diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5803b48c8c30fbeb7ade63153bf4257e769a7a91
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,184 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.DS_Store
+/models
+/logs
+
+filelists/*
+!/filelists/esd.list
+!/default_config.yml
+/Web/
+/emotional/*/*.bin
+/slm/*/*.bin
+/bert/*/*.bin
+/bert/*/*.h5
+/bert/*/*.model
+/bert/*/*.safetensors
+/bert/*/*.msgpack
+asr_transcript.py
+extract_list.py
+dataset
+Model
+raw/
+logs/
+/onnx
+/.vs
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..0ad25db4bd1d86c452db3f9602ccdbe172438f52
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+  A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
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+incorporate.  Many developers of free software are heartened and
+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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+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community.  It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
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+  "This License" refers to version 3 of the GNU Affero General Public License.
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+License of the Program or a work on which the Program is based.  The
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+  A patent license is "discriminatory" if it does not include within
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
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+  13. Remote Network Interaction; Use with the GNU General Public License.
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+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
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+3 of the GNU General Public License.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time.  Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
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+Program specifies that a certain numbered version of the GNU Affero General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
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+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+  16. Limitation of Liability.
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+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+solutions will be better for different programs; see section 13 for the
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+
+  You should also get your employer (if you work as a programmer) or school,
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+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..59677595cdf289229827147be52651e45d316037
--- /dev/null
+++ b/app.py
@@ -0,0 +1,560 @@
+# flake8: noqa: E402
+import os
+import logging
+import re_matching
+from tools.sentence import split_by_language
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+logging.getLogger("markdown_it").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+
+logging.basicConfig(
+    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
+)
+
+logger = logging.getLogger(__name__)
+
+import torch
+import utils
+from infer import infer, latest_version, get_net_g, infer_multilang
+import gradio as gr
+import webbrowser
+import numpy as np
+from config import config
+from tools.translate import translate
+import librosa
+
+net_g = None
+
+device = config.webui_config.device
+if device == "mps":
+    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
+
+def generate_audio(
+    slices,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    speaker,
+    language,
+    reference_audio,
+    emotion,
+    style_text,
+    style_weight,
+    skip_start=False,
+    skip_end=False,
+):
+    audio_list = []
+    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
+    with torch.no_grad():
+        for idx, piece in enumerate(slices):
+            skip_start = idx != 0
+            skip_end = idx != len(slices) - 1
+            audio = infer(
+                piece,
+                reference_audio=reference_audio,
+                emotion=emotion,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                sid=speaker,
+                language=language,
+                hps=hps,
+                net_g=net_g,
+                device=device,
+                skip_start=skip_start,
+                skip_end=skip_end,
+                style_text=style_text,
+                style_weight=style_weight,
+            )
+            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
+            audio_list.append(audio16bit)
+    return audio_list
+
+
+def generate_audio_multilang(
+    slices,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    speaker,
+    language,
+    reference_audio,
+    emotion,
+    skip_start=False,
+    skip_end=False,
+):
+    audio_list = []
+    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
+    with torch.no_grad():
+        for idx, piece in enumerate(slices):
+            skip_start = idx != 0
+            skip_end = idx != len(slices) - 1
+            audio = infer_multilang(
+                piece,
+                reference_audio=reference_audio,
+                emotion=emotion,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                sid=speaker,
+                language=language[idx],
+                hps=hps,
+                net_g=net_g,
+                device=device,
+                skip_start=skip_start,
+                skip_end=skip_end,
+            )
+            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
+            audio_list.append(audio16bit)
+    return audio_list
+
+
+def tts_split(
+    text: str,
+    speaker,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    language,
+    cut_by_sent,
+    interval_between_para,
+    interval_between_sent,
+    reference_audio,
+    emotion,
+    style_text,
+    style_weight,
+):
+    while text.find("\n\n") != -1:
+        text = text.replace("\n\n", "\n")
+    text = text.replace("|", "")
+    para_list = re_matching.cut_para(text)
+    para_list = [p for p in para_list if p != ""]
+    audio_list = []
+    for p in para_list:
+        if not cut_by_sent:
+            audio_list += process_text(
+                p,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                reference_audio,
+                emotion,
+                style_text,
+                style_weight,
+            )
+            silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
+            audio_list.append(silence)
+        else:
+            audio_list_sent = []
+            sent_list = re_matching.cut_sent(p)
+            sent_list = [s for s in sent_list if s != ""]
+            for s in sent_list:
+                audio_list_sent += process_text(
+                    s,
+                    speaker,
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    language,
+                    reference_audio,
+                    emotion,
+                    style_text,
+                    style_weight,
+                )
+                silence = np.zeros((int)(44100 * interval_between_sent))
+                audio_list_sent.append(silence)
+            if (interval_between_para - interval_between_sent) > 0:
+                silence = np.zeros(
+                    (int)(44100 * (interval_between_para - interval_between_sent))
+                )
+                audio_list_sent.append(silence)
+            audio16bit = gr.processing_utils.convert_to_16_bit_wav(
+                np.concatenate(audio_list_sent)
+            )  # 对完整句子做音量归一
+            audio_list.append(audio16bit)
+    audio_concat = np.concatenate(audio_list)
+    return ("Success", (hps.data.sampling_rate, audio_concat))
+
+
+def process_mix(slice):
+    _speaker = slice.pop()
+    _text, _lang = [], []
+    for lang, content in slice:
+        content = content.split("|")
+        content = [part for part in content if part != ""]
+        if len(content) == 0:
+            continue
+        if len(_text) == 0:
+            _text = [[part] for part in content]
+            _lang = [[lang] for part in content]
+        else:
+            _text[-1].append(content[0])
+            _lang[-1].append(lang)
+            if len(content) > 1:
+                _text += [[part] for part in content[1:]]
+                _lang += [[lang] for part in content[1:]]
+    return _text, _lang, _speaker
+
+
+def process_auto(text):
+    _text, _lang = [], []
+    for slice in text.split("|"):
+        if slice == "":
+            continue
+        temp_text, temp_lang = [], []
+        sentences_list = split_by_language(slice, target_languages=["zh", "ja", "en"])
+        for sentence, lang in sentences_list:
+            if sentence == "":
+                continue
+            temp_text.append(sentence)
+            temp_lang.append(lang.upper())
+        _text.append(temp_text)
+        _lang.append(temp_lang)
+    return _text, _lang
+
+
+def process_text(
+    text: str,
+    speaker,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    language,
+    reference_audio,
+    emotion,
+    style_text=None,
+    style_weight=0,
+):
+    audio_list = []
+    if language == "mix":
+        bool_valid, str_valid = re_matching.validate_text(text)
+        if not bool_valid:
+            return str_valid, (
+                hps.data.sampling_rate,
+                np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
+            )
+        for slice in re_matching.text_matching(text):
+            _text, _lang, _speaker = process_mix(slice)
+            if _speaker is None:
+                continue
+            print(f"Text: {_text}\nLang: {_lang}")
+            audio_list.extend(
+                generate_audio_multilang(
+                    _text,
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    _speaker,
+                    _lang,
+                    reference_audio,
+                    emotion,
+                )
+            )
+    elif language.lower() == "auto":
+        _text, _lang = process_auto(text)
+        print(f"Text: {_text}\nLang: {_lang}")
+        audio_list.extend(
+            generate_audio_multilang(
+                _text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                speaker,
+                _lang,
+                reference_audio,
+                emotion,
+            )
+        )
+    else:
+        audio_list.extend(
+            generate_audio(
+                text.split("|"),
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                speaker,
+                language,
+                reference_audio,
+                emotion,
+                style_text,
+                style_weight,
+            )
+        )
+    return audio_list
+
+
+def tts_fn(
+    text: str,
+    speaker,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    language,
+    reference_audio,
+    emotion,
+    prompt_mode,
+    style_text=None,
+    style_weight=0,
+):
+    if style_text == "":
+        style_text = None
+    if prompt_mode == "Audio prompt":
+        if reference_audio == None:
+            return ("Invalid audio prompt", None)
+        else:
+            reference_audio = load_audio(reference_audio)[1]
+    else:
+        reference_audio = None
+
+    audio_list = process_text(
+        text,
+        speaker,
+        sdp_ratio,
+        noise_scale,
+        noise_scale_w,
+        length_scale,
+        language,
+        reference_audio,
+        emotion,
+        style_text,
+        style_weight,
+    )
+
+    audio_concat = np.concatenate(audio_list)
+    return "Success", (hps.data.sampling_rate, audio_concat)
+
+
+def format_utils(text, speaker):
+    _text, _lang = process_auto(text)
+    res = f"[{speaker}]"
+    for lang_s, content_s in zip(_lang, _text):
+        for lang, content in zip(lang_s, content_s):
+            res += f"<{lang.lower()}>{content}"
+        res += "|"
+    return "mix", res[:-1]
+
+
+def load_audio(path):
+    audio, sr = librosa.load(path, 48000)
+    # audio = librosa.resample(audio, 44100, 48000)
+    return sr, audio
+
+
+def gr_util(item):
+    if item == "Text prompt":
+        return {"visible": True, "__type__": "update"}, {
+            "visible": False,
+            "__type__": "update",
+        }
+    else:
+        return {"visible": False, "__type__": "update"}, {
+            "visible": True,
+            "__type__": "update",
+        }
+
+
+if __name__ == "__main__":
+    if config.webui_config.debug:
+        logger.info("Enable DEBUG-LEVEL log")
+        logging.basicConfig(level=logging.DEBUG)
+    hps = utils.get_hparams_from_file(config.webui_config.config_path)
+    # 若config.json中未指定版本则默认为最新版本
+    version = hps.version if hasattr(hps, "version") else latest_version
+    net_g = get_net_g(
+        model_path=config.webui_config.model, version=version, device=device, hps=hps
+    )
+    speaker_ids = hps.data.spk2id
+    speakers = list(speaker_ids.keys())
+    languages = ["ZH", "JP", "EN", "mix", "auto"]
+    with gr.Blocks() as app:
+        with gr.Row():
+            with gr.Column():
+                text = gr.TextArea(
+                    label="输入文本内容",
+                    placeholder="""
+                    如果你选择语言为\'mix\'，必须按照格式输入，否则报错:
+                        格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi):
+                         [说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。
+                         [说话人2]<zh>你好吗？<jp>元気ですか？
+                         [说话人3]<zh>谢谢。<jp>どういたしまして。
+                         ...
+                    另外，所有的语言选项都可以用'|'分割长段实现分句生成。
+                    """,
+                    value="光合作用係指植物以光作為能源，將二氧化碳同水，轉化成為葡萄糖同氧氣嘅過程。植物會利用大氣中嘅二氧化碳，泥土中吸返嚟嘅水，加埋自己本身有嘅葉綠素，利用太陽光供給嘅能量，製造成糖分。好多粒糖聚埋一齊就會變成澱粉，植物會以澱粉嘅形式嚟儲存過多嘅萄葡糖。除咗糖粉之外，氧氣係光合作用嘅副產品（亦有佢係新陳代謝廢物嘅講法），呢啲氧氣會被釋放出大氣，維持大氣中氧氣嘅含量。光合作用除咗係自養生物嘅食物來源，重係呢個世界上其中一個非常重要嘅生物化學作用，因為全地球嘅生物都依賴佢所製造出嚟嘅氧氣同糖份嚟生存。好多綠色嘅生物，好似藻類，高級植物，某啲細菌，都會做光合作用。\n",
+                )
+                trans = gr.Button("中翻日", variant="primary")
+                slicer = gr.Button("快速切分", variant="primary")
+                formatter = gr.Button("检测语言，并整理为 MIX 格式", variant="primary")
+                speaker = gr.Dropdown(
+                    choices=speakers, value=speakers[0], label="Speaker"
+                )
+                _ = gr.Markdown(
+                    value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n",
+                    visible=False,
+                )
+                prompt_mode = gr.Radio(
+                    ["Text prompt", "Audio prompt"],
+                    label="Prompt Mode",
+                    value="Text prompt",
+                    visible=False,
+                )
+                text_prompt = gr.Textbox(
+                    label="Text prompt",
+                    placeholder="用文字描述生成风格。如：Happy",
+                    value="Happy",
+                    visible=False,
+                )
+                audio_prompt = gr.Audio(
+                    label="Audio prompt", type="filepath", visible=False
+                )
+                sdp_ratio = gr.Slider(
+                    minimum=0, maximum=1, value=0.5, step=0.1, label="SDP Ratio"
+                )
+                noise_scale = gr.Slider(
+                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise"
+                )
+                noise_scale_w = gr.Slider(
+                    minimum=0.1, maximum=2, value=0.9, step=0.1, label="Noise_W"
+                )
+                length_scale = gr.Slider(
+                    minimum=0.1, maximum=2, value=1.05, step=0.1, label="Length"
+                )
+                language = gr.Dropdown(
+                    choices=languages, value=languages[0], label="Language"
+                )
+                btn = gr.Button("生成音频！", variant="primary")
+            with gr.Column():
+                with gr.Accordion("融合文本语义", open=False):
+                    gr.Markdown(
+                        value="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
+                        "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
+                        "效果较不明确，留空即为不使用该功能"
+                    )
+                    style_text = gr.Textbox(label="辅助文本")
+                    style_weight = gr.Slider(
+                        minimum=0,
+                        maximum=1,
+                        value=0.7,
+                        step=0.1,
+                        label="Weight",
+                        info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
+                    )
+                with gr.Row():
+                    with gr.Column():
+                        interval_between_sent = gr.Slider(
+                            minimum=0,
+                            maximum=5,
+                            value=0.2,
+                            step=0.1,
+                            label="句间停顿(秒)，勾选按句切分才生效",
+                        )
+                        interval_between_para = gr.Slider(
+                            minimum=0,
+                            maximum=10,
+                            value=1,
+                            step=0.1,
+                            label="段间停顿(秒)，需要大于句间停顿才有效",
+                        )
+                        opt_cut_by_sent = gr.Checkbox(
+                            label="按句切分    在按段落切分的基础上再按句子切分文本"
+                        )
+                        slicer = gr.Button("切分生成", variant="primary")
+                text_output = gr.Textbox(label="状态信息")
+                audio_output = gr.Audio(
+                    label="输出音频",
+                    value="https://github.com/Naozumi520/Bert-VITS2-Yue/raw/master/sample.wav",
+                )
+                # explain_image = gr.Image(
+                #     label="参数解释信息",
+                #     show_label=True,
+                #     show_share_button=False,
+                #     show_download_button=False,
+                #     value=os.path.abspath("./img/参数说明.png"),
+                # )
+        btn.click(
+            tts_fn,
+            inputs=[
+                text,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                audio_prompt,
+                text_prompt,
+                prompt_mode,
+                style_text,
+                style_weight,
+            ],
+            outputs=[text_output, audio_output],
+        )
+
+        trans.click(
+            translate,
+            inputs=[text],
+            outputs=[text],
+        )
+        slicer.click(
+            tts_split,
+            inputs=[
+                text,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                opt_cut_by_sent,
+                interval_between_para,
+                interval_between_sent,
+                audio_prompt,
+                text_prompt,
+                style_text,
+                style_weight,
+            ],
+            outputs=[text_output, audio_output],
+        )
+
+        prompt_mode.change(
+            lambda x: gr_util(x),
+            inputs=[prompt_mode],
+            outputs=[text_prompt, audio_prompt],
+        )
+
+        audio_prompt.upload(
+            lambda x: load_audio(x),
+            inputs=[audio_prompt],
+            outputs=[audio_prompt],
+        )
+
+        formatter.click(
+            format_utils,
+            inputs=[text, speaker],
+            outputs=[language, text],
+        )
+
+    print("推理页面已开启!")
+    webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
+    app.launch(
+        share=config.webui_config.share,
+        server_name="0.0.0.0",
+        server_port=config.webui_config.port,
+    )
diff --git a/attentions.py b/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ba2407267ecd425d2095a6428015b5b4ebc0bda
--- /dev/null
+++ b/attentions.py
@@ -0,0 +1,464 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
+            device=x.device, dtype=x.dtype
+        )
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # pad along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/bert_gen.py b/bert_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..96f6899927793323a4f05721b533f7d588e9671d
--- /dev/null
+++ b/bert_gen.py
@@ -0,0 +1,93 @@
+import torch
+from multiprocessing import Pool
+import commons
+import utils
+from tqdm import tqdm
+from text import check_bert_models, cleaned_text_to_sequence, get_bert
+import argparse
+import torch.multiprocessing as mp
+from config import config
+
+
+def process_line(x):
+    line, add_blank = x
+    device = config.bert_gen_config.device
+    if config.bert_gen_config.use_multi_device:
+        rank = mp.current_process()._identity
+        rank = rank[0] if len(rank) > 0 else 0
+        if torch.cuda.is_available():
+            gpu_id = rank % torch.cuda.device_count()
+            device = torch.device(f"cuda:{gpu_id}")
+        else:
+            device = torch.device("cpu")
+    wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|")
+    phone = phones.split(" ")
+    tone = [int(i) for i in tone.split(" ")]
+    word2ph = [int(i) for i in word2ph.split(" ")]
+    word2ph = [i for i in word2ph]
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+
+    bert_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".bert.pt")
+
+    try:
+        bert = torch.load(bert_path)
+        assert bert.shape[0] == 2048
+    except Exception:
+        try:
+            bert = get_bert(text, word2ph, language_str, device)
+            assert bert.shape[-1] == len(phone)
+            torch.save(bert, bert_path)
+        except Exception as e:
+            print(f"Error: {text=}")
+            print(f"Error: {phone=}")
+            print(f"Error: {tone=}")
+            print(f"Error: {word2ph=}")
+            print(f"Error: {language_str=}")
+            print(f"Error: {device=}")
+            print(f"Error: {add_blank=}")
+            print(line.strip().split("|"))
+
+            raise e
+
+
+preprocess_text_config = config.preprocess_text_config
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c", "--config", type=str, default=config.bert_gen_config.config_path
+    )
+    parser.add_argument(
+        "--num_processes", type=int, default=config.bert_gen_config.num_processes
+    )
+    args, _ = parser.parse_known_args()
+    config_path = args.config
+    hps = utils.get_hparams_from_file(config_path)
+    check_bert_models()
+    lines = []
+    with open(hps.data.training_files, encoding="utf-8") as f:
+        lines.extend(f.readlines())
+
+    with open(hps.data.validation_files, encoding="utf-8") as f:
+        lines.extend(f.readlines())
+    add_blank = [hps.data.add_blank] * len(lines)
+
+    if len(lines) != 0:
+        num_processes = args.num_processes
+        with Pool(processes=num_processes) as pool:
+            for _ in tqdm(
+                pool.imap_unordered(process_line, zip(lines, add_blank)),
+                total=len(lines),
+            ):
+                # 这里是缩进的代码块，表示循环体
+                pass  # 使用pass语句作为占位符
+
+    print(f"bert生成完毕!, 共有{len(lines)}个bert.pt生成!")
diff --git a/commons.py b/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..7af7f66cb35b035a2487e0bd0738894943e41915
--- /dev/null
+++ b/commons.py
@@ -0,0 +1,158 @@
+import math
+import torch
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def intersperse(lst, item):
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += (
+        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    )
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    gather_indices = ids_str.view(x.size(0), 1, 1).repeat(
+        1, x.size(1), 1
+    ) + torch.arange(segment_size, device=x.device)
+    return torch.gather(x, 2, gather_indices)
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0)
+    ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+        num_timescales - 1
+    )
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm
diff --git a/compress_model.py b/compress_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..901490c2bf5476e8950b8361551caad720ace5fa
--- /dev/null
+++ b/compress_model.py
@@ -0,0 +1,89 @@
+from collections import OrderedDict
+from text.symbols import symbols
+import torch
+
+from tools.log import logger
+import utils
+from models import SynthesizerTrn
+import os
+
+
+def copyStateDict(state_dict):
+    if list(state_dict.keys())[0].startswith("module"):
+        start_idx = 1
+    else:
+        start_idx = 0
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        name = ",".join(k.split(".")[start_idx:])
+        new_state_dict[name] = v
+    return new_state_dict
+
+
+def removeOptimizer(config: str, input_model: str, ishalf: bool, output_model: str):
+    hps = utils.get_hparams_from_file(config)
+
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    )
+
+    optim_g = torch.optim.AdamW(
+        net_g.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+
+    state_dict_g = torch.load(input_model, map_location="cpu")
+    new_dict_g = copyStateDict(state_dict_g)
+    keys = []
+    for k, v in new_dict_g["model"].items():
+        if "enc_q" in k:
+            continue  # noqa: E701
+        keys.append(k)
+
+    new_dict_g = (
+        {k: new_dict_g["model"][k].half() for k in keys}
+        if ishalf
+        else {k: new_dict_g["model"][k] for k in keys}
+    )
+
+    torch.save(
+        {
+            "model": new_dict_g,
+            "iteration": 0,
+            "optimizer": optim_g.state_dict(),
+            "learning_rate": 0.0001,
+        },
+        output_model,
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-c", "--config", type=str, default="configs/config.json")
+    parser.add_argument("-i", "--input", type=str)
+    parser.add_argument("-o", "--output", type=str, default=None)
+    parser.add_argument(
+        "-hf", "--half", action="store_true", default=False, help="Save as FP16"
+    )
+
+    args = parser.parse_args()
+
+    output = args.output
+
+    if output is None:
+        import os.path
+
+        filename, ext = os.path.splitext(args.input)
+        half = "_half" if args.half else ""
+        output = filename + "_release" + half + ext
+
+    removeOptimizer(args.config, args.input, args.half, output)
+    logger.info(f"压缩模型成功, 输出模型: {os.path.abspath(output)}")
diff --git a/config.py b/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..09fef335972178e99b03dab41caf3648b44192f1
--- /dev/null
+++ b/config.py
@@ -0,0 +1,261 @@
+"""
+@Desc: 全局配置文件读取
+"""
+
+import argparse
+import yaml
+from typing import Dict, List
+import os
+import shutil
+import sys
+
+
+class Resample_config:
+    """重采样配置"""
+
+    def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
+        self.sampling_rate: int = sampling_rate  # 目标采样率
+        self.in_dir: str = in_dir  # 待处理音频目录路径
+        self.out_dir: str = out_dir  # 重采样输出路径
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        """从字典中生成实例"""
+
+        # 不检查路径是否有效，此逻辑在resample.py中处理
+        data["in_dir"] = os.path.join(dataset_path, data["in_dir"])
+        data["out_dir"] = os.path.join(dataset_path, data["out_dir"])
+
+        return cls(**data)
+
+
+class Preprocess_text_config:
+    """数据预处理配置"""
+
+    def __init__(
+        self,
+        transcription_path: str,
+        cleaned_path: str,
+        train_path: str,
+        val_path: str,
+        config_path: str,
+        val_per_lang: int = 5,
+        max_val_total: int = 10000,
+        clean: bool = True,
+    ):
+        self.transcription_path: str = (
+            transcription_path  # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
+        )
+        self.cleaned_path: str = (
+            cleaned_path  # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
+        )
+        self.train_path: str = (
+            train_path  # 训练集路径，可以不填。不填则将在原始文本目录生成
+        )
+        self.val_path: str = (
+            val_path  # 验证集路径，可以不填。不填则将在原始文本目录生成
+        )
+        self.config_path: str = config_path  # 配置文件路径
+        self.val_per_lang: int = val_per_lang  # 每个speaker的验证集条数
+        self.max_val_total: int = (
+            max_val_total  # 验证集最大条数，多于的会被截断并放到训练集中
+        )
+        self.clean: bool = clean  # 是否进行数据清洗
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        """从字典中生成实例"""
+
+        data["transcription_path"] = os.path.join(
+            dataset_path, data["transcription_path"]
+        )
+        if data["cleaned_path"] == "" or data["cleaned_path"] is None:
+            data["cleaned_path"] = None
+        else:
+            data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"])
+        data["train_path"] = os.path.join(dataset_path, data["train_path"])
+        data["val_path"] = os.path.join(dataset_path, data["val_path"])
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Bert_gen_config:
+    """bert_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 2,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        self.device = device
+        self.use_multi_device = use_multi_device
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Emo_gen_config:
+    """emo_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 2,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        self.device = device
+        self.use_multi_device = use_multi_device
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Train_ms_config:
+    """训练配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        env: Dict[str, any],
+        base: Dict[str, any],
+        model: str,
+        num_workers: int,
+        spec_cache: bool,
+        keep_ckpts: int,
+    ):
+        self.env = env  # 需要加载的环境变量
+        self.base = base  # 底模配置
+        self.model = (
+            model  # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录
+        )
+        self.config_path = config_path  # 配置文件路径
+        self.num_workers = num_workers  # worker数量
+        self.spec_cache = spec_cache  # 是否启用spec缓存
+        self.keep_ckpts = keep_ckpts  # ckpt数量
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        # data["model"] = os.path.join(dataset_path, data["model"])
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Webui_config:
+    """webui 配置"""
+
+    def __init__(
+        self,
+        device: str,
+        model: str,
+        config_path: str,
+        language_identification_library: str,
+        port: int = 7860,
+        share: bool = False,
+        debug: bool = False,
+    ):
+        self.device: str = device
+        self.model: str = model  # 端口号
+        self.config_path: str = config_path  # 是否公开部署，对外网开放
+        self.port: int = port  # 是否开启debug模式
+        self.share: bool = share  # 模型路径
+        self.debug: bool = debug  # 配置文件路径
+        self.language_identification_library: str = (
+            language_identification_library  # 语种识别库
+        )
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+        data["model"] = os.path.join(dataset_path, data["model"])
+        return cls(**data)
+
+
+class Server_config:
+    def __init__(
+        self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda"
+    ):
+        self.models: List[Dict[str, any]] = models  # 需要加载的所有模型的配置
+        self.port: int = port  # 端口号
+        self.device: str = device  # 模型默认使用设备
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, any]):
+        return cls(**data)
+
+
+class Translate_config:
+    """翻译api配置"""
+
+    def __init__(self, app_key: str, secret_key: str):
+        self.app_key = app_key
+        self.secret_key = secret_key
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, any]):
+        return cls(**data)
+
+
+class Config:
+    def __init__(self, config_path: str):
+        if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
+            shutil.copy(src="default_config.yml", dst=config_path)
+            print(
+                f"已根据默认配置文件default_config.yml生成配置文件{config_path}。请按该配置文件的说明进行配置后重新运行。"
+            )
+            print("如无特殊需求，请勿修改default_config.yml或备份该文件。")
+            sys.exit(0)
+        with open(file=config_path, mode="r", encoding="utf-8") as file:
+            yaml_config: Dict[str, any] = yaml.safe_load(file.read())
+            dataset_path: str = yaml_config["dataset_path"]
+            openi_token: str = yaml_config["openi_token"]
+            self.dataset_path: str = dataset_path
+            self.mirror: str = yaml_config["mirror"]
+            self.openi_token: str = openi_token
+            self.resample_config: Resample_config = Resample_config.from_dict(
+                dataset_path, yaml_config["resample"]
+            )
+            self.preprocess_text_config: Preprocess_text_config = (
+                Preprocess_text_config.from_dict(
+                    dataset_path, yaml_config["preprocess_text"]
+                )
+            )
+            self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
+                dataset_path, yaml_config["bert_gen"]
+            )
+            self.emo_gen_config: Emo_gen_config = Emo_gen_config.from_dict(
+                dataset_path, yaml_config["emo_gen"]
+            )
+            self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
+                dataset_path, yaml_config["train_ms"]
+            )
+            self.webui_config: Webui_config = Webui_config.from_dict(
+                dataset_path, yaml_config["webui"]
+            )
+            self.server_config: Server_config = Server_config.from_dict(
+                yaml_config["server"]
+            )
+            self.translate_config: Translate_config = Translate_config.from_dict(
+                yaml_config["translate"]
+            )
+
+
+parser = argparse.ArgumentParser()
+# 为避免与以前的config.json起冲突，将其更名如下
+parser.add_argument("-y", "--yml_config", type=str, default="config.yml")
+args, _ = parser.parse_known_args()
+config = Config(args.yml_config)
diff --git a/config.yml b/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a594d95bb85274066e95a24c98ad5341c3b46915
--- /dev/null
+++ b/config.yml
@@ -0,0 +1,177 @@
+# 全局配置
+# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml
+
+# 拟提供通用路径配置，统一存放数据，避免数据放得很乱
+# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
+# 不填或者填空则路径为相对于项目根目录的路径
+dataset_path: "data/finetuned"
+
+# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
+mirror: ""
+openi_token: ""  # openi token
+
+# resample 音频重采样配置
+# 注意， “:” 后需要加空格
+resample:
+  # 目标重采样率
+  sampling_rate: 44100
+  # 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样
+  # 请填入相对于datasetPath的相对路径
+  in_dir: "audios/raw" # 相对于根目录的路径为 /datasetPath/in_dir
+  # 音频文件重采样后输出路径
+  out_dir: "audios/wavs"
+
+
+# preprocess_text 数据集预处理相关配置
+# 注意， “:” 后需要加空格
+preprocess_text:
+  # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
+  transcription_path: "filelists/你的数据集文本.list"
+  # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
+  cleaned_path: ""
+  # 训练集路径
+  train_path: "filelists/train.list"
+  # 验证集路径
+  val_path: "filelists/val.list"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 每个语言的验证集条数
+  val_per_lang: 400
+  # 验证集最大条数，多于的会被截断并放到训练集中
+  max_val_total: 1200
+  # 是否进行数据清洗
+  clean: true
+
+
+# bert_gen 相关配置
+# 注意， “:” 后需要加空格
+bert_gen:
+  # 训练数据集配置文件路径
+  config_path: "configs/config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
+  # 该选项同时决定了get_bert_feature的默认设备
+  device: "cuda"
+  # 使用多卡推理
+  use_multi_device: false
+
+# emo_gen 相关配置
+# 注意， “:” 后需要加空格
+emo_gen:
+  # 训练数据集配置文件路径
+  config_path: "configs/config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
+  device: "cuda"
+  # 使用多卡推理
+  use_multi_device: false
+
+# train 训练配置
+# 注意， “:” 后需要加空格
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+    # 可以填写任意名的环境变量
+    # THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
+  # 底模设置
+  base:
+    use_base_model: false
+    repo_id: "Stardust_minus/Bert-VITS2"
+    model_image: "Bert-VITS2_2.3底模" # openi网页的模型名
+  # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
+  model: "models"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 训练使用的worker，不建议超过CPU核心数
+  num_workers: 16
+  # 关闭此项可以节约接近70%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。
+  spec_cache: False
+  # 保存的检查点数量，多于此数目的权重会被删除来节省空间。
+  keep_ckpts: 8
+
+
+# webui webui配置
+# 注意， “:” 后需要加空格
+webui:
+  # 推理设备
+  device: "cuda"
+  # 模型路径
+  model: "models/G_43000.pth"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 端口号
+  port: 7860
+  # 是否公开部署，对外网开放
+  share: false
+  # 是否开启debug模式
+  debug: false
+  # 语种识别库，可选langid, fastlid
+  language_identification_library: "langid"
+
+
+# server-fastapi配置
+# 注意， “:” 后需要加空格
+# 注意，本配置下的所有配置均为相对于根目录的路径
+server:
+  # 端口号
+  port: 5000
+  # 模型默认使用设备：但是当前并没有实现这个配置。
+  device: "cuda"
+  # 需要加载的所有模型的配置，可以填多个模型，也可以不填模型，等网页成功后手动加载模型
+  # 不加载模型的配置格式：删除默认给的两个模型配置，给models赋值 [ ]，也就是空列表。参考模型2的speakers 即 models: [ ]
+  # 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。
+  # 也可以不填模型，等网页加载成功后手动填写models。
+  models:
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cuda"
+      # 模型默认使用的语言
+      language: "YUE"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      # 暂时不用填写，当前尚未实现按人区分配置
+      speakers:
+        - speaker: "科比"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1
+        - speaker: "五条悟"
+          sdp_ratio: 0.3
+          noise_scale: 0.7
+          noise_scale_w: 0.8
+          length_scale: 0.5
+        - speaker: "安倍晋三"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1.2
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cpu"
+      # 模型默认使用的语言
+      language: "JP"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      speakers: [ ] # 也可以不填
+
+# 百度翻译开放平台 api配置
+# api接入文档 https://api.fanyi.baidu.com/doc/21
+# 请不要在github等网站公开分享你的app id 与 key
+translate:
+  # 你的APPID
+  "app_key": ""
+  # 你的密钥
+  "secret_key": ""
diff --git a/configs/config.json b/configs/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8772a1d15488df98b283f4cad4aa441b79d185d1
--- /dev/null
+++ b/configs/config.json
@@ -0,0 +1,955 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.0002,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 22,
+    "bf16_run": false,
+    "lr_decay": 0.99995,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": true,
+    "freeze_YUE_bert": false,
+    "freeze_emo": false
+  },
+  "data": {
+    "training_files": "filelists/train.list",
+    "validation_files": "filelists/val.list",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 850,
+    "cleaned_text": true,
+    "spk2id": {
+      "派蒙_ZH": 0,
+      "纳西妲_ZH": 1,
+      "凯亚_ZH": 2,
+      "阿贝多_ZH": 3,
+      "温迪_ZH": 4,
+      "枫原万叶_ZH": 5,
+      "钟离_ZH": 6,
+      "荒泷一斗_ZH": 7,
+      "八重神子_ZH": 8,
+      "艾尔海森_ZH": 9,
+      "提纳里_ZH": 10,
+      "迪希雅_ZH": 11,
+      "卡维_ZH": 12,
+      "宵宫_ZH": 13,
+      "那维莱特_ZH": 14,
+      "莱依拉_ZH": 15,
+      "赛诺_ZH": 16,
+      "莫娜_ZH": 17,
+      "诺艾尔_ZH": 18,
+      "托马_ZH": 19,
+      "凝光_ZH": 20,
+      "林尼_ZH": 21,
+      "北斗_ZH": 22,
+      "柯莱_ZH": 23,
+      "神里绫华_ZH": 24,
+      "可莉_ZH": 25,
+      "芭芭拉_ZH": 26,
+      "雷电将军_ZH": 27,
+      "娜维娅_ZH": 28,
+      "芙宁娜_ZH": 29,
+      "珊瑚宫心海_ZH": 30,
+      "鹿野院平藏_ZH": 31,
+      "迪奥娜_ZH": 32,
+      "琴_ZH": 33,
+      "五郎_ZH": 34,
+      "班尼特_ZH": 35,
+      "达达利亚_ZH": 36,
+      "安柏_ZH": 37,
+      "莱欧斯利_ZH": 38,
+      "夜兰_ZH": 39,
+      "妮露_ZH": 40,
+      "辛焱_ZH": 41,
+      "丽莎_ZH": 42,
+      "珐露珊_ZH": 43,
+      "魈_ZH": 44,
+      "香菱_ZH": 45,
+      "迪卢克_ZH": 46,
+      "砂糖_ZH": 47,
+      "烟绯_ZH": 48,
+      "早柚_ZH": 49,
+      "云堇_ZH": 50,
+      "刻晴_ZH": 51,
+      "重云_ZH": 52,
+      "优菈_ZH": 53,
+      "胡桃_ZH": 54,
+      "流浪者_ZH": 55,
+      "久岐忍_ZH": 56,
+      "神里绫人_ZH": 57,
+      "甘雨_ZH": 58,
+      "戴因斯雷布_ZH": 59,
+      "菲谢尔_ZH": 60,
+      "白术_ZH": 61,
+      "行秋_ZH": 62,
+      "九条裟罗_ZH": 63,
+      "夏洛蒂_ZH": 64,
+      "雷泽_ZH": 65,
+      "申鹤_ZH": 66,
+      "荧_ZH": 67,
+      "空_ZH": 68,
+      "迪娜泽黛_ZH": 69,
+      "凯瑟琳_ZH": 70,
+      "多莉_ZH": 71,
+      "坎蒂丝_ZH": 72,
+      "琳妮特_ZH": 73,
+      "萍姥姥_ZH": 74,
+      "罗莎莉亚_ZH": 75,
+      "埃德_ZH": 76,
+      "爱贝尔_ZH": 77,
+      "伊迪娅_ZH": 78,
+      "留云借风真君_ZH": 79,
+      "绮良良_ZH": 80,
+      "陌生人_ZH": 81,
+      "七七_ZH": 82,
+      "式大将_ZH": 83,
+      "瑶瑶_ZH": 84,
+      "奥兹_ZH": 85,
+      "菲米尼_ZH": 86,
+      "米卡_ZH": 87,
+      "哲平_ZH": 88,
+      "浮游水蕈兽·元素生命_ZH": 89,
+      "大肉丸_ZH": 90,
+      "托克_ZH": 91,
+      "蒂玛乌斯_ZH": 92,
+      "昆钧_ZH": 93,
+      "欧菲妮_ZH": 94,
+      "塞琉斯_ZH": 95,
+      "仆人_ZH": 96,
+      "迈勒斯_ZH": 97,
+      "希格雯_ZH": 98,
+      "阿守_ZH": 99,
+      "拉赫曼_ZH": 100,
+      "杜拉夫_ZH": 101,
+      "伊利亚斯_ZH": 102,
+      "阿晃_ZH": 103,
+      "旁白_ZH": 104,
+      "爱德琳_ZH": 105,
+      "埃洛伊_ZH": 106,
+      "德沃沙克_ZH": 107,
+      "玛乔丽_ZH": 108,
+      "塞塔蕾_ZH": 109,
+      "柊千里_ZH": 110,
+      "海芭夏_ZH": 111,
+      "九条镰治_ZH": 112,
+      "阿娜耶_ZH": 113,
+      "笼钓瓶一心_ZH": 114,
+      "回声海螺_ZH": 115,
+      "劳维克_ZH": 116,
+      "元太_ZH": 117,
+      "阿扎尔_ZH": 118,
+      "查尔斯_ZH": 119,
+      "阿洛瓦_ZH": 120,
+      "埃勒曼_ZH": 121,
+      "纳比尔_ZH": 122,
+      "莎拉_ZH": 123,
+      "康纳_ZH": 124,
+      "博来_ZH": 125,
+      "玛塞勒_ZH": 126,
+      "阿祇_ZH": 127,
+      "博士_ZH": 128,
+      "玛格丽特_ZH": 129,
+      "迪尔菲_ZH": 130,
+      "宛烟_ZH": 131,
+      "羽生田千鹤_ZH": 132,
+      "海妮耶_ZH": 133,
+      "旅行者_ZH": 134,
+      "霍夫曼_ZH": 135,
+      "佐西摩斯_ZH": 136,
+      "鹿野奈奈_ZH": 137,
+      "舒伯特_ZH": 138,
+      "天叔_ZH": 139,
+      "艾莉丝_ZH": 140,
+      "龙二_ZH": 141,
+      "莺儿_ZH": 142,
+      "嘉良_ZH": 143,
+      "一心传名刀_ZH": 144,
+      "珊瑚_ZH": 145,
+      "言笑_ZH": 146,
+      "久利须_ZH": 147,
+      "嘉玛_ZH": 148,
+      "艾文_ZH": 149,
+      "克洛琳德_ZH": 150,
+      "丹吉尔_ZH": 151,
+      "女士_ZH": 152,
+      "白老先生_ZH": 153,
+      "天目十五_ZH": 154,
+      "老孟_ZH": 155,
+      "巴达维_ZH": 156,
+      "长生_ZH": 157,
+      "吴船长_ZH": 158,
+      "拉齐_ZH": 159,
+      "艾伯特_ZH": 160,
+      "松浦_ZH": 161,
+      "埃泽_ZH": 162,
+      "阿圆_ZH": 163,
+      "莫塞伊思_ZH": 164,
+      "阿拉夫_ZH": 165,
+      "杜吉耶_ZH": 166,
+      "石头_ZH": 167,
+      "百闻_ZH": 168,
+      "波洛_ZH": 169,
+      "斯坦利_ZH": 170,
+      "博易_ZH": 171,
+      "迈蒙_ZH": 172,
+      "掇星攫辰天君_ZH": 173,
+      "毗伽尔_ZH": 174,
+      "芙卡洛斯_ZH": 175,
+      "恶龙_ZH": 176,
+      "恕筠_ZH": 177,
+      "知易_ZH": 178,
+      "克列门特_ZH": 179,
+      "大慈树王_ZH": 180,
+      "西拉杰_ZH": 181,
+      "上杉_ZH": 182,
+      "阿尔卡米_ZH": 183,
+      "纯水精灵_ZH": 184,
+      "常九爷_ZH": 185,
+      "沙扎曼_ZH": 186,
+      "田铁嘴_ZH": 187,
+      "克罗索_ZH": 188,
+      "阿巴图伊_ZH": 189,
+      "阿佩普_ZH": 190,
+      "埃尔欣根_ZH": 191,
+      "萨赫哈蒂_ZH": 192,
+      "塔杰·拉德卡尼_ZH": 193,
+      "安西_ZH": 194,
+      "陆行岩本真蕈·元素生命_ZH": 195,
+      "派蒙_JP": 196,
+      "纳西妲_JP": 197,
+      "凯亚_JP": 198,
+      "阿贝多_JP": 199,
+      "温迪_JP": 200,
+      "枫原万叶_JP": 201,
+      "钟离_JP": 202,
+      "荒泷一斗_JP": 203,
+      "八重神子_JP": 204,
+      "艾尔海森_JP": 205,
+      "提纳里_JP": 206,
+      "迪希雅_JP": 207,
+      "卡维_JP": 208,
+      "宵宫_JP": 209,
+      "那维莱特_JP": 210,
+      "莱依拉_JP": 211,
+      "赛诺_JP": 212,
+      "莫娜_JP": 213,
+      "诺艾尔_JP": 214,
+      "托马_JP": 215,
+      "凝光_JP": 216,
+      "林尼_JP": 217,
+      "北斗_JP": 218,
+      "柯莱_JP": 219,
+      "神里绫华_JP": 220,
+      "可莉_JP": 221,
+      "芭芭拉_JP": 222,
+      "雷电将军_JP": 223,
+      "娜维娅_JP": 224,
+      "芙宁娜_JP": 225,
+      "珊瑚宫心海_JP": 226,
+      "鹿野院平藏_JP": 227,
+      "迪奥娜_JP": 228,
+      "琴_JP": 229,
+      "五郎_JP": 230,
+      "班尼特_JP": 231,
+      "达达利亚_JP": 232,
+      "安柏_JP": 233,
+      "莱欧斯利_JP": 234,
+      "夜兰_JP": 235,
+      "妮露_JP": 236,
+      "辛焱_JP": 237,
+      "丽莎_JP": 238,
+      "珐露珊_JP": 239,
+      "魈_JP": 240,
+      "香菱_JP": 241,
+      "迪卢克_JP": 242,
+      "砂糖_JP": 243,
+      "烟绯_JP": 244,
+      "早柚_JP": 245,
+      "云堇_JP": 246,
+      "刻晴_JP": 247,
+      "重云_JP": 248,
+      "优菈_JP": 249,
+      "胡桃_JP": 250,
+      "流浪者_JP": 251,
+      "久岐忍_JP": 252,
+      "神里绫人_JP": 253,
+      "甘雨_JP": 254,
+      "戴因斯雷布_JP": 255,
+      "菲谢尔_JP": 256,
+      "白术_JP": 257,
+      "行秋_JP": 258,
+      "九条裟罗_JP": 259,
+      "夏洛蒂_JP": 260,
+      "雷泽_JP": 261,
+      "申鹤_JP": 262,
+      "空_JP": 263,
+      "荧_JP": 264,
+      "迪娜泽黛_JP": 265,
+      "凯瑟琳_JP": 266,
+      "多莉_JP": 267,
+      "坎蒂丝_JP": 268,
+      "琳妮特_JP": 269,
+      "萍姥姥_JP": 270,
+      "罗莎莉亚_JP": 271,
+      "埃德_JP": 272,
+      "爱贝尔_JP": 273,
+      "伊迪娅_JP": 274,
+      "留云借风真君_JP": 275,
+      "绮良良_JP": 276,
+      "陌生人_JP": 277,
+      "七七_JP": 278,
+      "式大将_JP": 279,
+      "瑶瑶_JP": 280,
+      "奥兹_JP": 281,
+      "菲米尼_JP": 282,
+      "米卡_JP": 283,
+      "哲平_JP": 284,
+      "浮游水蕈兽·元素生命_JP": 285,
+      "大肉丸_JP": 286,
+      "托克_JP": 287,
+      "蒂玛乌斯_JP": 288,
+      "昆钧_JP": 289,
+      "欧菲妮_JP": 290,
+      "塞琉斯_JP": 291,
+      "仆人_JP": 292,
+      "迈勒斯_JP": 293,
+      "希格雯_JP": 294,
+      "阿守_JP": 295,
+      "拉赫曼_JP": 296,
+      "杜拉夫_JP": 297,
+      "伊利亚斯_JP": 298,
+      "阿晃_JP": 299,
+      "旁白_JP": 300,
+      "爱德琳_JP": 301,
+      "埃洛伊_JP": 302,
+      "德沃沙克_JP": 303,
+      "玛乔丽_JP": 304,
+      "塞塔蕾_JP": 305,
+      "柊千里_JP": 306,
+      "海芭夏_JP": 307,
+      "九条镰治_JP": 308,
+      "阿娜耶_JP": 309,
+      "笼钓瓶一心_JP": 310,
+      "回声海螺_JP": 311,
+      "劳维克_JP": 312,
+      "元太_JP": 313,
+      "阿扎尔_JP": 314,
+      "查尔斯_JP": 315,
+      "阿洛瓦_JP": 316,
+      "埃勒曼_JP": 317,
+      "纳比尔_JP": 318,
+      "莎拉_JP": 319,
+      "康纳_JP": 320,
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+      "紫月季_EN": 843,
+      "幻胧_EN": 844,
+      "女声_EN": 845,
+      "陆景和": 846,
+      "莫弈": 847,
+      "左然": 848,
+      "夏彦": 849
+    }
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": true,
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [
+      3,
+      7,
+      11
+    ],
+    "resblock_dilation_sizes": [
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ]
+    ],
+    "upsample_rates": [
+      8,
+      8,
+      2,
+      2,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      8,
+      2,
+      2
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 512,
+    "slm": {
+      "model": "./slm/wavlm-base-plus",
+      "sr": 16000,
+      "hidden": 768,
+      "nlayers": 13,
+      "initial_channel": 64
+    }
+  },
+  "version": "2.3"
+}
\ No newline at end of file
diff --git a/css/custom.css b/css/custom.css
new file mode 100644
index 0000000000000000000000000000000000000000..5066448e8f2f1c49a9727ca426bf967d3c96c074
--- /dev/null
+++ b/css/custom.css
@@ -0,0 +1,18 @@
+
+#yml_code {
+    height: 600px;
+    flex-grow: inherit;
+    overflow-y: auto;
+}
+
+#json_code {
+    height: 600px;
+    flex-grow: inherit;
+    overflow-y: auto;
+}
+
+#gpu_code {
+    height: 300px;
+    flex-grow: inherit;
+    overflow-y: auto;
+}
diff --git a/data/finetuned/configs/config.json b/data/finetuned/configs/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..07c9e677c66fcd9d1e0ac666d976fc27e8cd0ebc
--- /dev/null
+++ b/data/finetuned/configs/config.json
@@ -0,0 +1,106 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.0002,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 12,
+    "bf16_run": false,
+    "lr_decay": 0.99995,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": true,
+    "freeze_YUE_bert": false,
+    "freeze_emo": false
+  },
+  "data": {
+    "training_files": "data/finetune/train.list",
+    "validation_files": "data/finetune/val.list",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 1,
+    "cleaned_text": true,
+    "spk2id": {
+      "SPK1": 0
+    }
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": true,
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [
+      3,
+      7,
+      11
+    ],
+    "resblock_dilation_sizes": [
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ]
+    ],
+    "upsample_rates": [
+      8,
+      8,
+      2,
+      2,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      8,
+      2,
+      2
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 512,
+    "slm": {
+      "model": "./slm/wavlm-base-plus",
+      "sr": 16000,
+      "hidden": 768,
+      "nlayers": 13,
+      "initial_channel": 64
+    }
+  },
+  "version": "2.3"
+}
\ No newline at end of file
diff --git a/data/finetuned/models/G_43000.pth b/data/finetuned/models/G_43000.pth
new file mode 100644
index 0000000000000000000000000000000000000000..fedc778c20c4076c0855213e23ff33e71600ed60
--- /dev/null
+++ b/data/finetuned/models/G_43000.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06a5cb843b6eef268351b5c9ffe0886c9bd3ad1da07c3d96664d58e339b0cb66
+size 721223374
diff --git a/data_utils.py b/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee8d34c778114baeae5ad9b64ad28ef5d82a51f3
--- /dev/null
+++ b/data_utils.py
@@ -0,0 +1,371 @@
+import os
+import random
+import torch
+import torch.utils.data
+from tqdm import tqdm
+from tools.log import logger
+import commons
+from mel_processing import spectrogram_torch, mel_spectrogram_torch
+from utils import load_wav_to_torch, load_filepaths_and_text
+from text import cleaned_text_to_sequence
+from config import config
+
+"""Multi speaker version"""
+
+
+class TextAudioSpeakerLoader(torch.utils.data.Dataset):
+    """
+    1) loads audio, speaker_id, text pairs
+    2) normalizes text and converts them to sequences of integers
+    3) computes spectrograms from audio files.
+    """
+
+    def __init__(self, audiopaths_sid_text, hparams):
+        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
+        self.max_wav_value = hparams.max_wav_value
+        self.sampling_rate = hparams.sampling_rate
+        self.filter_length = hparams.filter_length
+        self.hop_length = hparams.hop_length
+        self.win_length = hparams.win_length
+        self.sampling_rate = hparams.sampling_rate
+        self.spk_map = hparams.spk2id
+        self.hparams = hparams
+
+        self.use_mel_spec_posterior = getattr(
+            hparams, "use_mel_posterior_encoder", False
+        )
+        if self.use_mel_spec_posterior:
+            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
+
+        self.cleaned_text = getattr(hparams, "cleaned_text", False)
+
+        self.add_blank = hparams.add_blank
+        self.min_text_len = getattr(hparams, "min_text_len", 1)
+        self.max_text_len = getattr(hparams, "max_text_len", 384)
+
+        random.seed(1234)
+        random.shuffle(self.audiopaths_sid_text)
+        self._filter()
+
+    def _filter(self):
+        """
+        Filter text & store spec lengths
+        """
+        # Store spectrogram lengths for Bucketing
+        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
+        # spec_length = wav_length // hop_length
+
+        audiopaths_sid_text_new = []
+        lengths = []
+        skipped = 0
+        logger.info("Init dataset...")
+        for _id, spk, language, text, phones, tone, word2ph in tqdm(
+            self.audiopaths_sid_text
+        ):
+            audiopath = f"{_id}"
+            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
+                phones = phones.split(" ")
+                tone = [int(i) for i in tone.split(" ")]
+                word2ph = [int(i) for i in word2ph.split(" ")]
+                audiopaths_sid_text_new.append(
+                    [audiopath, spk, language, text, phones, tone, word2ph]
+                )
+                lengths.append(os.path.getsize(audiopath) //
+                               (2 * self.hop_length))
+            else:
+                skipped += 1
+        logger.info(
+            "skipped: "
+            + str(skipped)
+            + ", total: "
+            + str(len(self.audiopaths_sid_text))
+        )
+        self.audiopaths_sid_text = audiopaths_sid_text_new
+        self.lengths = lengths
+
+    def get_audio_text_speaker_pair(self, audiopath_sid_text):
+        # separate filename, speaker_id and text
+        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
+
+        phones, tone, language = self.get_text(
+            text, word2ph, phones, tone, language, audiopath
+        )
+
+        spec, wav = self.get_audio(audiopath)
+        sid = torch.LongTensor([int(self.spk_map[sid])])
+
+        return (phones, spec, wav, sid, tone, language)
+
+    def get_audio(self, filename):
+        audio, sampling_rate = load_wav_to_torch(filename)
+        if sampling_rate != self.sampling_rate:
+            raise ValueError(
+                "{} {} SR doesn't match target {} SR".format(
+                    filename, sampling_rate, self.sampling_rate
+                )
+            )
+        audio_norm = audio / self.max_wav_value
+        audio_norm = audio_norm.unsqueeze(0)
+        spec_filename = filename.replace(".wav", ".spec.pt")
+        if self.use_mel_spec_posterior:
+            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
+        try:
+            spec = torch.load(spec_filename)
+        except:
+            if self.use_mel_spec_posterior:
+                spec = mel_spectrogram_torch(
+                    audio_norm,
+                    self.filter_length,
+                    self.n_mel_channels,
+                    self.sampling_rate,
+                    self.hop_length,
+                    self.win_length,
+                    self.hparams.mel_fmin,
+                    self.hparams.mel_fmax,
+                    center=False,
+                )
+            else:
+                spec = spectrogram_torch(
+                    audio_norm,
+                    self.filter_length,
+                    self.sampling_rate,
+                    self.hop_length,
+                    self.win_length,
+                    center=False,
+                )
+            spec = torch.squeeze(spec, 0)
+            if config.train_ms_config.spec_cache:
+                torch.save(spec, spec_filename)
+        return spec, audio_norm
+
+    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
+        phone, tone, language = cleaned_text_to_sequence(
+            phone, tone, language_str)
+        if self.add_blank:
+            phone = commons.intersperse(phone, 0)
+            tone = commons.intersperse(tone, 0)
+            language = commons.intersperse(language, 0)
+            for i in range(len(word2ph)):
+                word2ph[i] = word2ph[i] * 2
+            word2ph[0] += 1
+
+        phone = torch.LongTensor(phone)
+        tone = torch.LongTensor(tone)
+        language = torch.LongTensor(language)
+        return phone, tone, language
+
+    def get_sid(self, sid):
+        sid = torch.LongTensor([int(sid)])
+        return sid
+
+    def __getitem__(self, index):
+        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
+
+    def __len__(self):
+        return len(self.audiopaths_sid_text)
+
+
+class TextAudioSpeakerCollate:
+    """Zero-pads model inputs and targets"""
+
+    def __init__(self, return_ids=False):
+        self.return_ids = return_ids
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text, audio and speaker identities
+        PARAMS
+        ------
+        batch: [text_normalized, spec_normalized, wav_normalized, sid]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        _, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
+        )
+
+        max_text_len = max([len(x[0]) for x in batch])
+        max_spec_len = max([x[1].size(1) for x in batch])
+        max_wav_len = max([x[2].size(1) for x in batch])
+
+        text_lengths = torch.LongTensor(len(batch))
+        spec_lengths = torch.LongTensor(len(batch))
+        wav_lengths = torch.LongTensor(len(batch))
+        sid = torch.LongTensor(len(batch))
+
+        text_padded = torch.LongTensor(len(batch), max_text_len)
+        tone_padded = torch.LongTensor(len(batch), max_text_len)
+        language_padded = torch.LongTensor(len(batch), max_text_len)
+
+        spec_padded = torch.FloatTensor(
+            len(batch), batch[0][1].size(0), max_spec_len)
+        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+        text_padded.zero_()
+        tone_padded.zero_()
+        language_padded.zero_()
+        spec_padded.zero_()
+        wav_padded.zero_()
+
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+
+            text = row[0]
+            text_padded[i, : text.size(0)] = text
+            text_lengths[i] = text.size(0)
+
+            spec = row[1]
+            spec_padded[i, :, : spec.size(1)] = spec
+            spec_lengths[i] = spec.size(1)
+
+            wav = row[2]
+            wav_padded[i, :, : wav.size(1)] = wav
+            wav_lengths[i] = wav.size(1)
+
+            sid[i] = row[3]
+
+            tone = row[4]
+            tone_padded[i, : tone.size(0)] = tone
+
+            language = row[5]
+            language_padded[i, : language.size(0)] = language
+
+        return (
+            text_padded,
+            text_lengths,
+            spec_padded,
+            spec_lengths,
+            wav_padded,
+            wav_lengths,
+            sid,
+            tone_padded,
+            language_padded,
+        )
+
+
+class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
+    """
+    Maintain similar input lengths in a batch.
+    Length groups are specified by boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
+
+    It removes samples which are not included in the boundaries.
+    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
+    """
+
+    def __init__(
+        self,
+        dataset,
+        batch_size,
+        boundaries,
+        num_replicas=None,
+        rank=None,
+        shuffle=True,
+    ):
+        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+        self.lengths = dataset.lengths
+        self.batch_size = batch_size
+        self.boundaries = boundaries
+
+        self.buckets, self.num_samples_per_bucket = self._create_buckets()
+        self.total_size = sum(self.num_samples_per_bucket)
+        self.num_samples = self.total_size // self.num_replicas
+
+    def _create_buckets(self):
+        buckets = [[] for _ in range(len(self.boundaries) - 1)]
+        for i in range(len(self.lengths)):
+            length = self.lengths[i]
+            idx_bucket = self._bisect(length)
+            if idx_bucket != -1:
+                buckets[idx_bucket].append(i)
+
+        try:
+            for i in range(len(buckets) - 1, 0, -1):
+                if len(buckets[i]) == 0:
+                    buckets.pop(i)
+                    self.boundaries.pop(i + 1)
+            assert all(len(bucket) > 0 for bucket in buckets)
+        # When one bucket is not traversed
+        except Exception as e:
+            print("Bucket warning ", e)
+            for i in range(len(buckets) - 1, -1, -1):
+                if len(buckets[i]) == 0:
+                    buckets.pop(i)
+                    self.boundaries.pop(i + 1)
+
+        num_samples_per_bucket = []
+        for i in range(len(buckets)):
+            len_bucket = len(buckets[i])
+            total_batch_size = self.num_replicas * self.batch_size
+            rem = (
+                total_batch_size - (len_bucket % total_batch_size)
+            ) % total_batch_size
+            num_samples_per_bucket.append(len_bucket + rem)
+        return buckets, num_samples_per_bucket
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+
+        indices = []
+        if self.shuffle:
+            for bucket in self.buckets:
+                indices.append(torch.randperm(
+                    len(bucket), generator=g).tolist())
+        else:
+            for bucket in self.buckets:
+                indices.append(list(range(len(bucket))))
+
+        batches = []
+        for i in range(len(self.buckets)):
+            bucket = self.buckets[i]
+            len_bucket = len(bucket)
+            if len_bucket == 0:
+                continue
+            ids_bucket = indices[i]
+            num_samples_bucket = self.num_samples_per_bucket[i]
+
+            # add extra samples to make it evenly divisible
+            rem = num_samples_bucket - len_bucket
+            ids_bucket = (
+                ids_bucket
+                + ids_bucket * (rem // len_bucket)
+                + ids_bucket[: (rem % len_bucket)]
+            )
+
+            # subsample
+            ids_bucket = ids_bucket[self.rank:: self.num_replicas]
+
+            # batching
+            for j in range(len(ids_bucket) // self.batch_size):
+                batch = [
+                    bucket[idx]
+                    for idx in ids_bucket[
+                        j * self.batch_size: (j + 1) * self.batch_size
+                    ]
+                ]
+                batches.append(batch)
+
+        if self.shuffle:
+            batch_ids = torch.randperm(len(batches), generator=g).tolist()
+            batches = [batches[i] for i in batch_ids]
+        self.batches = batches
+
+        assert len(self.batches) * self.batch_size == self.num_samples
+        return iter(self.batches)
+
+    def _bisect(self, x, lo=0, hi=None):
+        if hi is None:
+            hi = len(self.boundaries) - 1
+
+        if hi > lo:
+            mid = (hi + lo) // 2
+            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
+                return mid
+            elif x <= self.boundaries[mid]:
+                return self._bisect(x, lo, mid)
+            else:
+                return self._bisect(x, mid + 1, hi)
+        else:
+            return -1
+
+    def __len__(self):
+        return self.num_samples // self.batch_size
diff --git a/default_config.yml b/default_config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..72358f5ca95a2908bd4cfbbeac0f9b66eb855b93
--- /dev/null
+++ b/default_config.yml
@@ -0,0 +1,177 @@
+# 全局配置
+# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml
+
+# 拟提供通用路径配置，统一存放数据，避免数据放得很乱
+# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
+# 不填或者填空则路径为相对于项目根目录的路径
+dataset_path: "Data/"
+
+# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
+mirror: ""
+openi_token: ""  # openi token
+
+# resample 音频重采样配置
+# 注意， “:” 后需要加空格
+resample:
+  # 目标重采样率
+  sampling_rate: 44100
+  # 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样
+  # 请填入相对于datasetPath的相对路径
+  in_dir: "audios/raw" # 相对于根目录的路径为 /datasetPath/in_dir
+  # 音频文件重采样后输出路径
+  out_dir: "audios/wavs"
+
+
+# preprocess_text 数据集预处理相关配置
+# 注意， “:” 后需要加空格
+preprocess_text:
+  # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
+  transcription_path: "filelists/你的数据集文本.list"
+  # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
+  cleaned_path: ""
+  # 训练集路径
+  train_path: "filelists/train.list"
+  # 验证集路径
+  val_path: "filelists/val.list"
+  # 配置文件路径
+  config_path: "config.json"
+  # 每个语言的验证集条数
+  val_per_lang: 4
+  # 验证集最大条数，多于的会被截断并放到训练集中
+  max_val_total: 12
+  # 是否进行数据清洗
+  clean: true
+
+
+# bert_gen 相关配置
+# 注意， “:” 后需要加空格
+bert_gen:
+  # 训练数据集配置文件路径
+  config_path: "config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
+  # 该选项同时决定了get_bert_feature的默认设备
+  device: "cuda"
+  # 使用多卡推理
+  use_multi_device: false
+
+# emo_gen 相关配置
+# 注意， “:” 后需要加空格
+emo_gen:
+  # 训练数据集配置文件路径
+  config_path: "config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
+  device: "cuda"
+  # 使用多卡推理
+  use_multi_device: false
+
+# train 训练配置
+# 注意， “:” 后需要加空格
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+    # 可以填写任意名的环境变量
+    # THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
+  # 底模设置
+  base:
+    use_base_model: false
+    repo_id: "Stardust_minus/Bert-VITS2"
+    model_image: "Bert-VITS2_2.3底模" # openi网页的模型名
+  # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
+  model: "models"
+  # 配置文件路径
+  config_path: "config.json"
+  # 训练使用的worker，不建议超过CPU核心数
+  num_workers: 16
+  # 关闭此项可以节约接近70%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。
+  spec_cache: False
+  # 保存的检查点数量，多于此数目的权重会被删除来节省空间。
+  keep_ckpts: 8
+
+
+# webui webui配置
+# 注意， “:” 后需要加空格
+webui:
+  # 推理设备
+  device: "cuda"
+  # 模型路径
+  model: "models/G_8000.pth"
+  # 配置文件路径
+  config_path: "config.json"
+  # 端口号
+  port: 7860
+  # 是否公开部署，对外网开放
+  share: false
+  # 是否开启debug模式
+  debug: false
+  # 语种识别库，可选langid, fastlid
+  language_identification_library: "langid"
+
+
+# server-fastapi配置
+# 注意， “:” 后需要加空格
+# 注意，本配置下的所有配置均为相对于根目录的路径
+server:
+  # 端口号
+  port: 5000
+  # 模型默认使用设备：但是当前并没有实现这个配置。
+  device: "cuda"
+  # 需要加载的所有模型的配置，可以填多个模型，也可以不填模型，等网页成功后手动加载模型
+  # 不加载模型的配置格式：删除默认给的两个模型配置，给models赋值 [ ]，也就是空列表。参考模型2的speakers 即 models: [ ]
+  # 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。
+  # 也可以不填模型，等网页加载成功后手动填写models。
+  models:
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cuda"
+      # 模型默认使用的语言
+      language: "HAKKA"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      # 暂时不用填写，当前尚未实现按人区分配置
+      speakers:
+        - speaker: "科比"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1
+        - speaker: "五条悟"
+          sdp_ratio: 0.3
+          noise_scale: 0.7
+          noise_scale_w: 0.8
+          length_scale: 0.5
+        - speaker: "安倍晋三"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1.2
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cpu"
+      # 模型默认使用的语言
+      language: "JP"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      speakers: [ ] # 也可以不填
+
+# 百度翻译开放平台 api配置
+# api接入文档 https://api.fanyi.baidu.com/doc/21
+# 请不要在github等网站公开分享你的app id 与 key
+translate:
+  # 你的APPID
+  "app_key": ""
+  # 你的密钥
+  "secret_key": ""
diff --git a/emotional/clap-htsat-fused/.gitattributes b/emotional/clap-htsat-fused/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..c7d9f3332a950355d5a77d85000f05e6f45435ea
--- /dev/null
+++ b/emotional/clap-htsat-fused/.gitattributes
@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/emotional/clap-htsat-fused/README.md b/emotional/clap-htsat-fused/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9153cca1e30833467025c36c0d14d1cd9d4d9c37
--- /dev/null
+++ b/emotional/clap-htsat-fused/README.md
@@ -0,0 +1,107 @@
+---
+license: apache-2.0
+---
+# Model card for CLAP
+
+Model card for CLAP: Contrastive Language-Audio Pretraining
+
+![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png)
+
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Usage](#usage)
+3. [Uses](#uses)
+4. [Citation](#citation)
+
+# TL;DR
+
+The abstract of the paper states that:
+
+> Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.
+
+
+# Usage
+
+You can use this model for zero shot audio classification or extracting audio and/or textual features.
+
+# Uses
+
+## Perform zero-shot audio classification
+
+### Using `pipeline`
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+dataset = load_dataset("ashraq/esc50")
+audio = dataset["train"]["audio"][-1]["array"]
+
+audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-fused")
+output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
+print(output)
+>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
+```
+
+## Run the model:
+
+You can also get the audio and text embeddings using `ClapModel`
+
+### Run the model on CPU:
+
+```python
+from datasets import load_dataset
+from transformers import ClapModel, ClapProcessor
+
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = librispeech_dummy[0]
+
+model = ClapModel.from_pretrained("laion/clap-htsat-fused")
+processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
+
+inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
+audio_embed = model.get_audio_features(**inputs)
+```
+
+### Run the model on GPU:
+
+```python
+from datasets import load_dataset
+from transformers import ClapModel, ClapProcessor
+
+librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = librispeech_dummy[0]
+
+model = ClapModel.from_pretrained("laion/clap-htsat-fused").to(0)
+processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
+
+inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
+audio_embed = model.get_audio_features(**inputs)
+```
+
+
+# Citation
+
+If you are using this model for your work, please consider citing the original paper:
+```
+@misc{https://doi.org/10.48550/arxiv.2211.06687,
+  doi = {10.48550/ARXIV.2211.06687},
+
+  url = {https://arxiv.org/abs/2211.06687},
+
+  author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
+
+  keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
+
+  title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation},
+
+  publisher = {arXiv},
+
+  year = {2022},
+
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```
diff --git a/emotional/clap-htsat-fused/config.json b/emotional/clap-htsat-fused/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..7907d3cde2ed499c697371287e288aa2beded3f9
--- /dev/null
+++ b/emotional/clap-htsat-fused/config.json
@@ -0,0 +1,207 @@
+{
+  "_commit_hash": null,
+  "architectures": [
+    "ClapModel"
+  ],
+  "audio_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "aff_block_r": 4,
+    "architectures": null,
+    "attention_probs_dropout_prob": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "depths": [
+      2,
+      2,
+      6,
+      2
+    ],
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "drop_path_rate": 0.0,
+    "early_stopping": false,
+    "enable_fusion": true,
+    "enable_patch_fusion": true,
+    "enable_patch_layer_norm": true,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "flatten_patch_embeds": true,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "fusion_num_hidden_layers": 2,
+    "fusion_type": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "mlp_ratio": 4.0,
+    "model_type": "clap_audio_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": [
+      4,
+      8,
+      16,
+      32
+    ],
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_classes": 527,
+    "num_hidden_layers": 4,
+    "num_mel_bins": 64,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_embed_input_channels": 1,
+    "patch_embeds_hidden_size": 96,
+    "patch_size": 4,
+    "patch_stride": [
+      4,
+      4
+    ],
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+    "projection_dim": 512,
+    "projection_hidden_act": "relu",
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+    "pruned_heads": {},
+    "qkv_bias": true,
+    "remove_invalid_values": false,
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+    "tf_legacy_loss": false,
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+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
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+    "torchscript": false,
+    "transformers_version": "4.27.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "window_size": 8
+  },
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 14.285714285714285,
+  "model_type": "clap",
+  "num_hidden_layers": 16,
+  "projection_dim": 512,
+  "projection_hidden_act": "relu",
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
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+    "classifier_dropout": null,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "fusion_hidden_size": 768,
+    "fusion_num_hidden_layers": 2,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-12,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 514,
+    "min_length": 0,
+    "model_type": "clap_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim": 512,
+    "projection_hidden_act": "relu",
+    "projection_hidden_size": 768,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.27.0.dev0",
+    "type_vocab_size": 1,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 50265
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null
+}
diff --git a/emotional/clap-htsat-fused/merges.txt b/emotional/clap-htsat-fused/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956
--- /dev/null
+++ b/emotional/clap-htsat-fused/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
+Ġ t
+Ġ a
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+Ġ P
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+Ġc on
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+Ġe x
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+Ġha ve
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+Ġs h
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+igh t
+ar t
+m ent
+Ġa l
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+en d
+- -
+al l
+Ġ O
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+Ġc h
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+â Ģ
+ou t
+Ġ J
+Ġa b
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+Ġh is
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diff --git a/emotional/clap-htsat-fused/preprocessor_config.json b/emotional/clap-htsat-fused/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d439910a3317697a54f06ad5083af1810c27d803
--- /dev/null
+++ b/emotional/clap-htsat-fused/preprocessor_config.json
@@ -0,0 +1,22 @@
+{
+  "chunk_length_s": 10,
+  "feature_extractor_type": "ClapFeatureExtractor",
+  "feature_size": 64,
+  "fft_window_size": 1024,
+  "frequency_max": 14000,
+  "frequency_min": 50,
+  "hop_length": 480,
+  "max_length_s": 10,
+  "n_fft": 1024,
+  "nb_frequency_bins": 513,
+  "nb_max_frames": 1000,
+  "nb_max_samples": 480000,
+  "padding": "repeatpad",
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "ClapProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 48000,
+  "top_db": null,
+  "truncation": "fusion"
+}
diff --git a/emotional/clap-htsat-fused/special_tokens_map.json b/emotional/clap-htsat-fused/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e
--- /dev/null
+++ b/emotional/clap-htsat-fused/special_tokens_map.json
@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}
diff --git a/emotional/clap-htsat-fused/tokenizer.json b/emotional/clap-htsat-fused/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..1d192010b9fa42aebf86daf4d585098ab39e7f99
--- /dev/null
+++ b/emotional/clap-htsat-fused/tokenizer.json
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+      "Ġint ensive",
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+      "j in",
+      "Ġadv ise",
+      "p al",
+      "ĠH alo",
+      "Ġdes cent",
+      "Ġsimpl er",
+      "Ġbo oth",
+      "ST R",
+      "L ater",
+      "ĠC ave",
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+      "Ġf ist",
+      "Ġshot gun",
+      "su pp",
+      "Ġrob bery",
+      "E ffect",
+      "Ġobsc ure",
+      "ĠProf essional",
+      "Ġemb assy",
+      "Ġmilit ant",
+      "Ġinc arcer",
+      "Ġgener ates",
+      "Ġlaun ches",
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+      "Ġsh aft",
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+      "ĠJo el",
+      "ĠD rew",
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+      "ĠApp arently",
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+      "ĠF E",
+      "Ġb ore",
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+      "Ġbroad ly",
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+      "Ġh ills",
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+      "h art",
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+      "C ong",
+      "ĠOr acle",
+      "ĠL ords",
+      "Ġscholars hip",
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+      "ro v",
+      "Ġqu o",
+      "Ġprem ise",
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+      "cl ub",
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+      "Ġn od",
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+      "Ġdecl aring",
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+      "Ġdistingu ished",
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+      "int eg",
+      "ĠC ele",
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+      "Ġhier archy",
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+      "ĠÎ ¼",
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+      "Ġur ine",
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+      "ourge ois",
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+      "Ġfil med",
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+      "Ġz ombies",
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+      "Ġnon etheless",
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+      "s ix",
+      "ĠSh a",
+      "Ġrest oration",
+      ") \\",
+      "W S",
+      "oy s",
+      "Ġillust rate",
+      "with out",
+      "12 6",
+      "ĠâĶ Ĥ",
+      "Ġpick up",
+      "n els",
+      "Ġ ....",
+      "f ood",
+      "ĠF en",
+      ") ?",
+      "Ġphenomen a",
+      "Ġcompan ions",
+      "ĠW rite",
+      "Ġsp ill",
+      "Ġbr idges",
+      "ĠUp dated",
+      "ĠF o",
+      "Ġinsect s",
+      "ASH INGTON",
+      "Ġsc are",
+      "il tr",
+      "ĠZh ang",
+      "Ġsever ity",
+      "Ġind ul",
+      "14 9",
+      "ĠCo ffee",
+      "Ġnorm s",
+      "Ġp ulse",
+      "ĠF T",
+      "Ġhorr ific",
+      "ĠDest roy",
+      "ĠJ SON",
+      "Ġo live",
+      "Ġdiscuss es",
+      "R est",
+      "E lect",
+      "ĠW inn",
+      "ĠSurv iv",
+      "ĠH ait",
+      "S ure",
+      "op ed",
+      "Ġro oted",
+      "ĠS ke",
+      "ĠBron ze",
+      "Ġl ol",
+      "Def ault",
+      "Ġcommod ity",
+      "red ited",
+      "Ġliber tarian",
+      "Ġforb idden",
+      "Ġgr an",
+      "à ¨",
+      "Ġl ag",
+      "en z",
+      "dri ve",
+      "Ġmathemat ics",
+      "Ġw ires",
+      "Ġcrit ically",
+      "Ġcarb ohyd",
+      "ĠChance llor",
+      "ĠEd die",
+      "Ġban ning",
+      "ĠF ri",
+      "Ġcompl ications",
+      "et ric",
+      "ĠBangl adesh",
+      "Ġband width",
+      "St op",
+      "ĠOrig inally",
+      "Ġhalf way",
+      "yn asty",
+      "sh ine",
+      "Ġt ales",
+      "rit ies",
+      "av ier",
+      "Ġspin ning",
+      "ĠWH O",
+      "Ġneighbour hood",
+      "b ach",
+      "Ġcommer ce",
+      "ĠS le",
+      "B U",
+      "Ġentreprene ur",
+      "Ġpecul iar",
+      "ĠCom ments",
+      "f re",
+      "3 20",
+      "IC S",
+      "Ġimag ery",
+      "ĠCan on",
+      "ĠElect ronic",
+      "sh ort",
+      "( (",
+      "D ig",
+      "Ġcomm em",
+      "u ced",
+      "Ġincl ined",
+      "ĠSum mon",
+      "Ġcl iff",
+      "ĠMed iterranean",
+      "Ġpo etry",
+      "Ġprosper ity",
+      "ĠRe ce",
+      "Ġp ills",
+      "m ember",
+      "Ġfin ale",
+      "un c",
+      "ĠG ig",
+      "ä ½",
+      "Ġl od",
+      "Ġback ward",
+      "- +",
+      "ĠFor ward",
+      "Ġth ri",
+      "s ure",
+      "Ġso ap",
+      "ĠF X",
+      "R ES",
+      "ĠSe xual",
+      "oul os",
+      "Ġfool ish",
+      "Ġright eous",
+      "Ġco ff",
+      "terror ism",
+      "ust ain",
+      "ot er",
+      "Ġab uses",
+      "ne xt",
+      "Ġab usive",
+      "Ġthere after",
+      "Ġprohib ition",
+      "ĠS UP",
+      "Ġd ip",
+      "Ġr ipped",
+      "Ġinher ited",
+      "Ġb ats",
+      "st ru",
+      "G T",
+      "Ġflaw ed",
+      "ph abet",
+      "Ġf og",
+      "do ors",
+      "Ġim aging",
+      "Ġdig its",
+      "ĠHung ary",
+      "Ġar rog",
+      "Ġteach ings",
+      "Ġprotocol s",
+      "ĠB anks",
+      "à ¸",
+      "p ound",
+      "ĠC urt",
+      ".\" )",
+      ". /",
+      "Ġex emption",
+      "end ix",
+      "ĠM ull",
+      "Ġimpro ves",
+      "ĠG amer",
+      "d imensional",
+      "I con",
+      "ĠMarg aret",
+      "St atus",
+      "d ates",
+      "Ġint ends",
+      "Ġdep ict",
+      "Ġpark ed",
+      "J oe",
+      "ĠMar ines",
+      "chn ology",
+      "! ).",
+      "Ġjud ged",
+      "Ġwe ights",
+      "R ay",
+      "Ġapart ments",
+      "he ster",
+      "Ġrein force",
+      "Ġoff ender",
+      "occ up",
+      "Ġs ore",
+      "e pt",
+      "ĠPH P",
+      "ĠB row",
+      "Ġauthor ization",
+      "ĠR isk",
+      "ĠDel aware",
+      "ĠQ U",
+      "Ġnot ifications",
+      "Ġsun light",
+      "Ġex clude",
+      "d at",
+      "Ġm esh",
+      "ĠSud an",
+      "Ġbelong ed",
+      "Ġsub way",
+      "Ġno on",
+      "ĠInter ior",
+      "ol ics",
+      "ĠL akers",
+      "Ġc oding",
+      "Dis claimer",
+      "Cal if",
+      "O ld",
+      "Ġdis l",
+      "???? ?",
+      "Ġconfir ms",
+      "Ġrecruit ment",
+      "Ġhom icide",
+      "Cons ider",
+      "ĠJeff rey",
+      "ft y",
+      "} ;",
+      "Ġobject ion",
+      "do ing",
+      "ĠLe o",
+      "W ant",
+      "Ġgl ow",
+      "ĠClar ke",
+      "ĠNorm an",
+      "Ġver ification",
+      "Ġpack et",
+      "ĠForm ula",
+      "Ġpl ag",
+      "es ville",
+      "Ġshout ing",
+      "Ġo v",
+      "ĠR EC",
+      "ĠB ub",
+      "Ġn inth",
+      "Ġener g",
+      "Ġvalid ity",
+      "Ġup s",
+      "j ack",
+      "Ġneighbor ing",
+      "ĠN ec",
+      "ew orks",
+      "ĠH ab",
+      "are z",
+      "Ġsp ine",
+      "Ġevent ual",
+      "ĠLe aders",
+      "ĠC arn",
+      "Ġprob ation",
+      "Ġrom ance",
+      "ms g",
+      "ĠMechan ical",
+      "ER Y",
+      "R ock",
+      "Ġpart isan",
+      "N ode",
+      "ass ets",
+      "min ent",
+      "Ġforeign ers",
+      "Ġtest ify",
+      "ĠUs ually",
+      "l ords",
+      "ĠG ren",
+      "ĠPow ell",
+      "BI L",
+      "Ġs r",
+      "Ġadd ict",
+      "Ġshell s",
+      "Ġs igh",
+      "ĠY ale",
+      "tern ity",
+      "Ġ7 50",
+      "E U",
+      "ĠR ifle",
+      "Ġpat ron",
+      "em a",
+      "ĠB annon",
+      "an ity",
+      "Ġtrop ical",
+      "ĠV II",
+      "c ross",
+      "Every thing",
+      "ĠIS O",
+      "Ġhum ble",
+      "ass ing",
+      "ĠF IG",
+      "Ġupd ating",
+      "ys on",
+      "Ġcal cium",
+      "Ġcompet ent",
+      "Ġste ering",
+      "Pro t",
+      "ĠS Y",
+      "ĠFin als",
+      "ĠR ug",
+      "15 9",
+      "13 7",
+      "ĠG olf",
+      "Ġ12 6",
+      "Ġaccommod ation",
+      "ĠHug hes",
+      "Ġaest hetic",
+      "art isan",
+      "ĠTw ilight",
+      "Ġpr ince",
+      "ĠAgric ulture",
+      "ĠDis co",
+      "Ġpreced ent",
+      "Ġtyp ing",
+      "author ized",
+      "O ption",
+      "ĠA ub",
+      "l ishes",
+      "ach t",
+      "m ag",
+      "P eter",
+      "ĠU FO",
+      "mont on",
+      "ĠL ith",
+      "Ġa rom",
+      "Ġsec uring",
+      "Ġconf ined",
+      "priv ate",
+      "Ġsw ords",
+      "Ġmark ers",
+      "Ġmetab olic",
+      "se lect",
+      "ĠCur se",
+      "ĠO t",
+      "g ressive",
+      "Ġinc umb",
+      "ĠS aga",
+      "Ġpr iced",
+      "Ġclear ance",
+      "Cont ent",
+      "Ġdr illing",
+      "Ġnot ices",
+      "Ġb ourgeois",
+      "Ġv est",
+      "Ġcook ie",
+      "ĠGuard ians",
+      "ry s",
+      "in yl",
+      "Ġ12 4",
+      "Ġpl ausible",
+      "on gh",
+      "ĠOd in",
+      "Ġconcept ion",
+      "ĠY uk",
+      "ĠBaghd ad",
+      "ĠFl ag",
+      "Aust ral",
+      "ĠI BM",
+      "Ġintern ationally",
+      "ĠWiki Leaks",
+      "I ED",
+      "Ġc yn",
+      "Ġcho oses",
+      "ĠP ill",
+      "Ġcomb ining",
+      "Ġrad i",
+      "ĠMoh ammed",
+      "def ense",
+      "atch ing",
+      "Sub ject",
+      "ic iency",
+      "Fr ame",
+      "Ġ{ \"",
+      "Ġche ss",
+      "Ġtim er",
+      "19 0",
+      "Ġt in",
+      "Ġord inance",
+      "emet ery",
+      "Ġacc using",
+      "Ġnotice able",
+      "Ġcent res",
+      "Ġl id",
+      "ĠM ills",
+      "img ur",
+      "Ġz oom",
+      "erg ic",
+      "Ġcomp ression",
+      "pr im",
+      "f ind",
+      "Ġsur g",
+      "Ġp and",
+      "ĠK ee",
+      "ĠCh ad",
+      "cell ence",
+      "oy le",
+      "Ġsocial ism",
+      "ĠT ravis",
+      "ĠM Hz",
+      "Ġgu ild",
+      "ALL Y",
+      "ĠSub scribe",
+      "ĠRel ated",
+      "Ġoccur rence",
+      "itch ing",
+      "Ġfict ional",
+      "Ġcr ush",
+      "ĠE A",
+      "c od",
+      "m ix",
+      "ĠTri ple",
+      "Ġretrie ve",
+      "Ġstimul us",
+      "Ġpsych iat",
+      "ĠDo or",
+      "Ġhomosexual ity",
+      "Ġelement ary",
+      "Ġcell ular",
+      "id ian",
+      "ĠL aun",
+      "Ġintrig uing",
+      "Ġfo am",
+      "ĠB ass",
+      "id i",
+      "its u",
+      "Ġass ure",
+      "Ġcongr at",
+      "Ġbusiness man",
+      "ĠBo ost",
+      "cl ose",
+      "Ġl ied",
+      "Ġsc iences",
+      "ĠO mega",
+      "ĠG raphics",
+      "Ġ< =",
+      "sp oken",
+      "Ġconnect ivity",
+      "S aturday",
+      "ĠAven gers",
+      "Ġto ggle",
+      "Ġank le",
+      "Ġnational ist",
+      "mod el",
+      "ĠP ool",
+      "ophob ia",
+      "V ar",
+      "ĠM ons",
+      "ator ies",
+      "Ġaggress ively",
+      "C lear",
+      "For ge",
+      "act ers",
+      "Ġhed ge",
+      "Ġpip es",
+      "Ġbl unt",
+      "Ġs q",
+      "Ġremote ly",
+      "W ed",
+      "as ers",
+      "Ġref riger",
+      "Ġt iles",
+      "Ġresc ued",
+      "Ġcompr ised",
+      "ins ky",
+      "Ġman if",
+      "avan augh",
+      "Ġprol ifer",
+      "Ġal igned",
+      "x ml",
+      "Ġtri v",
+      "Ġcoord ination",
+      "ĠP ER",
+      "ĠQu ote",
+      "13 4",
+      "b f",
+      "ĠS aw",
+      "Ġtermin ation",
+      "Ġ19 0",
+      "Ġadd itions",
+      "Ġtri o",
+      "Ġproject ions",
+      "Ġpositive ly",
+      "Ġin clusive",
+      "Ġmem br",
+      "19 90",
+      "old er",
+      "Ġpract iced",
+      "ink le",
+      "Ar ch",
+      "Ġstar ters",
+      "ari us",
+      "Ġinter mediate",
+      "ĠBen ef",
+      "ĠK iller",
+      "Ġinter ventions",
+      "ĠK il",
+      "ĠF lying",
+      "In v",
+      "Ġprem ature",
+      "Ġpsych iatric",
+      "Ġind ie",
+      "Ġcoll ar",
+      "ĠRain bow",
+      "af i",
+      "Ġdis ruption",
+      "ĠFO X",
+      "cast ing",
+      "Ġmis dem",
+      "c ro",
+      "Ġw ipe",
+      "ard on",
+      "Ġb ast",
+      "ĠTom my",
+      "ĠRepresent ative",
+      "Ġbell y",
+      "ĠP O",
+      "ĠBre itbart",
+      "13 2",
+      "Ġmess aging",
+      "Sh ould",
+      "Ref erences",
+      "ĠG RE",
+      "ist ical",
+      "L P",
+      "ĠC av",
+      "ĠC razy",
+      "Ġintu itive",
+      "ke eping",
+      "ĠM oss",
+      "Ġdiscont in",
+      "ĠMod ule",
+      "Ġun related",
+      "ĠPract ice",
+      "ĠTrans port",
+      "Ġstatist ically",
+      "orn s",
+      "Ġs ized",
+      "p u",
+      "Ġca f",
+      "ĠWorld s",
+      "ĠRod gers",
+      "ĠL un",
+      "ĠCom ic",
+      "l iving",
+      "Ġc ared",
+      "Ġclim bed",
+      ") {",
+      "Ġconsist ed",
+      "Ġmed ieval",
+      "fol k",
+      "Ġh acked",
+      "Ġd ire",
+      "ĠHerm ione",
+      "Ġt ended",
+      "ce ans",
+      "D aniel",
+      "w ent",
+      "Ġlegisl ators",
+      "Ġred es",
+      "g ames",
+      "Ġg n",
+      "am iliar",
+      "Ġ+ +",
+      "gg y",
+      "th reat",
+      "Ġmag net",
+      "Ġper ceive",
+      "Ġz ip",
+      "Ġindict ment",
+      "Ġcrit ique",
+      "g ard",
+      "ĠSaf e",
+      "ĠC ream",
+      "Ġad vent",
+      "ob a",
+      "Ġv owed",
+      "ous ands",
+      "Ġsk i",
+      "Ġabort ions",
+      "u art",
+      "Ġstun ned",
+      "Ġadv ancing",
+      "Ġlack ed",
+      "Ġ\\ \"",
+      "Ġsch izophren",
+      "Ġeleg ant",
+      "Ġconf erences",
+      "Ġcance led",
+      "ĠHud son",
+      "ĠHop efully",
+      "Ġtr ump",
+      "Ġfrequ encies",
+      "Ġmet eor",
+      "ĠJun ior",
+      "ĠFle et",
+      "ĠMal colm",
+      "ĠT ools",
+      "Ġ ........",
+      "Ġh obby",
+      "ĠEurope ans",
+      "Ġ15 00",
+      "ĠInt o",
+      "Ġs way",
+      "ĠApp ro",
+      "ĠCom pl",
+      "Comm unity",
+      "Ġt ide",
+      "ĠSum mit",
+      "ä »",
+      "Ġinter vals",
+      "ĠE ther",
+      "Ġhabit at",
+      "ĠSteven s",
+      "lish ing",
+      "ĠDom ain",
+      "Ġtrig gers",
+      "Ġch asing",
+      "Ġchar m",
+      "ĠFl ower",
+      "it ored",
+      "Ġbless ing",
+      "Ġtext ures",
+      "F ive",
+      "Ġliqu or",
+      "R P",
+      "F IN",
+      "Ġ19 62",
+      "C AR",
+      "Un known",
+      "Ġres il",
+      "ĠL ily",
+      "Ġabund ance",
+      "Ġpredict able",
+      "r ar",
+      "Ġbull shit",
+      "le en",
+      "che t",
+      "M or",
+      "M uch",
+      "ä ¹",
+      "Ġemphas ized",
+      "Ġcr ust",
+      "Ġprim itive",
+      "Ġenjoy able",
+      "ĠPict ures",
+      "Ġteam mate",
+      "pl er",
+      "ĠT ol",
+      "ĠK ane",
+      "Ġsummon ed",
+      "th y",
+      "ram a",
+      "ĠH onda",
+      "Ġreal izing",
+      "Ġquick er",
+      "Ġconcent rate",
+      "cle ar",
+      "Ġ2 10",
+      "ĠErd ogan",
+      "ar is",
+      "Ġrespond s",
+      "ĠB I",
+      "Ġelig ibility",
+      "Ġpus hes",
+      "ĠId aho",
+      "Ġagg rav",
+      "Ġru ins",
+      "ur ations",
+      "Ġb ans",
+      "Ġan at",
+      "sh are",
+      "Ġgr ind",
+      "h in",
+      "um en",
+      "Ġut ilities",
+      "ĠYan kees",
+      "Ġdat abases",
+      "ĠD D",
+      "Ġdispl aced",
+      "Ġdepend encies",
+      "Ġstim ulation",
+      "h un",
+      "h ouses",
+      "ĠP retty",
+      "ĠRaven s",
+      "ĠTOD AY",
+      "Ġassoci ates",
+      "Ġthe rape",
+      "cl ed",
+      "Ġde er",
+      "Ġrep airs",
+      "rent ice",
+      "Ġrecept ors",
+      "Ġrem ed",
+      "ĠC e",
+      "Ġmar riages",
+      "Ġball ots",
+      "ĠSold ier",
+      "Ġhilar ious",
+      "op l",
+      "13 8",
+      "Ġinherent ly",
+      "Ġignor ant",
+      "Ġb ounce",
+      "ĠE aster",
+      "REL ATED",
+      "ĠCur rency",
+      "E V",
+      "ãĥ ŀ",
+      "ĠLe ad",
+      "Ġdece ased",
+      "B rien",
+      "ĠMus k",
+      "J S",
+      "Ġmer ge",
+      "heart ed",
+      "c reat",
+      "m itt",
+      "m und",
+      "ĠâĢ ĭ",
+      "ĠB ag",
+      "Ġproject ion",
+      "Ġj ava",
+      "ĠStand ards",
+      "ĠLeon ard",
+      "Ġcoc onut",
+      "ĠPop ulation",
+      "Ġtra ject",
+      "Ġimp ly",
+      "Ġcur iosity",
+      "ĠD B",
+      "ĠF resh",
+      "ĠP or",
+      "Ġheav ier",
+      "ne ys",
+      "gom ery",
+      "Ġdes erved",
+      "Ġphr ases",
+      "ĠG C",
+      "Ġye ast",
+      "d esc",
+      "De ath",
+      "Ġreb oot",
+      "Ġmet adata",
+      "IC AL",
+      "Ġrep ay",
+      "ĠInd ependence",
+      "Ġsubur ban",
+      "ical s",
+      "Ġat op",
+      "Ġall ocation",
+      "gener ation",
+      "ĠG ram",
+      "Ġmoist ure",
+      "Ġp ine",
+      "ĠLiber als",
+      "Ġa ides",
+      "Ġund erest",
+      "ĠBer ry",
+      "Ġcere mon",
+      "3 70",
+      "ast rous",
+      "ĠPir ates",
+      "Ġt ense",
+      "ĠIndust ries",
+      "ĠApp eals",
+      "ĠN ear",
+      "Ġè£ı ç",
+      "Ġlo vers",
+      "ĠC AP",
+      "ĠC raw",
+      "Ġg iants",
+      "Ġeffic acy",
+      "E lement",
+      "ĠBeh avior",
+      "ĠToy ota",
+      "Ġint est",
+      "P riv",
+      "A I",
+      "Ġmaneu ver",
+      "Ġperfect ion",
+      "Ġb ang",
+      "p aper",
+      "r ill",
+      "Ge orge",
+      "b order",
+      "in ters",
+      "ĠS eth",
+      "Ġcl ues",
+      "ĠLe vi",
+      "ĠRe venue",
+      "14 7",
+      "Ġv apor",
+      "Ġfortun ate",
+      "Ġthreat ens",
+      "Ġve t",
+      "Ġdepend ency",
+      "ers ed",
+      "art icle",
+      "ĠBl izzard",
+      "Ġch lor",
+      "Ġmin us",
+      "ĠB ills",
+      "Ġcryptoc urrency",
+      "Ġmetabol ism",
+      "ter ing",
+      "Ġp estic",
+      "step s",
+      "ĠTre asure",
+      "ract ed",
+      "ĠConst ant",
+      "Ġtem p",
+      "13 9",
+      "ĠDet ective",
+      "ur ally",
+      "Ġrecover ing",
+      "Ġcort ex",
+      "Ġ14 4",
+      "cl osed",
+      "Ġprejud ice",
+      "aun ted",
+      "Ġstorm s",
+      "ĠN OW",
+      "Ġmach inery",
+      "Add ress",
+      "Ġcompe lled",
+      "27 0",
+      "Ġdesp air",
+      "b ane",
+      "Ġveget able",
+      "Ġbed s",
+      "Lear n",
+      "Ġcolor ful",
+      "Ġsp ike",
+      "Ġmarg ins",
+      "Ġsymp athy",
+      "Ġworks hop",
+      "ĠC BC",
+      "S at",
+      "Ġburn s",
+      "ĠG ender",
+      "Ġ12 9",
+      "ĠC able",
+      "Ġdeb ts",
+      "ĠThe resa",
+      "Ġreflect ing",
+      "Ġa irst",
+      "Ġr im",
+      "ram id",
+      "Ġweakness es",
+      "W rit",
+      "ogg le",
+      "t i",
+      "ĠCh arge",
+      "Ġwe ighed",
+      "Ġ( .",
+      "Ġl aughter",
+      "Ġrou ter",
+      "ĠDemocr acy",
+      "D ear",
+      "Ġhas ht",
+      "Ġd y",
+      "Ġhint s",
+      "run ning",
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+      "ar us",
+      "M ass",
+      "res ult",
+      "asc us",
+      "Ġv intage",
+      "Ġcon qu",
+      "Ġwild ly",
+      "ac ist",
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+      "Ġprot agonist",
+      "st rom",
+      "te enth",
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+      "m ac",
+      "f illed",
+      "Ġre nown",
+      "it ives",
+      "Ġmot ive",
+      "ĠAnt ar",
+      "ĠM ann",
+      "ĠAd just",
+      "Ġrock ets",
+      "Ġtrou bling",
+      "e i",
+      "Ġorgan isms",
+      "ass is",
+      "Christ ian",
+      "Ġ14 5",
+      "ĠH ass",
+      "Ġsw all",
+      "Ġw ax",
+      "ĠSurv ival",
+      "V S",
+      "ĠM urd",
+      "v d",
+      "stand ard",
+      "Ġdrag ons",
+      "Ġacceler ation",
+      "r ational",
+      "f inal",
+      "Ġp aired",
+      "ĠE thereum",
+      "Ġinterf aces",
+      "Ġres ent",
+      "Ġartif acts",
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+      "Ġcompet itor",
+      "ĠNich olas",
+      "ĠSur face",
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+      "ĠT ot",
+      "Ġeconom ically",
+      "Ġorgan ised",
+      "Ġen forced",
+      "in ho",
+      "Ġvar ieties",
+      "Ġab dom",
+      "ĠBa iley",
+      "id av",
+      "ĠSal v",
+      "p aid",
+      "Ġalt itude",
+      "ess ert",
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+      "op oulos",
+      "Ġprofess ors",
+      "igg s",
+      "ĠF ate",
+      "he y",
+      "Ġ3 000",
+      "D ist",
+      "Ġtw ins",
+      "c ill",
+      "ĠM aps",
+      "Ġtra ps",
+      "Ġwe ed",
+      "ĠK iss",
+      "Ġy oga",
+      "Ġrecip ients",
+      "ĠWest minster",
+      "Ġpool s",
+      "ĠWal mart",
+      "18 8",
+      "ĠSchool s",
+      "att ack",
+      "ĠAR M",
+      "par agraph",
+      "W arning",
+      "j l",
+      "Ġself ish",
+      "anche z",
+      "ĠHe ights",
+      "F re",
+      "ĠS oph",
+      "Ġ --------------------------------",
+      "t ml",
+      "33 3",
+      "Ġraid s",
+      "Ġsatell ites",
+      "KE Y",
+      "Ġlast s",
+      "Ñ Ĥ",
+      "In s",
+      "ĠD ame",
+      "Ġunp redict",
+      "// /",
+      "gh ai",
+      "Ġart illery",
+      "Ġcru ise",
+      "Ġg el",
+      "ĠCabin et",
+      "Ġbl ows",
+      "ĠE sp",
+      "Ġprox imity",
+      "ot he",
+      "ĠSk ills",
+      "ĠU pper",
+      "ob o",
+      "ĠN DP",
+      "Ġenjoy s",
+      "Ġrepe ating",
+      "ĠConst ruction",
+      "ĠQuest ions",
+      "H illary",
+      "Ġu int",
+      "Ġprocess ors",
+      "ĠGib son",
+      "ĠMult iple",
+      "q a",
+      "ĠB om",
+      "ĠM iles",
+      "vent ional",
+      "Ġhur ts",
+      "s kin",
+      "ĠA IDS",
+      "Ġadvis ers",
+      "ĠR oot",
+      "Ġmethod ology",
+      "ĠD ale",
+      "Ġdet on",
+      "ĠKnow ledge",
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+      "Ġ12 1",
+      "Ġconnect s",
+      "C y",
+      "ĠD anger",
+      "Ġcontribut ors",
+      "ĠB ent",
+      "Ġbr ass",
+      "ĠGun s",
+      "int o",
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+      "Ġbro ker",
+      "bal ance",
+      "Ġlength s",
+      "Ġv ic",
+      "Ġaver aging",
+      "Ġappropri ately",
+      "ĠCamer a",
+      "Ġsand wich",
+      "ĠCD C",
+      "Ġcoord inate",
+      "Ġnav ig",
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+      "l aim",
+      "Ġbra ke",
+      "Ġextrem ist",
+      "ĠW ake",
+      "ĠM end",
+      "ĠT iny",
+      "ĠC OL",
+      "ĠR F",
+      "ĠD ual",
+      "ĠW ine",
+      "C ase",
+      "Ġref ined",
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+      "Ġb apt",
+      "ĠCar b",
+      "ĠS add",
+      "ĠMin neapolis",
+      "PD F",
+      "Ear ly",
+      "ĠH idden",
+      "I ts",
+      "ĠT IME",
+      "Ġp ap",
+      "Ġcommission ed",
+      "ĠF ew",
+      "ĠCol ts",
+      "ĠB ren",
+      "Ġbot hered",
+      "Ġlike wise",
+      "Ex per",
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+      "n n",
+      "ĠM itch",
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+      "Ġregist ry",
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+      "00 000",
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+      "D ead",
+      "H uman",
+      "En abled",
+      "ĠMar ie",
+      "Ġch ill",
+      "ĠParad ise",
+      "Ġstar ring",
+      "ĠLat ino",
+      "ĠProt ocol",
+      "ĠE VER",
+      "Ġsuppl iers",
+      "m essage",
+      "ĠBro ck",
+      "Ġser um",
+      "âĸĪâĸĪ âĸĪâĸĪ",
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+      "ues e",
+      "Ġar rows",
+      "And rew",
+      "Ġanten na",
+      "Ġ19 61",
+      "ĠB ark",
+      "Ġb ool",
+      "ãĤ ª",
+      "ĠSt orage",
+      "Ġrail way",
+      "Ġtoug her",
+      "ĠC ad",
+      "Ġwas hing",
+      "P y",
+      "' ]",
+      "em bed",
+      "ĠMem phis",
+      "ack le",
+      "Ġfam ously",
+      "ĠF ortunately",
+      "ov ies",
+      "Ġmind set",
+      "Ġsne ak",
+      "ĠD h",
+      "RA W",
+      "ĠSim pson",
+      "Ġliv est",
+      "Ġland mark",
+      "Ġc ement",
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+      "ĠCour se",
+      "in el",
+      "Ġch uck",
+      "id ate",
+      "gl obal",
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+      "ĠBr anch",
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+      "ĠM K",
+      "Ġunbel iev",
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+      "Orig inal",
+      "ĠRe ceived",
+      "ĠTrans fer",
+      "ĠAr g",
+      "In put",
+      "ĠStr ategy",
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+      "the ning",
+      "D ri",
+      "Ġsent encing",
+      "umbn ail",
+      "Ġp ins",
+      "re cy",
+      "Ġs iblings",
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+      "ĠNorth west",
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+      "ĠSak ura",
+      "C omb",
+      "ĠB our",
+      "Ġinadequ ate",
+      "ĠK ash",
+      "Ġus ername",
+      "ĠImpro ve",
+      "Ġbatt ling",
+      "ĠM AC",
+      "Ġcurric ulum",
+      "Ġs oda",
+      "ĠC annon",
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+      "sp ons",
+      "De cember",
+      "Ġw icked",
+      "ĠP engu",
+      "Ġdict ators",
+      "ĠHe arts",
+      "og yn",
+      "Ġsimilar ities",
+      "ĠSt ats",
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+      "it ations",
+      "\": [",
+      "Ġh over",
+      "ĠList en",
+      "s ch",
+      "S und",
+      "Ġc ad",
+      "ĠPar ks",
+      "Ġl ur",
+      "Ġhy pe",
+      "ĠL em",
+      "N AME",
+      "is ure",
+      "Fr iday",
+      "Ġshoot s",
+      "Ġclos es",
+      "Ġd b",
+      "ĠR idge",
+      "ĠDiff erent",
+      "Ġrepl ies",
+      "ĠBroad way",
+      "op ers",
+      "Ġint oler",
+      "ĠZe us",
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+      "Ġpropri etary",
+      "Ġrequest ing",
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+      "ĠM IN",
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+      "Ġexp ans",
+      "Ġoil s",
+      "B ot",
+      "ĠCh and",
+      "Ġpr inter",
+      "Ġto pped",
+      "ĠP OL",
+      "ĠEar lier",
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+      "B rown",
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+      "ro e",
+      "ĠPro blem",
+      "ĠQu ad",
+      "Ġdecent ral",
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+      "Ġbo il",
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+      "Ġnurs es",
+      "Ob viously",
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+      "Ġcom r",
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+      "pt r",
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+      "Ġcar pet",
+      "ĠSp art",
+      "Ġj ungle",
+      "l ists",
+      "Ġbull ying",
+      "ĠNob el",
+      "ĠGl en",
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+      "Ġcho pped",
+      "gl ass",
+      "ĠW rest",
+      "Ġneutral ity",
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+      "Ġshel ves",
+      "Ġun constitutional",
+      "Ġreprodu ction",
+      "Ġmer chant",
+      "m ia",
+      "Ġmet rics",
+      "Ġexplos ives",
+      "ĠSon ia",
+      "Ġbod ily",
+      "Ġthick ness",
+      "Ġpredomin antly",
+      "ĠAb ility",
+      "Ġmon itored",
+      "IC H",
+      "Ġ] .",
+      "ĠMart inez",
+      "Ġvis ibility",
+      "Ġqu eries",
+      "Ġgen ocide",
+      "ĠWar fare",
+      "Qu ery",
+      "Ġstud ios",
+      "Ġemb ry",
+      "Ġcorrid or",
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+      "com plete",
+      "ĠM H",
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+      "ING S",
+      "Ġimpact ed",
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+      "w ik",
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+      "Ġam p",
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+      "ĠBeh ind",
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+      "Ġ( *",
+      "Ġt ennis",
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+      "Ġj ets",
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+      "Ġu mb",
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+      "Ġcoh ort",
+      "Ġmerch andise",
+      "Ġimp osing",
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+      "iv ia",
+      "ĠN aval",
+      "Ġoff ences",
+      "Ġmir acle",
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+      "ed ience",
+      "K it",
+      "ĠB in",
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+      "ĠC ities",
+      "Ġrig id",
+      "ĠRE AD",
+      "ĠNob le",
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+      "o iler",
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+      "d id",
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+      "Ġprint s",
+      "ĠMot ion",
+      "ron ics",
+      "ĠS ans",
+      "oss ibly",
+      "u ctions",
+      "Ġcolon ies",
+      "ĠDan ish",
+      "un it",
+      "Ġsp oil",
+      "Ġadvis ory",
+      "ber ries",
+      "Pl an",
+      "Ġspecific ation",
+      "op hers",
+      "ĠRes ource",
+      "Ġsh irts",
+      "prising ly",
+      "commun ications",
+      "Ġtriv ial",
+      "Ġmention ing",
+      "ise xual",
+      "Ġsupp lements",
+      "Ġsuper vision",
+      "B P",
+      "v or",
+      "Ġw it",
+      "Ġco oldown",
+      "Ġplaint iff",
+      "ĠReview s",
+      "ĠS ri",
+      "ĠM int",
+      "ĠSug ar",
+      "Ġafter ward",
+      "ĠPri est",
+      "ĠInvest ment",
+      "og ene",
+      "ĠT aking",
+      "Ġstretch ing",
+      "Ġinflamm ation",
+      "ĠTe hran",
+      "Ġl ining",
+      "Ġfree zing",
+      "ĠEnt ity",
+      "Ġins piring",
+      "spe cial",
+      "pr ice",
+      "Ġsu e",
+      "ĠP orter",
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+      "ET A",
+      "ĠD erek",
+      "ĠLu is",
+      "u o",
+      "ym ph",
+      "Ġex terior",
+      "ih il",
+      "ĠAsh ley",
+      "in ator",
+      "Ġnut rients",
+      "ĠTh rones",
+      "Ġfin ances",
+      "ĠIn spect",
+      "Ġspe cially",
+      "ĠRequ ired",
+      "ĠP TS",
+      "ĠViol ence",
+      "oint ed",
+      "sh ots",
+      "Ġex cerpt",
+      "co on",
+      "IN S",
+      "ĠG ri",
+      "Ġrecogn ised",
+      "We ek",
+      "You ng",
+      "Ġv om",
+      "is le",
+      "ĠCur ry",
+      "ĠBudd h",
+      "Ġnot ebook",
+      "Ġd urable",
+      "/ ?",
+      "ĠG ad",
+      "ĠP upp",
+      "Ġforg ive",
+      "p ark",
+      "Ġpersonal ities",
+      "an alysis",
+      "cl amation",
+      "Ġelev ator",
+      "Ġware house",
+      "ĠR ole",
+      "un n",
+      "Ġillust ration",
+      "ĠSc an",
+      "Ġatmosp heric",
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+      "AN C",
+      "rict ed",
+      "f u",
+      "01 0",
+      "Ġar che",
+      "Ġreward ed",
+      "akespe are",
+      "Ġintern ally",
+      "ĠR BI",
+      "alk er",
+      "Ġeleph ant",
+      "ow itz",
+      "ĠP izza",
+      "Ġbip artisan",
+      "é s",
+      "Ġslow ed",
+      "ĠSt ark",
+      "Ġover ride",
+      "OU S",
+      "Ġ3 20",
+      "undred s",
+      "ĠDe ck",
+      "ĠC ensus",
+      "be e",
+      "14 6",
+      "ot or",
+      "Ġ ip",
+      "Ġu b",
+      "oc ations",
+      "ĠBut ton",
+      "r ice",
+      "Ġc ripp",
+      "ff f",
+      "Ġorig inated",
+      "Ġoverwhel med",
+      "app a",
+      "Ġfore most",
+      "âĢ ij",
+      "ĠL EG",
+      "re lease",
+      "eat ured",
+      "at ches",
+      "Ġre ps",
+      "Ġl ending",
+      "ĠRe ference",
+      "ĠCl ient",
+      "16 5",
+      "vent h",
+      "Com plete",
+      "ĠPat rol",
+      "Ġsw orn",
+      "c am",
+      "Ġshut tle",
+      "ĠR alph",
+      "Ġh ometown",
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+      "on al",
+      "ĠB P",
+      "å ı",
+      "Ġpersu ade",
+      "ĠAlex and",
+      "Ġcomb ines",
+      "Ġv ivid",
+      "ĠL ag",
+      "Ġenc oding",
+      "Ġsal vation",
+      "w en",
+      "ĠRec overy",
+      "i ya",
+      "Un iversity",
+      "ĠB iden",
+      "Ġbud gets",
+      "ĠTex ans",
+      "f its",
+      "Ġhon ored",
+      "Ġp ython",
+      "T D",
+      "## #",
+      "cl one",
+      "Ġbl ink",
+      "ĠL iquid",
+      "Ġunemploy ed",
+      "Ġcl ashes",
+      "ĠCoun sel",
+      "Ġdirect ing",
+      "Ġpun ct",
+      "ĠFal cons",
+      "Ġsh ark",
+      "ĠDam ascus",
+      "Ġje ans",
+      "Ġemb ark",
+      "Ġse ize",
+      "Ġup wards",
+      "2 80",
+      "ĠE z",
+      "ĠAny thing",
+      "Ġex otic",
+      "l ower",
+      "ĠCreat or",
+      "ĠU m",
+      "Ġsubur bs",
+      "ber ger",
+      "ĠW end",
+      "Ġm int",
+      "ĠX X",
+      "ĠD ro",
+      "Ġsuff ers",
+      "Ġher b",
+      "t ree",
+      "Ġfrag ile",
+      "Ġflood ed",
+      "ĠAl cohol",
+      "ole an",
+      "ny der",
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+      "20 6",
+      "ĠMarket ing",
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+      "ĠT rou",
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+      "ble ms",
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+      "Ġper t",
+      "Ġuns ure",
+      "4 80",
+      "ale z",
+      "ult z",
+      "ĠOut side",
+      "Ġst yl",
+      "ĠUnder ground",
+      "Ġp anc",
+      "Ġd ictionary",
+      "Ġf oe",
+      "rim inal",
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+      "Ġj ailed",
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+      "ĠLu cy",
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+      "Ch o",
+      "Ġuns igned",
+      "ĠZe lda",
+      "ĠIns ider",
+      "ĠContin ued",
+      "Ġ13 3",
+      "ĠNar uto",
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+      "ĠW o",
+      "ãĤ ĵ",
+      "Ġpast or",
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+      "an throp",
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+      "Ġy elling",
+      "Ġdr astically",
+      "Ġe ject",
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+      "Ġth rive",
+      "ĠFre qu",
+      "ou x",
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+      "Ġ5 12",
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+      "ĠRes ident",
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+      "r il",
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+      "Ġhuge ly",
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+      "ustom ed",
+      "Ġsk ate",
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+      "Ġrecogn izing",
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+      "St ore",
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+      "C oll",
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+      "@@ @@",
+      "ĠT all",
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+      "names e",
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+      "c ig",
+      "iz ational",
+      "R ound",
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+      "ĠCole man",
+      "Ġadm itting",
+      "ĠPl ug",
+      "Ġbit coins",
+      "ĠSu z",
+      "Ġfair ness",
+      "Ġsupp lier",
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+      "o qu",
+      "M arc",
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+      "g ie",
+      "Ġend angered",
+      "Ġdest iny",
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+      "ol ia",
+      "ax is",
+      "Ġche at",
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+      "IC O",
+      "qu ote",
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+      "ا ÙĦ",
+      "Ġincumb ent",
+      "Ġcr unch",
+      "t ro",
+      "Ġsc oop",
+      "Ġblog ger",
+      "Ġsell ers",
+      "ure n",
+      "Ġmedic ines",
+      "ĠC aps",
+      "ĠAnim ation",
+      "ox y",
+      "Ġout ward",
+      "Ġinqu iries",
+      "22 9",
+      "Ġpsych ologist",
+      "ĠS ask",
+      "ev il",
+      "Ġcontam inated",
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+      "he rence",
+      "Ġbrand ed",
+      "ĠAbd ul",
+      "z h",
+      "Ġparagraph s",
+      "Ġmin s",
+      "Ġcor related",
+      "er b",
+      "Ġimp art",
+      "Ġmil estone",
+      "ĠSol utions",
+      "ot le",
+      "Ġunder cover",
+      "Ġmar ched",
+      "ĠCharg ers",
+      "f ax",
+      "ĠSec rets",
+      "Ġr uth",
+      "we ather",
+      "Ġfemin ine",
+      "Ġsh am",
+      "Ġprest igious",
+      "igg ins",
+      "Ġs ung",
+      "hist ory",
+      "ett le",
+      "gg ie",
+      "Ġout dated",
+      "ol and",
+      "Ġper ceptions",
+      "ĠS ession",
+      "ĠDod gers",
+      "u j",
+      "ĠE ND",
+      "D oc",
+      "Ġdefic iency",
+      "Gr and",
+      "ĠJ oker",
+      "Ġretro spect",
+      "Ġdiagn ostic",
+      "Ġharm less",
+      "Ġro gue",
+      "ĠA val",
+      "E qu",
+      "Ġtrans c",
+      "ĠRoberts on",
+      "ĠDep ending",
+      "ĠBurn s",
+      "iv o",
+      "Ġhost ility",
+      "F eatures",
+      "ĵ ĺ",
+      "Ġdis comfort",
+      "ĠL CD",
+      "spec ified",
+      "ĠEx pect",
+      "3 40",
+      "Ġimper ative",
+      "ĠReg ular",
+      "Ch inese",
+      "Ġstate wide",
+      "Ġsy mm",
+      "Ġlo ops",
+      "Ġaut umn",
+      "N ick",
+      "Ġsh aping",
+      "Ġqu ot",
+      "Ġc herry",
+      "ĠCross ref",
+      "è¦ ļéĨĴ",
+      "Stand ard",
+      "he ed",
+      "ĠD ell",
+      "ĠViet namese",
+      "Ġo st",
+      "ĠV alkyrie",
+      "O A",
+      "Ass ad",
+      "Ġreb ound",
+      "ĠTra ffic",
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+      "æ ĺ",
+      "ĠB uc",
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+      "Ġshel ters",
+      "Ġins isting",
+      "ĠCertain ly",
+      "ĠKenn eth",
+      "ĠT CP",
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+      "he ard",
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+      "iz a",
+      "ĠF Y",
+      "it cher",
+      "ĠD L",
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+      "Ġquarterback s",
+      "Ġh ull",
+      "Ġgo ogle",
+      "Ġto dd",
+      "ĠSter ling",
+      "ĠPl ate",
+      "Ġsp ying",
+      "mb ol",
+      "ĠReal m",
+      "ĠPro ced",
+      "ĠCr ash",
+      "Ġtermin ate",
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+      "gu ided",
+      "Ġun cover",
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+      "s ound",
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+      "Ġ13 9",
+      "Ġdesc ended",
+      "Ġrehab ilitation",
+      "Ġborrow ing",
+      "ĠB uch",
+      "Ġbl ur",
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+      "en za",
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+      "Ġtransl ates",
+      "M IN",
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+      "Ġpl ague",
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+      "d oms",
+      "Ġstret ches",
+      "ĠSp y",
+      "Ġstory telling",
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+      "ĠP ush",
+      "Ġtra ction",
+      "Ġin effective",
+      "ĠL una",
+      "Ġt apes",
+      "Ġanaly tics",
+      "erc ise",
+      "Ġprogram mes",
+      "ĠCar bon",
+      "Ġbeh old",
+      "he avy",
+      "ĠConserv ation",
+      "ĠF IR",
+      "Ġs ack",
+      "ter min",
+      "ric ks",
+      "Ġhous ed",
+      "Ġunus ually",
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+      "ĠMor oc",
+      "ed ay",
+      "Ġed itions",
+      "Ġsm arter",
+      "ĠB A",
+      "Ġout law",
+      "Ġvan ished",
+      "ib a",
+      "AL SE",
+      "ĠSil va",
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+      "C ould",
+      "Ġphilos opher",
+      "Ġevac uated",
+      "Sec ret",
+      "14 2",
+      "Ġvis as",
+      "ãĤ ¬",
+      "ĠM alt",
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+      "ĠN iger",
+      "ĠC airo",
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+      "ĠX ML",
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+      "Ġrein forced",
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+      "Ġscrew s",
+      "parent s",
+      "Ġo ceans",
+      "ma res",
+      "Ġbra kes",
+      "vas ive",
+      "Ġhell o",
+      "ĠS IM",
+      "rim p",
+      "Ġo re",
+      "ĠArm our",
+      "24 7",
+      "Ġterr ific",
+      "Ġt ones",
+      "14 1",
+      "ĠMin utes",
+      "Ep isode",
+      "Ġcur ves",
+      "Ġinflamm atory",
+      "Ġbat ting",
+      "ĠBeaut iful",
+      "L ay",
+      "Ġunp op",
+      "v able",
+      "Ġr iots",
+      "ĠTact ics",
+      "b augh",
+      "ĠC ock",
+      "Ġorg asm",
+      "ĠS as",
+      "Ġconstruct or",
+      "et z",
+      "G ov",
+      "Ġant agon",
+      "Ġthe at",
+      "Ġde eds",
+      "ha o",
+      "c uts",
+      "ĠMc Cl",
+      "Ġu m",
+      "ĠScient ists",
+      "Ġgrass roots",
+      "ys sey",
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+      "Ġsurf aced",
+      "Ġsh ades",
+      "Ġneighb ours",
+      "Ġad vertis",
+      "oy a",
+      "Ġmer ged",
+      "Up on",
+      "Ġg ad",
+      "Ġanticip ate",
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+      "Ġsl ogan",
+      "Ġdis respect",
+      "I ran",
+      "ĠT B",
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+      "Ġsubp oen",
+      "medi ately",
+      "OO OO",
+      "Ġwa iver",
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+      "ott esville",
+      "ĠHuff ington",
+      "J osh",
+      "ĠD H",
+      "M onday",
+      "ĠEll en",
+      "K now",
+      "x on",
+      "it ems",
+      "22 8",
+      "Ġf ills",
+      "ĠN ike",
+      "Ġcum ulative",
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+      "I r",
+      "Ġ ì",
+      "Ġfr iction",
+      "ig ator",
+      "Ġsc ans",
+      "ĠVi enna",
+      "ld om",
+      "Ġperform ers",
+      "P rim",
+      "Ġb idding",
+      "M ur",
+      "Ġlean ed",
+      "ĠPri x",
+      "al ks",
+      "Ġ[ âĢ¦]",
+      "ĠTw itch",
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+      "ĠG ir",
+      "Ġcall back",
+      "Ab stract",
+      "Ġacc ustomed",
+      "Ġfreed oms",
+      "ĠP G",
+      "ur acy",
+      "Ġl ump",
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+      "19 92",
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+      "Ġwor m",
+      "M atch",
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+      "I J",
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+      "Tri via",
+      "com pl",
+      "Ġnew born",
+      "Ġfant as",
+      "O wn",
+      "Ġ19 59",
+      "Ġsymp ath",
+      "Ġub iqu",
+      "Ġoutput s",
+      "Ġal lev",
+      "Ġpr ag",
+      "K evin",
+      "Ġfav ors",
+      "Ġbur ial",
+      "Ġn urt",
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+      "c ache",
+      "Ġ15 6",
+      "Ġunl ocks",
+      "te chn",
+      "M aking",
+      "Ġcon quer",
+      "ad ic",
+      "æ ĸ",
+      "Ġel f",
+      "Ġelect orate",
+      "ĠKurd s",
+      "ĠSt ack",
+      "ĠSam urai",
+      "Ġâ ĺħ",
+      "Ġ{ }",
+      "ĠS aid",
+      "ĠFall out",
+      "Ġkind ness",
+      "ĠCustom s",
+      "ĠBou levard",
+      "Ġhelicop ters",
+      "ot ics",
+      "ĠVe get",
+      "com ment",
+      "Ġcritic ised",
+      "Ġpol ished",
+      "ĠRem ix",
+      "ĠC ultural",
+      "Ġrec ons",
+      "Ġdo i",
+      "at em",
+      "Sc reen",
+      "Ġbar red",
+      "Com ments",
+      "ĠGener ally",
+      "Ġsl ap",
+      "7 20",
+      "V ari",
+      "p ine",
+      "Ġem pt",
+      "Ġh ats",
+      "ĠPlay ing",
+      "l ab",
+      "a verage",
+      "form s",
+      "ĠC otton",
+      "Ġcan s",
+      "ĠD ON",
+      "ĠSom alia",
+      "C rypt",
+      "ĠIncre ases",
+      "E ver",
+      "mod ern",
+      "Ġsur geon",
+      "3 000",
+      "Ġrandom ized",
+      "================================ ================================",
+      "B ern",
+      "im pl",
+      "ĠC OR",
+      "Ġpro claim",
+      "th ouse",
+      "Ġto es",
+      "Ġam ple",
+      "Ġpres erving",
+      "Ġdis bel",
+      "gr and",
+      "B esides",
+      "Ġsil k",
+      "ĠPat tern",
+      "h m",
+      "Ġenter prises",
+      "Ġaffidav it",
+      "ĠAdvis ory",
+      "Ġadvert ised",
+      "ĠRel igious",
+      "se ctions",
+      "psy ch",
+      "ĠField s",
+      "aw ays",
+      "Ġhasht ag",
+      "ĠNight mare",
+      "Ġv ampire",
+      "Ġfore nsic",
+      "rosso ver",
+      "n ar",
+      "Ġn avy",
+      "Ġvac ant",
+      "ĠD uel",
+      "Ġhall way",
+      "Ġface book",
+      "ident ally",
+      "ĠN RA",
+      "Ġm att",
+      "Ġhur ricane",
+      "ĠKir by",
+      "ĠP uzzle",
+      "Ġsk irt",
+      "ou st",
+      "du llah",
+      "Ġanal ogy",
+      "in ion",
+      "Ġtomat oes",
+      "ĠN V",
+      "ĠPe ak",
+      "ĠMe yer",
+      "Ġappoint ments",
+      "Ġm asc",
+      "Ġal ley",
+      "re hend",
+      "Ġchar ities",
+      "Ġund o",
+      "Ġdest inations",
+      "ĠTest ing",
+      "\"> </",
+      "Ġdest ined",
+      "Ġimp lements",
+      "ĠHar old",
+      "RE CT",
+      "Ġoptim ization",
+      "Ġkilomet res",
+      "Ġc md",
+      "Ġimpair ment",
+      "Ġun successful",
+      "Ġswift ly",
+      "ĠGlas gow",
+      "art en",
+      "ĠSh ares",
+      "ĠAn swer",
+      "ĠAl bum",
+      "Ġnut ritional",
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+      "ĠF ut",
+      "Ġbl oc",
+      "ĠN FC",
+      "Ġwholes ale",
+      "ĠC W",
+      "Ġneg lected",
+      "Ġlaun cher",
+      "Ġannounce ments",
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+      "com b",
+      "Ġrot ating",
+      "Ġrest s",
+      "ĠT icket",
+      "ched el",
+      "L ou",
+      "ĠV ic",
+      "Ġ\" '",
+      "Ġtem plates",
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+      "Ar c",
+      ":: ::",
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+      "Ġsched ules",
+      "Ġheter osexual",
+      "L INE",
+      "Ġhere in",
+      "Ġco erc",
+      "Ġdecre asing",
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+      "s udo",
+      "ĠInd igenous",
+      "Ġweigh s",
+      "Al ong",
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+      "ĠBeng als",
+      "70 7",
+      "Ġjoint s",
+      "ver ts",
+      "Ġ14 9",
+      "na ire",
+      "Ġsimpl est",
+      "Ġl ore",
+      "10 80",
+      "f iction",
+      "ĠDat abase",
+      "Ġreserv ation",
+      "Ġs ou",
+      "Ġsan ctuary",
+      "aud io",
+      "ap le",
+      "Ġveget arian",
+      "Ġanticip ation",
+      "m icro",
+      "Ġend uring",
+      "Ġdepart ed",
+      "Ġsidew alk",
+      "Ġprohib its",
+      "ĠF ont",
+      "Ġcomp ute",
+      "ĠS ect",
+      "Ġ15 8",
+      "B attle",
+      "Ġbom ber",
+      "Ġdist raction",
+      "Ġend ured",
+      "Ġpractition ers",
+      "Ġdistur bed",
+      "Ġdr ank",
+      "ord ered",
+      "Ġsurpr ises",
+      "se at",
+      "Sec urity",
+      "ĠW isdom",
+      "og o",
+      "Ġsub paragraph",
+      "ĠPen insula",
+      "ĠOrig ins",
+      "ire n",
+      "ĠP av",
+      "igg le",
+      "Ġgrat itude",
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+      "im an",
+      "ct r",
+      "ĠCa esar",
+      "c ould",
+      "g em",
+      "Ġsk ies",
+      "Ġch amp",
+      "Ġagree ing",
+      "F amily",
+      "D iv",
+      "17 6",
+      "Ġmess y",
+      "um ption",
+      "F ederal",
+      "ern o",
+      "ĠCh at",
+      "Bey ond",
+      "Ġdev ote",
+      "ĠW alsh",
+      "Ġdump ed",
+      "Ġaccum ulation",
+      "st ad",
+      "hib ition",
+      "Ġsm okers",
+      "Ġinspect or",
+      "F rench",
+      "iss an",
+      "ĠV ita",
+      "Ġresearch ing",
+      "R AM",
+      "ĠCelt ics",
+      "Ġcl oak",
+      "ĠTer ra",
+      "M ary",
+      "so ld",
+      "ĠD OM",
+      "mod s",
+      "Int el",
+      "Ġmult itude",
+      "ĠImpro ved",
+      "Ġrel iance",
+      "Ġartif act",
+      "Ġalarm ing",
+      "P rom",
+      "h on",
+      "T ION",
+      "med ium",
+      "Ġref lex",
+      "ĠEx cel",
+      "Ġweaken ed",
+      "16 3",
+      "2 24",
+      "Ġcost umes",
+      "Ġunique ly",
+      "Ġs orrow",
+      "Ġm ansion",
+      "w p",
+      "Ġsal v",
+      "ĠGro ve",
+      "bs p",
+      "ĠSn iper",
+      "ĠSh ipping",
+      "ĠP OW",
+      "Ġund is",
+      "Ġbrand ing",
+      "G irl",
+      "ĠAh mad",
+      "ĠL akes",
+      "ĠCore y",
+      "Ġinherit ance",
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+      "Ġpack ing",
+      "ĠP rest",
+      "D est",
+      "F W",
+      "Ġregul ator",
+      "l ocked",
+      "Ġcont ested",
+      "ĠMel issa",
+      "ĠD uc",
+      "Ġunpop ular",
+      "Ġst acked",
+      "Ġ19 17",
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+      "Ġst are",
+      "Ġassess ing",
+      "Ã ¸",
+      "Ġbe verages",
+      "Ġcompet itions",
+      "Ġstreng thening",
+      "al ong",
+      "ĠL ud",
+      "Ġmel ted",
+      "stan bul",
+      "Ġb ounty",
+      "EN C",
+      "ĠL ands",
+      "Ġdecl ares",
+      "Ġcustom ize",
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+      "ãĥ ¬",
+      "C M",
+      "ograph ics",
+      "ĠTem p",
+      "Ġcont ender",
+      "Ġins ign",
+      "ĠL AN",
+      "Ġdis asters",
+      "ins pired",
+      "Ġjud gments",
+      "ustain able",
+      "urs ion",
+      "Ġvar iance",
+      "ĠUlt imately",
+      "Ġ --------",
+      "u ador",
+      "ĠR X",
+      "Ġmel ting",
+      "ĠExt ended",
+      "ĠT we",
+      "M ajor",
+      "ĠB il",
+      "Ġsy rup",
+      "qu ick",
+      "ĠHold er",
+      "Ġinnoc ence",
+      "U LE",
+      "ĠM ight",
+      "99 99",
+      "Ġf al",
+      "Ġcontinu ity",
+      "Ġ19 53",
+      "ĠB S",
+      "st ill",
+      "L at",
+      "ĠAb use",
+      "Ġun supported",
+      "xxxx xxxx",
+      "Ġinst itute",
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+      "ĠP ep",
+      "W estern",
+      "ĠC ause",
+      "ĠFr ag",
+      "ĠAr s",
+      "à ¥",
+      "ast ics",
+      "Ġb ishop",
+      "Ġcross es",
+      "Ġ15 4",
+      "ĠUp grade",
+      "Ġmit igate",
+      "ĠRay mond",
+      "Mod s",
+      "Ġtom ato",
+      "Ġst umbled",
+      "Ġdiff ers",
+      "In itial",
+      "ĠR aspberry",
+      "Ġign ores",
+      "Ġt ant",
+      "Ã ł",
+      "Ġrel ay",
+      "Ġb isexual",
+      "Ġconf ession",
+      "Ġd ement",
+      "in as",
+      "ĠHe ather",
+      "pl atform",
+      "dri ving",
+      "bour g",
+      "ĠM ush",
+      "Ġhy ster",
+      "Det ails",
+      "Ġdr ift",
+      "ĠW ald",
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+      "or f",
+      "Ġexp ire",
+      "ĠP unch",
+      "zy me",
+      "g old",
+      "Ġunp aid",
+      "ĠT rent",
+      "Ġun armed",
+      "Ġill icit",
+      "ĠT ottenham",
+      "Ġsm ash",
+      "Intern ational",
+      "ink er",
+      "Ġst ing",
+      "ĠSadd am",
+      "ĠAR T",
+      "Ġtruth s",
+      "b irth",
+      "Ġso ber",
+      "ĠN it",
+      "Ġ ib",
+      "Ġus able",
+      "Ġst acks",
+      "ĠSy lv",
+      "Ġnort heast",
+      "Ġdom ination",
+      "ĠM our",
+      "EN SE",
+      "ĠMe asure",
+      "Ġprogram mer",
+      "Ġ< -",
+      "18 2",
+      "ĠCond ition",
+      "Ġback yard",
+      "ir ling",
+      "ĠJ eb",
+      "ĠCre ed",
+      "ĠH ang",
+      "ĠCOM P",
+      "F ER",
+      "ĠIs h",
+      "Ġdetect ives",
+      "------------ ---",
+      "ĠMess enger",
+      "Ġlo oph",
+      "Ġgate way",
+      "15 1",
+      "ĠMaterial s",
+      "ĠD T",
+      "Ġdo omed",
+      "od o",
+      "Ġslic es",
+      "Ġemail ed",
+      "ĠPer l",
+      "Ġren ov",
+      "UT H",
+      "ody nam",
+      "ĠSouth west",
+      "get ic",
+      "ĠT PP",
+      "Ġoptim ism",
+      "ĠT ow",
+      "ul ators",
+      "prot ected",
+      "y les",
+      "Â «",
+      "Ġex ile",
+      "en v",
+      "P rop",
+      "ĠZimmer man",
+      "Ù İ",
+      "C a",
+      "om aly",
+      "ãĥ Ĩ",
+      "Ġrail road",
+      "L ee",
+      "23 2",
+      "Ġrepl icate",
+      "Ġcomfort ably",
+      "act ly",
+      "Ġr av",
+      "Ġtelesc ope",
+      "Ġhonest y",
+      "ĠPe pper",
+      "ĠBr ing",
+      "Ġric hest",
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+      "Ġapp end",
+      "Ġstub born",
+      "ĠC ind",
+      "T ell",
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+      "Ġunbeliev able",
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+      "EC K",
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+      "Ġac et",
+      "Ġsuccess ive",
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+      "Ġ14 2",
+      "ort ium",
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+      "Ġp ear",
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+      "Ġc ues",
+      "Ġunder went",
+      "ĠW S",
+      "Ġexagger ated",
+      "Ġdub ious",
+      "Ġfl ashing",
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+      "Ġcomp ressed",
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+      "Ġpin ch",
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+      "Ġcollabor ative",
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+      "m is",
+      "ĠRes ult",
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+      "el ia",
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+      "Soft ware",
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+      "Ġprim aries",
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+      "osh op",
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+      "Ġsett ling",
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+      "Ġreb uilding",
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+      "ĠR um",
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+      "ĠF uel",
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+      "Ġupgr ading",
+      "M ale",
+      "Ġe lic",
+      "Ġrelent less",
+      "ĠCo venant",
+      "ap esh",
+      "ĠT rop",
+      "T y",
+      "pro duction",
+      "art y",
+      "Ġpun ches",
+      "ak o",
+      "cyclop edia",
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+      "ĠHD MI",
+      "Ġ14 1",
+      "Ġf oil",
+      "Item Image",
+      "ĠF G",
+      "Ġimplement ations",
+      "ĠP om",
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+      "Ġaw ait",
+      "Ġ3 30",
+      "am us",
+      "Ġumb rella",
+      "Ġfore see",
+      "se par",
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+      "Ġperipher al",
+      "S ay",
+      "ĠExper t",
+      "In c",
+      "Ġwithd rew",
+      "ĠAnd ers",
+      "f ried",
+      "Ġradio active",
+      "ĠOp ening",
+      "Ġboard ing",
+      "ĠN D",
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+      "W P",
+      "ĠAct s",
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+      "v ic",
+      "ĠM ighty",
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+      "a er",
+      "Ġthank ful",
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+      "mo il",
+      "Ġpredict ing",
+      "26 6",
+      "ch oice",
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+      "oss om",
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+      "P en",
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+      "Ġpack aged",
+      "Ġheight ened",
+      "c ot",
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+      "Ġst amps",
+      "m age",
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+      "Ġextrem ism",
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+      "Ġcl own",
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+      "ĠL ar",
+      "Ġconsequ ently",
+      "p resent",
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+      "th ose",
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+      "d an",
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+      "Ġfair y",
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+      "link ed",
+      "Ġur gency",
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+      "Ġcoun cill",
+      "r ab",
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+      "Ġconf essed",
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+      "Ġharm on",
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+      "ĠNe utral",
+      "D ouble",
+      "Ġst aple",
+      "ĠSH A",
+      "Brit ish",
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+      "u ph",
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+      "29 5",
+      "Ġpin ned",
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+      "ĠD ocker",
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+      "f orth",
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+      "ĠA AA",
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+      "ĠS ally",
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+      "Ġpro ps",
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+      "Ġc ove",
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+      "We ight",
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+      "ĠHe ath",
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+      "ĠJ UST",
+      "Ġw aving",
+      "u pload",
+      "Ġins ider",
+      "ĠPro gressive",
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+      "c oll",
+      "F B",
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+      "ĠCann ot",
+      "ĠM ile",
+      "Ġembr acing",
+      "ĠSil k",
+      "39 3",
+      "ot ers",
+      "F D",
+      "Ġday light",
+      "al ias",
+      "ĠBrig ade",
+      "ĠHann ah",
+      "Ġcler gy",
+      "Ġs outheast",
+      "Ġalcohol ic",
+      "Ġpropos es",
+      "liv ion",
+      "Ġcalcul ating",
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+      "e ight",
+      "ĠInd y",
+      "pl ays",
+      "ĠP ik",
+      "Ġdom est",
+      "Ġforg iveness",
+      "ĠR ings",
+      "pat ient",
+      "kins on",
+      "M ont",
+      "ig ible",
+      "; \"",
+      "Ġperiod ically",
+      "amm ad",
+      "ĠBr itt",
+      "p ard",
+      "Ġarbit ration",
+      "ĠSchne ider",
+      "ĠCorpor ate",
+      "ĠMay a",
+      "Ġsn akes",
+      "a um",
+      "Ġbl asted",
+      "Ġmyster ies",
+      "Ġrev ive",
+      "oc amp",
+      "ĠD odge",
+      "ĠOper a",
+      "27 9",
+      "Ġor phan",
+      "Ġspec ifies",
+      "ĠM ets",
+      "D uration",
+      "H en",
+      "Ġfire works",
+      "Ġprosec ute",
+      "ĠTill erson",
+      "d p",
+      "us age",
+      "l iness",
+      "ĠDeb ian",
+      "Ġ2 24",
+      "ris es",
+      "ĠIn fect",
+      "at ra",
+      "ĠR R",
+      "ĠL or",
+      "d iff",
+      "ĠCharl eston",
+      "Ġac oustic",
+      "Ġam use",
+      "3 30",
+      "Ġc er",
+      "ĠT ac",
+      "Ġ[ +",
+      "Ġcard iac",
+      "ĠRestaur ant",
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+      "Ġf uzz",
+      "Ġbit es",
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+      "Ġbr ighter",
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+      "ext ra",
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+      "ĠChrist ine",
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+      "Ġant it",
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+      "B IT",
+      "ãĤ °",
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+      "ĠIr ving",
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+      "t aking",
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+      "Ġl ig",
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+      "Ġforecast s",
+      "Ġsp iders",
+      "Or gan",
+      "ĠParticip ants",
+      "ĠO ps",
+      "des ign",
+      "Cl ose",
+      "Ġfact o",
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+      "Ġcor ro",
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+      "ĠBrend an",
+      "ĠM X",
+      "ĠT S",
+      "ĠSt ri",
+      "Ġstake holders",
+      "ĠMillenn ium",
+      "Ġtransfer ring",
+      "J ud",
+      "Ġt ac",
+      "Ġ16 00",
+      "ĠSD K",
+      "r b",
+      "Ġinterpret ations",
+      "ĠS G",
+      "Ġup stairs",
+      "ĠHar vest",
+      "Ġvag ina",
+      "Ġing est",
+      "x f",
+      "ĠOr ion",
+      "ĠJoe y",
+      "Ġsand wic",
+      "Ġimm ortal",
+      "Ġfl ipped",
+      "ort ex",
+      "threat ening",
+      "Ġsn iper",
+      "Ġconver ts",
+      "Ġinstall ations",
+      "ĠBul gar",
+      "ors che",
+      "m ails",
+      "Ġl ure",
+      "Ġnarrow ly",
+      "Ġgren ade",
+      "ĠG ing",
+      "Ġunder wear",
+      "------------ --",
+      "Ġch ased",
+      "ĠV AL",
+      "Ġparent ing",
+      "ĠH amb",
+      "ĠBl az",
+      "Ġanarch ist",
+      "ĠMed ian",
+      "ĠProgram s",
+      "Î ½",
+      "Ġob j",
+      "ĠN okia",
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+      "at ism",
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+      "Ġric her",
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+      "Ġf ury",
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+      "Ġo ak",
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+      "ee le",
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+      "Ġdepri ved",
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+      "Ġair lines",
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+      "Form at",
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+      "ãĥ Ĭ",
+      "dri ver",
+      "Ġtrans cend",
+      "S ettings",
+      "ĠPro secut",
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+      "F K",
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+      "ĠSp icer",
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+      "Ġcorrect ions",
+      "Ġdop amine",
+      "ĠA Z",
+      "C ut",
+      "Ġro omm",
+      "Ġspec ulate",
+      "H ash",
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+      "on el",
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+      "lo op",
+      "Ġl ent",
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+      "an ic",
+      "d uty",
+      "28 5",
+      "Ġthank ed",
+      "Ġbetray ed",
+      "l ift",
+      "Ġsouth west",
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+      "ĠB od",
+      "Ġkey words",
+      "A verage",
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+      "á ¹",
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+      "iox id",
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+      "Ġpast a",
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+      "Ġcontext s",
+      "ĠAm ount",
+      "ĠSp ike",
+      "Ġtot als",
+      "Ġorgan izer",
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+      "Y Y",
+      "ĠNort heast",
+      "m oney",
+      "grad ation",
+      "Ġgoal keeper",
+      "ĠH ear",
+      "Ġste ak",
+      "ĠBuzz Feed",
+      "Ġsole mn",
+      "ĠSc and",
+      "Ġpo pping",
+      "Ġad here",
+      "ĠAl leg",
+      "by te",
+      "ĠW olver",
+      "Ġun in",
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+      "it ud",
+      "Ġmim ic",
+      "ib us",
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+      "ĠKee per",
+      "i ating",
+      "Ġde ception",
+      "Ġlear nt",
+      "Ġdi ary",
+      "Ġcond itional",
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+      "Ġinv oke",
+      "ien ced",
+      "å Ī",
+      "ĠP ont",
+      "Ġcell phone",
+      "Ġspeed ing",
+      "Ġtack ling",
+      "Ġn ude",
+      "op ened",
+      "ĠMan afort",
+      "Ġ19 52",
+      "Ġmaj ors",
+      "ĠSil ence",
+      "Ġlog istics",
+      "Ġweight ed",
+      "ĠPsych iat",
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+      "Ġsick ness",
+      "Ġdivid ends",
+      "z on",
+      "Re lease",
+      "ĠKe ys",
+      "ĠI ch",
+      "Ġen z",
+      "ĠF ernand",
+      "ĠÎ ±",
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+      "Ġp enny",
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+      "Ġl ar",
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+      "w m",
+      "ĠIs le",
+      "Ġregul ating",
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+      "ices ter",
+      "ĠChe f",
+      "Ġpop s",
+      "ĠLaun cher",
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+      "Ġrival ry",
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+      "Rec ent",
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+      "W ord",
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+      "h om",
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+      "Ġdictators hip",
+      "idd ler",
+      "Ġannoun ces",
+      "ĠThe m",
+      "ĠNept une",
+      "Ġde ity",
+      "ĠY i",
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+      "AR R",
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+      "ĠArc ane",
+      "Ġdis charged",
+      "Ġevangel ical",
+      "Ġinter change",
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+      "ĠEnd less",
+      "Ġ29 0",
+      "ĠSalv ador",
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+      "ĠSign al",
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+      "l ot",
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+      "Ġarr ays",
+      "Ġaccomplish ments",
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+      "29 7",
+      "Ġdisp atched",
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+      "Ġdile mma",
+      "d emon",
+      "S pl",
+      "Ġhook s",
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+      "Ġburg l",
+      "Ġqu ir",
+      "AC A",
+      "ĠBren nan",
+      "Ġdur ability",
+      "Ġbomb ings",
+      "ĠWind ow",
+      "Ġculp rit",
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+      "Ġen forcing",
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+      "al pha",
+      "Ġsin ister",
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+      "ĠSl ash",
+      "Ġnight club",
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+      "ĠPal adin",
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+      "opp y",
+      "Ġmon keys",
+      "ĠHaw k",
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+      "p oon",
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+      "ĠB IG",
+      "Sand ers",
+      "im ates",
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+      "Ġfl ick",
+      "Ġr azor",
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+      "ĠChar acters",
+      "Ġword ing",
+      "Ġpopul ist",
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+      "Ġm use",
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+      "ĠThe ft",
+      "icult ural",
+      "Ġgovern ors",
+      "Ġ ����",
+      "Ġ16 3",
+      "Ġtime out",
+      "ĠA uth",
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+      "L o",
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+      "b omb",
+      "Ġcustom ized",
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+      "T ree",
+      "Ġm apped",
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+      "Ġdisgr ace",
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+      "ĠA the",
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+      "bal anced",
+      "................ ........",
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+      "Ġfor ge",
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+      "Ġsuper star",
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+      "U k",
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+      "ĠN iet",
+      "Ġper ks",
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+      "phas is",
+      "Ġdiscrim inate",
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+      "v c",
+      "Ġgr inding",
+      "ĠJenn y",
+      "Ġast hma",
+      "hol es",
+      "ĠHom er",
+      "Ġreg isters",
+      "ĠGl ad",
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+      "ĠTur ks",
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+      "Ġb ake",
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+      "ĠM aid",
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+      "t ri",
+      "Ġfl ux",
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+      "Ġmort g",
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+      "ĠT ort",
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+      "met al",
+      "ĠAs ide",
+      "Ġindu ction",
+      "Ġinf l",
+      "Ġathe ists",
+      "amp h",
+      "Ġe ther",
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+      "ĠTsu k",
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+      "Ġtwent ieth",
+      "################ ################",
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+      "ĠG arn",
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+      "ĠAT M",
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+      "ĠL S",
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+      "En large",
+      "Ġtw ists",
+      "Ġfault y",
+      "Ġpir acy",
+      "Ġimp over",
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+      "Ġs ands",
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+      "ĠNo ise",
+      "ãĥ Ĺ",
+      "Ġg reens",
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+      "Ġd ynasty",
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+      "Ġo t",
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+      "Ġblock ade",
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+      "L V",
+      "Ġdeb it",
+      "ĠR FC",
+      "ĠMil ton",
+      "ĠPress ure",
+      "Ġwill ingly",
+      "Ġdisproportion ate",
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+      "Ġdiamond s",
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+      "Ġbell s",
+      "Ġimperial ism",
+      "Ġ2 27",
+      "Ġexpl oding",
+      "ĠE clipse",
+      "Ġ19 19",
+      "Ġr ant",
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+      "Ġpeace fully",
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+      "ĠF UCK",
+      "Ġvib ration",
+      "mal ink",
+      "Ġro pes",
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+      "ĠBrew ery",
+      "ĠBook er",
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+      "ĠSn ape",
+      "Ġ19 1",
+      "39 5",
+      "Ġ2 99",
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+      "Ġdisc s",
+      "Ġprom inently",
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+      "Ġsk ipping",
+      "l ves",
+      "Ġtsun ami",
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+      "ĠU rug",
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+      "p hen",
+      "Ġfault s",
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+      "ĠGu idelines",
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+      "ĠT arg",
+      "ãĥ ģ",
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+      "Ġelectrom agnetic",
+      "Ġamid st",
+      "ĠVik tor",
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+      "Ġfluct uations",
+      "6 40",
+      "Ġnood les",
+      "ĠCop enhagen",
+      "ĠT ide",
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+      "ĠS EE",
+      "Ġpip elines",
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+      "ĠE TF",
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+      "Ġvis c",
+      "ĠRecomm ended",
+      "Ġj umper",
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+      "Ġspoil ers",
+      "Ġsucceed s",
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+      "Ġs ip",
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+      "ĠSle eping",
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+      "or por",
+      "ĠK lan",
+      "ĠDar th",
+      "h ack",
+      "ĠNi elsen",
+      "Ġtum ors",
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+      "ĠY orkshire",
+      "Ġra ided",
+      "K Y",
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+      "Ġ// [",
+      "ĠDis order",
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+      "Ġcere bral",
+      "Ġbip olar",
+      "ĠC N",
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+      "Ġdeb ating",
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+      "ĠWar p",
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+      "Ġms g",
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+      "Ġp amph",
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+      "à ¹",
+      "ĠE h",
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+      "ĠQ in",
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+      "ĠG ast",
+      "ĠN ir",
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+      "Ġsystem d",
+      "Ġdesc ending",
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+      "33 7",
+      "Ġf ug",
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+      "Ġvolcan ic",
+      "Ġwom b",
+      "ĠM IL",
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+      "Ġrest rained",
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+      "onna issance",
+      "Ġassault ing",
+      "ĠL ug",
+      "CH AT",
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+      "Ġintoler ance",
+      "ĠSnap chat",
+      "ĠSh arks",
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+      "Ġcondem ning",
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+      "Ġpant ies",
+      "ĠPre vent",
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+      "34 2",
+      "external ActionCode",
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+      "Ġru pt",
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+      "Ġprof iling",
+      "Ð º",
+      "St rike",
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+      "ĠEthiop ia",
+      "ĠC ivic",
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+      "ĠS aul",
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+      "p rem",
+      "Ġbuild ers",
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+      "ĠB ie",
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+      "Ġbegin ners",
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+      "Ġimm oral",
+      "Ġf ries",
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+      "Ġt ion",
+      "x s",
+      "Ġwith hold",
+      "Ġw aged",
+      "ĠLiber ties",
+      "Fl ag",
+      "Ġcomfort ing",
+      "45 4",
+      "ĠI ris",
+      "are rs",
+      "Ġr ag",
+      "Ġrel ocated",
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+      "Ġgam ma",
+      "uber ty",
+      "ĠLock heed",
+      "g res",
+      "Ġgr illed",
+      "ĠLow e",
+      "st ats",
+      "ĠR ocks",
+      "Ġsens ing",
+      "Ġrent ing",
+      "ĠGe ological",
+      "ا Ø",
+      "ot rop",
+      "Ġse w",
+      "Ġimproper ly",
+      "48 6",
+      "Ġâĸ ł",
+      "Ġstar ving",
+      "ĠB j",
+      "Disc ussion",
+      "3 28",
+      "ĠCom bo",
+      "ĠFix es",
+      "N AT",
+      "Ġstri ving",
+      "th ora",
+      "Ġharvest ed",
+      "ĠP ing",
+      "Ġplay ful",
+      "Ġaven ues",
+      "Ġoccup ational",
+      "Ġw akes",
+      "ĠCou rier",
+      "Ġdrum mer",
+      "ĠBrow ser",
+      "ĠH outh",
+      "it u",
+      "Ġapp arel",
+      "p aste",
+      "Ġhun ted",
+      "ĠSecond ly",
+      "l ain",
+      "X Y",
+      "ĠP IN",
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+      "Ġcock tails",
+      "Ġs izable",
+      "Ġhurd les",
+      "est inal",
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+      "64 8",
+      "ĠD ied",
+      "m int",
+      "Ġfinger prints",
+      "Ġdis pose",
+      "ĠBos nia",
+      "ts y",
+      "22 00",
+      "Ġins pected",
+      "ĠF ou",
+      "Ġf uss",
+      "Ġamb ush",
+      "ĠR ak",
+      "Ġmanif ested",
+      "Pro secut",
+      "Ġsuff ice",
+      "ren ces",
+      "Ġcompens ated",
+      "ĠC yrus",
+      "Ġgen us",
+      "ĠWolver ine",
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+      "Ġh ikes",
+      "ĠSe en",
+      "Ġen rol",
+      "C old",
+      "Ġpol itely",
+      "ĠSl av",
+      "ĠRu pert",
+      "Ġey ewitness",
+      "ĠAl to",
+      "Ġun comp",
+      "Ġposter ior",
+      "M ust",
+      "ĠHer z",
+      "Ġprogress ively",
+      "Ġ23 4",
+      "Ġind ifference",
+      "ĠCunning ham",
+      "Ġacadem ia",
+      "Ġse wer",
+      "Ġast ounding",
+      "ĠA ES",
+      "r ather",
+      "Ġeld est",
+      "Ġclim bs",
+      "ĠAdd s",
+      "Ġout cry",
+      "Ġcont ag",
+      "ĠH ouses",
+      "Ġpe pt",
+      "ĠMel ania",
+      "interest ed",
+      "ĠU CH",
+      "ĠR oots",
+      "ĠHub bard",
+      "ĠT BD",
+      "ĠRoman ian",
+      "fil ename",
+      "St one",
+      "ĠIm pl",
+      "Ġchromos ome",
+      "C le",
+      "d x",
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+      "ĠP t",
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+      "OP LE",
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+      "p ipe",
+      "Ġinj uring",
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+      "J C",
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+      "d k",
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+      "hem oth",
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+      "Ġcar rots",
+      "ĠL ayer",
+      "ĠH acker",
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+      "Ġmoder ation",
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+      "Ġcharacter ize",
+      "ĠTe resa",
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+      "tr aining",
+      "ĠPaul o",
+      "ph ys",
+      "Ġtrust worthy",
+      "Ġembod ied",
+      "ĠMer ch",
+      "c urrency",
+      "ĠPrior ity",
+      "Ġte asing",
+      "Ġabsor bing",
+      "Ġunf inished",
+      "ĠCompar ison",
+      "Ġdis ple",
+      "writ ers",
+      "Ġprofess ions",
+      "ĠPengu in",
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+      "ĠL INK",
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+      "ĠCor respond",
+      "Ġprev ailed",
+      "Ġcart el",
+      "l p",
+      "as ms",
+      "ĠRed emption",
+      "ĠIslam ists",
+      "effect s",
+      "d ose",
+      "ĠL atter",
+      "ĠHal ifax",
+      "Ġv as",
+      "ĠTop ics",
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+      "zz a",
+      "IC ES",
+      "Ġret arded",
+      "ach able",
+      "ĠPupp et",
+      "ĠItem Level",
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+      "Ġident ifiable",
+      "A aron",
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+      "hel le",
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+      "B a",
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+      "é Ģ",
+      "ĠSir i",
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+      "Ġshort ened",
+      "Ġ3 12",
+      "Ġdiscrep ancy",
+      "ĠTem perature",
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+      "p olit",
+      "Ġrem ix",
+      "Ġconnect ors",
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+      "im ble",
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+      "ĠC ao",
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+      "j p",
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+      "C ool",
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+      "P ast",
+      "Ġch ars",
+      "ĠB ender",
+      "ĠShe ldon",
+      "Ġpain ter",
+      "Ġbe acon",
+      "ĠCreat ures",
+      "Ġdownt urn",
+      "Ġh inder",
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+      "et rical",
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+      "Ġsen ate",
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+      "Ġgru esome",
+      "ost ics",
+      "ĠUb isoft",
+      "ĠKel ley",
+      "Ġw rench",
+      "Ġbourgeois ie",
+      "IB LE",
+      "ĠPrest on",
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+      "ar ist",
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+      "ar ine",
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+      "EN N",
+      "Ġche sts",
+      "Ġground water",
+      "ann ot",
+      "ĠTr ay",
+      "ĠLoc ke",
+      "ĠC TR",
+      "Ġd udes",
+      "ĠEx ternal",
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+      "Ġpar amed",
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+      "80 9",
+      "ĠD inner",
+      "rup al",
+      "g z",
+      "ĠG um",
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+      "j ee",
+      "Ġd h",
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+      "Ġen qu",
+      "ĠEp stein",
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+      "Ġfriends hips",
+      "ĠAr d",
+      "Ġ23 1",
+      "ĠRub in",
+      "ĠDist ance",
+      "Ġsp urred",
+      "Ġd ossier",
+      "Ġover looking",
+      "\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\",
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+      "ĠCom es",
+      "\\ \",",
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+      "L aughs",
+      "Ġcur ry",
+      "ĠKing ston",
+      "Ġsqu ash",
+      "Ġcat alogue",
+      "Ġabnormal ities",
+      "Ġdigest ive",
+      ".... .....",
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+      "og ly",
+      "Ġ24 9",
+      "M iddle",
+      "Ġmass ac",
+      "Ġburg ers",
+      "Ġdown stairs",
+      "Ġ19 31",
+      "39 4",
+      "ĠV G",
+      "Ġl asers",
+      "ĠS ikh",
+      "ĠAlex a",
+      "der ived",
+      "Ġcycl ist",
+      "ãģ® éŃĶ",
+      "onel iness",
+      "!!!! !!!!",
+      "Ġbuff s",
+      "leg ate",
+      "Ġrap ing",
+      "Ġrecomm ending",
+      "ro red",
+      "Ġmult icultural",
+      "un ique",
+      "Ġbusiness men",
+      "Ġune asy",
+      "ĠM AP",
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+      "ij a",
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+      "af er",
+      "Ġunfold ed",
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+      "Ġbre ached",
+      "ĠH earing",
+      "Ġn at",
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+      "Ġant agonist",
+      "ĠYes terday",
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+      "m ere",
+      "Ġbe wild",
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+      "V irgin",
+      "ph rase",
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+      "ĠH IGH",
+      "Ġprot ester",
+      "Ġgar ner",
+      "unk nown",
+      "Ġk ay",
+      "Ġabduct ed",
+      "Ġst alking",
+      "am n",
+      "Ġdes erving",
+      "ĠR iv",
+      "ĠJ orge",
+      "Ġscratch ing",
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+      "ip ing",
+      "Ġte ase",
+      "Ġmission ary",
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+      "T IME",
+      "P resent",
+      "Ġchem otherapy",
+      "tern ess",
+      "ĠH omes",
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+      "Ġst aunch",
+      "ĠWhit ney",
+      "ĠTH ERE",
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+      "ĠDe ploy",
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+      "F ML",
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+      "Ġex ited",
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+      "Ġner d",
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+      "ĠAm en",
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+      "Ġâ ĺ",
+      "D IR",
+      "Ġad herence",
+      "Ġche w",
+      "ĠCo ke",
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+      "dig ital",
+      "ĠNe ck",
+      "g ently",
+      "enth al",
+      "/ )",
+      "Ġwe ary",
+      "Ġgu ise",
+      "ĠConc ord",
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+      "at cher",
+      "Ġb inge",
+      "ĠDirect ive",
+      "Ġman ned",
+      "ans k",
+      "Ġill usions",
+      "Ġbillion aires",
+      "38 3",
+      "oly n",
+      "odynam ic",
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+      "Ġcol oured",
+      "ĠN AFTA",
+      "ab o",
+      "Ġmac ros",
+      "ind ependent",
+      "s weet",
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+      "ĠK abul",
+      "Ġ Ä",
+      "em e",
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+      "ĠCr icket",
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+      "Ġanalys ed",
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+      "ĠBlaz ers",
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+      "Ġ29 5",
+      "Ġw itches",
+      "ĠIn cluded",
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+      "G am",
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+      "ĠI B",
+      "ĠRam irez",
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+      "re ro",
+      "ĠN g",
+      "ĠWar lock",
+      "t p",
+      "Ġover he",
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+      "Ġreg iment",
+      "Ġpist ols",
+      "Ġblank ets",
+      "Ġep ist",
+      "Ġbowl s",
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+      "Ġde an",
+      "Ġj ung",
+      "Ġasc end",
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+      "ĠSant iago",
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+      "any l",
+      "Ġdele ting",
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+      "ĠR ox",
+      "ĠMil ky",
+      "F ather",
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+      "44 7",
+      "Ġprec ursor",
+      "Ġm aiden",
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+      "Ġvent ilation",
+      "ĠSchwar z",
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+      "ĠHe ads",
+      "ĠB G",
+      "Ġl umber",
+      "ĠSp iel",
+      "Ġis Enabled",
+      "Ġancest ral",
+      "ĠSh ips",
+      "Ġwrest ler",
+      "ph i",
+      "Ġy uan",
+      "ĠRebell ion",
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+      "Ġdivers ion",
+      "ar ro",
+      "yth m",
+      "ĠR iders",
+      "ĠRob bie",
+      "ĠK ara",
+      "ĠMain tenance",
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+      "p acked",
+      "ĠFe instein",
+      "Ġmarry ing",
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+      "ĠR ates",
+      "Ġ18 80",
+      "Ġwr ink",
+      "ĠUn ch",
+      "ĠTor ch",
+      "desc ribed",
+      "Ġhuman oid",
+      "ilit ating",
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+      "ĠFe ld",
+      "IGH TS",
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+      "ort mund",
+      "ets y",
+      "arre tt",
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+      "ĠI ke",
+      "ĠC NBC",
+      "ĠW AY",
+      "ĠMD MA",
+      "ĠIndividual s",
+      "Ġsupplement al",
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+      "F ocus",
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+      "idd led",
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+      "iling ual",
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+      "Ġdis abling",
+      "ĠCard iff",
+      "Ġmemor andum",
+      "ĠTr ace",
+      "Ġ?? ?",
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+      "ĠY ates",
+      "ĠFree ze",
+      "Ġbl adder",
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+      "ant asy",
+      "Ġforesee able",
+      "ĠC ologne",
+      "cont ainer",
+      "ĠTre es",
+      "ĠG ors",
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+      "Ġbar ring",
+      "key e",
+      "Ġsl ashed",
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+      "é ĩ",
+      "Ġâĸ º",
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+      "Ġdr illed",
+      "ĠF urn",
+      "44 3",
+      "Ġse wage",
+      "Ġhome page",
+      "Ġcour tyard",
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+      "aj o",
+      "direct ory",
+      "Ġam mon",
+      "V ers",
+      "charg es",
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+      "Ġ24 6",
+      "n ob",
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+      "Ġg rit",
+      "Ġfisher men",
+      "ĠIraq is",
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+      "ĠF ULL",
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+      "Ġcooper ating",
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+      "Ġweak est",
+      "ĠBull dogs",
+      "U A",
+      "ĠAll oy",
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+      "oc ene",
+      "ĠWilliam son",
+      "s pr",
+      ", âĢĶ",
+      "ĠG F",
+      "itt ens",
+      "Be at",
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+      "iph ate",
+      "ĠFarm ers",
+      "ĠBit coins",
+      "ig ers",
+      "d h",
+      "ĠL oyal",
+      "p ayer",
+      "Ġentert ained",
+      "Ġpenn ed",
+      "Ġcoup on",
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+      "Ġweaken ing",
+      "c arry",
+      "Ġunderest imate",
+      "Ġshoot out",
+      "Ġcharism atic",
+      "ĠProced ure",
+      "Ġprud ent",
+      "in ances",
+      "Ġric hes",
+      "Ġcort ical",
+      "Ġstr ides",
+      "Ġd rib",
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+      "5 40",
+      "ĠPer form",
+      "ĠBang kok",
+      "Ġe uth",
+      "S ER",
+      "Ġsimpl istic",
+      "t ops",
+      "camp aign",
+      "Q uality",
+      "Ġimpover ished",
+      "ĠEisen hower",
+      "Ġaug ment",
+      "ĠH arden",
+      "Ġinterven ed",
+      "Ġlist ens",
+      "ĠK ok",
+      "Ġs age",
+      "Ġrub bish",
+      "ĠD ed",
+      "Ġm ull",
+      "pe lling",
+      "Ġvide ot",
+      "Produ ction",
+      "D J",
+      "m iah",
+      "Ġadapt ations",
+      "Ġmed ically",
+      "Ġboard ed",
+      "Ġarrog ance",
+      "Ġscra pped",
+      "Ġopp ress",
+      "FORM ATION",
+      "Ġj unction",
+      "4 15",
+      "EE EE",
+      "S kill",
+      "Ġsub du",
+      "ĠSug gest",
+      "ĠP ett",
+      "Ġle tt",
+      "ĠMan ip",
+      "ĠC af",
+      "ĠCooper ation",
+      "T her",
+      "Ġreg ained",
+      "¶ æ",
+      "ref lect",
+      "Ġth ugs",
+      "ĠShel by",
+      "Ġdict ates",
+      "ĠWe iner",
+      "ĠH ale",
+      "Ġbatt leground",
+      "s child",
+      "Ġcond ol",
+      "h unt",
+      "osit ories",
+      "Ġacc uses",
+      "Fil ename",
+      "Ġsh ri",
+      "Ġmotiv ate",
+      "Ġreflect ions",
+      "N ull",
+      "ĠL obby",
+      "¥ µ",
+      "ĠS ATA",
+      "ĠBack up",
+      "Ñ ĥ",
+      "n in",
+      "ĠCor rection",
+      "Ġju icy",
+      "ut ra",
+      "ĠP ric",
+      "Ġrest raining",
+      "ĠAir bnb",
+      "ĠAr rest",
+      "Ġappropri ations",
+      "Ġsl opes",
+      "Ġmans laughter",
+      "Ġwork ings",
+      "ĠH uss",
+      "ĠF rey",
+      "Le ave",
+      "ĠHarm ony",
+      "ĠF eder",
+      "Ġ4 30",
+      "Ġt rench",
+      "Ġglad ly",
+      "Ġbull pen",
+      "ĠG au",
+      "b ones",
+      "Ġgro ove",
+      "Ġpre text",
+      "ã ħĭ",
+      "Ġtransm itter",
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+      "Ġunder age",
+      "ĠEm pires",
+      "T ile",
+      "Ġo y",
+      "ĠMar vin",
+      "ĠC AS",
+      "Ġbl oss",
+      "Ġrepl icated",
+      "ĠMar iners",
+      "Marc us",
+      "ĠBl ocks",
+      "Ġliber ated",
+      "Ġbutter fly",
+      "Fe el",
+      "Ġfer mentation",
+      "Ġyou tube",
+      "Ġoff end",
+      "ĠTer m",
+      "res ist",
+      "Ġcess ation",
+      "Ġinsurg ency",
+      "Ġb ir",
+      "ĠRa ise",
+      "59 5",
+      "Ġhypothes es",
+      "50 2",
+      "Ġpl aque",
+      "ocr at",
+      "Ġjack ets",
+      "ĠHuff Post",
+      "am ong",
+      "Ġconf er",
+      "48 7",
+      "ĠL illy",
+      "Ġadapt ing",
+      "ĠF ay",
+      "Ġsh oved",
+      "ve c",
+      "Ġref ine",
+      "Ġg on",
+      "Ġgun men",
+      "z ai",
+      "ĠShut tle",
+      "ĠI zan",
+      "Ġ19 13",
+      "Ġple thora",
+      "· ·",
+      "Ġ5 10",
+      "Ġp uberty",
+      "Ġ24 1",
+      "ĠWe alth",
+      "ĠAl ma",
+      "ĠM EM",
+      "ĠAd ults",
+      "C as",
+      "pr ison",
+      "R ace",
+      "Ġwater proof",
+      "Ġathlet icism",
+      "Ġcapital ize",
+      "ĠJu ice",
+      "Ġillum inated",
+      "ĠP ascal",
+      "Ġirrit ation",
+      "ĠWitness es",
+      "ad le",
+      "ĠAst ro",
+      "Ġf ax",
+      "ĠEl vis",
+      "Prim ary",
+      "ĠL ich",
+      "ĠEl ves",
+      "Ġres iding",
+      "Ġst umble",
+      "3 19",
+      "ĠP KK",
+      "Ġadvers aries",
+      "D OS",
+      "ĠR itual",
+      "Ġsm ear",
+      "Ġar son",
+      "ident al",
+      "Ġsc ant",
+      "Ġmon archy",
+      "Ġhal ftime",
+      "Ġresid ue",
+      "Ġind ign",
+      "ĠSh aun",
+      "ĠEl m",
+      "aur i",
+      "A ff",
+      "W ATCH",
+      "ĠLy on",
+      "hel ps",
+      "36 1",
+      "Ġlobby ist",
+      "Ġdimin ishing",
+      "Ġout breaks",
+      "Ġgo ats",
+      "f avorite",
+      "ĠN ah",
+      "son ian",
+      "ĠBo oster",
+      "Ġsand box",
+      "ĠF are",
+      "ĠMalt a",
+      "Ġatt Rot",
+      "ĠM OR",
+      "ld e",
+      "Ġnavig ating",
+      "T ouch",
+      "Ġunt rue",
+      "ĠDis aster",
+      "Ġl udicrous",
+      "Pass word",
+      "ĠJ FK",
+      "blog spot",
+      "4 16",
+      "ĠUN DER",
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+      "Ġdelay ing",
+      "T OP",
+      "Ġimpl ants",
+      "ĠAV G",
+      "ĠH uge",
+      "att r",
+      "Ġjournal istic",
+      "ĠPe yton",
+      "ĠI A",
+      "R ap",
+      "go al",
+      "ĠProgram me",
+      "Ġsm ashing",
+      "w ives",
+      "print ln",
+      "ĠPl ague",
+      "in us",
+      "EE P",
+      "Ġcru iser",
+      "ĠPar ish",
+      "umin ium",
+      "Ġoccup ants",
+      "ĠJ ihad",
+      "m op",
+      "Ġp int",
+      "Ġhe ct",
+      "ĠMe cca",
+      "direct or",
+      "ĠFund ing",
+      "ĠM ixed",
+      "Ġst ag",
+      "T ier",
+      "Ġg ust",
+      "Ġbright ly",
+      "ors i",
+      "Ġup hill",
+      "R D",
+      "Ġles ions",
+      "ĠBund y",
+      "liv ious",
+      "Ġbi ologist",
+      "ĠFac ulty",
+      "ĠAuthor ization",
+      "Ġ24 4",
+      "All ow",
+      "ï ¸",
+      "ĠGi ul",
+      "Ġpert inent",
+      "ot aur",
+      "es se",
+      "ĠRo of",
+      "Ġunman ned",
+      "35 1",
+      "ĠSh ak",
+      "ĠO rient",
+      "Ġend anger",
+      "D ir",
+      "Ġrepl en",
+      "ed ient",
+      "Ġtail or",
+      "Ġgad gets",
+      "Ġaud ible",
+      "âĺ Ĩ",
+      "N ice",
+      "Ġbomb ard",
+      "ĠR ape",
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+      "ĠTW O",
+      "ĠFilip ino",
+      "Ġunaff ected",
+      "erv atives",
+      "Ġso ared",
+      "ĠBol ton",
+      "Ġcomprom ising",
+      "ĠBrew ers",
+      "R AL",
+      "ĠA HL",
+      "icy cle",
+      "Ġv ampires",
+      "Ġdi pped",
+      "oy er",
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+      "Ġsidew ays",
+      "ĠW aste",
+      "ĠD iss",
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+      "ĠBe ef",
+      "tr uth",
+      "tr ained",
+      "spl it",
+      "R us",
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+      "ĠB ram",
+      "RE P",
+      "p id",
+      "è£ ħ",
+      "ĠMut ant",
+      "An im",
+      "ĠMar ina",
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+      "hig hest",
+      "f requency",
+      "Ġepile psy",
+      "Ġcop ing",
+      "Ġconc ise",
+      "Ġtr acing",
+      "ĠS UN",
+      "pan el",
+      "ĠSoph ie",
+      "ĠCrow ley",
+      "ĠAd olf",
+      "ĠShoot er",
+      "Ġsh aky",
+      "ĠI G",
+      "ĠL ies",
+      "ĠBar ber",
+      "p kg",
+      "Ġupt ake",
+      "Ġpred atory",
+      "UL TS",
+      "/ **",
+      "Ġintox icated",
+      "ĠWest brook",
+      "od der",
+      "he ment",
+      "Ġbas eman",
+      "AP D",
+      "st orage",
+      "ĠFif ty",
+      "ed itor",
+      "G EN",
+      "UT ION",
+      "ir ting",
+      "Ġse wing",
+      "r ift",
+      "Ġag ony",
+      "ĠS ands",
+      "Ġ25 4",
+      "C ash",
+      "Ġl odge",
+      "Ġp unt",
+      "N atural",
+      "ĠIde as",
+      "Ġerrone ous",
+      "ĠSens or",
+      "ĠHann ity",
+      "Ġ19 21",
+      "Ġm ould",
+      "ĠG on",
+      "kay a",
+      "Ġanonym ously",
+      "ĠK EY",
+      "Ġsim ulator",
+      "W inter",
+      "Ġstream ed",
+      "50 7",
+      "? \",",
+      "Ġte ased",
+      "Ġco efficient",
+      "Ġwart ime",
+      "ĠTH R",
+      "' '.",
+      "ĠBank ing",
+      "mp ire",
+      "Ġf andom",
+      "Ġl ia",
+      "G a",
+      "Ġdown hill",
+      "Ġinterpre ting",
+      "Ind ividual",
+      "N orm",
+      "Ġjealous y",
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+      "Ġple asures",
+      "ĠToy s",
+      "ĠChev rolet",
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+      "IZ E",
+      "Ġrecept ions",
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+      "C ro",
+      "Ġ26 2",
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+      "U ES",
+      "an z",
+      "19 81",
+      "ĠWork er",
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+      "contin ental",
+      "T on",
+      "ĠPr ism",
+      "ĠShe ep",
+      "Ġ28 8",
+      "n ox",
+      "ĠV og",
+      "O rd",
+      "Ġreal ms",
+      "te k",
+      "Ġirrig ation",
+      "Ġbicy cles",
+      "Ġelectron ically",
+      "p oly",
+      "t all",
+      "() );",
+      "Ġaest hetics",
+      "ĠInteg rated",
+      "Expl ore",
+      "Ġd unk",
+      "47 6",
+      "p ain",
+      "ĠJac ques",
+      "ĠD mit",
+      "Fram es",
+      "Ġreun ited",
+      "Ġhum id",
+      "D ro",
+      "P olitical",
+      "Ġyouth ful",
+      "Ġent ails",
+      "Ġmosqu ito",
+      "36 3",
+      "spe cies",
+      "Ġcoord inating",
+      "ĠMay hem",
+      "ĠMagn us",
+      "M ount",
+      "Impro ved",
+      "ĠST ATE",
+      "ATT LE",
+      "Ġflow ed",
+      "Ġtack led",
+      "Ġfashion ed",
+      "Ġre organ",
+      "iv ari",
+      "f inger",
+      "Ġreluct antly",
+      "et ting",
+      "ĠV and",
+      "you ng",
+      "ĠGar land",
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+      "Ġamen ities",
+      "ĠPle asant",
+      "on ential",
+      "ĠO xy",
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+      "ĠY ah",
+      "Read y",
+      "Sim on",
+      "En h",
+      "D emon",
+      "Ġcl ich",
+      "Mon itor",
+      "ĠD U",
+      "Ġwel comes",
+      "Ġstand out",
+      "Ġdread ful",
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+      "h ooting",
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+      "Ġsuff ix",
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+      "ĠP ru",
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+      "ĠL OL",
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+      "ĠS AS",
+      "Ġsty ling",
+      "Ġpunish ments",
+      "Ġnum b",
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+      "Ġperpet rated",
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+      "ĠOld er",
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+      "ĠCommun ities",
+      "Ġabol ish",
+      "ĠPart ner",
+      "pt oms",
+      "Ġ7 77",
+      "ĠFo ley",
+      "ĠC ases",
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+      "ĠPat ient",
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+      "Ġhomosexual s",
+      "Ġ---- ---",
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+      "Ġturb ines",
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+      "lad en",
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+      "Ġsubscript ions",
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+      "Ġper plex",
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+      "in formed",
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+      "P retty",
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+      "â ī",
+      "ĠSunder land",
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+      "V oice",
+      "???? ????",
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+      "Ġbow el",
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+      "Ġd ancers",
+      "Ġid iots",
+      "major ity",
+      "ĠNev ille",
+      "ĠFree BSD",
+      "Ġt art",
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+      "69 0",
+      "Ġcoc oa",
+      "Ġsam pled",
+      "Ġlook up",
+      "Ind ust",
+      "Ġinject ions",
+      "gen re",
+      "Ġa u",
+      "Ġroad way",
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+      "K ind",
+      "ĠEx aminer",
+      "ĠY az",
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+      "Ġpar alysis",
+      "ĠAl uminum",
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+      "Ġsl oppy",
+      "ĠTun nel",
+      "pos ium",
+      "ner y",
+      "en ic",
+      "Ġher bal",
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+      "ĠBuild er",
+      "Ġinc ur",
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+      "cons uming",
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+      "ĠKN OW",
+      "ĠG ret",
+      "ĠM IC",
+      "Ġtough ness",
+      "ĠEx hibit",
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+      "ĠSCH OOL",
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+      "ĠN ull",
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+      "m ouse",
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+      "Ġrev ol",
+      "ĠLaw son",
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+      "eb ted",
+      "ĠS aunders",
+      "Ġ3 13",
+      "ĠW inc",
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+      "Ġw edge",
+      "ch ip",
+      "ĠT ina",
+      "b g",
+      "Ġinf uri",
+      "r n",
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+      "ĠSy nc",
+      "ĠEx am",
+      "ĠComm it",
+      "ĠDi ary",
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+      "6 55",
+      "Ġempower ing",
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+      "ĠE TH",
+      "ĠBox ing",
+      "=\" /",
+      "Ġfacilit ated",
+      "p oke",
+      "ĠPars ons",
+      "ĠMod er",
+      "tra vel",
+      "Ġcivil izations",
+      "Ġliber tarians",
+      "Ġrun e",
+      "ĠCl arks",
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+      "Ġcampaign ers",
+      "ĠDis patch",
+      "ĠFah renheit",
+      "ĠCap com",
+      "-------- --",
+      "Ġl ace",
+      "Ġdr aining",
+      "Ġl iner",
+      "ĠArt ificial",
+      "é n",
+      "t ask",
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+      "ĠGM O",
+      "ĠOper ator",
+      "ord inary",
+      "ĠInf luence",
+      "ĠU ps",
+      "Ġpot ency",
+      "uss en",
+      "osp ons",
+      "ĠSw im",
+      "ĠDead line",
+      "Un ity",
+      "Ġcul inary",
+      "Ġenlight enment",
+      "Ġwe arer",
+      "Ġmin ed",
+      "Ġp ly",
+      "Ġinc est",
+      "ĠDVD s",
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+      "Tr ade",
+      "Ġdev al",
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+      "Ġmisc ar",
+      "ĠV ulkan",
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+      "è re",
+      "ĠS ap",
+      "Ġmer ging",
+      "ĠCar ly",
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+      "Ġbr isk",
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+      "Ġdark est",
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+      "ĠIllust rated",
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+      "qu art",
+      "Ġadam ant",
+      "ĠM ew",
+      "Ġren ovation",
+      "Ġcerv ical",
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+      "en ers",
+      "ĠKim ber",
+      "Ġflo ats",
+      "Ġde x",
+      "ĠW itcher",
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+      "d m",
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+      "ĠM n",
+      "ĠA ck",
+      "ĠR iding",
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+      "Ġhom ophobic",
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+      "Ġchim pan",
+      "ĠR azor",
+      "Ġt icks",
+      "Ġcouncill or",
+      "Ġpilgr image",
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+      "ĠQ C",
+      "ĠA uction",
+      "x ia",
+      "ik k",
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+      "Ġhum orous",
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+      "Ġlur king",
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+      "ĠMatt is",
+      "Ġmisogyn y",
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+      "Read ing",
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+      "Ult ra",
+      "Ġend point",
+      "H ig",
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+      "L ua",
+      "ĠMess i",
+      "a qu",
+      "ĠPsy Net",
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+      "Ġa venue",
+      "v p",
+      "J er",
+      "Ġsh ady",
+      "ĠRes ist",
+      "ĠArt emis",
+      "Ġcare less",
+      "Ġbro kers",
+      "Ġtemper ament",
+      "Ġ5 20",
+      "T ags",
+      "ĠTurn ing",
+      "Ġut tered",
+      "Ġp edd",
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+      "ĠEss ence",
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+      "ĠRest oration",
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+      "ĠPok er",
+      "ĠAugust ine",
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+      "ĠSE AL",
+      "or ama",
+      "Ġth wart",
+      "se ek",
+      "Ġp agan",
+      "Â º",
+      "cp u",
+      "Ġg arn",
+      "Ġass ortment",
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+      "t ower",
+      "Recomm ended",
+      "Ġun born",
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+      "Ġparaly zed",
+      "Ġeru ption",
+      "Ġinter sect",
+      "ĠSt oke",
+      "ĠS co",
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+      "ĠP NG",
+      "ĠNeg ative",
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+      "M H",
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+      "Ġlong itudinal",
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+      "Ġsw ell",
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+      "ĠA im",
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+      "Ġm L",
+      "D X",
+      "Ġb isc",
+      "ĠBill board",
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+      "ga ard",
+      "Ġdist ressed",
+      "former ly",
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+      "ĠC omet",
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+      "3 11",
+      "ĠW B",
+      "Ġcont raction",
+      "ĠT OTAL",
+      "D ual",
+      "Ġstart led",
+      "Ġunderstand ably",
+      "Ġsung lasses",
+      "ETH OD",
+      "Ġd ocker",
+      "Ġsurf ing",
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+      "Ġsh alt",
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+      "c ussion",
+      "Ġunrest ricted",
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+      "Ġre name",
+      "employ ed",
+      "Ġeduc ating",
+      "Ġgrin ned",
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+      "ĠV elvet",
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+      "ig or",
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+      "Ġfore ground",
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+      "ĠT ie",
+      "E y",
+      "Ġt urtles",
+      "ĠRail road",
+      "ĠKat z",
+      "ĠFram es",
+      "Ġmen ace",
+      "ĠFell owship",
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+      "ugg ish",
+      "Ġdri p",
+      "ch witz",
+      "ĠKy oto",
+      "s b",
+      "ĠN ina",
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+      "Ġal arms",
+      "ĠCl aud",
+      "Ġpione ering",
+      "Ġchief ly",
+      "ĠSc ream",
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+      "ĠRonald o",
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+      "st rip",
+      "ĠDisney land",
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+      "S oul",
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+      "Ġc iv",
+      "ĠAs he",
+      "Ġdiv ides",
+      "ĠD agger",
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+      "Ġs ushi",
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+      "W I",
+      "Ġdisadvant aged",
+      "Ġbatt alion",
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+      "Ġb unk",
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+      "pro xy",
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+      "D avis",
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+      "Ġtim eless",
+      "ĠNE ED",
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+      "Ġå ¤",
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+      "lu aj",
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+      "Ġf ooth",
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+      "Ġe b",
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+      "cont aining",
+      "Ġescal ate",
+      "ĠOP EN",
+      "Ġtwe aked",
+      "Ġst ash",
+      "Ġsp arks",
+      "ĠEs sex",
+      "ĠE cc",
+      "Ġconv ict",
+      "Ġblog ging",
+      "I ER",
+      "ĠH L",
+      "Ġmurd erers",
+      "75 9",
+      "ĠH ib",
+      "Ġde pl",
+      "ĠJ ord",
+      "S ac",
+      "Ġdis sect",
+      "ĠHow e",
+      "os her",
+      "Ġcustom izable",
+      "ĠFran z",
+      "Ġat ro",
+      "Ä ĩ",
+      "Ġ000 4",
+      "Ġout post",
+      "R oss",
+      "Ġglyph osate",
+      "ĠHast ings",
+      "ĠBE FORE",
+      "Ġsh ove",
+      "o pped",
+      "ĠSc ala",
+      "Ġam ulet",
+      "an ian",
+      "Ġexacerb ated",
+      "Ġe ater",
+      "47 1",
+      "UM E",
+      "Ġpul p",
+      "izont al",
+      "ĠZ am",
+      "ĠAT I",
+      "imm une",
+      "aby tes",
+      "Ġunnecess arily",
+      "ĠC AT",
+      "ĠAx is",
+      "Ġvisual ize",
+      "Ã ī",
+      "ĠRad ical",
+      "f m",
+      "Doc uments",
+      "ĠFor rest",
+      "Ġcontext ual",
+      "ĠSy mbol",
+      "Ġtent ative",
+      "ĠDO ES",
+      "ĠGood s",
+      "Ġintermitt ent",
+      "} :",
+      "medi ated",
+      "Ġridic ule",
+      "Ġathe ism",
+      "Ġpath ogens",
+      "ĠM um",
+      "Ġre introdu",
+      "Ġ30 7",
+      "i HUD",
+      "Ġflash light",
+      "Ġsw earing",
+      "Ġp engu",
+      "B u",
+      "Ġrot ated",
+      "ĠCr ane",
+      "Ġ() );",
+      "Ġfashion able",
+      "Ġendors ing",
+      "46 3",
+      ") [",
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+      "Ġcook s",
+      "Ġ9 50",
+      "ot omy",
+      "ĠIm am",
+      "Ġk a",
+      "Ġte aser",
+      "ĠGhost s",
+      "ĠãĤ µ",
+      "19 69",
+      "Ï ĥ",
+      "ub by",
+      "Ġconver ter",
+      "zan ne",
+      "end e",
+      "ĠPre par",
+      "ĠNic kel",
+      "ĠChim era",
+      "h im",
+      "ĠTyr ann",
+      "ĠSabb ath",
+      "ĠNich ols",
+      "Ġra pt",
+      "ih ar",
+      "Ġshe lling",
+      "Ġillum inate",
+      "Ġdent ist",
+      "ut or",
+      "ĠInteg ration",
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+      "ĠLiter ary",
+      "Be aut",
+      "Ġp archment",
+      "ag ara",
+      "Br and",
+      "Ġder og",
+      "âĢ¦ )",
+      "ĠNor se",
+      "Ġunw itting",
+      "Ġc uc",
+      "Ġborder line",
+      "Ġupset ting",
+      "Ġrec ourse",
+      "Ġd raped",
+      "ĠRad ar",
+      "Ġcold er",
+      "ĠPep si",
+      "im inary",
+      "], [",
+      "65 8",
+      "V i",
+      "ĠF rem",
+      "ĠP es",
+      "Ġveter inary",
+      "ĠT ED",
+      "ĠEp idem",
+      "n ova",
+      "k id",
+      "Ġdev out",
+      "o ct",
+      "j ad",
+      "M oh",
+      "ĠP AY",
+      "Ġge ometric",
+      "Ġ3 23",
+      "Ġcircum ference",
+      "ich ick",
+      "19 75",
+      "ĠY uri",
+      "ĠSh all",
+      "ĠH over",
+      "un in",
+      "S pr",
+      "Ġg raft",
+      "ĠHapp iness",
+      "Ġdisadvant ages",
+      "att acks",
+      "Ġhub s",
+      "ĠStar Craft",
+      "é ĸ",
+      "Ġgall eries",
+      "ĠKor ra",
+      "Ġgrocer ies",
+      "ĠGors uch",
+      "Ġrap ists",
+      "Ġfun gi",
+      "ĠTyph oon",
+      "V ector",
+      "ĠEm press",
+      "b attle",
+      "4 68",
+      "Ġparas ite",
+      "ĠBom ber",
+      "S G",
+      "ex ist",
+      "ĠP f",
+      "Ġun se",
+      "Ġsurge ons",
+      "B irth",
+      "ĠUn sure",
+      "ĠPrint ed",
+      "ĠBehavior al",
+      "ĠA ster",
+      "Pak istan",
+      "Ġun ethical",
+      "Ġs v",
+      "ĠIo T",
+      "Ġlay outs",
+      "P ain",
+      "Ġconst ants",
+      "ĠL W",
+      "ĠB ake",
+      "Ġtow els",
+      "Ġdeterior ation",
+      "ĠBol ivia",
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+      "ĠW arden",
+      "ĠMist ress",
+      "Ġon stage",
+      "Ġcl ans",
+      "ĠB EST",
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+      "Ġrhet orical",
+      "ĠPer cy",
+      "ĠRw anda",
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+      "Ġtra umat",
+      "ĠParliament ary",
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+      "id ia",
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+      "se eking",
+      "gen ic",
+      "Ġdim ensional",
+      "H ide",
+      "èĢ ħ",
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+      "in se",
+      "Ġle ases",
+      "Ġapp rentices",
+      "w ashing",
+      "Ġ19 26",
+      "V ILLE",
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+      "s cl",
+      "Ġbed rooms",
+      "on ics",
+      "ĠCr unch",
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+      "Ġpos itives",
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+      "odd y",
+      "ĠMahm oud",
+      "As ia",
+      "Ġskull s",
+      "ĠEx odus",
+      "ĠComp et",
+      "ĠL IC",
+      "ĠM ansion",
+      "ĠA me",
+      "Ġconsolid ate",
+      "storm s",
+      "ont ent",
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+      "Ġcl en",
+      "Ġm ummy",
+      "fl at",
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+      "ĠV OL",
+      "oter ic",
+      "n en",
+      "ĠMin ute",
+      "S ov",
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+      "ĠJohann es",
+      "ĠGall agher",
+      "Ġgym n",
+      "S uddenly",
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+      "k r",
+      "i ator",
+      "T a",
+      "Ġhippocamp us",
+      "N PR",
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+      "Ġsquare ly",
+      "Ġmod elling",
+      "ĠFor ums",
+      "ĠL isp",
+      "ĠKrish na",
+      "Ġ3 24",
+      "Ġr ushes",
+      "Ġens ued",
+      "Ġcre eping",
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+      "il ater",
+      "ĠHorn ets",
+      "Ġob livious",
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+      "Ġjeopard y",
+      "Ġdistingu ishing",
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+      "Ġbeg s",
+      "sim ilar",
+      "ph ot",
+      "5 30",
+      "ĠPark way",
+      "Ġs inks",
+      "ĠHearth stone",
+      "ib ur",
+      "ĠBat on",
+      "Av oid",
+      "Ġd ancer",
+      "Ġmag istrate",
+      "ary n",
+      "Ġdisturb ances",
+      "ĠRom ero",
+      "Ġpar aph",
+      "Ġmis chief",
+      "âĸ ĵ",
+      "ĠSh aria",
+      "Ġur inary",
+      "r oute",
+      "iv as",
+      "f itted",
+      "Ġeject ed",
+      "ĠAl buquerque",
+      "Ġ4 70",
+      "Ġirrit ated",
+      "ĠZ ip",
+      "ĠB iol",
+      "Ã į",
+      "Ġden ounce",
+      "Ġbin aries",
+      "ĠVer se",
+      "Ġopp os",
+      "ĠKend rick",
+      "ĠG PL",
+      "Ġsp ew",
+      "ĠEl ijah",
+      "ĠE as",
+      "Ġdr ifted",
+      "so far",
+      "Ġannoy ance",
+      "ĠB ET",
+      "47 4",
+      "ĠSt rongh",
+      "it ates",
+      "ĠCogn itive",
+      "oph one",
+      "ĠIdent ification",
+      "ocr ine",
+      "connect ion",
+      "Ġbox er",
+      "ĠAS D",
+      "ĠAre as",
+      "Y ang",
+      "t ch",
+      "ull ah",
+      "Ġdece ive",
+      "Comb at",
+      "ep isode",
+      "cre te",
+      "W itness",
+      "Ġcondol ences",
+      "ht ar",
+      "Ġhe als",
+      "Ġbuck ets",
+      "ĠLA W",
+      "B lu",
+      "Ġsl ab",
+      "ĠOR DER",
+      "oc l",
+      "att on",
+      "ĠSteven son",
+      "ĠG inger",
+      "ĠFriend ly",
+      "ĠVander bilt",
+      "sp irit",
+      "ig l",
+      "ĠReg arding",
+      "ĠPR OG",
+      "Ġse aling",
+      "start ing",
+      "Ġcard inal",
+      "ĠV ec",
+      "ĠBe ir",
+      "Ġmillisec onds",
+      "we ak",
+      "per se",
+      "Ġster ile",
+      "ĠCont emporary",
+      "ĠPh ant",
+      "ĠCl o",
+      "Ġout p",
+      "Ġex iled",
+      "Ġ27 7",
+      "Ġself ie",
+      "Ġman ic",
+      "Ġn ano",
+      "ter ms",
+      "Alex ander",
+      "Ġres olves",
+      "Ġmillenn ia",
+      "Ġexpl odes",
+      "Ġconst ellation",
+      "Ġadul tery",
+      "m otion",
+      "D OC",
+      "Ġbroad casters",
+      "Ġkinderg arten",
+      "ĠMay weather",
+      "ĠE co",
+      "ich o",
+      "Ġ28 7",
+      "l aun",
+      "Ġm ute",
+      "Ġdisc reet",
+      "Ġpres chool",
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+      "De lete",
+      "ĠFre ed",
+      "P i",
+      "H K",
+      "Ġblock er",
+      "ĠC umber",
+      "Ġw rought",
+      "d ating",
+      "Ġins urer",
+      "Ġquot as",
+      "Ġpre ached",
+      "Ġev iction",
+      "ĠReg ina",
+      "ĠP ens",
+      "Ġsevent een",
+      "ĠN ass",
+      "D ick",
+      "Ġfold s",
+      "Ġd otted",
+      "ĠA ad",
+      "Un iversal",
+      "Ġp izz",
+      "ĠG uru",
+      "Ġso ils",
+      "Ġno vice",
+      "ĠNe ander",
+      "Ġst ool",
+      "Ġdeton ated",
+      "ĠPik achu",
+      "ĠMass ive",
+      "IV ER",
+      "ĠAb del",
+      "Ġsubdu ed",
+      "Ġtall est",
+      "Ġprec arious",
+      "Ġa y",
+      "r ification",
+      "ĠOb j",
+      "c ale",
+      "Ġun question",
+      "cul osis",
+      "ad as",
+      "igr ated",
+      "D ays",
+      "Ġque ens",
+      "ĠGaz ette",
+      "ĠCol our",
+      "ĠBow man",
+      "ĠJ J",
+      "ï ve",
+      "Ġdomin ates",
+      "Stud ent",
+      "Ġm u",
+      "Ġback log",
+      "ĠElect ro",
+      "Tr uth",
+      "48 3",
+      "Ġcond ensed",
+      "r ules",
+      "ĠCons piracy",
+      "Ġacron ym",
+      "hand led",
+      "ĠMat te",
+      "j ri",
+      "ĠImp ossible",
+      "l ude",
+      "cre ation",
+      "Ġwar med",
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+      "Ġmis led",
+      "Ġfer ment",
+      "ĠK ah",
+      "ink i",
+      "ke leton",
+      "cy l",
+      "ĠKar in",
+      "Hun ter",
+      "Reg ister",
+      "ĠSur rey",
+      "Ġst ares",
+      "ĠW idth",
+      "ĠN ay",
+      "ĠSk i",
+      "Ġblack list",
+      "uck et",
+      "Ġexp ulsion",
+      "im et",
+      "Ġret weet",
+      "vant age",
+      "Fe ature",
+      "Ġtro opers",
+      "Ġhom ers",
+      "9 69",
+      "Ġconting ency",
+      "ĠW TC",
+      "ĠBrew er",
+      "fore ign",
+      "W are",
+      "S olar",
+      "Ġund ue",
+      "RE C",
+      "ulner able",
+      "path ic",
+      "ĠBo ise",
+      "Ġ3 22",
+      "Ġarous ed",
+      "ĠY ing",
+      "ä¸ į",
+      "uel ess",
+      "Ġp as",
+      "Ġmor p",
+      "Ġfl oral",
+      "Ex press",
+      "ud ging",
+      "k B",
+      "ĠGr anted",
+      "Ø ¯",
+      "ĠMich a",
+      "ĠGoth ic",
+      "ĠSPEC IAL",
+      "ĠRic ardo",
+      "F ran",
+      "Ġadminister ing",
+      "6 20",
+      "por a",
+      "ĠÂ ®",
+      "Ġcomprom ises",
+      "Ġb itten",
+      "Ac cept",
+      "Th irty",
+      "Ð ²",
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+      "ĠTer r",
+      "ig matic",
+      "ch ains",
+      "Ġdo ve",
+      "stad t",
+      "Mar vel",
+      "FA ULT",
+      "Ġwind shield",
+      "Ġ3 36",
+      "ad ier",
+      "Ġsw apping",
+      "Ġflaw less",
+      "ĠPred ator",
+      "ĠMiche le",
+      "Ġprop ulsion",
+      "ĠPsych ic",
+      "Ġassign ing",
+      "Ġfabric ation",
+      "Ġbar ley",
+      "l ust",
+      "Ġtow ering",
+      "Ġalter cation",
+      "ĠBent ley",
+      "Sp here",
+      "Ġtun a",
+      "ĠClass es",
+      "Fre edom",
+      "un er",
+      "L ady",
+      "v oice",
+      "Ġcool est",
+      "or r",
+      "Ġpal p",
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+      "Ġhyster ia",
+      "ĠMet atron",
+      "p ants",
+      "Ġspawn ing",
+      "Exper ts",
+      "ĠInvest ors",
+      "ĠAn archy",
+      "Ġshr unk",
+      "ĠVict im",
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+      "Ġec stasy",
+      "ĠB inding",
+      "58 5",
+      "ĠMel ody",
+      "57 8",
+      "ot ally",
+      "ĠE tsy",
+      "lig a",
+      "Ġapplaud ed",
+      "Ġswe ating",
+      "Ġredist ributed",
+      "Ġpop corn",
+      "Ġsem inal",
+      "f ur",
+      "ĠNeuro science",
+      "R and",
+      "ĠO st",
+      "ĠMadd en",
+      "ĠIncre asing",
+      "ĠDaw kins",
+      "ĠSub way",
+      "Ġar sen",
+      "cons erv",
+      "B UR",
+      "Ġsp iked",
+      "ĠLy ft",
+      "ĠImper ium",
+      "ĠDrop box",
+      "Ġfav oured",
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+      "gh ost",
+      "Ġins pires",
+      "Ġbur geoning",
+      "ĠY oshi",
+      "ĠVert ical",
+      "ĠAud itor",
+      "Ġint ending",
+      "Ġfilib uster",
+      "Bl oom",
+      "f ac",
+      "ĠCav s",
+      "ign ing",
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+      "ĠBarb arian",
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+      "ason ry",
+      "Col lege",
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+      "ĠPsych o",
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+      "Ġlav ish",
+      "Ġhierarch ical",
+      "ĠDr one",
+      "ou k",
+      "Ġcripp led",
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+      "ĠV id",
+      "if ling",
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+      "d ial",
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+      "ĠF as",
+      "T ed",
+      "Ġr aspberry",
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+      "Ġsp aced",
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+      "ĠF ern",
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+      "Ġneck lace",
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+      "F U",
+      "ĠBrav o",
+      "Ġab users",
+      "ĠAn ita",
+      "met adata",
+      "ĠG ithub",
+      "ait o",
+      "ĠF aster",
+      "ĠWass erman",
+      "ĠF lesh",
+      "Ġth orn",
+      "r arily",
+      "ĠMer ry",
+      "w ine",
+      "Ġpopul ace",
+      "ĠL ann",
+      "Ġrepair ing",
+      "Ġpsy che",
+      "Ġmod ulation",
+      "aw aru",
+      "âĢĭ âĢĭ",
+      "ari j",
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+      "ĠG arg",
+      "app ly",
+      "Ġgive away",
+      "ĠFl an",
+      "ĠWy att",
+      "U ber",
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+      "Ġtorped o",
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+      "Ġam assed",
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+      "ĠBe et",
+      "Ġrev oke",
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+      "ĠH iggins",
+      "F ail",
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+      "h ound",
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+      "ag ically",
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+      "H OME",
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+      "f ee",
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+      "ĠD ent",
+      "Sh ut",
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+      "ĠG ins",
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+      "Ġm uddy",
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+      "Ġwear able",
+      "Ġ3 28",
+      "our met",
+      "ah s",
+      "ĠRob ots",
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+      "inst ance",
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+      "ĠSk ies",
+      "01 00",
+      "Ġfall acy",
+      "S ocket",
+      "ĠRece iver",
+      "Ġsol ves",
+      "ĠButter fly",
+      "ĠSho pping",
+      "ĠFI RE",
+      "65 4",
+      "Med ic",
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+      "ĠNeed less",
+      "'' ''",
+      "isher s",
+      "ĠD ive",
+      "58 8",
+      "Ġselect ively",
+      "Ġcl umsy",
+      "88 9",
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+      "ĠF IGHT",
+      "am eless",
+      "ĠMead ows",
+      "ĠHar baugh",
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+      "ĠCON C",
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+      "ĠS utton",
+      "Ġtouch screen",
+      "Ġembry os",
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+      "ĠK rug",
+      "com bat",
+      "ĠPet roleum",
+      "Ġam d",
+      "ĠCos mos",
+      "Ġpresc ribing",
+      "Ġconform ity",
+      "ours es",
+      "Ġplent iful",
+      "Ġdis illusion",
+      "ĠEc ology",
+      "itt al",
+      "Ġf anc",
+      "Ġassass inated",
+      "regn ancy",
+      "Ġperenn ial",
+      "ĠBul lets",
+      "Ġst ale",
+      "Ġc ached",
+      "ĠJud ith",
+      "ĠDise ases",
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+      "Ġl as",
+      "Ġsh ards",
+      "ĠSu arez",
+      "ĠFriend ship",
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+      "ĠSupp orters",
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+      "ĠIm ran",
+      "ĠW im",
+      "Ġnew found",
+      "ĠM b",
+      "An imal",
+      "Ġd arling",
+      "and e",
+      "Ġrh y",
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+      "pos al",
+      "yn ski",
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+      "ĠK iw",
+      "uy omi",
+      "Ġwell being",
+      "ĠL au",
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+      "Ġunm ist",
+      "Ġmac OS",
+      "Ġrest room",
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+      "Ġrad ios",
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+      "ias co",
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+      "Ġdef icient",
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+      "ĠF ault",
+      "ĠCost ume",
+      "ĠM ub",
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+      "C trl",
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+      "ĠWra ith",
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+      "ĠWonder ful",
+      "ĠT aco",
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+      "Ġlo af",
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+      "Ġiniti ating",
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+      "Ġbrew ed",
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+      "ĠAdd iction",
+      "ĠPad res",
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+      "ĠCa vern",
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+      "ĠMand ela",
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+      "amp oo",
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+      "ĠK H",
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+      "Ġendeav ors",
+      "ĠHav ana",
+      "ĠSt out",
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+      "ĠT ens",
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+      "Ġop ium",
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+      "ich ael",
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+      "ĠZ oom",
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+      "inc erity",
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+      "Ġlong ing",
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+      "Ġshowc asing",
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+      "ĠK ass",
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+      "ĠL unch",
+      "ĠF oss",
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+      "ĠOne Plus",
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+      "ĠCorrect ions",
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+      "Ġqual ifiers",
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+      "ĠH undred",
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+      "ĠT OR",
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+      "ĠWall s",
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+      "! \",",
+      "al on",
+      "K rist",
+      "Ġmeas urable",
+      "Ġimagin able",
+      "Ġapost les",
+      "Y N",
+      "7 60",
+      "Ġster oid",
+      "Ġspecific ity",
+      "ĠL ocated",
+      "ĠBeck er",
+      "ĠE du",
+      "ĠDiet ary",
+      "uts ch",
+      "ĠMar ilyn",
+      "Ġbl ister",
+      "ĠM EP",
+      "ĠK oz",
+      "ĠC MS",
+      "y ahoo",
+      "ĠCar ney",
+      "Ġbo asting",
+      "ĠC aleb",
+      "By te",
+      "read s",
+      "ad en",
+      "Pro blem",
+      "ĠWood ward",
+      "S we",
+      "S up",
+      "ĠK GB",
+      "Set up",
+      "Ġtac it",
+      "Ġret ribution",
+      "Ġd ues",
+      "ĠM ü",
+      ". ?",
+      "ä¸ Ń",
+      "p ots",
+      "Ġcame o",
+      "ĠP AL",
+      "educ ation",
+      "A my",
+      "like ly",
+      "g ling",
+      "Ġconstitution ally",
+      "ĠHam m",
+      "ĠSpe ak",
+      "Ġwid gets",
+      "br ate",
+      "Ġcra ppy",
+      "ĠI ter",
+      "Ġanticip ating",
+      "ĠB out",
+      "P ixel",
+      "ĠY ep",
+      "ĠLaur ie",
+      "Ġh ut",
+      "Ġbullet in",
+      "ĠSal vation",
+      "Ġch ats",
+      "ear able",
+      "Honest ly",
+      "AL TH",
+      "onse qu",
+      "c ult",
+      "isco very",
+      "ovy ch",
+      "Ġse lves",
+      "ĠSat oshi",
+      "S ounds",
+      "Ġconver gence",
+      "ĠRosen berg",
+      "19 74",
+      "Ġnas al",
+      "Ġfull est",
+      "Ġfer ocious",
+      "x us",
+      "ist e",
+      "AM S",
+      "Ġlobb ied",
+      "Ġso othing",
+      "ĠGun n",
+      "t oday",
+      "0 24",
+      "Ġinspir ational",
+      "ĠN BN",
+      "p b",
+      "g ewater",
+      "or ah",
+      "all owed",
+      "ĠCol iseum",
+      "Ġspecial izing",
+      "Ġinsane ly",
+      "ĠT ape",
+      "del ay",
+      "Ġt arn",
+      "ĠP ound",
+      "Ġmel anch",
+      "Ġdeploy ments",
+      "il and",
+      "Ġless en",
+      "Ġfur ry",
+      "ĠUE FA",
+      "Ġblood shed",
+      "ĠMe ier",
+      "ither ing",
+      "Ġhe irs",
+      "ĠJ aw",
+      "ax ter",
+      "ĠPublic ations",
+      "Ġal ters",
+      "int ention",
+      "ĠWinc hester",
+      "d etermination",
+      "ĠLif etime",
+      "th in",
+      "Mon ster",
+      "7 80",
+      "Ġapprox imation",
+      "Ġsuper markets",
+      "ĠSecond s",
+      "or os",
+      "h uge",
+      "Ġb ribe",
+      "ĠLIM ITED",
+      "un ed",
+      "Ġmis interpret",
+      "ĠIn jury",
+      "Ġ3 67",
+      "Ġthreshold s",
+      "ĠCarn ival",
+      "Ġgastro intestinal",
+      "Ġguid eline",
+      "Ġde ceived",
+      "f eatures",
+      "Ġpurported ly",
+      "ĠRon nie",
+      "ĠNew t",
+      "Ġsp acious",
+      "as us",
+      "Ġsuperhero es",
+      "ĠCyn thia",
+      "le gged",
+      "k amp",
+      "ch io",
+      "Ġth umbnail",
+      "ĠShir ley",
+      "ill ation",
+      "Ġshe ds",
+      "ĠZ y",
+      "E PA",
+      "Ġdam s",
+      "Ġy awn",
+      "n ah",
+      "ĠPe ggy",
+      "ĠE rie",
+      "ĠJu ventus",
+      "ĠF ountain",
+      "r x",
+      "don ald",
+      "al bum",
+      "ĠComp rehensive",
+      "Ġc aching",
+      "ĠU z",
+      "ulner ability",
+      "ĠPrinc iple",
+      "ĠJ ian",
+      "ing ers",
+      "cast s",
+      "ĠOs iris",
+      "ch art",
+      "t ile",
+      "ĠTiff any",
+      "ĠPatt on",
+      "ĠWh ip",
+      "Ġovers ized",
+      "J e",
+      "ĠCind erella",
+      "ĠB orders",
+      "ĠDa esh",
+      "M ah",
+      "Ġdog ma",
+      "Ġcommun ists",
+      "v u",
+      "Coun cil",
+      "Ġfresh water",
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+      "Ġdeb acle",
+      "Ġyoung ster",
+      "Ġthread ed",
+      "ĠB ots",
+      "ĠSav ings",
+      "ãģ Ĥ",
+      "ol ing",
+      "oh o",
+      "Ġillum ination",
+      "M RI",
+      "Ġlo osen",
+      "tr ump",
+      "ag ency",
+      "ur ion",
+      "Ġmoment arily",
+      "ĠCh un",
+      "ĠBud apest",
+      "ĠAl ley",
+      "D isk",
+      "Ġaston ished",
+      "ĠCon quer",
+      "ĠAccount ing",
+      "h aving",
+      "ĠWe in",
+      "ĠAl right",
+      "Ġrev olver",
+      "Ġdel usion",
+      "Ġrelic s",
+      "Ġad herent",
+      "qu ant",
+      "Ġhand made",
+      "or io",
+      "Ġcomb ating",
+      "c oded",
+      "Ġquad ru",
+      "re th",
+      "N ik",
+      "ĠTrib al",
+      "ĠMyster ious",
+      "Ġin hal",
+      "ĠWin ning",
+      "ĠClass ification",
+      "ch anged",
+      "Ġun ab",
+      "Ġsc orn",
+      "icip ated",
+      "w l",
+      "ond uctor",
+      "Ġrein forcing",
+      "ĠChild hood",
+      "an ova",
+      "Ġadventure r",
+      "Ġdoctor al",
+      "ĠStrateg ies",
+      "Ġengulf ed",
+      "ĠEnc ounter",
+      "Ġl ashes",
+      "Crit ical",
+      "ric ular",
+      "ĠU TF",
+      "oci ation",
+      "check ing",
+      "ĠConsult ing",
+      "Run time",
+      "per iod",
+      "ĠAs gard",
+      "Ġdist illed",
+      "ĠPas adena",
+      "ĠD ying",
+      "ĠCOUN TY",
+      "Ġgran ite",
+      "Ġsm ack",
+      "Ġparach ute",
+      "ĠS UR",
+      "Virgin ia",
+      "ĠF urious",
+      "78 7",
+      "ĠO kin",
+      "Ġcam el",
+      "ĠM bps",
+      "19 72",
+      "ĠCh ao",
+      "ĠC yan",
+      "j oice",
+      "ef er",
+      "ĠW rap",
+      "ĠDeb ate",
+      "S eg",
+      "Ġfore arm",
+      "ĠIgn ore",
+      "Ġtim estamp",
+      "Ġprob ing",
+      "ĠNo on",
+      "ĠGra il",
+      "f en",
+      "Ġdorm ant",
+      "ĠFirst ly",
+      "ĠE ighth",
+      "ĠH UN",
+      "ĠDes ire",
+      "or as",
+      "Girl s",
+      "ĠDes mond",
+      "z ar",
+      "am ines",
+      "O AD",
+      "exec ute",
+      "Ġbo obs",
+      "ĠAT L",
+      "_ (",
+      "Chel sea",
+      "Ġmasturb ation",
+      "ĠCo C",
+      "Ġdestroy er",
+      "ĠCh omsky",
+      "Ġsc atter",
+      "ĠAss ets",
+      "79 6",
+      "ĠC argo",
+      "Ġrecept ive",
+      "ĠSc ope",
+      "Ġmarket ers",
+      "Ġlaun chers",
+      "Ġax le",
+      "ĠSE A",
+      "se q",
+      "ĠM off",
+      "f inding",
+      "ĠGib bs",
+      "Georg ia",
+      "extreme ly",
+      "N J",
+      "Ġlab orers",
+      "st als",
+      "Ġmed iation",
+      "ĠH edge",
+      "at own",
+      "Ġi od",
+      "des pite",
+      "v ill",
+      "J ane",
+      "ex istence",
+      "Ġcoinc ided",
+      "ĠUt ilities",
+      "ĠChe ap",
+      "Ġlog istical",
+      "Ġcul mination",
+      "ĠNic otine",
+      "p ak",
+      "F older",
+      "Ġrod ents",
+      "st uff",
+      "Ġlaw fully",
+      "Ġreper to",
+      "io ch",
+      "j j",
+      "Dial ogue",
+      "HH HH",
+      "lic tion",
+      "Look s",
+      "Ġ29 7",
+      "Ġtur rets",
+      "ĠAb andon",
+      "Ġinc ess",
+      "ĠTraff ord",
+      "Ġcur led",
+      "Ġprefer ring",
+      "Ġprivat ization",
+      "Ġir resist",
+      "ĠP anda",
+      "ĠSh ake",
+      "ĠMc Gr",
+      "ãĥ Ħ",
+      "und ers",
+      "Ġdiscrim inated",
+      "Ġbart ender",
+      "I LE",
+      "Atl antic",
+      "Ġprop ensity",
+      "ĠW iz",
+      "ĠG im",
+      "con ference",
+      "Ġrein forces",
+      "G h",
+      "w agon",
+      "Ġe erie",
+      "F al",
+      "Ġhug ged",
+      "rac ist",
+      "R IC",
+      "F u",
+      "Ġf iller",
+      "ĠSt ub",
+      "Ġeng raved",
+      "ĠWrest le",
+      "Ġimagin ative",
+      "ĠPe er",
+      "ĠFact ors",
+      "an us",
+      "ĠDrac ula",
+      "mon itor",
+      "Ġrou ters",
+      "ib ia",
+      "ĠBoo lean",
+      "end ale",
+      "ĠSl aughter",
+      "ĠSh ack",
+      "R FC",
+      "ĠSpiel berg",
+      "S ax",
+      "ĠPH OTO",
+      "ĠCl over",
+      "ĠR ae",
+      "Dep ending",
+      "ĠMem or",
+      "ar am",
+      "Ġpier ced",
+      "Ġcur tains",
+      "v ale",
+      "ĠInqu isition",
+      "ĠP oke",
+      "Ġforecast ing",
+      "Ġcompl ains",
+      "S ense",
+      "ĠHer mes",
+      "isc overed",
+      "Ġb ible",
+      "ĠMor ph",
+      "Ġg erm",
+      "78 5",
+      "D ON",
+      "Ġcon gen",
+      "Ġcr ane",
+      "ĠD PR",
+      "Ġrespect fully",
+      "R oom",
+      "ĠN aw",
+      "ĠDal ai",
+      "re ason",
+      "ĠAng us",
+      "Educ ation",
+      "ĠTitan ic",
+      "Ë ľ",
+      "Ġo val",
+      "un ited",
+      "Ġthird s",
+      "Ġmoist ur",
+      "ĠC PC",
+      "M iami",
+      "Ġtent acles",
+      "ĠPol aris",
+      "ex c",
+      "ex clusive",
+      "ĠPra irie",
+      "Ġcol ossal",
+      "ĠBl end",
+      "sur prisingly",
+      "ÃŃ s",
+      "Ġindo ctr",
+      "Ġbas al",
+      "ĠMP EG",
+      "und o",
+      "Spl it",
+      "Develop ment",
+      "Ġlan tern",
+      "19 71",
+      "Ġprov ocation",
+      "Ġang uish",
+      "ĠB ind",
+      "ĠLe ia",
+      "duc ers",
+      "ipp y",
+      "conserv ancy",
+      "Ġinitial ize",
+      "ĠTw ice",
+      "ĠSu k",
+      "Ġpred ic",
+      "Ġdi ploma",
+      "Ġsoc iop",
+      "Ing redients",
+      "Ġhamm ered",
+      "ĠIr ma",
+      "Q aida",
+      "Ġglim ps",
+      "ĠB ian",
+      "Ġst acking",
+      "Ġf end",
+      "gov track",
+      "Ġun n",
+      "dem ocratic",
+      "ig ree",
+      "Ġ5 80",
+      "Ġ29 4",
+      "Ġstraw berry",
+      "ID ER",
+      "Ġcher ished",
+      "ĠH ots",
+      "Ġinfer red",
+      "Ġ8 08",
+      "ĠS ocrates",
+      "O regon",
+      "ĠR oses",
+      "ĠFO IA",
+      "Ġins ensitive",
+      "Ġ40 8",
+      "Recomm end",
+      "ĠSh ine",
+      "Ġpain staking",
+      "UG E",
+      "ĠHell er",
+      "ĠEnter prises",
+      "I OR",
+      "ad j",
+      "N RS",
+      "L G",
+      "Ġalien ated",
+      "Ġacknowled gement",
+      "ĠA UD",
+      "ĠRen eg",
+      "Ġvou chers",
+      "Ġ9 60",
+      "Ġm oot",
+      "ĠDim ensions",
+      "Ġc abbage",
+      "B right",
+      "g at",
+      "ĠK lu",
+      "Ġlat ent",
+      "Ġz e",
+      "ĠM eng",
+      "Ġdis perse",
+      "Ġpand emonium",
+      "H Q",
+      "Ġvirt uous",
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+      "ee per",
+      "prov ided",
+      "Ġse ams",
+      "ĠW T",
+      "iz o",
+      "PR OV",
+      "Ġtit anium",
+      "Ġrecol lection",
+      "Ġcr an",
+      "Ġ7 80",
+      "ĠN F",
+      "49 1",
+      "64 2",
+      "p acking",
+      "59 8",
+      "text ure",
+      "Sp ider",
+      "fre edom",
+      "cipl ed",
+      "ĠTAM ADRA",
+      "âĻ ¦",
+      "aut hent",
+      "ĠW ANT",
+      "r ified",
+      "Ġr ites",
+      "Ġuter us",
+      "k iss",
+      "Ġâī ¤",
+      "Ġsk illet",
+      "Ġdis enfranch",
+      "ĠGa al",
+      "Comp an",
+      "Ġage ing",
+      "gu ide",
+      "B alt",
+      "Ġiter ator",
+      "Ġdiscretion ary",
+      "t ips",
+      "Ġprim ates",
+      "ĠTechn ique",
+      "ĠPay ments",
+      "az el",
+      "ĠR OCK",
+      "stant ial",
+      "0 60",
+      "Ġd mg",
+      "ĠJack ets",
+      "ĠPlay off",
+      "Ġnurs ery",
+      "ĠSy mb",
+      "art on",
+      "Ġannex ation",
+      "Color ado",
+      "Ġco ils",
+      "ĠSh oes",
+      "âĦ¢ :",
+      "ĠRo z",
+      "COM PLE",
+      "ĠEve rest",
+      "ĠTri umph",
+      "J oy",
+      "G rid",
+      "à ¼",
+      "process or",
+      "ĠPros per",
+      "ĠSever us",
+      "ĠSelect ed",
+      "r g",
+      "ĠTay yip",
+      "St ra",
+      "Ġski ing",
+      "Ġ? )",
+      "Ġpe g",
+      "Tes la",
+      "Ġtime frame",
+      "Ġmaster mind",
+      "ĠN B",
+      "scient ific",
+      "ĠSh it",
+      "gener ic",
+      "IN TER",
+      "N UM",
+      "Ġst roll",
+      "ĠEn ix",
+      "ĠM MR",
+      "ĠE MS",
+      "m ovie",
+      "Ĥ ª",
+      "Ġminim izing",
+      "idd ling",
+      "Ġilleg itimate",
+      "Ġprot otyp",
+      "Ġpremature ly",
+      "Ġmanual s",
+      "obb ies",
+      "ĠCass idy",
+      "D EC",
+      "des ktop",
+      "Ġaer os",
+      "Ġscreen ings",
+      "Ġdeb ilitating",
+      "ĠGr ind",
+      "nature conservancy",
+      "Ġf ades",
+      "ter mination",
+      "assets adobe",
+      "F actor",
+      "Ġdefinitive ly",
+      "P oké",
+      "ap ult",
+      "ĠLaf ayette",
+      "C orn",
+      "ĠCor al",
+      "Ġstagn ant",
+      "T ue",
+      "Ġdissatisf action",
+      "G ender",
+      "Ġkid neys",
+      "ĠG ow",
+      "ĠDef eat",
+      "ĠAsh ton",
+      "Ġcart els",
+      "Ġfore closure",
+      "ĠExpl ore",
+      "stre ngth",
+      "ot in",
+      "Ġveterin arian",
+      "Ġf umble",
+      "Ġpar ap",
+      "ĠSt rait",
+      "r ils",
+      "Ġpr ick",
+      "ĠBerm uda",
+      "ĠAm munition",
+      "skin ned",
+      "Ġab ound",
+      "ĠB raz",
+      "Ġshar per",
+      "ĠAsc ension",
+      "Ġ9 78",
+      "Ġpreview s",
+      "Ġcommun ion",
+      "ĠX Y",
+      "Ġph ony",
+      "Ġnewcom er",
+      "Ġ3 32",
+      ".\" ,\"",
+      "Ġredist ribution",
+      "Prot ect",
+      "ĠSo f",
+      "K al",
+      "Ġlip stick",
+      "w orst",
+      "Ġtang led",
+      "Ġretrospect ive",
+      "int eger",
+      "Ġvolunte ering",
+      "Ġ19 07",
+      "Ġ --------------------",
+      "ic hen",
+      "Ġunve iling",
+      "Ġsen seless",
+      "Ġfisher ies",
+      "\\ -",
+      "Ġh inges",
+      "Ġcalcul us",
+      "My th",
+      "Ġund efeated",
+      "Ġoptim izations",
+      "Ġdep ress",
+      "Ġbill board",
+      "ĠY ad",
+      "ĠPy ramid",
+      "Is n",
+      "I de",
+      "Ġleg ion",
+      "ĠK ramer",
+      "ent anyl",
+      "Ġpenet rating",
+      "ĠHaw th",
+      "ĠPR ODUCT",
+      "ĠGer ard",
+      "ĠP act",
+      "ĠIn cluding",
+      "ĠEl ias",
+      "ĠEl aine",
+      "vis ual",
+      "Ġhum ming",
+      "Ġcond esc",
+      "ĠF asc",
+      "ä¸ Ĭ",
+      "Ġe galitarian",
+      "Ġdev s",
+      "ĠD ahl",
+      "O ps",
+      "D H",
+      "ĠB ounce",
+      "id ated",
+      "ald o",
+      "Ġrepublic an",
+      "Ġh amb",
+      "ĠS ett",
+      "ograph ies",
+      "CH APTER",
+      "Ġtrans sexual",
+      "Ġsky rocket",
+      "ans wer",
+      "Ġmark up",
+      "Ø ª",
+      "Ġhero ine",
+      "Comp are",
+      "ĠT av",
+      "Be ast",
+      "Ġsuccess ors",
+      "Ġna ïve",
+      "ĠBuck ley",
+      "st ress",
+      "me at",
+      "Ġdownload able",
+      "Ġindex ed",
+      "Ġsc aff",
+      "ĠL ump",
+      "ĠHom o",
+      "Stud io",
+      "In sp",
+      "Ġr acked",
+      "far ious",
+      "ĠPet ty",
+      "Ex ternal",
+      "Ġ19 09",
+      "W ars",
+      "com mit",
+      "put ers",
+      "Ġun ob",
+      "ĠEr r",
+      "ĠE G",
+      "ĠAl am",
+      "ĠSiber ia",
+      "ĠAtmosp heric",
+      "IS TER",
+      "ĠSatan ic",
+      "trans lation",
+      "ĠL oud",
+      "tra umatic",
+      "l ique",
+      "Ġreson ate",
+      "ĠWel ch",
+      "Ġspark ing",
+      "ĠT OM",
+      "t one",
+      "Ġout l",
+      "Ġhandc uffed",
+      "ĠSer ie",
+      "8 01",
+      "Ġland marks",
+      "ĠRee ves",
+      "Ġsoft ened",
+      "Ġdazz ling",
+      "ĠW anted",
+      "month s",
+      "Mag ikarp",
+      "Ġunt reated",
+      "ĠBed ford",
+      "M i",
+      "ĠDynam o",
+      "O re",
+      "79 5",
+      "Ġwrong ful",
+      "Ġl ured",
+      "Ġcort isol",
+      "Ġve x",
+      "d rawn",
+      "ile t",
+      "Download ha",
+      "ĠF action",
+      "Ġlab yrinth",
+      "Ġhij acked",
+      "w aters",
+      "er ick",
+      "Ġsuper iors",
+      "ĠRow ling",
+      "ĠGu inness",
+      "Ġt d",
+      "99 2",
+      "Ġune arthed",
+      "Ġcentr if",
+      "Ġsham eless",
+      "P od",
+      "ĠF ib",
+      "Ġ icing",
+      "Ġpredict or",
+      "Ġ29 2",
+      "fore station",
+      "con struct",
+      "C and",
+      "@ #",
+      "Ġag itated",
+      "Ġre pr",
+      "OV A",
+      "Ġkn itting",
+      "ĠLim a",
+      "Ġf odder",
+      "68 4",
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+      "ĠGu pta",
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+      "ri et",
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+      "Ġexperiment ed",
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+      "ĠGentle man",
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+      "k illed",
+      "ed ited",
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+      "Ġnon partisan",
+      "ĠCons umption",
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+      "Ġdeduct ible",
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+      "4 11",
+      "ĠUn ified",
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+      "Ġfurn ace",
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+      "Ġest ates",
+      "ĠH F",
+      "Ġha ha",
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+      "ĠO CT",
+      "Ġpl astics",
+      "Ġincident ally",
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+      "Ġch anted",
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+      "F ed",
+      "Ġhide ous",
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+      "ĠSynd icate",
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+      "ĠD AV",
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+      "c rew",
+      "Ġfundamental ist",
+      "Ġtruth ful",
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+      "Ġl il",
+      "Ġdown ed",
+      "Ġen chanted",
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+      "gorith m",
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+      "ĠK T",
+      "Ġ3 37",
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+      "F UN",
+      "D oug",
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+      "Ġcal c",
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+      "an u",
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+      "Ġab norm",
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+      "x p",
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+      "Ġsc apego",
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+      "oth al",
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+      "Dep ths",
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+      "ĠIll uminati",
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+      "ĠD aredevil",
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+      "ĠRom o",
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+      "ĠAer ospace",
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+      "Ġ3 47",
+      "Ir ish",
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+      "ĠHon olulu",
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+      "5 99",
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+      "il ial",
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+      "Am id",
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+      "Ġbe friend",
+      "m obi",
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+      "h ak",
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+      "ĠF EMA",
+      "Ġev olves",
+      "Ġest e",
+      "ĠP ets",
+      "ĠM é",
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+      "ch apter",
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+      "Sp anish",
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+      "n orth",
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+      "ent imes",
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+      "ĠQU EST",
+      "ĠCONT IN",
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+      "ĠSam oa",
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+      "Ð ´",
+      "userc ontent",
+      "ĠMod erate",
+      "y eah",
+      "ĠK itt",
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+      "Ġstuff ing",
+      "ĠInter vention",
+      "ĠD ign",
+      "Ġware houses",
+      "ĠF iji",
+      "Ġpel lets",
+      "Ġtake away",
+      "ĠT ABLE",
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+      "col lection",
+      "Ġland fall",
+      "ĠMus cle",
+      "Ġsett les",
+      "ĠAD V",
+      "Ġ3 44",
+      "L aura",
+      "Ġf ared",
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+      "55 1",
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+      "ĠO CD",
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+      "IR O",
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+      "ĠL ift",
+      "um o",
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+      "ĠL ori",
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+      "dep ending",
+      "ĠLow ry",
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+      "R achel",
+      "Min imum",
+      "Ġcontest ant",
+      "ĠRed uce",
+      "ĠMor se",
+      "ĠGrass ley",
+      "ĠClick er",
+      "Ġexp r",
+      "Ġs incerity",
+      "Ġmar qu",
+      "Ġelic it",
+      "ĠPro position",
+      "ĠDemon ic",
+      "Ġtac os",
+      "G reek",
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+      "ĠP ork",
+      "Ġ35 2",
+      "doctor al",
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+      "ic i",
+      "AL D",
+      "ĠUS L",
+      "ĠF ISA",
+      "ĠAm pl",
+      "ĠAlex andra",
+      "ine lli",
+      "Tr ain",
+      "Ġsign ify",
+      "ĠVers us",
+      "Ġob fusc",
+      "Ġk h",
+      "Ġagg ro",
+      "ĠRen ault",
+      "Ġ3 48",
+      "5 18",
+      "ox icity",
+      "0 22",
+      "ĠTw ist",
+      "Ġgoof y",
+      "D ynamic",
+      "Ġbrief ings",
+      "m ight",
+      "8 99",
+      "Ġderog atory",
+      "T ro",
+      "Ġfor ging",
+      "ĠKor an",
+      "ĠMar ried",
+      "ĠBuc s",
+      "Ġpal ate",
+      "ĠCon version",
+      "m able",
+      "4 13",
+      "Ġ( _",
+      "Ġs iph",
+      "ĠN EO",
+      "col lege",
+      "Ġmarg inally",
+      "Ġfl irt",
+      "ĠTra ps",
+      "ĠP ace",
+      "é »Ĵ",
+      "Ġgoalt ender",
+      "Ġforb ids",
+      "Ġcler ks",
+      "ĠT ant",
+      "ĠRobb ins",
+      "ĠPrint ing",
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+      "ĠT G",
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+      "ĠK odi",
+      "rib une",
+      "ĠL ena",
+      "Elect ric",
+      "Ġref inery",
+      "Ġend owed",
+      "Ġcounsel ors",
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+      "hib ited",
+      "Beg in",
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+      "ĠV or",
+      "ĠShar if",
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+      "est ate",
+      "Ġj ams",
+      "Pro xy",
+      "Ġband its",
+      "ĠPresbyter ian",
+      "ĠPrem iere",
+      "t iny",
+      "ĠCru el",
+      "Test ing",
+      "Ġhom er",
+      "ĠV ERS",
+      "ĠPro l",
+      "ĠDep osit",
+      "ĠCoff in",
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+      "ĠDef endants",
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+      "ĠR ats",
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+      "Ġfootball er",
+      "ĠG ER",
+      "Ġdetect able",
+      "ĠSupport ing",
+      "ĠCS V",
+      "oc ally",
+      "k Hz",
+      "Ġund e",
+      "Ġsh one",
+      "Ġbud ding",
+      "tra k",
+      "Stand ing",
+      "ĠStar craft",
+      "ĠKem p",
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+      "Ġthw arted",
+      "ĠGround s",
+      "ath i",
+      "L isa",
+      "Dial og",
+      "ĠS X",
+      "V ision",
+      "Ġingen ious",
+      "Ù IJ",
+      "Ġfost ering",
+      "ĠZ a",
+      "ĠIn gram",
+      "Ġ\" @",
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+      "6 16",
+      "0 35",
+      "ĠF AC",
+      "H mm",
+      "55 4",
+      "Ġacceler ator",
+      "ĠV end",
+      "Ġsun screen",
+      "Ġtuber culosis",
+      "rav iolet",
+      "ĠFunction al",
+      "ĠEr rors",
+      "ed ar",
+      "19 66",
+      "ĠSpect re",
+      "ĠRec ipes",
+      "88 5",
+      "ĠM ankind",
+      "L iverpool",
+      "Ġ| --",
+      "Ġsubst itutes",
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+      "Ġinc o",
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+      "E va",
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+      "Ġaf ar",
+      "ĠH MS",
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+      "ĠEm il",
+      "im ov",
+      "........ .....",
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+      "ĠFer dinand",
+      "burn ing",
+      "Af ee",
+      "Ġtyr ann",
+      "ĠI w",
+      "ĠBo one",
+      "100 7",
+      "ĠRe pt",
+      "Ċ Âł",
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+      "ĠB oh",
+      "Ġ3 49",
+      "Ġdr ains",
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+      "ĠLib re",
+      "ĠD aryl",
+      "Ġ# ##",
+      "S ys",
+      ". ,\"",
+      "Ġfree zes",
+      "est ablishment",
+      "and owski",
+      "Ġcum bers",
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+      "ĠBom bs",
+      "Ġleg ions",
+      "Ġhand writing",
+      "Ġgr un",
+      "ĠC ah",
+      "sequ ent",
+      "Ġm oth",
+      "ĠMS M",
+      "Ins ert",
+      "F if",
+      "Ġmot el",
+      "Ġdex ter",
+      "ĠB ild",
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+      "Ġpro pe",
+      "ĠText ure",
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+      "oc ard",
+      "ĠVer a",
+      "ĠBar th",
+      "Ġμ g",
+      "Ġl ashed",
+      "Ġ35 1",
+      "ĠZ amb",
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+      "ĠCork er",
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+      "0 33",
+      "ĠG OLD",
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+      "Ġbaff led",
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+      "Ġc rave",
+      "Ġ ______",
+      "Ġentrepreneurs hip",
+      "ĠDirector ate",
+      "Ġ' [",
+      "Ġv ines",
+      "Ġasc ended",
+      "ĠGR OUP",
+      "ĠGood bye",
+      "Ġdo gged",
+      "ãĥ´ ãĤ¡",
+      "Man ufact",
+      "Ġunimagin able",
+      "ri ots",
+      "ier rez",
+      "Ġrel ativity",
+      "ĠCraft ing",
+      "ra ught",
+      "ud en",
+      "c ookie",
+      "Ġassass ins",
+      "Ġdissatisf ied",
+      "ac ci",
+      "Ġcondu it",
+      "Sp read",
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+      "n ice",
+      "izz le",
+      "Ġsc ares",
+      "ĠWH Y",
+      "ph ans",
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+      "ĠKrist en",
+      "5 36",
+      "ĠSc rib",
+      "ĠNe h",
+      "Ġtwent ies",
+      "Ġpredic ament",
+      "Ġhandc uffs",
+      "Ġfruit ful",
+      "ĠU L",
+      "ĠLud wig",
+      "Ġatt est",
+      "ĠBre aker",
+      "Ġbi ologically",
+      "ĠDeal er",
+      "Ġrenov ations",
+      "f w",
+      "ess en",
+      "Al ice",
+      "ĠHen ri",
+      "Ġun ilaterally",
+      "ĠS idd",
+      "h ai",
+      "ĠSt retch",
+      "S ales",
+      "Ġcumbers ome",
+      "ĠJ avier",
+      "Ġtrend y",
+      "Ġrot ting",
+      "ĠChall enges",
+      "Ġscra ps",
+      "Ġfac ets",
+      "ĠVer onica",
+      "ĠVer ge",
+      "ĠS ana",
+      "Al ien",
+      "ĠR ih",
+      "Ġrad ial",
+      "ect ar",
+      "Ġ6 30",
+      "cl i",
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+      "ĠCat o",
+      "h ander",
+      "Ġwait ress",
+      "Ġch ops",
+      "ĠS ECTION",
+      "Ġblunt ly",
+      "ĠCat alog",
+      "n ian",
+      "stud y",
+      "Ġpat rolling",
+      "ĠT enth",
+      "nex us",
+      "ĠN ON",
+      "op sy",
+      "Ġsc athing",
+      "s ie",
+      "Ġdeterior ated",
+      "V B",
+      "Naz is",
+      "Ġdep ictions",
+      "Ġauthent icated",
+      "ĠCon ce",
+      "k rit",
+      "Ġpromul g",
+      "ĠL ONG",
+      "U FC",
+      "ĠVis itors",
+      "ĠRec all",
+      "Ġrehab ilit",
+      "ĠSL I",
+      "Ġglac ier",
+      "ĠB ite",
+      "Ġ50 3",
+      "Ġvom it",
+      "Ġfer mented",
+      "ĠKh alid",
+      "Ġgrad ed",
+      "ĠMag icka",
+      "ĠIch igo",
+      "power ful",
+      "ic ators",
+      "75 3",
+      "Ġsh rew",
+      "Ġ35 6",
+      "Ġlegal izing",
+      "Ġall otted",
+      "ĠArch demon",
+      "ith ing",
+      "igg urat",
+      "V OL",
+      "Le od",
+      "Ġo ily",
+      "Ġindu cing",
+      "Ġamy gdala",
+      "Ġadm ins",
+      "ĠAcqu isition",
+      "C AN",
+      "Ġsche matic",
+      "Ġmo an",
+      "ĠCamer oon",
+      "Ġt ink",
+      "Ġmer ry",
+      "Ġbutter flies",
+      "ĠGo ff",
+      "Ġworks pace",
+      "ĠCor ona",
+      "Ġj avascript",
+      "ĠD olphin",
+      "ĠCant or",
+      "4 64",
+      "to e",
+      "AP S",
+      "ĠAg ing",
+      "Ġpadd ed",
+      "ĠZ heng",
+      "ĠHe ld",
+      "Ġest ranged",
+      "Ġ7 70",
+      ". }",
+      "ĠDun ham",
+      "Ġsm okes",
+      "Ġcap itals",
+      "und ai",
+      "Sh in",
+      "ĠFound ing",
+      "Ġent itle",
+      "Ġcenter piece",
+      "D iscover",
+      "Ġthere to",
+      "al ert",
+      "ĠN ou",
+      "ĠAnaly st",
+      "l c",
+      "F H",
+      "FI ELD",
+      "ĠP OV",
+      "gr ay",
+      "Ġar cs",
+      "ĠH OT",
+      "Ġr s",
+      "Ġoblig atory",
+      "ĠArchitect s",
+      "ĠS ven",
+      "ĠF EC",
+      "0 200",
+      "Christ mas",
+      "ĠAlban ia",
+      "rat om",
+      "58 7",
+      "Ġhard ships",
+      "Ġaut os",
+      "ĠCharg es",
+      "Ġap es",
+      "Ġ3 76",
+      "wal let",
+      "Ġintox ication",
+      "Ġgobl in",
+      "Ġ5 70",
+      "++++++++ ++++++++",
+      "ĠYel p",
+      "ĠMag netic",
+      "ĠBr iggs",
+      "R ail",
+      "Ġspawn s",
+      "ĠW iggins",
+      "Ġshowc ased",
+      "Ġres orted",
+      "ub en",
+      "Ġwh ipping",
+      "Ġim itate",
+      "Ġdigest ion",
+      "ĠUS PS",
+      "ĠG est",
+      "Ġye a",
+      "ĠT ight",
+      "ind al",
+      "ic as",
+      "` .",
+      "C AST",
+      "'' ;",
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+      "opath ic",
+      "In valid",
+      "Ġregrett ed",
+      "Ġbro ccoli",
+      "ĠSc ores",
+      "e ve",
+      "Ġpost ings",
+      "Ġaccum ulating",
+      "Ġneed less",
+      "elf th",
+      "Ġmay ors",
+      "Ġsc rib",
+      "Ġanecd otes",
+      "Ġbot ched",
+      "ĠRib bon",
+      "ĠConstant ine",
+      "i uses",
+      "ess es",
+      "Ġdev ise",
+      "Comp ared",
+      "Ġp udding",
+      "Ġg arg",
+      "Ġev oke",
+      "79 7",
+      "Ġdet ox",
+      "9 09",
+      "ĠPie ces",
+      "ĠMcC artney",
+      "Ġmet ast",
+      "ĠK rypt",
+      "P OR",
+      "Ġt ending",
+      "ĠMerch ants",
+      "Pro of",
+      "ĠV arg",
+      "ĠPort able",
+      "ãĥ¼ãĥĨ ãĤ£",
+      "B rain",
+      "25 00",
+      "Ġfol iage",
+      "Ø ¹",
+      "Ġment ors",
+      "ĠA ires",
+      "Ġminimal ist",
+      "Ġing ested",
+      "ĠTro jan",
+      "ĠQ ian",
+      "inv olved",
+      "0 27",
+      "Ġer oded",
+      "RA FT",
+      "Ġbl urry",
+      "M ob",
+      "Ġbuff et",
+      "ĠFn atic",
+      "ae a",
+      "KN OWN",
+      "ĠIn it",
+      "s afety",
+      "en um",
+      "ACT ION",
+      "ĠCrus her",
+      "ĠD ates",
+      "Ġ ................",
+      "c alling",
+      "ak ov",
+      "Ġvent ured",
+      "Ġ5 55",
+      "au ga",
+      "H art",
+      "ĠA ero",
+      "M AC",
+      "Ġthin ly",
+      "Ġar ra",
+      "ST ATE",
+      "ild e",
+      "ĠJac qu",
+      "ĠFem ales",
+      "Ġthe orem",
+      "Ġ3 46",
+      "Ġsmart est",
+      "ĠPU BLIC",
+      "ĠK ron",
+      "ĠB its",
+      "ĠV essel",
+      "ĠTele phone",
+      "Ġdec ap",
+      "Ġadj unct",
+      "ĠS EN",
+      "mer ga",
+      "Ġred acted",
+      "Ġpre historic",
+      "Ġexplan atory",
+      "ĠRun s",
+      "ĠUtt ar",
+      "ĠM anny",
+      "ĠAUTH OR",
+      "ĠUnle ashed",
+      "ĠBow ling",
+      "be ans",
+      "79 3",
+      "Ġunivers es",
+      "Ġsens it",
+      "ĠK ung",
+      "re peat",
+      "ctr l",
+      "Ġp aced",
+      "Ġfull er",
+      "Cl ock",
+      "Ġrec omb",
+      "ĠF aul",
+      "ĠB unker",
+      "Ġpool ed",
+      "Ġan a",
+      "ĠM outh",
+      "LL OW",
+      "hum ane",
+      "Ġbull do",
+      "ĠMicha els",
+      "f am",
+      "Ġwreck ed",
+      "Ġport rays",
+      "ĠWh ale",
+      "ĠH es",
+      "Ġguess es",
+      "ĠBrow se",
+      "ĠL APD",
+      "Ġconsequ ential",
+      "ĠInn ocent",
+      "ĠD RAG",
+      "Ġtrans gress",
+      "ĠO aks",
+      "Ġtri via",
+      "ĠRes on",
+      "ĠA DS",
+      "-- +",
+      "ĠT oll",
+      "Ġgrasp ing",
+      "ĠTHE M",
+      "ĠT ags",
+      "ĠCon clusion",
+      "Ġpract icable",
+      "Ġho op",
+      "Ġunintention ally",
+      "Ġign ite",
+      "ĠM ov",
+      "ur ized",
+      "le hem",
+      "Ter min",
+      "Ġcolour ful",
+      "ĠLin ear",
+      "ĠEll ie",
+      "G y",
+      "Ġman power",
+      "Ġj s",
+      "Ġem oji",
+      "ĠSHAR ES",
+      "_ .",
+      "0000 7",
+      "Ġsophistic ation",
+      "Ġunders core",
+      "Ġpract ise",
+      "Ġbl ob",
+      "op ens",
+      "Uk raine",
+      "Ke eping",
+      "Y C",
+      "J R",
+      "ult imate",
+      "Cl aim",
+      "Ġautom obiles",
+      "99 3",
+      "ste el",
+      "Ġpart ing",
+      "ĠL ank",
+      "... ?",
+      "Ġ38 5",
+      "Ġremem brance",
+      "Ġe ased",
+      "Ġcov ari",
+      "ĠS ind",
+      "Effect ive",
+      "Ġdisse mination",
+      "ĠMo ose",
+      "ĠCl apper",
+      "br ates",
+      "App ly",
+      "Ġinv is",
+      "Ġwors ened",
+      "âĢĶ -",
+      "Ġlegisl ator",
+      "ĠL ol",
+      "ĠRow e",
+      "Ġdealers hip",
+      "um ar",
+      "id ences",
+      "Ġinvestig ates",
+      "Ġc ascade",
+      "Ġbid der",
+      "ĠB EN",
+      "Iron ically",
+      "Ġpres iding",
+      "Ġd ing",
+      "Ġcontrad icted",
+      "Ġshut s",
+      "ĠF IX",
+      "Ġ3 66",
+      "Dist rict",
+      "Ġsin ful",
+      "ĠChar isma",
+      "o ops",
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+      "D one",
+      "Ġw ig",
+      "Ġmiscon ceptions",
+      "Cor p",
+      "W an",
+      "Ġvener able",
+      "ĠNot ably",
+      "ĠKling on",
+      "an imate",
+      "Bo ost",
+      "ĠS AY",
+      "miss ing",
+      "ibli ography",
+      "mel on",
+      "Ġpay day",
+      "Ø ³",
+      "bo le",
+      "Ġve iled",
+      "ĠAl phabet",
+      "It alian",
+      "Ġever lasting",
+      "ĠR IS",
+      "ĠC ree",
+      "rom pt",
+      "Ġh ating",
+      "Ġgrin ning",
+      "Ġge ographically",
+      "OS H",
+      "Ġwe eping",
+      "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł",
+      "Ġimpe cc",
+      "Let ter",
+      "Ġblo ated",
+      "PL A",
+      "ĠFe in",
+      "Ġper sever",
+      "Th under",
+      "Ġa ur",
+      "ĠR L",
+      "Ġpit falls",
+      "âĸ º",
+      "Ġpredomin ant",
+      "Ġ5 25",
+      "7 18",
+      "AP E",
+      "7 14",
+      "Ġfarm land",
+      "ĠQ iao",
+      "Ġv iolet",
+      "ĠBah amas",
+      "Ġinflic ting",
+      "ĠE fficiency",
+      "Ġhome brew",
+      "Ġundert ook",
+      "Ġcur ly",
+      "ĠHard ing",
+      "man ia",
+      "59 6",
+      "Ġtem pered",
+      "Ġhar rowing",
+      "ĠP ledge",
+      "ĠFranken stein",
+      "è ª",
+      "M otion",
+      "Ġpredict ably",
+      "ĠExpl osion",
+      "oc using",
+      "er d",
+      "col o",
+      "FF ER",
+      "Ġback field",
+      "ĠV IDE",
+      "ue bl",
+      "N arr",
+      "ĠArg ument",
+      "Ġgen omic",
+      "Ġbout ique",
+      "Ġbatt ed",
+      "ĠB inary",
+      "Ġg amb",
+      "ĠRh ythm",
+      "67 3",
+      "Ġa float",
+      "ĠOlymp ia",
+      "Y ING",
+      "Ġend if",
+      "is in",
+      "Ġwin ters",
+      "Ġsc attering",
+      "I v",
+      "D istance",
+      "Ġtr u",
+      "ĠCom fort",
+      "Ġne xus",
+      "Ġair flow",
+      "ĠByz antine",
+      "p ayers",
+      "con i",
+      "ĠB etsy",
+      "D eal",
+      "ĠN ug",
+      "ĠContin ent",
+      "red ibly",
+      "Ġoptim izing",
+      "al beit",
+      "Ġec static",
+      "ĠPro to",
+      "ç ·",
+      "iv ot",
+      "âĸ Ħ",
+      "em p",
+      "rou nder",
+      "Ġcl out",
+      "ĠI ST",
+      "66 3",
+      "ĠDoll ars",
+      "ĠD AC",
+      "Ġsubsc ribed",
+      "Ġrehears al",
+      "Ġam ps",
+      "ĠSh ang",
+      "es m",
+      "Ġspr inkle",
+      "Ġassail ant",
+      "ĠO o",
+      "ĠCoin base",
+      "T act",
+      "Ġret ina",
+      "Ġn uns",
+      "R ON",
+      "att o",
+      "Ġj ug",
+      "ĠSV G",
+      "Ġb ikini",
+      "ĠFI LE",
+      "ĠFound ers",
+      "ep ort",
+      "ĠK P",
+      "Ġrest ores",
+      "ĠTh ick",
+      "Ġash ore",
+      "Ġappro vals",
+      "R ender",
+      "M AG",
+      "G raham",
+      "ĠCort ana",
+      "ãĥ³ ãĤ¸",
+      "ss h",
+      "or ians",
+      "ars ity",
+      "ĠInsp ired",
+      "u pper",
+      "Ġsign alling",
+      "Ġreb uke",
+      "Ġfl ares",
+      "Ġdownt ime",
+      "Stud ies",
+      "Ġstagn ation",
+      "ĠSequ ence",
+      "Ġgr unt",
+      "Ġass ures",
+      "ĠPL A",
+      "59 2",
+      "Ġintra ven",
+      "d epend",
+      "Sus an",
+      "ĠManz iel",
+      "Man ia",
+      "Cont ract",
+      "Ġsl ams",
+      "Ġcult ured",
+      "Ġcred itor",
+      "L IST",
+      "ĠH UM",
+      "ĠChatt anooga",
+      "serv ed",
+      "Ġclo aked",
+      "ĠF TP",
+      "p owder",
+      "ĠSt ella",
+      "uct ive",
+      "Ġcheap ly",
+      "ĠMU CH",
+      "ĠGalile o",
+      "Ġsu ites",
+      "spe ech",
+      "Ġdeliber ations",
+      "ĠCh ips",
+      "« ĺ",
+      "Bal ance",
+      "ĠWyn ne",
+      "ĠAk ron",
+      "Ass et",
+      "Ġhon oured",
+      "Ġed ged",
+      "Like wise",
+      "anim ous",
+      "ĠW age",
+      "ĠEz ek",
+      "ad vertisement",
+      "ĠRT X",
+      "ĠM AD",
+      "Ġmigr ating",
+      "ĠS QU",
+      "Ġ4 75",
+      "Ed ited",
+      "Ġshorth and",
+      "ĠBas ics",
+      "Ġcro tch",
+      "ĠEV EN",
+      "Ġv m",
+      "effic iency",
+      "Ġcal ves",
+      "ĠF rie",
+      "ĠBrill iant",
+      "Ġstri kers",
+      "Ġrepent ance",
+      "Ġarter ies",
+      "r l",
+      "B ed",
+      "h ap",
+      "Ġcrypt ography",
+      "ĠSab res",
+      "Ġ4 14",
+      "vi ks",
+      "ih ara",
+      "aps es",
+      "T alking",
+      "Ġintertw ined",
+      "Ġdoc ks",
+      "Ġalle le",
+      "ĠArt ifact",
+      "ĠH IM",
+      "t orn",
+      "ç ķ",
+      "Ġop acity",
+      "ĠE ly",
+      "os uke",
+      "Ġn ipple",
+      "Ġhand written",
+      "ĠV K",
+      "ĠChamber lain",
+      "ĠLa os",
+      "ig raph",
+      "g row",
+      "Ġtr illions",
+      "Ġdescend ant",
+      "ĠSail or",
+      "as uring",
+      "Ġce ilings",
+      "ĠWare house",
+      "f lying",
+      "ĠGl ow",
+      "Ġn ont",
+      "Ġmiscar riage",
+      "Ġrig s",
+      "Ġmin istries",
+      "Ġelabor ated",
+      "Ġdel usional",
+      "ĠHum ane",
+      "Ġ3 79",
+      "n ets",
+      "Ġblack out",
+      "add ers",
+      "Ġn p",
+      "ĠT ire",
+      "ro sc",
+      "Ġsub div",
+      "Ġlink age",
+      "Ġchron ological",
+      "ĠHER O",
+      "Ġres ettlement",
+      "ĠVin yl",
+      "Ġpast oral",
+      "ĠMob il",
+      "ĠBar bar",
+      "Co oldown",
+      "ĠF ritz",
+      "c riminal",
+      "re pe",
+      "Ġbell ig",
+      "ĠBre ed",
+      "Ġ4 18",
+      "Ġsem blance",
+      "ij k",
+      "Ġcur tail",
+      "Ġclin ch",
+      "cont ained",
+      "ĠProm pt",
+      "ast on",
+      "Ġw i",
+      "Ġpursu its",
+      "5 15",
+      "ĠGl oss",
+      "Ġfl ips",
+      "Ġcoup ons",
+      "Ġcl oning",
+      "ĠLike ly",
+      "Rem oved",
+      "ĠQu artz",
+      "r ices",
+      "ĠSpe ars",
+      "Ġp ious",
+      "Ġdep reciation",
+      "ĠD are",
+      "oun ces",
+      "am az",
+      "O nt",
+      "Ġp innacle",
+      "d ocker",
+      "0 26",
+      "ĠW yr",
+      "ĠPro per",
+      "Ë Ī",
+      "n il",
+      "By tes",
+      "Ġseek er",
+      "t rial",
+      "Ġunf olds",
+      "ĠMar se",
+      "Ġextravag ant",
+      "ĠSurviv ors",
+      "RED ACTED",
+      "ĠSpeed way",
+      "ĠCra igslist",
+      "sub mit",
+      "ĠGener ations",
+      "Ġup holding",
+      "Ġblood stream",
+      "ĠMiss ions",
+      "ĠL awn",
+      "Ġlim bo",
+      "ene i",
+      "H uh",
+      "ĠWild cats",
+      "pre p",
+      "ĠMark us",
+      "ĠFor bidden",
+      "rit ic",
+      "IN O",
+      "Ġexhib iting",
+      "requ ent",
+      "ch uk",
+      "Ġhabit ual",
+      "ĠComp atibility",
+      "Dr ag",
+      "RIP T",
+      "uj ah",
+      "GR OUND",
+      "Ġdelinqu ent",
+      "Ġburn er",
+      "Ġcontempor aries",
+      "Ġgimm ick",
+      "load s",
+      "Ġno zzle",
+      "p odcast",
+      "ĠW ak",
+      "ĠStat en",
+      "ĠK uh",
+      "ãģ ĵ",
+      "inter rupted",
+      "Ġinv incible",
+      "ĠBurn ett",
+      "cig arette",
+      "ĠPeb ble",
+      "ĠTem porary",
+      "ĠMar ino",
+      "58 2",
+      "Ġwast eland",
+      "ident ly",
+      "T x",
+      "Ġr ite",
+      "ĠPan asonic",
+      "ĠM iddles",
+      "ĠHort on",
+      "ae us",
+      "Ġc uring",
+      "Ġm ats",
+      "Ġadj ourn",
+      "Ġfears ome",
+      "pe z",
+      "bo ats",
+      "Ġpro pell",
+      "Ġconflic ted",
+      "ĠAng er",
+      "Ġinsurg ent",
+      "K arl",
+      "Ġco ales",
+      "Ġsouth western",
+      "Ġdis su",
+      "ĠO vert",
+      "******** ****",
+      "Ġbox ed",
+      "ĠBr une",
+      "aa a",
+      "Ġgard ening",
+      "ĠEng el",
+      "tr acks",
+      "Ġpur ified",
+      "Ġplace holder",
+      "ĠL ikes",
+      "Ġd an",
+      "G ab",
+      "Ġe ct",
+      "ĠF aw",
+      "ĠEl iot",
+      "Ġ' ,",
+      "otrop ic",
+      "ĠRu in",
+      "hed on",
+      "Ġca ul",
+      "Ġa ft",
+      "ĠCad illac",
+      "gh a",
+      "ass ian",
+      "ud eb",
+      "ĠT ick",
+      "Ġadjust s",
+      "AR GET",
+      "5 37",
+      "isc he",
+      "ant y",
+      "ĠFried rich",
+      "ĠBl izz",
+      "ĠA OL",
+      "Camp aign",
+      "Ġmamm al",
+      "ĠVe il",
+      "ĠK ev",
+      "ĠMaur it",
+      "ĠDam ien",
+      "N ation",
+      "E astern",
+      "Ġ{ :",
+      "Ġ= ================================",
+      "Ġstereotyp ical",
+      "Ġatt ic",
+      "ĠCy borg",
+      "requ ire",
+      "Ġaward ing",
+      "ĠPap ua",
+      "bt n",
+      "b ent",
+      "B oo",
+      "Ġ( =",
+      "ĠX ander",
+      "ĠSomers et",
+      "Ġcatch y",
+      "Ġcert ify",
+      "STR UCT",
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+      "Ġt ides",
+      "ĠBr ands",
+      "G ray",
+      "comp etitive",
+      "Ġcur ator",
+      "ĠD G",
+      "omin ium",
+      "ĠGM Os",
+      "ci ating",
+      "ĠCarm en",
+      "ow ard",
+      "Balt imore",
+      "Ġr gb",
+      "C u",
+      "Ġwip es",
+      "spe ll",
+      "IT NESS",
+      "Ġsummar izes",
+      "ĠRe vis",
+      "Ġwhistlebl owers",
+      "ĠBre ach",
+      "Ġcro chet",
+      "k os",
+      "ews ki",
+      "Ġrep et",
+      "Ġcrim son",
+      "ĠKar achi",
+      "read able",
+      "dim ension",
+      "ĠI gor",
+      "ild ed",
+      "ĠZ ed",
+      "ĠKe ane",
+      "ĠCos metic",
+      "DE P",
+      "Ġretreat ing",
+      "ĠU A",
+      "ens ical",
+      "Ġd usk",
+      "ĠDick ens",
+      "Ġaren as",
+      "ĠPass age",
+      "level s",
+      "Ġcur v",
+      "P ope",
+      "Ġch ores",
+      "ĠEl ise",
+      "ĠComp ass",
+      "b ub",
+      "Ġmamm alian",
+      "ĠSans krit",
+      "ĠAN C",
+      "ĠCr ack",
+      "Q ual",
+      "L aun",
+      "amp unk",
+      "Ġlearn ers",
+      "Ġglam orous",
+      "Ġfur the",
+      "erm ott",
+      "c and",
+      "Gener ic",
+      "Ġnarr ated",
+      "Ġdisorder ly",
+      "ĠTrans actions",
+      "ĠDet ention",
+      "ĠR oku",
+      "Ä į",
+      "Ġunder statement",
+      "ĠS aur",
+      "ĠRodrig o",
+      "ĠAS AP",
+      "S in",
+      "Ġre joice",
+      "Method s",
+      "Ġelectro de",
+      "Ġworsh ipped",
+      "Ġid i",
+      "ĠPhys icians",
+      "Ġpop up",
+      "Ġde ft",
+      "ĠRem oval",
+      "ĠBu enos",
+      "ver bs",
+      "Ġfun k",
+      "ush a",
+      "rict ion",
+      "ore a",
+      "ĠBang alore",
+      "ĠKen obi",
+      "zz i",
+      "Ġnorm ative",
+      "Ġgobl ins",
+      "Ġcaf es",
+      "ĠUN CLASSIFIED",
+      "ĠF ired",
+      "S IGN",
+      "Ġs clerosis",
+      "ĠV oter",
+      "ĠSon ny",
+      "ĠExt end",
+      "ĠEV s",
+      "Ar senal",
+      "Ġp si",
+      "Ġwid est",
+      "ĠT us",
+      "Ġlo oms",
+      "Ġjust ifying",
+      "ĠGr anger",
+      "è ¯",
+      "Ref er",
+      "58 3",
+      "Ġflour ishing",
+      "ab re",
+      "Ġr ave",
+      "ĠCont ra",
+      "Ġ18 98",
+      "Add s",
+      "Ġf ul",
+      "ĠCo oke",
+      "some one",
+      "= #",
+      "67 1",
+      "Ġy ak",
+      "Ġar te",
+      "ĠMis cellaneous",
+      "ĠDet ection",
+      "ĠCl ancy",
+      "â ģ",
+      "ass ies",
+      "Ġval iant",
+      "ĠFemin ist",
+      "cor ruption",
+      "V el",
+      "P ear",
+      "Ġsucc inct",
+      "Ġquick est",
+      "k w",
+      "Ġsp itting",
+      "ĠL ibraries",
+      "åħ ī",
+      "ant z",
+      "D ad",
+      "ĠSpec ifications",
+      "rup ulous",
+      "and r",
+      "RES ULTS",
+      "Ġsnow ball",
+      "Ġpred is",
+      "ĠB axter",
+      "ĠNurs ing",
+      "ĠCh aff",
+      "s we",
+      "Ġout age",
+      "Ġnest ing",
+      "Ġnotor iety",
+      "tr igger",
+      "on ite",
+      "j on",
+      "Ġf ou",
+      "ook ed",
+      "ĠCelebr ity",
+      "re ality",
+      "Ġfat ig",
+      "Ġhug ging",
+      "Ġbother s",
+      "ĠPan zer",
+      "ĠCh andra",
+      "fig ured",
+      "Ġvol ts",
+      "ĠCloud s",
+      "Ġfee ble",
+      "ĠCur ve",
+      "ĠAs us",
+      "78 6",
+      "abs or",
+      "ĠV ICE",
+      "ĠH ess",
+      "Ġmanufact ures",
+      "Ġgri zz",
+      "ĠPower ful",
+      "ac id",
+      "Ġsub sections",
+      "ĠKrug man",
+      "ĠAl ps",
+      "is u",
+      "Ġsequ est",
+      "ĠUlt ron",
+      "ĠT inker",
+      "ĠGo ose",
+      "Ġmism atch",
+      "Att orney",
+      "Ġmorph ology",
+      "ĠSix ers",
+      "ut tered",
+      "ĠE LECT",
+      "gr an",
+      "Rus sell",
+      "ĠG SL",
+      "Ġfort night",
+      "Ġ. )",
+      "Ġapost le",
+      "pr one",
+      "el ist",
+      "Unt itled",
+      "ĠIm plementation",
+      "ist ors",
+      "Ġtank er",
+      "Ġpl ush",
+      "Ġattend ants",
+      "ĠT ik",
+      "ĠGreen wich",
+      "ĠY on",
+      "ĠSP L",
+      "cell s",
+      "unt led",
+      "S olution",
+      "ĠQu é",
+      "Ġvac ated",
+      "Ġupt ick",
+      "ĠMer idian",
+      "æ ĥ",
+      "ĠDr ill",
+      "9 25",
+      "58 4",
+      "Ġrenov ated",
+      "ĠKub rick",
+      "zy k",
+      "Ġl ousy",
+      "pp el",
+      "ohyd rate",
+      "ĠI zzy",
+      "lesi astical",
+      "CC C",
+      "ĠAj ax",
+      "Ġad apters",
+      "ĠPetra eus",
+      "Ġaffirm ation",
+      "ĠST OR",
+      "le ms",
+      "ad oes",
+      "ĠConstantin ople",
+      "Ġp onies",
+      "Ġl ighthouse",
+      "Ġadherent s",
+      "ĠBre es",
+      "omorph ic",
+      "Fight ing",
+      "Ġpl aster",
+      "ĠP VC",
+      "ĠOb st",
+      "Ġdear ly",
+      "ĠTo oth",
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+      "Ġsh aming",
+      "P lex",
+      "A gg",
+      "ĠâĢ¦ \"",
+      "Ġsub reddits",
+      "Ġpige on",
+      "ĠResident ial",
+      "ĠPass ing",
+      "Ġl um",
+      "ĠP ension",
+      "Ġpessim istic",
+      "Ġ4 32",
+      "z inski",
+      "c ade",
+      "0 75",
+      "Ġapolog ised",
+      "iy ah",
+      "Put ting",
+      "Ġgloom y",
+      "ĠLy me",
+      "=-=-=-=- =-=-=-=-",
+      "ĠT ome",
+      "ĠPsych iatric",
+      "ĠH IT",
+      "c ms",
+      "ap olog",
+      "Ġbreak er",
+      "Ġdeep en",
+      "Ġtheor ist",
+      "ĠHigh lands",
+      "Ġb aker",
+      "Ġst aples",
+      "Ġinterf ered",
+      "ĠAb ortion",
+      "jo ined",
+      "ch u",
+      "Ġform ulate",
+      "Ġvacc inations",
+      "Ġban ter",
+      "phe us",
+      "Ġoutfield er",
+      "ĠM eter",
+      "Ġ# ####",
+      "Ġ18 95",
+      "Ġnarrow ing",
+      "ĠST ORY",
+      "f p",
+      "ĠC ST",
+      "ign ore",
+      "Ġproclaim ing",
+      "ĠR U",
+      "ĠB ALL",
+      "yn a",
+      "65 3",
+      "Ġpos it",
+      "P RE",
+      "59 4",
+      "ĠRegist rar",
+      "ĠPil grim",
+      "ic io",
+      "Ġpre tt",
+      "Ġlif eless",
+      "Ġ__ _",
+      "Ne igh",
+      "ĠCh urches",
+      "orn o",
+      "Ġor cs",
+      "Ġkind red",
+      "ĠAud it",
+      "Ġmillenn ial",
+      "ĠPers ia",
+      "g ravity",
+      "ĠDis ability",
+      "ĠD ARK",
+      "W s",
+      "od on",
+      "Ġgrand daughter",
+      "ĠBro oke",
+      "ĠA DA",
+      "ER A",
+      "Ġpick ups",
+      "ĠWil kinson",
+      "ĠSh ards",
+      "ĠN K",
+      "Ġexp el",
+      "ĠKis lyak",
+      "Ġj argon",
+      "Ġpolar ized",
+      "ian e",
+      "Pub lisher",
+      "Ġreb utt",
+      "Ġapprehens ion",
+      "ĠK essler",
+      "Ġpr ism",
+      "F UL",
+      "19 64",
+      "ĠL oll",
+      "ä ¿",
+      "le thal",
+      "Å Ł",
+      "Ġg hetto",
+      "Ġb oulder",
+      "ĠSlow ly",
+      "ĠOsc ars",
+      "ĠInst ruction",
+      "ĠUl tr",
+      "ĠM oe",
+      "N ich",
+      "ĠP ATH",
+      "( *",
+      "ĠRE LEASE",
+      "un ing",
+      "rou se",
+      "en eg",
+      "Ġre imb",
+      "ĠDet ected",
+      "Do S",
+      "Ġster ling",
+      "Ġaggreg ation",
+      "ĠLone ly",
+      "ĠAtt end",
+      "hig her",
+      "Ġairst rike",
+      "ks on",
+      "SE LECT",
+      "Ġdef lation",
+      "ĠHer rera",
+      "C ole",
+      "rit ch",
+      "Ġadvis able",
+      "F ax",
+      "Ġwork around",
+      "Ġp id",
+      "mort em",
+      "ers en",
+      "Ġtyp o",
+      "Ġal um",
+      "78 2",
+      "ĠJam al",
+      "script s",
+      "Ġcapt ives",
+      "ĠPres ence",
+      "ĠLie berman",
+      "angel o",
+      "Ġalcohol ism",
+      "ass i",
+      "Ġrec ite",
+      "Ġgap ing",
+      "Ġbask ets",
+      "ĠG ou",
+      "Brow ser",
+      "ne au",
+      "Ġcorrect ive",
+      "und a",
+      "sc oring",
+      "ĠX D",
+      "Ġfil ament",
+      "Ġdeep ening",
+      "ĠStain less",
+      "Int eger",
+      "Ġbu ggy",
+      "Ġten ancy",
+      "ĠMub arak",
+      "Ġt uple",
+      "ĠD roid",
+      "ĠS itting",
+      "Ġforfe it",
+      "ĠRasm ussen",
+      "ixt ies",
+      "es i",
+      "ĠKim mel",
+      "Ġmetic ulously",
+      "Ġap opt",
+      "ĠS eller",
+      "08 8",
+      "ec ake",
+      "hem atically",
+      "T N",
+      "Ġmind less",
+      "Ġdig s",
+      "ĠAcc ord",
+      "ons ense",
+      "em ing",
+      "br ace",
+      "Ġe Book",
+      "ĠDist ribut",
+      "ĠInvest ments",
+      "w t",
+      "] ),",
+      "beh avior",
+      "56 3",
+      "Ġbl inding",
+      "ĠPro testers",
+      "top ia",
+      "Ġreb orn",
+      "ĠKel vin",
+      "ĠDo ver",
+      "ĠD airy",
+      "ĠOut s",
+      "Ġ[ /",
+      "Ï Ģ",
+      "b p",
+      "ĠVan ity",
+      "ĠRec ap",
+      "ĠHOU SE",
+      "ĠF ACE",
+      "Ġ4 22",
+      "69 2",
+      "ĠAnt ioch",
+      "cook ed",
+      "Ġcoll ide",
+      "Ġa pr",
+      "Ġsle eper",
+      "ĠJar vis",
+      "Ġalternative ly",
+      "ĠLe aves",
+      "ĠM aw",
+      "Ġantiqu ity",
+      "ĠAdin ida",
+      "Ġab user",
+      "Poké mon",
+      "Ġass orted",
+      "ĠRev ision",
+      "ĠP iano",
+      "ĠG ideon",
+      "O cean",
+      "Ġsal on",
+      "Ġbust ling",
+      "ogn itive",
+      "ĠRah man",
+      "Ġwa iter",
+      "Ġpres ets",
+      "ĠO sh",
+      "ĠG HC",
+      "oper ator",
+      "Ġrept iles",
+      "Ġ4 13",
+      "ĠG arr",
+      "ĠCh ak",
+      "Ġhas hes",
+      "Ġfail ings",
+      "Ġfolk lore",
+      "Ġab l",
+      "ĠC ena",
+      "ĠMac Arthur",
+      "ĠCOUR T",
+      "Ġperipher y",
+      "app ers",
+      "Ġreck oned",
+      "ĠInf lu",
+      "ĠC ET",
+      "Ġ3 72",
+      "ĠDefin itive",
+      "ass ault",
+      "4 21",
+      "Ġreservoir s",
+      "Ġd ives",
+      "ĠCo il",
+      "DA Q",
+      "Ġvivid ly",
+      "ĠR J",
+      "ĠBel lev",
+      "Ġec lectic",
+      "ĠShow down",
+      "ĠK M",
+      "ip ed",
+      "reet ings",
+      "ĠAs uka",
+      "L iberal",
+      "ĠÏ Ħ",
+      "Ġbystand ers",
+      "ĠGood win",
+      "uk ong",
+      "S it",
+      "ĠT rem",
+      "Ġcrim inally",
+      "ĠCirc us",
+      "ch rome",
+      "88 7",
+      "Ġnan op",
+      "ĠOb i",
+      "ĠL OW",
+      "o gh",
+      "ĠAuth ors",
+      "ob yl",
+      "Ur ban",
+      "Ġt i",
+      "ĠWe ir",
+      "t rap",
+      "ag y",
+      "Ġparent heses",
+      "Ġout numbered",
+      "Ġcounter productive",
+      "ĠTob ias",
+      "ub is",
+      "P arser",
+      "ST AR",
+      "Ġsyn aptic",
+      "ĠG ears",
+      "Ġh iber",
+      "Ġdebunk ed",
+      "Ġex alted",
+      "aw atts",
+      "H OU",
+      "Ch urch",
+      "ĠPix ie",
+      "ĠU ri",
+      "ĠForm ation",
+      "ĠPred iction",
+      "C EO",
+      "Ġthro tt",
+      "ĠBrit ann",
+      "ĠMad agascar",
+      "ë ĭ",
+      "Ġbill boards",
+      "ĠRPG s",
+      "ĠBe es",
+      "complete ly",
+      "F IL",
+      "Ġdoes nt",
+      "ĠGreen berg",
+      "re ys",
+      "Ġsl ing",
+      "Ġempt ied",
+      "ĠPix ar",
+      "ĠDh arma",
+      "l uck",
+      "ingu ished",
+      "Ġend ot",
+      "Ġbab ys",
+      "05 9",
+      "che st",
+      "r ats",
+      "Ġr idden",
+      "Ġbeet les",
+      "Ġillum inating",
+      "Ġfict itious",
+      "ĠProv incial",
+      "Ġ7 68",
+      "Ġshe pherd",
+      "ĠR ender",
+      "Ġ18 96",
+      "C rew",
+      "Ġmold ed",
+      "ĠXia omi",
+      "ĠSp iral",
+      "Ġdel im",
+      "Ġorgan ising",
+      "Ġho ops",
+      "ĠBe i",
+      "z hen",
+      "Ġfuck in",
+      "Ġdec ad",
+      "Ġun biased",
+      "am my",
+      "sw ing",
+      "Ġsmugg led",
+      "Ġk ios",
+      "ĠP ERSON",
+      "ĠInquis itor",
+      "Ġsnow y",
+      "Ġscrap ing",
+      "ĠBurg ess",
+      "P tr",
+      "ag ame",
+      "R W",
+      "Ġdro id",
+      "ĠL ys",
+      "ĠCass andra",
+      "Jac ob",
+      "Ġ35 4",
+      "Ġpast ure",
+      "Ġfr anc",
+      "ĠScot ch",
+      "ĠEnd s",
+      "ĠI GF",
+      "def inition",
+      "Ġhyster ical",
+      "ĠBrown e",
+      "77 1",
+      "Ġmobil ization",
+      "æ ķ",
+      "iqu eness",
+      "Th or",
+      "Ġspear headed",
+      "Ġembro iled",
+      "Ġconject ure",
+      "jud icial",
+      "Ch oice",
+      "Ġpaper back",
+      "P ir",
+      "Ġrec overs",
+      "ĠSur ge",
+      "ĠSh ogun",
+      "ĠPed iatrics",
+      "ãģ ł",
+      "Ġsweep s",
+      "ĠLabor atories",
+      "ĠP acks",
+      "al us",
+      "add in",
+      "Ġhead lights",
+      "g ra",
+      "Ev idence",
+      "COL OR",
+      "Ad min",
+      "Ĭ ±",
+      "Ġconco ct",
+      "s ufficient",
+      "Ġun marked",
+      "Ġrich ness",
+      "Ġdiss ertation",
+      "Ġseason ing",
+      "Ġg ib",
+      "ĠM ages",
+      "un ctions",
+      "ĠN id",
+      "che at",
+      "ĠTM Z",
+      "c itizens",
+      "ĠCatholic ism",
+      "n b",
+      "Ġdisemb ark",
+      "ĠPROG RAM",
+      "a ques",
+      "Ty ler",
+      "Or g",
+      "ĠSl ay",
+      "ĠN ero",
+      "ĠTown send",
+      "IN TON",
+      "te le",
+      "Ġmes mer",
+      "9 01",
+      "Ġfire ball",
+      "ev idence",
+      "aff iliated",
+      "ĠFrench man",
+      "ĠAugust a",
+      "0 21",
+      "Ġs led",
+      "Ġre used",
+      "ĠImmun ity",
+      "Ġwrest le",
+      "assemb led",
+      "Mar ia",
+      "Ġgun shots",
+      "ĠBarb ie",
+      "Ġcannabin oids",
+      "ĠTo ast",
+      "ĠK inder",
+      "IR D",
+      "Ġre juven",
+      "Ġg ore",
+      "Ġrupt ure",
+      "Ġbre aching",
+      "ĠCart oon",
+      "Ġ4 55",
+      "ĠPale o",
+      "6 14",
+      "Ġspe ars",
+      "ĠAm es",
+      "ab us",
+      "Mad ison",
+      "GR OUP",
+      "Ġab orted",
+      "y ah",
+      "Ġfel on",
+      "Ġcaus ation",
+      "Ġprep aid",
+      "Ġp itted",
+      "op lan",
+      "ĠShel ley",
+      "ĠRus so",
+      "ĠP agan",
+      "Ġwill fully",
+      "ĠCan aver",
+      "und rum",
+      "ĠSal ary",
+      "ĠAr paio",
+      "read er",
+      "ĠR ational",
+      "ĠOver se",
+      "ĠCa uses",
+      "Ġ* .",
+      "Ġw ob",
+      "Ke ith",
+      "ĠCons ent",
+      "man ac",
+      "77 3",
+      "6 23",
+      "Ġfate ful",
+      "et imes",
+      "Ġspir ited",
+      "ĠD ys",
+      "Ġhe gemony",
+      "Ġboy cot",
+      "ĠEn rique",
+      "em outh",
+      "Ġtim elines",
+      "ĠSah ara",
+      "ĠRel ax",
+      "ĠQuin cy",
+      "ĠLess ons",
+      "ĠE QU",
+      "SE A",
+      "N K",
+      "ĠCost co",
+      "Incre ase",
+      "Ġmotiv ating",
+      "ĠCh ong",
+      "am aru",
+      "ĠDiv ide",
+      "Ġped igree",
+      "ĠTasman ia",
+      "ĠPrel ude",
+      "L as",
+      "9 40",
+      "57 4",
+      "Ġch au",
+      "ĠSp iegel",
+      "un ic",
+      "-- >",
+      "ĠPhil ips",
+      "ĠKaf ka",
+      "Ġuphe aval",
+      "Ġsent imental",
+      "Ġsa x",
+      "ĠAk ira",
+      "ser ial",
+      "Mat rix",
+      "Ġelect ing",
+      "Ġcomment er",
+      "ĠNeb ula",
+      "ple ts",
+      "ĠNad u",
+      "ĠAd ren",
+      "Ġen shr",
+      "ĠR AND",
+      "fin ancial",
+      "ĠCly de",
+      "uther ford",
+      "Ġsign age",
+      "Ġde line",
+      "Ġphosph ate",
+      "rovers ial",
+      "f ascist",
+      "ĠV all",
+      "ĠBeth lehem",
+      "Ġfor s",
+      "Ġeng lish",
+      "S olid",
+      "N ature",
+      "Ġv a",
+      "ĠGu ests",
+      "Ġtant al",
+      "Ġauto immune",
+      ";;;;;;;; ;;;;",
+      "ĠTot ally",
+      "ĠO v",
+      "Ġdef ences",
+      "ĠCoc onut",
+      "Ġtranqu il",
+      "Ġpl oy",
+      "Ġflav ours",
+      "ĠFl ask",
+      "ãĤ¨ ãĥ«",
+      "ĠWest on",
+      "ĠVol vo",
+      "8 70",
+      "Ġmicro phones",
+      "ver bal",
+      "R PG",
+      "Ġi ii",
+      "; }",
+      "0 28",
+      "Ġhead lined",
+      "Ġprim ed",
+      "Ġho ard",
+      "ĠSh ad",
+      "ĠEN TER",
+      "Ġtri angular",
+      "Ġcap it",
+      "l ik",
+      "ĠAn cients",
+      "Ġl ash",
+      "Ġconv ol",
+      "Ġcolon el",
+      "en emy",
+      "G ra",
+      "Ġpub s",
+      "ut ters",
+      "Ġassign s",
+      "ĠPen et",
+      "ĠMon strous",
+      "ĠBow en",
+      "il ver",
+      "H aunted",
+      "ĠD ing",
+      "start ed",
+      "pl in",
+      "Ġcontamin ants",
+      "ĠDO E",
+      "ff en",
+      "ĠTechn ician",
+      "R y",
+      "Ġrob bers",
+      "Ġhot line",
+      "ĠGuard iola",
+      "ĠKau fman",
+      "row er",
+      "ĠDres den",
+      "ĠAl pine",
+      "E lf",
+      "Ġf mt",
+      "ĠS ard",
+      "urs es",
+      "g pu",
+      "Un ix",
+      "Ġunequiv ocally",
+      "ĠCitizens hip",
+      "qu ad",
+      "m ire",
+      "ĠS weeney",
+      "B attery",
+      "6 15",
+      "Ġpanc akes",
+      "Ġo ats",
+      "M aps",
+      "ĠCont rast",
+      "mbuds man",
+      "ĠE PS",
+      "Ġsub committee",
+      "Ġsour cing",
+      "Ġs izing",
+      "ĠBuff er",
+      "ĠMand atory",
+      "Ġmoder ates",
+      "ĠPattern s",
+      "ĠCh ocobo",
+      "ĠZ an",
+      "ĠSTAT ES",
+      "ĠJud ging",
+      "ĠIn her",
+      "* :",
+      "Ġb il",
+      "ĠY en",
+      "Ġexh ilar",
+      "oll ower",
+      "z ers",
+      "Ġsn ug",
+      "max imum",
+      "Ġdesp icable",
+      "ĠP ACK",
+      "ĠAn nex",
+      "Ġsarcast ic",
+      "Ġlate x",
+      "Ġt amp",
+      "ĠS ao",
+      "b ah",
+      "ĠRe verend",
+      "ĠChin atown",
+      "ĠA UT",
+      "d ocumented",
+      "ĠGA BA",
+      "ĠCan aan",
+      "ĠÙ ħ",
+      "Ġgovern s",
+      "pre v",
+      "E sc",
+      "ĠEst imates",
+      "OS P",
+      "Ġendeav our",
+      "ĠCl osing",
+      "omet ime",
+      "every one",
+      "Ġwor sen",
+      "Ġsc anners",
+      "Ġdev iations",
+      "ĠRobot ics",
+      "ĠCom pton",
+      "Ġsorce rer",
+      "Ġend ogenous",
+      "Ġem ulation",
+      "ĠPier cing",
+      "ĠA ph",
+      "ĠS ocket",
+      "Ġb ould",
+      "ĠO U",
+      "ĠBorder lands",
+      "Ġ18 63",
+      "G ordon",
+      "ĠW TO",
+      "Ġrestrict s",
+      "Ġmosa ic",
+      "Ġmel odies",
+      "ç Ħ",
+      "T ar",
+      "Ġdis son",
+      "ĠProv ides",
+      "Ġ ......",
+      "b ek",
+      "F IX",
+      "Ġbro om",
+      "ans hip",
+      "Do ctors",
+      "Ġner ds",
+      "ĠReg ions",
+      "na issance",
+      "Ġmet e",
+      "Ġcre pt",
+      "pl ings",
+      "Ġgirlfriend s",
+      "kn it",
+      "ig ent",
+      "ow e",
+      "Ġus hered",
+      "ĠB az",
+      "M obil",
+      "4 34",
+      "ĠPres ents",
+      "orig in",
+      "Ġins omnia",
+      "ĠA ux",
+      "4 39",
+      "ĠCh ili",
+      "irs ch",
+      "G AME",
+      "Ġgest ation",
+      "alg ia",
+      "rom ising",
+      "$ ,",
+      "c row",
+      "ĠIn spection",
+      "at omic",
+      "Rel ations",
+      "J OHN",
+      "rom an",
+      "ĠClock work",
+      "ĠBak r",
+      "m one",
+      "M ET",
+      "Ġthirst y",
+      "Ġb c",
+      "Ġfacult ies",
+      "R um",
+      "Ġnu ance",
+      "ĠD arius",
+      "ple ting",
+      "fter s",
+      "etch up",
+      "Reg istration",
+      "ĠK E",
+      "R ah",
+      "Ġpref erential",
+      "ĠL ash",
+      "ĠH H",
+      "Val id",
+      "ĠN AV",
+      "Ġstar ve",
+      "ĠG ong",
+      "z ynski",
+      "ĠAct ress",
+      "Ġw ik",
+      "Ġun accompanied",
+      "lv l",
+      "Br ide",
+      "AD S",
+      "ĠCommand o",
+      "ĠVaugh n",
+      "Wal let",
+      "Ġho pping",
+      "ĠV ie",
+      "Ġcave ats",
+      "Ġal as",
+      "if led",
+      "ab use",
+      "66 1",
+      "Ġib n",
+      "Ġg ul",
+      "Ġrob bing",
+      "t il",
+      "IL A",
+      "Ġmit igating",
+      "Ġapt ly",
+      "Ġty rant",
+      "Ġmid day",
+      "ĠGil more",
+      "ĠDe cker",
+      "Ġ§ §",
+      "part ial",
+      "Ex actly",
+      "Ġphen otype",
+      "Ġ[+ ]",
+      "ĠP lex",
+      "ĠI ps",
+      "vers ions",
+      "Ġe book",
+      "Ġch ic",
+      "g ross",
+      "\":\" \"},{\"",
+      "ĠSur prisingly",
+      "M organ",
+      "Ġresid ues",
+      "ĠConf ederation",
+      "in feld",
+      "Ġl yr",
+      "mod erate",
+      "Ġperpend icular",
+      "V K",
+      "Ġsynchron ized",
+      "Ġrefres hed",
+      "Ġad ore",
+      "ĠTor ment",
+      "ol ina",
+      "Ġ26 00",
+      "Item Tracker",
+      "Ġp ies",
+      "ĠF AT",
+      "ĠR HP",
+      "0 48",
+      "ĠRES P",
+      "ĠB J",
+      "all ows",
+      "P and",
+      "Ġunw elcome",
+      "ĠV oc",
+      "ĠBast ard",
+      "ĠO W",
+      "ĠL AR",
+      "ĠHeal er",
+      "Environment al",
+      "ĠKen yan",
+      "ĠTr ance",
+      "ĠP ats",
+      "Ġali ases",
+      "ĠGar field",
+      "Ġcampaign er",
+      "Ġadvance ments",
+      "ĠOkin awa",
+      "ĠC oh",
+      "ows ky",
+      "Ġstar ved",
+      "Ġsize able",
+      "Ġ: -)",
+      "Ġm RNA",
+      "Ġsusp ensions",
+      "ist ar",
+      "Scot land",
+      "Pr in",
+      "-------------------------------- ----------------",
+      "Ġ50 2",
+      "Ġteasp oons",
+      "Ġ10 50",
+      "Ġcoerc ive",
+      "ĠMason ic",
+      "edd ed",
+      "ĠPass enger",
+      "Ġl att",
+      "Ġbr aces",
+      "ĠSt eal",
+      "ĠNY T",
+      "ĠK ats",
+      "ĠCel est",
+      "ae z",
+      "T u",
+      "ĠCoul ter",
+      "ðŁ ĺ",
+      "Fl ickr",
+      "ĠWil mington",
+      "ith s",
+      "++ ;",
+      "Ġv ending",
+      "Ġneg ro",
+      "ĠPh i",
+      "ĠYellow stone",
+      "Call back",
+      "Ġsh ampoo",
+      "ĠSh ades",
+      "w at",
+      "Ġsuper human",
+      "Ġridic uled",
+      "Ġhol iest",
+      "om bo",
+      "Ġintern s",
+      "Ġh one",
+      "ĠPar agu",
+      "UR I",
+      "Ġd angling",
+      "ãĤ »",
+      "so v",
+      "ict ional",
+      "av ailability",
+      "Ġrev ocation",
+      "Ġd ow",
+      "in ic",
+      "ĠTHE IR",
+      "Ġis o",
+      "Ġout ings",
+      "ĠLeth al",
+      "Ġ) ))",
+      "Ġinacc ur",
+      "Ġout landish",
+      "Ġan us",
+      "let ico",
+      "id on",
+      "l ol",
+      "Ġun regulated",
+      "Ġsuccumb ed",
+      "Ġc uff",
+      "ĠWast eland",
+      "let al",
+      "Ġsub str",
+      "Ġcoff ers",
+      "Ġautom akers",
+      "ov i",
+      "ĠX ue",
+      "ĠDayton a",
+      "Ġjar ring",
+      "Ġf umes",
+      "Ġdisband ed",
+      "z ik",
+      "itt on",
+      "Ġstriking ly",
+      "Ġsp ores",
+      "Ad apter",
+      ".) :",
+      "ĠLynd on",
+      "ival ry",
+      "Ġor ally",
+      "Ġtumult uous",
+      "Ġdisple asure",
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+      "or rect",
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+      "ĠBe gins",
+      "Ġit ching",
+      "ĠPh ar",
+      "Ass uming",
+      "Ġem itting",
+      "ĠML G",
+      "Ġbirth place",
+      "Ġt aunt",
+      "ĠL uffy",
+      "ĠAm it",
+      "Ġcir cled",
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+      "enn ett",
+      "Ġde forestation",
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+      "Ġn un",
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+      "ĠSe eking",
+      "ĠTr ash",
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+      "Ġnorth western",
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+      "ĠSat isf",
+      "x es",
+      "5 17",
+      "Ġreass ured",
+      "Ġspor adic",
+      "Ġ7 01",
+      "Ġmed ial",
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+      "Ġbarbar ic",
+      "Ġep is",
+      "ĠExplos ive",
+      "ĠD ough",
+      "Ġuns olved",
+      "Support ed",
+      "Ġacknowled gment",
+      "sp awn",
+      "Ġkit chens",
+      "Ġ- =",
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+      "ic ist",
+      "ĠPeg asus",
+      "ĠPS U",
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+      "76 2",
+      "ĠCl air",
+      "Ġdi aper",
+      "Ġbr ist",
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+      "ĠEvery where",
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+      "object s",
+      "ĠPel icans",
+      "Ġ39 2",
+      "Ġbl u",
+      "b ys",
+      "ĠA go",
+      "Ġinstruction al",
+      "Ġdiscrim inating",
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+      "ĠCorn el",
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+      "Ġty re",
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+      "ĠBrid gewater",
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+      "ĠCoc o",
+      "P ie",
+      "Ġdet ach",
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+      "Ġover heard",
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+      "ĠEmer ging",
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+      "Ġem its",
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+      "Ġst ink",
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+      "ĠMcC ann",
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+      "ilant ro",
+      "Ġreneg oti",
+      "Trans lation",
+      "Ġin quest",
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+      "Ġassass inate",
+      "Ġab original",
+      "Ġresc uing",
+      "ĠSh attered",
+      "lock ing",
+      "all ion",
+      "Ch anging",
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+      "ĠB ord",
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+      "ĠBuck et",
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+      "y k",
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+      "con cert",
+      "Ġshe nan",
+      "Ġrep aid",
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+      "Ġbas hing",
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+      "Ġpip ing",
+      "Ġmin ced",
+      "Ġd iam",
+      "ri eg",
+      "Ġmasc ara",
+      "Ġsuck er",
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+      "App s",
+      "ĠPe ck",
+      "Ġper v",
+      "ĠFl oat",
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+      "ĠN ish",
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+      "Ġarom atic",
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+      "ãĤ ¶",
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+      "ĠE aton",
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+      "E vil",
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+      "Ġwhis pering",
+      "ĠH att",
+      "D K",
+      "Ġdis infect",
+      "Ġdeduct ed",
+      "Ġpart ake",
+      "Ġdown grade",
+      "ĠEs ports",
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+      "Ġfled gling",
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+      "69 3",
+      "% :",
+      "w ake",
+      "5 26",
+      "ĠDis cipline",
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+      "ĠLeg ions",
+      "ĠFrank ie",
+      "int ent",
+      "Ġrest rooms",
+      "ĠRou ter",
+      "da q",
+      "Ġobjection able",
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+      "w ark",
+      "ĠRah ul",
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+      "activ ation",
+      "abs olute",
+      "ĠAccess ed",
+      "Ġ24 00",
+      "ogg les",
+      "Ġsecond ly",
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+      "wra pper",
+      "sh arp",
+      "7 29",
+      "Ġcommun icates",
+      "Ġadd on",
+      "ĠMil itia",
+      "H ong",
+      "Ġsl umped",
+      "ĠJP EG",
+      "ĠI car",
+      "ad ish",
+      "68 1",
+      "Ġmaj esty",
+      "ĠWolf gang",
+      "ĠEl astic",
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+      "Ġv iz",
+      "Ġunconscious ly",
+      "ĠST D",
+      "ĠS ass",
+      "Ġflower ing",
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+      "l io",
+      "ĠT BA",
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+      "ĠJ ournals",
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+      "Ġvow el",
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+      "Fin ish",
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+      "Ġhero ism",
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+      "ag na",
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+      "Ġpower fully",
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+      "04 3",
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+      "Ġcou rier",
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+      "Ġunbeliev ably",
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+      "Ġt ir",
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+      "Ġdisgr untled",
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+      "Ġang st",
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+      "ĠAd idas",
+      "Ġjoy ful",
+      "Ġwh ining",
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+      "Ġatt rition",
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diff --git a/emotional/clap-htsat-fused/tokenizer_config.json b/emotional/clap-htsat-fused/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..058e2e071e2a76af9dc9a10940053477ce8329d3
--- /dev/null
+++ b/emotional/clap-htsat-fused/tokenizer_config.json
@@ -0,0 +1,16 @@
+{
+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "processor_class": "ClapProcessor",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}
diff --git a/emotional/clap-htsat-fused/vocab.json b/emotional/clap-htsat-fused/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..3bf4a887f9c69a4c44c93582ddc2c80cd39b96ea
--- /dev/null
+++ b/emotional/clap-htsat-fused/vocab.json
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diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..dc427705af2afa78332be8217e71c78a95762a10
--- /dev/null
+++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md
@@ -0,0 +1,127 @@
+---
+language: en
+datasets:
+- msp-podcast
+inference: true
+tags:
+- speech
+- audio
+- wav2vec2
+- audio-classification
+- emotion-recognition
+license: cc-by-nc-sa-4.0
+pipeline_tag: audio-classification
+---
+
+# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
+
+The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
+Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
+
+# Usage
+
+```python
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import Wav2Vec2Processor
+from transformers.models.wav2vec2.modeling_wav2vec2 import (
+    Wav2Vec2Model,
+    Wav2Vec2PreTrainedModel,
+)
+
+
+class RegressionHead(nn.Module):
+    r"""Classification head."""
+
+    def __init__(self, config):
+
+        super().__init__()
+
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.final_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+
+        x = features
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+
+        return x
+
+
+class EmotionModel(Wav2Vec2PreTrainedModel):
+    r"""Speech emotion classifier."""
+
+    def __init__(self, config):
+
+        super().__init__(config)
+
+        self.config = config
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.classifier = RegressionHead(config)
+        self.init_weights()
+
+    def forward(
+            self,
+            input_values,
+    ):
+
+        outputs = self.wav2vec2(input_values)
+        hidden_states = outputs[0]
+        hidden_states = torch.mean(hidden_states, dim=1)
+        logits = self.classifier(hidden_states)
+
+        return hidden_states, logits
+
+
+
+# load model from hub
+device = 'cpu'
+model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = EmotionModel.from_pretrained(model_name)
+
+# dummy signal
+sampling_rate = 16000
+signal = np.zeros((1, sampling_rate), dtype=np.float32)
+
+
+def process_func(
+    x: np.ndarray,
+    sampling_rate: int,
+    embeddings: bool = False,
+) -> np.ndarray:
+    r"""Predict emotions or extract embeddings from raw audio signal."""
+
+    # run through processor to normalize signal
+    # always returns a batch, so we just get the first entry
+    # then we put it on the device
+    y = processor(x, sampling_rate=sampling_rate)
+    y = y['input_values'][0]
+    y = y.reshape(1, -1)
+    y = torch.from_numpy(y).to(device)
+
+    # run through model
+    with torch.no_grad():
+        y = model(y)[0 if embeddings else 1]
+
+    # convert to numpy
+    y = y.detach().cpu().numpy()
+
+    return y
+
+
+print(process_func(signal, sampling_rate))
+#  Arousal    dominance valence
+# [[0.5460754  0.6062266  0.40431657]]
+
+print(process_func(signal, sampling_rate, embeddings=True))
+# Pooled hidden states of last transformer layer
+# [[-0.00752167  0.0065819  -0.00746342 ...  0.00663632  0.00848748
+#    0.00599211]]
+```
diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a9596a6e736ce7ddf629657e6b22c1a3756a554d
--- /dev/null
+++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json
@@ -0,0 +1,122 @@
+{
+  "_name_or_path": "torch",
+  "activation_dropout": 0.1,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForSpeechClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 768,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.1,
+  "finetuning_task": "wav2vec2_reg",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "arousal",
+    "1": "dominance",
+    "2": "valence"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "arousal": 0,
+    "dominance": 1,
+    "valence": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 0,
+  "pooling_mode": "mean",
+  "problem_type": "regression",
+  "proj_codevector_dim": 768,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": null,
+  "xvector_output_dim": 512
+}
diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..73caa151574001d3d495fae897e1d38968249712
--- /dev/null
+++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json
@@ -0,0 +1,9 @@
+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..0967ef424bce6791893e9a57bb952f80fd536e93
--- /dev/null
+++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json
@@ -0,0 +1 @@
+{}
diff --git a/export_onnx.py b/export_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..498b0242fbd878b5326745dc628af7b51c99c79b
--- /dev/null
+++ b/export_onnx.py
@@ -0,0 +1,15 @@
+from onnx_modules import export_onnx
+import os
+
+if __name__ == "__main__":
+    export_path = "BertVits2.2PT"
+    model_path = "model\\G_0.pth"
+    config_path = "model\\config.json"
+    novq = False
+    dev = False
+    Extra = "chinese"  # japanese or chinese
+    if not os.path.exists("onnx"):
+        os.makedirs("onnx")
+    if not os.path.exists(f"onnx/{export_path}"):
+        os.makedirs(f"onnx/{export_path}")
+    export_onnx(export_path, model_path, config_path, novq, dev, Extra)
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diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a10c41d9496e34a80b43e4601fe8c6c7f6dcf5d9
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,366 @@
+"""
+版本管理、兼容推理及模型加载实现。
+版本说明：
+    1. 版本号与github的release版本号对应，使用哪个release版本训练的模型即对应其版本号
+    2. 请在模型的config.json中显示声明版本号，添加一个字段"version" : "你的版本号"
+特殊版本说明：
+    1.1.1-fix： 1.1.1版本训练的模型，但是在推理时使用dev的日语修复
+    2.3：当前版本
+"""
+
+import torch
+import commons
+from text import cleaned_text_to_sequence
+
+# from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
+from typing import Union
+from text.cleaner import clean_text
+import utils
+
+from models import SynthesizerTrn
+from text.symbols import symbols
+
+# from oldVersion.V220.models import SynthesizerTrn as V220SynthesizerTrn
+# from oldVersion.V220.text import symbols as V220symbols
+# from oldVersion.V210.models import SynthesizerTrn as V210SynthesizerTrn
+# from oldVersion.V210.text import symbols as V210symbols
+# from oldVersion.V200.models import SynthesizerTrn as V200SynthesizerTrn
+# from oldVersion.V200.text import symbols as V200symbols
+# from oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn
+# from oldVersion.V111.text import symbols as V111symbols
+# from oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn
+# from oldVersion.V110.text import symbols as V110symbols
+# from oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn
+# from oldVersion.V101.text import symbols as V101symbols
+# 
+# from oldVersion import V111, V110, V101, V200, V210, V220
+
+# 当前版本信息
+latest_version = "2.3"
+
+# # 版本兼容
+# SynthesizerTrnMap = {
+#     "2.2": V220SynthesizerTrn,
+#     "2.1": V210SynthesizerTrn,
+#     "2.0.2-fix": V200SynthesizerTrn,
+#     "2.0.1": V200SynthesizerTrn,
+#     "2.0": V200SynthesizerTrn,
+#     "1.1.1-fix": V111SynthesizerTrn,
+#     "1.1.1": V111SynthesizerTrn,
+#     "1.1": V110SynthesizerTrn,
+#     "1.1.0": V110SynthesizerTrn,
+#     "1.0.1": V101SynthesizerTrn,
+#     "1.0": V101SynthesizerTrn,
+#     "1.0.0": V101SynthesizerTrn,
+# }
+# 
+# symbolsMap = {
+#     "2.2": V220symbols,
+#     "2.1": V210symbols,
+#     "2.0.2-fix": V200symbols,
+#     "2.0.1": V200symbols,
+#     "2.0": V200symbols,
+#     "1.1.1-fix": V111symbols,
+#     "1.1.1": V111symbols,
+#     "1.1": V110symbols,
+#     "1.1.0": V110symbols,
+#     "1.0.1": V101symbols,
+#     "1.0": V101symbols,
+#     "1.0.0": V101symbols,
+# }
+
+
+# def get_emo_(reference_audio, emotion, sid):
+#     emo = (
+#         torch.from_numpy(get_emo(reference_audio))
+#         if reference_audio and emotion == -1
+#         else torch.FloatTensor(
+#             np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy")
+#         )
+#     )
+#     return emo
+
+
+def get_net_g(model_path: str, device: str, hps):
+    # 当前版本模型 net_g
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    ).to(device)
+    _ = net_g.eval()
+    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
+    return net_g
+
+
+def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
+    style_text = None if style_text == "" else style_text
+    # 在此处实现当前版本的get_text
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    del word2ph
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return phone, tone, language
+
+
+def infer(
+    text,
+    emotion: Union[int, str],
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    reference_audio=None,
+    skip_start=False,
+    skip_end=False,
+    style_text=None,
+    style_weight=0.7,
+):
+    # 2.2版本参数位置变了
+    # inferMap_V4 = {
+    #     "2.2": V220.infer,
+    # }
+    # # 2.1 参数新增 emotion reference_audio skip_start skip_end
+    # inferMap_V3 = {
+    #     "2.1": V210.infer,
+    # }
+    # # 支持中日英三语版本
+    # inferMap_V2 = {
+    #     "2.0.2-fix": V200.infer,
+    #     "2.0.1": V200.infer,
+    #     "2.0": V200.infer,
+    #     "1.1.1-fix": V111.infer_fix,
+    #     "1.1.1": V111.infer,
+    #     "1.1": V110.infer,
+    #     "1.1.0": V110.infer,
+    # }
+    # # 仅支持中文版本
+    # # 在测试中，并未发现两个版本的模型不能互相通用
+    # inferMap_V1 = {
+    #     "1.0.1": V101.infer,
+    #     "1.0": V101.infer,
+    #     "1.0.0": V101.infer,
+    # }
+    # version = hps.version if hasattr(hps, "version") else latest_version
+    # 非当前版本，根据版本号选择合适的infer
+    # if version != latest_version:
+    #     if version in inferMap_V4.keys():
+    #         return inferMap_V4[version](
+    #             text,
+    #             emotion,
+    #             sdp_ratio,
+    #             noise_scale,
+    #             noise_scale_w,
+    #             length_scale,
+    #             sid,
+    #             language,
+    #             hps,
+    #             net_g,
+    #             device,
+    #             reference_audio,
+    #             skip_start,
+    #             skip_end,
+    #             style_text,
+    #             style_weight,
+    #         )
+    #     if version in inferMap_V3.keys():
+    #         return inferMap_V3[version](
+    #             text,
+    #             sdp_ratio,
+    #             noise_scale,
+    #             noise_scale_w,
+    #             length_scale,
+    #             sid,
+    #             language,
+    #             hps,
+    #             net_g,
+    #             device,
+    #             reference_audio,
+    #             emotion,
+    #             skip_start,
+    #             skip_end,
+    #             style_text,
+    #             style_weight,
+    #         )
+    #     if version in inferMap_V2.keys():
+    #         return inferMap_V2[version](
+    #             text,
+    #             sdp_ratio,
+    #             noise_scale,
+    #             noise_scale_w,
+    #             length_scale,
+    #             sid,
+    #             language,
+    #             hps,
+    #             net_g,
+    #             device,
+    #         )
+    #     if version in inferMap_V1.keys():
+    #         return inferMap_V1[version](
+    #             text,
+    #             sdp_ratio,
+    #             noise_scale,
+    #             noise_scale_w,
+    #             length_scale,
+    #             sid,
+    #             hps,
+    #             net_g,
+    #             device,
+    #         )
+    # 在此处实现当前版本的推理
+    # emo = get_emo_(reference_audio, emotion, sid)
+    # if isinstance(reference_audio, np.ndarray):
+    #     emo = get_clap_audio_feature(reference_audio, device)
+    # else:
+    #     emo = get_clap_text_feature(emotion, device)
+    # emo = torch.squeeze(emo, dim=1)
+
+    phones, tones, lang_ids = get_text(
+        text,
+        language,
+        hps,
+        device,
+        style_text=style_text,
+        style_weight=style_weight,
+    )
+    if skip_start:
+        phones = phones[3:]
+        tones = tones[3:]
+        lang_ids = lang_ids[3:]
+    if skip_end:
+        phones = phones[:-2]
+        tones = tones[:-2]
+        lang_ids = lang_ids[:-2]
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        # emo = emo.to(device).unsqueeze(0)
+        del phones
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                speakers,
+                tones,
+                lang_ids,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )
+        del (
+            x_tst,
+            tones,
+            lang_ids,
+            x_tst_lengths,
+            speakers,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
+
+
+def infer_multilang(
+    text,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    reference_audio=None,
+    emotion=None,
+    skip_start=False,
+    skip_end=False,
+):
+    phones, tones, lang_ids = [], [], [], [], [], [], []
+    # emo = get_emo_(reference_audio, emotion, sid)
+    # if isinstance(reference_audio, np.ndarray):
+    #     emo = get_clap_audio_feature(reference_audio, device)
+    # else:
+    #     emo = get_clap_text_feature(emotion, device)
+    # emo = torch.squeeze(emo, dim=1)
+    for idx, (txt, lang) in enumerate(zip(text, language)):
+        _skip_start = (idx != 0) or (skip_start and idx == 0)
+        _skip_end = (idx != len(language) - 1) or skip_end
+        (
+            temp_phones,
+            temp_tones,
+            temp_lang_ids,
+        ) = get_text(txt, lang, hps, device)
+        if _skip_start:
+            temp_phones = temp_phones[3:]
+            temp_tones = temp_tones[3:]
+            temp_lang_ids = temp_lang_ids[3:]
+        if _skip_end:
+            temp_phones = temp_phones[:-2]
+            temp_tones = temp_tones[:-2]
+            temp_lang_ids = temp_lang_ids[:-2]
+        phones.append(temp_phones)
+        tones.append(temp_tones)
+        lang_ids.append(temp_lang_ids)
+    phones = torch.concatenate(phones, dim=0)
+    tones = torch.concatenate(tones, dim=0)
+    lang_ids = torch.concatenate(lang_ids, dim=0)
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        # emo = emo.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        del phones
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                speakers,
+                tones,
+                lang_ids,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )
+        del (
+            x_tst,
+            tones,
+            lang_ids,
+            x_tst_lengths,
+            speakers,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
diff --git a/losses.py b/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..62982fcca79e476953e4c1661bac635585991a6e
--- /dev/null
+++ b/losses.py
@@ -0,0 +1,153 @@
+import torch
+import torchaudio
+from transformers import AutoModel
+
+
+def feature_loss(fmap_r, fmap_g):
+    loss = 0
+    for dr, dg in zip(fmap_r, fmap_g):
+        for rl, gl in zip(dr, dg):
+            rl = rl.float().detach()
+            gl = gl.float()
+            loss += torch.mean(torch.abs(rl - gl))
+
+    return loss * 2
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+    loss = 0
+    r_losses = []
+    g_losses = []
+    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+        dr = dr.float()
+        dg = dg.float()
+        r_loss = torch.mean((1 - dr) ** 2)
+        g_loss = torch.mean(dg**2)
+        loss += r_loss + g_loss
+        r_losses.append(r_loss.item())
+        g_losses.append(g_loss.item())
+
+    return loss, r_losses, g_losses
+
+
+def generator_loss(disc_outputs):
+    loss = 0
+    gen_losses = []
+    for dg in disc_outputs:
+        dg = dg.float()
+        l = torch.mean((1 - dg) ** 2)
+        gen_losses.append(l)
+        loss += l
+
+    return loss, gen_losses
+
+
+def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
+    """
+    z_p, logs_q: [b, h, t_t]
+    m_p, logs_p: [b, h, t_t]
+    """
+    z_p = z_p.float()
+    logs_q = logs_q.float()
+    m_p = m_p.float()
+    logs_p = logs_p.float()
+    z_mask = z_mask.float()
+
+    kl = logs_p - logs_q - 0.5
+    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
+    kl = torch.sum(kl * z_mask)
+    l = kl / torch.sum(z_mask)
+    return l
+
+
+class WavLMLoss(torch.nn.Module):
+    def __init__(self, model, wd, model_sr, slm_sr=16000):
+        super(WavLMLoss, self).__init__()
+        self.wavlm = AutoModel.from_pretrained(model)
+        self.wd = wd
+        self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)
+        self.wavlm.eval()
+        for param in self.wavlm.parameters():
+            param.requires_grad = False
+
+    def forward(self, wav, y_rec):
+        with torch.no_grad():
+            wav_16 = self.resample(wav)
+            wav_embeddings = self.wavlm(
+                input_values=wav_16, output_hidden_states=True
+            ).hidden_states
+        y_rec_16 = self.resample(y_rec)
+        y_rec_embeddings = self.wavlm(
+            input_values=y_rec_16.squeeze(), output_hidden_states=True
+        ).hidden_states
+
+        floss = 0
+        for er, eg in zip(wav_embeddings, y_rec_embeddings):
+            floss += torch.mean(torch.abs(er - eg))
+
+        return floss.mean()
+
+    def generator(self, y_rec):
+        y_rec_16 = self.resample(y_rec)
+        y_rec_embeddings = self.wavlm(
+            input_values=y_rec_16, output_hidden_states=True
+        ).hidden_states
+        y_rec_embeddings = (
+            torch.stack(y_rec_embeddings, dim=1)
+            .transpose(-1, -2)
+            .flatten(start_dim=1, end_dim=2)
+        )
+        y_df_hat_g = self.wd(y_rec_embeddings)
+        loss_gen = torch.mean((1 - y_df_hat_g) ** 2)
+
+        return loss_gen
+
+    def discriminator(self, wav, y_rec):
+        with torch.no_grad():
+            wav_16 = self.resample(wav)
+            wav_embeddings = self.wavlm(
+                input_values=wav_16, output_hidden_states=True
+            ).hidden_states
+            y_rec_16 = self.resample(y_rec)
+            y_rec_embeddings = self.wavlm(
+                input_values=y_rec_16, output_hidden_states=True
+            ).hidden_states
+
+            y_embeddings = (
+                torch.stack(wav_embeddings, dim=1)
+                .transpose(-1, -2)
+                .flatten(start_dim=1, end_dim=2)
+            )
+            y_rec_embeddings = (
+                torch.stack(y_rec_embeddings, dim=1)
+                .transpose(-1, -2)
+                .flatten(start_dim=1, end_dim=2)
+            )
+
+        y_d_rs = self.wd(y_embeddings)
+        y_d_gs = self.wd(y_rec_embeddings)
+
+        y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs
+
+        r_loss = torch.mean((1 - y_df_hat_r) ** 2)
+        g_loss = torch.mean((y_df_hat_g) ** 2)
+
+        loss_disc_f = r_loss + g_loss
+
+        return loss_disc_f.mean()
+
+    def discriminator_forward(self, wav):
+        with torch.no_grad():
+            wav_16 = self.resample(wav)
+            wav_embeddings = self.wavlm(
+                input_values=wav_16, output_hidden_states=True
+            ).hidden_states
+            y_embeddings = (
+                torch.stack(wav_embeddings, dim=1)
+                .transpose(-1, -2)
+                .flatten(start_dim=1, end_dim=2)
+            )
+
+        y_d_rs = self.wd(y_embeddings)
+
+        return y_d_rs
diff --git a/mel_processing.py b/mel_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..a21af73d2b9cc296a8b443dce71c09d84ec257c0
--- /dev/null
+++ b/mel_processing.py
@@ -0,0 +1,142 @@
+import torch
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+import warnings
+
+# warnings.simplefilter(action='ignore', category=FutureWarning)
+warnings.filterwarnings(action="ignore")
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + "_" + str(y.device)
+    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
+            dtype=y.dtype, device=y.device
+        )
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + "_" + str(spec.device)
+    fmax_dtype_device = str(fmax) + "_" + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
+            dtype=spec.dtype, device=spec.device
+        )
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(
+    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
+):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + "_" + str(y.device)
+    fmax_dtype_device = str(fmax) + "_" + dtype_device
+    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
+            dtype=y.dtype, device=y.device
+        )
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
+            dtype=y.dtype, device=y.device
+        )
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec
\ No newline at end of file
diff --git a/models.py b/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..84f2ad3d2cf9cbe3311e6aa680b3d362062fe19c
--- /dev/null
+++ b/models.py
@@ -0,0 +1,1071 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+import attentions
+import monotonic_align
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from commons import init_weights, get_padding
+from text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        # it needs to be removed from future version.
+        filter_channels = in_channels
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = (
+                torch.randn(w.size(0), 2, w.size(2)).to(
+                    device=x.device, dtype=x.dtype)
+                * x_mask
+            )
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum(
+                (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
+            )
+            logq = (
+                torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2))
+                          * x_mask, [1, 2])
+                - logdet_tot_q
+            )
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = (
+                torch.sum(0.5 * (math.log(2 * math.pi) + (z**2))
+                          * x_mask, [1, 2])
+                - logdet_tot
+            )
+            return nll + logq  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            z = (
+                torch.randn(x.size(0), 2, x.size(2)).to(
+                    device=x.device, dtype=x.dtype)
+                * noise_scale
+            )
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+
+        self.encoder = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, g=None):
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+        self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4,
+                              initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(
+            Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=4,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get(
+            "mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def forward(
+        self,
+        x,
+        x_lengths,
+        y,
+        y_lengths,
+        sid,
+        tone,
+        language,
+    ):
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, g=g
+        )
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+        z_p = self.flow(z, y_mask, g=g)
+
+        with torch.no_grad():
+            # negative cross-entropy
+            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
+            neg_cent1 = torch.sum(
+                -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
+            )  # [b, 1, t_s]
+            neg_cent2 = torch.matmul(
+                -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
+            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+            neg_cent3 = torch.matmul(
+                z_p.transpose(1, 2), (m_p * s_p_sq_r)
+            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+            neg_cent4 = torch.sum(
+                -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
+            )  # [b, 1, t_s]
+            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+            if self.use_noise_scaled_mas:
+                epsilon = (
+                    torch.std(neg_cent)
+                    * torch.randn_like(neg_cent)
+                    * self.current_mas_noise_scale
+                )
+                neg_cent = neg_cent + epsilon
+
+            attn_mask = torch.unsqueeze(
+                x_mask, 2) * torch.unsqueeze(y_mask, -1)
+            attn = (
+                monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
+                .unsqueeze(1)
+                .detach()
+            )
+
+        w = attn.sum(2)
+
+        l_length_sdp = self.sdp(x, x_mask, w, g=g)
+        l_length_sdp = l_length_sdp / torch.sum(x_mask)
+
+        logw_ = torch.log(w + 1e-6) * x_mask
+        logw = self.dp(x, x_mask, g=g)
+        logw_sdp = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=1.0)
+        l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
+            x_mask
+        )  # for averaging
+        l_length_sdp += torch.sum((logw_sdp - logw_)
+                                  ** 2, [1, 2]) / torch.sum(x_mask)
+
+        l_length = l_length_dp + l_length_sdp
+
+        # expand prior
+        m_p = torch.matmul(attn.squeeze(
+            1), m_p.transpose(1, 2)).transpose(1, 2)
+        logs_p = torch.matmul(attn.squeeze(
+            1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+        z_slice, ids_slice = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        o = self.dec(z_slice, g=g)
+        return (
+            o,
+            l_length,
+            attn,
+            ids_slice,
+            x_mask,
+            y_mask,
+            (z, z_p, m_p, logs_p, m_q, logs_q),
+            (x, logw, logw_, logw_sdp),
+            g,
+        )
+
+    def infer(
+        self,
+        x,
+        x_lengths,
+        sid,
+        tone,
+        language,
+        noise_scale=0.667,
+        length_scale=1,
+        noise_scale_w=0.8,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
+        # g = self.gst(y)
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, g=g
+        )
+        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
+            sdp_ratio
+        ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
+        return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/modules.py b/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5c7e9a15c07c38fc7a1f77bd448ed5ae6147d1d
--- /dev/null
+++ b/modules.py
@@ -0,0 +1,580 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import commons
+from commons import init_weights, get_padding
+from transforms import piecewise_rational_quadratic_transform
+from attentions import Encoder
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        hidden_channels,
+        out_channels,
+        kernel_size,
+        n_layers,
+        p_dropout,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(
+            nn.Conv1d(
+                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
+            )
+        )
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(
+                nn.Conv1d(
+                    hidden_channels,
+                    hidden_channels,
+                    kernel_size,
+                    padding=kernel_size // 2,
+                )
+            )
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DDSConv(nn.Module):
+    """
+    Dilated and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    groups=channels,
+                    dilation=dilation,
+                    padding=padding,
+                )
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+        p_dropout=0,
+    ):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = (kernel_size,)
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(
+                gin_channels, 2 * hidden_channels * n_layers, 1
+            )
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(
+                hidden_channels,
+                2 * hidden_channels,
+                kernel_size,
+                dilation=dilation,
+                padding=padding,
+            )
+            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        p_dropout=0,
+        gin_channels=0,
+        mean_only=False,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            p_dropout=p_dropout,
+            gin_channels=gin_channels,
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+
+class ConvFlow(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        n_layers,
+        num_bins=10,
+        tail_bound=5.0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
+        self.proj = nn.Conv1d(
+            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
+        )
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
+            self.filter_channels
+        )
+        unnormalized_derivatives = h[..., 2 * self.num_bins :]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(
+            x1,
+            unnormalized_widths,
+            unnormalized_heights,
+            unnormalized_derivatives,
+            inverse=reverse,
+            tails="linear",
+            tail_bound=self.tail_bound,
+        )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
+
+
+class TransformerCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        n_layers,
+        n_heads,
+        p_dropout=0,
+        filter_channels=0,
+        mean_only=False,
+        wn_sharing_parameter=None,
+        gin_channels=0,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = (
+            Encoder(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=gin_channels,
+            )
+            if wn_sharing_parameter is None
+            else wn_sharing_parameter
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
diff --git a/monotonic_align/__init__.py b/monotonic_align/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aed94600a6b01f4322b371b0c57d5b05713c4dac
--- /dev/null
+++ b/monotonic_align/__init__.py
@@ -0,0 +1,16 @@
+from numpy import zeros, int32, float32
+from torch import from_numpy
+
+from .core import maximum_path_jit
+
+
+def maximum_path(neg_cent, mask):
+    device = neg_cent.device
+    dtype = neg_cent.dtype
+    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
+    path = zeros(neg_cent.shape, dtype=int32)
+
+    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
+    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
+    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
+    return from_numpy(path).to(device=device, dtype=dtype)
diff --git a/monotonic_align/core.py b/monotonic_align/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c962adea65543ef426034c4d53c4f0e615e8181
--- /dev/null
+++ b/monotonic_align/core.py
@@ -0,0 +1,46 @@
+import numba
+
+
+@numba.jit(
+    numba.void(
+        numba.int32[:, :, ::1],
+        numba.float32[:, :, ::1],
+        numba.int32[::1],
+        numba.int32[::1],
+    ),
+    nopython=True,
+    nogil=True,
+)
+def maximum_path_jit(paths, values, t_ys, t_xs):
+    b = paths.shape[0]
+    max_neg_val = -1e9
+    for i in range(int(b)):
+        path = paths[i]
+        value = values[i]
+        t_y = t_ys[i]
+        t_x = t_xs[i]
+
+        v_prev = v_cur = 0.0
+        index = t_x - 1
+
+        for y in range(t_y):
+            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+                if x == y:
+                    v_cur = max_neg_val
+                else:
+                    v_cur = value[y - 1, x]
+                if x == 0:
+                    if y == 0:
+                        v_prev = 0.0
+                    else:
+                        v_prev = max_neg_val
+                else:
+                    v_prev = value[y - 1, x - 1]
+                value[y, x] += max(v_prev, v_cur)
+
+        for y in range(t_y - 1, -1, -1):
+            path[y, index] = 1
+            if index != 0 and (
+                index == y or value[y - 1, index] < value[y - 1, index - 1]
+            ):
+                index = index - 1
diff --git a/onnx_infer.py b/onnx_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..389ecccec3db10f256418e576b9e888c2c6ba872
--- /dev/null
+++ b/onnx_infer.py
@@ -0,0 +1,60 @@
+from onnx_modules.V220_OnnxInference import OnnxInferenceSession
+import numpy as np
+
+Session = OnnxInferenceSession(
+    {
+        "enc": "onnx/BertVits2.2PT/BertVits2.2PT_enc_p.onnx",
+        "emb_g": "onnx/BertVits2.2PT/BertVits2.2PT_emb.onnx",
+        "dp": "onnx/BertVits2.2PT/BertVits2.2PT_dp.onnx",
+        "sdp": "onnx/BertVits2.2PT/BertVits2.2PT_sdp.onnx",
+        "flow": "onnx/BertVits2.2PT/BertVits2.2PT_flow.onnx",
+        "dec": "onnx/BertVits2.2PT/BertVits2.2PT_dec.onnx",
+    },
+    Providers=["CPUExecutionProvider"],
+)
+
+# 这里的输入和原版是一样的，只需要在原版预处理结果出来之后加上.numpy()即可
+x = np.array(
+    [
+        0,
+        97,
+        0,
+        8,
+        0,
+        78,
+        0,
+        8,
+        0,
+        76,
+        0,
+        37,
+        0,
+        40,
+        0,
+        97,
+        0,
+        8,
+        0,
+        23,
+        0,
+        8,
+        0,
+        74,
+        0,
+        26,
+        0,
+        104,
+        0,
+    ]
+)
+tone = np.zeros_like(x)
+language = np.zeros_like(x)
+sid = np.array([0])
+bert = np.random.randn(x.shape[0], 1024)
+ja_bert = np.random.randn(x.shape[0], 1024)
+en_bert = np.random.randn(x.shape[0], 1024)
+emo = np.random.randn(512, 1)
+
+audio = Session(x, tone, language, bert, ja_bert, en_bert, emo, sid)
+
+print(audio)
diff --git a/onnx_modules/V200/__init__.py b/onnx_modules/V200/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V200/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V200/attentions_onnx.py b/onnx_modules/V200/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V200/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V200/models_onnx.py b/onnx_modules/V200/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec6b5b4015f61ca50007ba971a6e3368bf07929a
--- /dev/null
+++ b/onnx_modules/V200/models_onnx.py
@@ -0,0 +1,990 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(
+            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
+
+    def forward_probability(self, x, x_mask, dur, g=None):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_1(x)
+        x = self.drop(x)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_2(x)
+        x = self.drop(x)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, bert, ja_bert, en_bert, g=None):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=4,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        ja_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        en_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, ja_bert, en_bert, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "bert_1",
+                "bert_2",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "bert_1": [0],
+                "bert_2": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g
+        )
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V200/text/__init__.py b/onnx_modules/V200/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V200/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V200/text/bert_utils.py b/onnx_modules/V200/text/bert_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e0c4a95e5d086726a1c684231cfe51ebd60aef4
--- /dev/null
+++ b/onnx_modules/V200/text/bert_utils.py
@@ -0,0 +1,23 @@
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+from config import config
+
+
+MIRROR: str = config.mirror
+
+
+def _check_bert(repo_id, files, local_path):
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            if MIRROR.lower() == "openi":
+                import openi
+
+                openi.model.download_model(
+                    "Stardust_minus/Bert-VITS2", repo_id.split("/")[-1], "./bert"
+                )
+            else:
+                hf_hub_download(
+                    repo_id, file, local_dir=local_path, local_dir_use_symlinks=False
+                )
diff --git a/onnx_modules/V200/text/chinese.py b/onnx_modules/V200/text/chinese.py
new file mode 100644
index 0000000000000000000000000000000000000000..66150b653627eec9f09086209a83ffa101b03cd7
--- /dev/null
+++ b/onnx_modules/V200/text/chinese.py
@@ -0,0 +1,198 @@
+import os
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+
+from .symbols import punctuation
+from .tone_sandhi import ToneSandhi
+
+current_file_path = os.path.dirname(__file__)
+pinyin_to_symbol_map = {
+    line.split("\t")[0]: line.strip().split("\t")[1]
+    for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
+}
+
+import jieba.posseg as psg
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+tone_modifier = ToneSandhi()
+
+
+def replace_punctuation(text):
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
+
+
+def g2p(text):
+    pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
+    phones, tones, word2ph = _g2p(sentences)
+    assert sum(word2ph) == len(phones)
+    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def _get_initials_finals(word):
+    initials = []
+    finals = []
+    orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
+    orig_finals = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
+    )
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+def _g2p(segments):
+    phones_list = []
+    tones_list = []
+    word2ph = []
+    for seg in segments:
+        # Replace all English words in the sentence
+        seg = re.sub("[a-zA-Z]+", "", seg)
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        for word, pos in seg_cut:
+            if pos == "eng":
+                continue
+            sub_initials, sub_finals = _get_initials_finals(word)
+            sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in punctuation
+                phone = [c]
+                tone = "0"
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in "12345"
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": "ui",
+                        "iou": "iu",
+                        "uen": "un",
+                    }
+                    if v_without_tone in v_rep_map.keys():
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        "ing": "ying",
+                        "i": "yi",
+                        "in": "yin",
+                        "u": "wu",
+                    }
+                    if pinyin in pinyin_rep_map.keys():
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            "v": "yu",
+                            "e": "e",
+                            "i": "y",
+                            "u": "w",
+                        }
+                        if pinyin[0] in single_rep_map.keys():
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
+                phone = pinyin_to_symbol_map[pinyin].split(" ")
+                word2ph.append(len(phone))
+
+            phones_list += phone
+            tones_list += [int(tone)] * len(phone)
+    return phones_list, tones_list, word2ph
+
+
+def text_normalize(text):
+    numbers = re.findall(r"\d+(?:\.?\d+)?", text)
+    for number in numbers:
+        text = text.replace(number, cn2an.an2cn(number), 1)
+    text = replace_punctuation(text)
+    return text
+
+
+def get_bert_feature(text, word2ph):
+    from text import chinese_bert
+
+    return chinese_bert.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    from text.chinese_bert import get_bert_feature
+
+    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"
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)
+
+
+# # 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试...."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试
diff --git a/onnx_modules/V200/text/chinese_bert.py b/onnx_modules/V200/text/chinese_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..36f1e2a09350584dfe5fca42b27402aa571aba3f
--- /dev/null
+++ b/onnx_modules/V200/text/chinese_bert.py
@@ -0,0 +1,101 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from config import config
+
+LOCAL_PATH = "./bert/chinese-roberta-wwm-ext-large"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
+
+
+if __name__ == "__main__":
+    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
+    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,
+        2,
+        2,
+        2,
+        1,
+    ]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 1024])
diff --git a/onnx_modules/V200/text/cleaner.py b/onnx_modules/V200/text/cleaner.py
new file mode 100644
index 0000000000000000000000000000000000000000..52d7ed0491c9b1952959c3a778ad6ac594531694
--- /dev/null
+++ b/onnx_modules/V200/text/cleaner.py
@@ -0,0 +1,28 @@
+from . import chinese, japanese, english, cleaned_text_to_sequence
+
+
+language_module_map = {"ZH": chinese, "JP": japanese, "EN": english}
+
+
+def clean_text(text, language):
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones, tones, word2ph = language_module.g2p(norm_text)
+    return norm_text, phones, tones, word2ph
+
+
+def clean_text_bert(text, language):
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones, tones, word2ph = language_module.g2p(norm_text)
+    bert = language_module.get_bert_feature(norm_text, word2ph)
+    return phones, tones, bert
+
+
+def text_to_sequence(text, language):
+    norm_text, phones, tones, word2ph = clean_text(text, language)
+    return cleaned_text_to_sequence(phones, tones, language)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/onnx_modules/V200/text/english.py b/onnx_modules/V200/text/english.py
new file mode 100644
index 0000000000000000000000000000000000000000..a71bdeedead188094ee9cbb677361a9b0f10698b
--- /dev/null
+++ b/onnx_modules/V200/text/english.py
@@ -0,0 +1,361 @@
+import pickle
+import os
+import re
+from g2p_en import G2p
+
+from . import symbols
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
+_g2p = G2p()
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "...": "…",
+        "v": "V",
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+
+def read_dict():
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0]
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    return g2p_dict
+
+
+eng_dict = get_dict()
+
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    return phn.lower(), tone
+
+
+def refine_syllables(syllables):
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for i in range(len(phn_list)):
+            phn = phn_list[i]
+            phn, tone = refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+import inflect
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("æ", "e"),
+        ("ɑ", "a"),
+        ("ɔ", "o"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ɛ", "e"),
+        ("ɪ", "i"),
+        ("ʊ", "u"),
+        ("ʒ", "ʥ"),
+        ("ʤ", "ʥ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ʒ", "ʑ"),
+        ("ʤ", "dʑ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
+]
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def text_normalize(text):
+    text = normalize_numbers(text)
+    return text
+
+
+def g2p(text):
+    phones = []
+    tones = []
+    word2ph = []
+    words = re.split(r"([,;.\-\?\!\s+])", text)
+    words = [word for word in words if word.strip() != ""]
+    for word in words:
+        if word.upper() in eng_dict:
+            phns, tns = refine_syllables(eng_dict[word.upper()])
+            phones += phns
+            tones += tns
+            word2ph.append(len(phns))
+        else:
+            phone_list = list(filter(lambda p: p != " ", _g2p(word)))
+            for ph in phone_list:
+                if ph in arpa:
+                    ph, tn = refine_ph(ph)
+                    phones.append(ph)
+                    tones.append(tn)
+                else:
+                    phones.append(ph)
+                    tones.append(0)
+            word2ph.append(len(phone_list))
+
+    phones = [post_replace_ph(i) for i in phones]
+
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+
+    return phones, tones, word2ph
+
+
+def get_bert_feature(text, word2ph):
+    from text import english_bert_mock
+
+    return english_bert_mock.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    # print(get_dict())
+    # print(eng_word_to_phoneme("hello"))
+    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
+    # all_phones = set()
+    # for k, syllables in eng_dict.items():
+    #     for group in syllables:
+    #         for ph in group:
+    #             all_phones.add(ph)
+    # print(all_phones)
diff --git a/onnx_modules/V200/text/english_bert_mock.py b/onnx_modules/V200/text/english_bert_mock.py
new file mode 100644
index 0000000000000000000000000000000000000000..4aba0fc1d534a8e7f01b00c2ddde5d6725043459
--- /dev/null
+++ b/onnx_modules/V200/text/english_bert_mock.py
@@ -0,0 +1,42 @@
+import sys
+
+import torch
+from transformers import DebertaV2Model, DebertaV2Tokenizer
+
+from config import config
+
+
+LOCAL_PATH = "./bert/deberta-v3-large"
+
+tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = DebertaV2Model.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+    # assert len(word2ph) == len(text)+2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
diff --git a/onnx_modules/V200/text/japanese.py b/onnx_modules/V200/text/japanese.py
new file mode 100644
index 0000000000000000000000000000000000000000..85f18da48f97732026b4376a4332324182579f52
--- /dev/null
+++ b/onnx_modules/V200/text/japanese.py
@@ -0,0 +1,403 @@
+# Convert Japanese text to phonemes which is
+# compatible with Julius https://github.com/julius-speech/segmentation-kit
+import re
+import unicodedata
+
+from transformers import AutoTokenizer
+
+from . import punctuation, symbols
+
+from num2words import num2words
+
+import pyopenjtalk
+import jaconv
+
+
+def kata2phoneme(text: str) -> str:
+    """Convert katakana text to phonemes."""
+    text = text.strip()
+    if text == "ー":
+        return ["ー"]
+    elif text.startswith("ー"):
+        return ["ー"] + kata2phoneme(text[1:])
+    res = []
+    prev = None
+    while text:
+        if re.match(_MARKS, text):
+            res.append(text)
+            text = text[1:]
+            continue
+        if text.startswith("ー"):
+            if prev:
+                res.append(prev[-1])
+            text = text[1:]
+            continue
+        res += pyopenjtalk.g2p(text).lower().replace("cl", "q").split(" ")
+        break
+    # res = _COLON_RX.sub(":", res)
+    return res
+
+
+def hira2kata(text: str) -> str:
+    return jaconv.hira2kata(text)
+
+
+_SYMBOL_TOKENS = set(list("・、。？！"))
+_NO_YOMI_TOKENS = set(list("「」『』―（）［］[]"))
+_MARKS = re.compile(
+    r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+
+def text2kata(text: str) -> str:
+    parsed = pyopenjtalk.run_frontend(text)
+
+    res = []
+    for parts in parsed:
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        if yomi:
+            if re.match(_MARKS, yomi):
+                if len(word) > 1:
+                    word = [replace_punctuation(i) for i in list(word)]
+                    yomi = word
+                    res += yomi
+                    sep += word
+                    continue
+                elif word not in rep_map.keys() and word not in rep_map.values():
+                    word = ","
+                yomi = word
+            res.append(yomi)
+        else:
+            if word in _SYMBOL_TOKENS:
+                res.append(word)
+            elif word in ("っ", "ッ"):
+                res.append("ッ")
+            elif word in _NO_YOMI_TOKENS:
+                pass
+            else:
+                res.append(word)
+    return hira2kata("".join(res))
+
+
+def text2sep_kata(text: str) -> (list, list):
+    parsed = pyopenjtalk.run_frontend(text)
+
+    res = []
+    sep = []
+    for parts in parsed:
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        if yomi:
+            if re.match(_MARKS, yomi):
+                if len(word) > 1:
+                    word = [replace_punctuation(i) for i in list(word)]
+                    yomi = word
+                    res += yomi
+                    sep += word
+                    continue
+                elif word not in rep_map.keys() and word not in rep_map.values():
+                    word = ","
+                yomi = word
+            res.append(yomi)
+        else:
+            if word in _SYMBOL_TOKENS:
+                res.append(word)
+            elif word in ("っ", "ッ"):
+                res.append("ッ")
+            elif word in _NO_YOMI_TOKENS:
+                pass
+            else:
+                res.append(word)
+        sep.append(word)
+    return sep, [hira2kata(i) for i in res], get_accent(parsed)
+
+
+def get_accent(parsed):
+    labels = pyopenjtalk.make_label(parsed)
+
+    phonemes = []
+    accents = []
+    for n, label in enumerate(labels):
+        phoneme = re.search(r"\-([^\+]*)\+", label).group(1)
+        if phoneme not in ["sil", "pau"]:
+            phonemes.append(phoneme.replace("cl", "q").lower())
+        else:
+            continue
+        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
+        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
+        if re.search(r"\-([^\+]*)\+", labels[n + 1]).group(1) in ["sil", "pau"]:
+            a2_next = -1
+        else:
+            a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
+        # Falling
+        if a1 == 0 and a2_next == a2 + 1:
+            accents.append(-1)
+        # Rising
+        elif a2 == 1 and a2_next == 2:
+            accents.append(1)
+        else:
+            accents.append(0)
+    return list(zip(phonemes, accents))
+
+
+_ALPHASYMBOL_YOMI = {
+    "#": "シャープ",
+    "%": "パーセント",
+    "&": "アンド",
+    "+": "プラス",
+    "-": "マイナス",
+    ":": "コロン",
+    ";": "セミコロン",
+    "<": "小なり",
+    "=": "イコール",
+    ">": "大なり",
+    "@": "アット",
+    "a": "エー",
+    "b": "ビー",
+    "c": "シー",
+    "d": "ディー",
+    "e": "イー",
+    "f": "エフ",
+    "g": "ジー",
+    "h": "エイチ",
+    "i": "アイ",
+    "j": "ジェー",
+    "k": "ケー",
+    "l": "エル",
+    "m": "エム",
+    "n": "エヌ",
+    "o": "オー",
+    "p": "ピー",
+    "q": "キュー",
+    "r": "アール",
+    "s": "エス",
+    "t": "ティー",
+    "u": "ユー",
+    "v": "ブイ",
+    "w": "ダブリュー",
+    "x": "エックス",
+    "y": "ワイ",
+    "z": "ゼット",
+    "α": "アルファ",
+    "β": "ベータ",
+    "γ": "ガンマ",
+    "δ": "デルタ",
+    "ε": "イプシロン",
+    "ζ": "ゼータ",
+    "η": "イータ",
+    "θ": "シータ",
+    "ι": "イオタ",
+    "κ": "カッパ",
+    "λ": "ラムダ",
+    "μ": "ミュー",
+    "ν": "ニュー",
+    "ξ": "クサイ",
+    "ο": "オミクロン",
+    "π": "パイ",
+    "ρ": "ロー",
+    "σ": "シグマ",
+    "τ": "タウ",
+    "υ": "ウプシロン",
+    "φ": "ファイ",
+    "χ": "カイ",
+    "ψ": "プサイ",
+    "ω": "オメガ",
+}
+
+
+_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+")
+_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
+_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])")
+_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?")
+
+
+def japanese_convert_numbers_to_words(text: str) -> str:
+    res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text)
+    res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res)
+    res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res)
+    return res
+
+
+def japanese_convert_alpha_symbols_to_words(text: str) -> str:
+    return "".join([_ALPHASYMBOL_YOMI.get(ch, ch) for ch in text.lower()])
+
+
+def japanese_text_to_phonemes(text: str) -> str:
+    """Convert Japanese text to phonemes."""
+    res = unicodedata.normalize("NFKC", text)
+    res = japanese_convert_numbers_to_words(res)
+    # res = japanese_convert_alpha_symbols_to_words(res)
+    res = text2kata(res)
+    res = kata2phoneme(res)
+    return res
+
+
+def is_japanese_character(char):
+    # 定义日语文字系统的 Unicode 范围
+    japanese_ranges = [
+        (0x3040, 0x309F),  # 平假名
+        (0x30A0, 0x30FF),  # 片假名
+        (0x4E00, 0x9FFF),  # 汉字 (CJK Unified Ideographs)
+        (0x3400, 0x4DBF),  # 汉字扩展 A
+        (0x20000, 0x2A6DF),  # 汉字扩展 B
+        # 可以根据需要添加其他汉字扩展范围
+    ]
+
+    # 将字符的 Unicode 编码转换为整数
+    char_code = ord(char)
+
+    # 检查字符是否在任何一个日语范围内
+    for start, end in japanese_ranges:
+        if start <= char_code <= end:
+            return True
+
+    return False
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "...": "…",
+    "···": "…",
+    "・・・": "…",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def replace_punctuation(text):
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+        + "".join(punctuation)
+        + r"]+",
+        "",
+        replaced_text,
+    )
+
+    return replaced_text
+
+
+def text_normalize(text):
+    res = unicodedata.normalize("NFKC", text)
+    res = japanese_convert_numbers_to_words(res)
+    # res = "".join([i for i in res if is_japanese_character(i)])
+    res = replace_punctuation(res)
+    return res
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def handle_long(sep_phonemes):
+    for i in range(len(sep_phonemes)):
+        if sep_phonemes[i][0] == "ー":
+            sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
+        if "ー" in sep_phonemes[i]:
+            for j in range(len(sep_phonemes[i])):
+                if sep_phonemes[i][j] == "ー":
+                    sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1]
+    return sep_phonemes
+
+
+tokenizer = AutoTokenizer.from_pretrained("./bert/deberta-v2-large-japanese")
+
+
+def align_tones(phones, tones):
+    res = []
+    for pho in phones:
+        temp = [0] * len(pho)
+        for idx, p in enumerate(pho):
+            if len(tones) == 0:
+                break
+            if p == tones[0][0]:
+                temp[idx] = tones[0][1]
+                if idx > 0:
+                    temp[idx] += temp[idx - 1]
+                tones.pop(0)
+        temp = [0] + temp
+        temp = temp[:-1]
+        if -1 in temp:
+            temp = [i + 1 for i in temp]
+        res.append(temp)
+    res = [i for j in res for i in j]
+    assert not any([i < 0 for i in res]) and not any([i > 1 for i in res])
+    return res
+
+
+def g2p(norm_text):
+    sep_text, sep_kata, acc = text2sep_kata(norm_text)
+    sep_tokenized = [tokenizer.tokenize(i) for i in sep_text]
+    sep_phonemes = handle_long([kata2phoneme(i) for i in sep_kata])
+    # 异常处理，MeCab不认识的词的话会一路传到这里来，然后炸掉。目前来看只有那些超级稀有的生僻词会出现这种情况
+    for i in sep_phonemes:
+        for j in i:
+            assert j in symbols, (sep_text, sep_kata, sep_phonemes)
+    tones = align_tones(sep_phonemes, acc)
+
+    word2ph = []
+    for token, phoneme in zip(sep_tokenized, sep_phonemes):
+        phone_len = len(phoneme)
+        word_len = len(token)
+
+        aaa = distribute_phone(phone_len, word_len)
+        word2ph += aaa
+    phones = ["_"] + [j for i in sep_phonemes for j in i] + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones)
+    return phones, tones, word2ph
+
+
+if __name__ == "__main__":
+    tokenizer = AutoTokenizer.from_pretrained("./bert/deberta-v2-large-japanese")
+    text = "hello,こんにちは、世界ー！……"
+    from text.japanese_bert import get_bert_feature
+
+    text = text_normalize(text)
+    print(text)
+
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)
diff --git a/onnx_modules/V200/text/japanese_bert.py b/onnx_modules/V200/text/japanese_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..b84687937410b4a1b41e55fb5b4460d5423b5d15
--- /dev/null
+++ b/onnx_modules/V200/text/japanese_bert.py
@@ -0,0 +1,58 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from config import config
+from .japanese import text2sep_kata
+
+LOCAL_PATH = "./bert/deberta-v2-large-japanese"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(text, word2ph, device=config.bert_gen_config.device):
+    sep_text, _, _ = text2sep_kata(text)
+    sep_tokens = [tokenizer.tokenize(t) for t in sep_text]
+    sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens]
+    sep_ids = [2] + [item for sublist in sep_ids for item in sublist] + [3]
+    return get_bert_feature_with_token(sep_ids, word2ph, device)
+
+
+def get_bert_feature_with_token(tokens, word2ph, device=config.bert_gen_config.device):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = torch.tensor(tokens).to(device).unsqueeze(0)
+        token_type_ids = torch.zeros_like(inputs).to(device)
+        attention_mask = torch.ones_like(inputs).to(device)
+        inputs = {
+            "input_ids": inputs,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+        }
+
+        # for i in inputs:
+        #     inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+    assert inputs["input_ids"].shape[-1] == len(word2ph)
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
diff --git a/onnx_modules/V200/text/opencpop-strict.txt b/onnx_modules/V200/text/opencpop-strict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1bede89a429e34b1a58781ee570fc313b80c0aee
--- /dev/null
+++ b/onnx_modules/V200/text/opencpop-strict.txt
@@ -0,0 +1,429 @@
+a	AA a
+ai	AA ai
+an	AA an
+ang	AA ang
+ao	AA ao
+ba	b a
+bai	b ai
+ban	b an
+bang	b ang
+bao	b ao
+bei	b ei
+ben	b en
+beng	b eng
+bi	b i
+bian	b ian
+biao	b iao
+bie	b ie
+bin	b in
+bing	b ing
+bo	b o
+bu	b u
+ca	c a
+cai	c ai
+can	c an
+cang	c ang
+cao	c ao
+ce	c e
+cei	c ei
+cen	c en
+ceng	c eng
+cha	ch a
+chai	ch ai
+chan	ch an
+chang	ch ang
+chao	ch ao
+che	ch e
+chen	ch en
+cheng	ch eng
+chi	ch ir
+chong	ch ong
+chou	ch ou
+chu	ch u
+chua	ch ua
+chuai	ch uai
+chuan	ch uan
+chuang	ch uang
+chui	ch ui
+chun	ch un
+chuo	ch uo
+ci	c i0
+cong	c ong
+cou	c ou
+cu	c u
+cuan	c uan
+cui	c ui
+cun	c un
+cuo	c uo
+da	d a
+dai	d ai
+dan	d an
+dang	d ang
+dao	d ao
+de	d e
+dei	d ei
+den	d en
+deng	d eng
+di	d i
+dia	d ia
+dian	d ian
+diao	d iao
+die	d ie
+ding	d ing
+diu	d iu
+dong	d ong
+dou	d ou
+du	d u
+duan	d uan
+dui	d ui
+dun	d un
+duo	d uo
+e	EE e
+ei	EE ei
+en	EE en
+eng	EE eng
+er	EE er
+fa	f a
+fan	f an
+fang	f ang
+fei	f ei
+fen	f en
+feng	f eng
+fo	f o
+fou	f ou
+fu	f u
+ga	g a
+gai	g ai
+gan	g an
+gang	g ang
+gao	g ao
+ge	g e
+gei	g ei
+gen	g en
+geng	g eng
+gong	g ong
+gou	g ou
+gu	g u
+gua	g ua
+guai	g uai
+guan	g uan
+guang	g uang
+gui	g ui
+gun	g un
+guo	g uo
+ha	h a
+hai	h ai
+han	h an
+hang	h ang
+hao	h ao
+he	h e
+hei	h ei
+hen	h en
+heng	h eng
+hong	h ong
+hou	h ou
+hu	h u
+hua	h ua
+huai	h uai
+huan	h uan
+huang	h uang
+hui	h ui
+hun	h un
+huo	h uo
+ji	j i
+jia	j ia
+jian	j ian
+jiang	j iang
+jiao	j iao
+jie	j ie
+jin	j in
+jing	j ing
+jiong	j iong
+jiu	j iu
+ju	j v
+jv	j v
+juan	j van
+jvan	j van
+jue	j ve
+jve	j ve
+jun	j vn
+jvn	j vn
+ka	k a
+kai	k ai
+kan	k an
+kang	k ang
+kao	k ao
+ke	k e
+kei	k ei
+ken	k en
+keng	k eng
+kong	k ong
+kou	k ou
+ku	k u
+kua	k ua
+kuai	k uai
+kuan	k uan
+kuang	k uang
+kui	k ui
+kun	k un
+kuo	k uo
+la	l a
+lai	l ai
+lan	l an
+lang	l ang
+lao	l ao
+le	l e
+lei	l ei
+leng	l eng
+li	l i
+lia	l ia
+lian	l ian
+liang	l iang
+liao	l iao
+lie	l ie
+lin	l in
+ling	l ing
+liu	l iu
+lo	l o
+long	l ong
+lou	l ou
+lu	l u
+luan	l uan
+lun	l un
+luo	l uo
+lv	l v
+lve	l ve
+ma	m a
+mai	m ai
+man	m an
+mang	m ang
+mao	m ao
+me	m e
+mei	m ei
+men	m en
+meng	m eng
+mi	m i
+mian	m ian
+miao	m iao
+mie	m ie
+min	m in
+ming	m ing
+miu	m iu
+mo	m o
+mou	m ou
+mu	m u
+na	n a
+nai	n ai
+nan	n an
+nang	n ang
+nao	n ao
+ne	n e
+nei	n ei
+nen	n en
+neng	n eng
+ni	n i
+nian	n ian
+niang	n iang
+niao	n iao
+nie	n ie
+nin	n in
+ning	n ing
+niu	n iu
+nong	n ong
+nou	n ou
+nu	n u
+nuan	n uan
+nun	n un
+nuo	n uo
+nv	n v
+nve	n ve
+o	OO o
+ou	OO ou
+pa	p a
+pai	p ai
+pan	p an
+pang	p ang
+pao	p ao
+pei	p ei
+pen	p en
+peng	p eng
+pi	p i
+pian	p ian
+piao	p iao
+pie	p ie
+pin	p in
+ping	p ing
+po	p o
+pou	p ou
+pu	p u
+qi	q i
+qia	q ia
+qian	q ian
+qiang	q iang
+qiao	q iao
+qie	q ie
+qin	q in
+qing	q ing
+qiong	q iong
+qiu	q iu
+qu	q v
+qv	q v
+quan	q van
+qvan	q van
+que	q ve
+qve	q ve
+qun	q vn
+qvn	q vn
+ran	r an
+rang	r ang
+rao	r ao
+re	r e
+ren	r en
+reng	r eng
+ri	r ir
+rong	r ong
+rou	r ou
+ru	r u
+rua	r ua
+ruan	r uan
+rui	r ui
+run	r un
+ruo	r uo
+sa	s a
+sai	s ai
+san	s an
+sang	s ang
+sao	s ao
+se	s e
+sen	s en
+seng	s eng
+sha	sh a
+shai	sh ai
+shan	sh an
+shang	sh ang
+shao	sh ao
+she	sh e
+shei	sh ei
+shen	sh en
+sheng	sh eng
+shi	sh ir
+shou	sh ou
+shu	sh u
+shua	sh ua
+shuai	sh uai
+shuan	sh uan
+shuang	sh uang
+shui	sh ui
+shun	sh un
+shuo	sh uo
+si	s i0
+song	s ong
+sou	s ou
+su	s u
+suan	s uan
+sui	s ui
+sun	s un
+suo	s uo
+ta	t a
+tai	t ai
+tan	t an
+tang	t ang
+tao	t ao
+te	t e
+tei	t ei
+teng	t eng
+ti	t i
+tian	t ian
+tiao	t iao
+tie	t ie
+ting	t ing
+tong	t ong
+tou	t ou
+tu	t u
+tuan	t uan
+tui	t ui
+tun	t un
+tuo	t uo
+wa	w a
+wai	w ai
+wan	w an
+wang	w ang
+wei	w ei
+wen	w en
+weng	w eng
+wo	w o
+wu	w u
+xi	x i
+xia	x ia
+xian	x ian
+xiang	x iang
+xiao	x iao
+xie	x ie
+xin	x in
+xing	x ing
+xiong	x iong
+xiu	x iu
+xu	x v
+xv	x v
+xuan	x van
+xvan	x van
+xue	x ve
+xve	x ve
+xun	x vn
+xvn	x vn
+ya	y a
+yan	y En
+yang	y ang
+yao	y ao
+ye	y E
+yi	y i
+yin	y in
+ying	y ing
+yo	y o
+yong	y ong
+you	y ou
+yu	y v
+yv	y v
+yuan	y van
+yvan	y van
+yue	y ve
+yve	y ve
+yun	y vn
+yvn	y vn
+za	z a
+zai	z ai
+zan	z an
+zang	z ang
+zao	z ao
+ze	z e
+zei	z ei
+zen	z en
+zeng	z eng
+zha	zh a
+zhai	zh ai
+zhan	zh an
+zhang	zh ang
+zhao	zh ao
+zhe	zh e
+zhei	zh ei
+zhen	zh en
+zheng	zh eng
+zhi	zh ir
+zhong	zh ong
+zhou	zh ou
+zhu	zh u
+zhua	zh ua
+zhuai	zh uai
+zhuan	zh uan
+zhuang	zh uang
+zhui	zh ui
+zhun	zh un
+zhuo	zh uo
+zi	z i0
+zong	z ong
+zou	z ou
+zu	z u
+zuan	z uan
+zui	z ui
+zun	z un
+zuo	z uo
diff --git a/onnx_modules/V200/text/symbols.py b/onnx_modules/V200/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V200/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V200/text/tone_sandhi.py b/onnx_modules/V200/text/tone_sandhi.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd39296ac72e61b2d6ba5b2e9a8a5ebf0c41523c
--- /dev/null
+++ b/onnx_modules/V200/text/tone_sandhi.py
@@ -0,0 +1,772 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List
+from typing import Tuple
+
+import jieba
+from pypinyin import lazy_pinyin
+from pypinyin import Style
+
+
+class ToneSandhi:
+    def __init__(self):
+        self.must_neural_tone_words = {
+            "麻烦",
+            "麻利",
+            "鸳鸯",
+            "高粱",
+            "骨头",
+            "骆驼",
+            "马虎",
+            "首饰",
+            "馒头",
+            "馄饨",
+            "风筝",
+            "难为",
+            "队伍",
+            "阔气",
+            "闺女",
+            "门道",
+            "锄头",
+            "铺盖",
+            "铃铛",
+            "铁匠",
+            "钥匙",
+            "里脊",
+            "里头",
+            "部分",
+            "那么",
+            "道士",
+            "造化",
+            "迷糊",
+            "连累",
+            "这么",
+            "这个",
+            "运气",
+            "过去",
+            "软和",
+            "转悠",
+            "踏实",
+            "跳蚤",
+            "跟头",
+            "趔趄",
+            "财主",
+            "豆腐",
+            "讲究",
+            "记性",
+            "记号",
+            "认识",
+            "规矩",
+            "见识",
+            "裁缝",
+            "补丁",
+            "衣裳",
+            "衣服",
+            "衙门",
+            "街坊",
+            "行李",
+            "行当",
+            "蛤蟆",
+            "蘑菇",
+            "薄荷",
+            "葫芦",
+            "葡萄",
+            "萝卜",
+            "荸荠",
+            "苗条",
+            "苗头",
+            "苍蝇",
+            "芝麻",
+            "舒服",
+            "舒坦",
+            "舌头",
+            "自在",
+            "膏药",
+            "脾气",
+            "脑袋",
+            "脊梁",
+            "能耐",
+            "胳膊",
+            "胭脂",
+            "胡萝",
+            "胡琴",
+            "胡同",
+            "聪明",
+            "耽误",
+            "耽搁",
+            "耷拉",
+            "耳朵",
+            "老爷",
+            "老实",
+            "老婆",
+            "老头",
+            "老太",
+            "翻腾",
+            "罗嗦",
+            "罐头",
+            "编辑",
+            "结实",
+            "红火",
+            "累赘",
+            "糨糊",
+            "糊涂",
+            "精神",
+            "粮食",
+            "簸箕",
+            "篱笆",
+            "算计",
+            "算盘",
+            "答应",
+            "笤帚",
+            "笑语",
+            "笑话",
+            "窟窿",
+            "窝囊",
+            "窗户",
+            "稳当",
+            "稀罕",
+            "称呼",
+            "秧歌",
+            "秀气",
+            "秀才",
+            "福气",
+            "祖宗",
+            "砚台",
+            "码头",
+            "石榴",
+            "石头",
+            "石匠",
+            "知识",
+            "眼睛",
+            "眯缝",
+            "眨巴",
+            "眉毛",
+            "相声",
+            "盘算",
+            "白净",
+            "痢疾",
+            "痛快",
+            "疟疾",
+            "疙瘩",
+            "疏忽",
+            "畜生",
+            "生意",
+            "甘蔗",
+            "琵琶",
+            "琢磨",
+            "琉璃",
+            "玻璃",
+            "玫瑰",
+            "玄乎",
+            "狐狸",
+            "状元",
+            "特务",
+            "牲口",
+            "牙碜",
+            "牌楼",
+            "爽快",
+            "爱人",
+            "热闹",
+            "烧饼",
+            "烟筒",
+            "烂糊",
+            "点心",
+            "炊帚",
+            "灯笼",
+            "火候",
+            "漂亮",
+            "滑溜",
+            "溜达",
+            "温和",
+            "清楚",
+            "消息",
+            "浪头",
+            "活泼",
+            "比方",
+            "正经",
+            "欺负",
+            "模糊",
+            "槟榔",
+            "棺材",
+            "棒槌",
+            "棉花",
+            "核桃",
+            "栅栏",
+            "柴火",
+            "架势",
+            "枕头",
+            "枇杷",
+            "机灵",
+            "本事",
+            "木头",
+            "木匠",
+            "朋友",
+            "月饼",
+            "月亮",
+            "暖和",
+            "明白",
+            "时候",
+            "新鲜",
+            "故事",
+            "收拾",
+            "收成",
+            "提防",
+            "挖苦",
+            "挑剔",
+            "指甲",
+            "指头",
+            "拾掇",
+            "拳头",
+            "拨弄",
+            "招牌",
+            "招呼",
+            "抬举",
+            "护士",
+            "折腾",
+            "扫帚",
+            "打量",
+            "打算",
+            "打点",
+            "打扮",
+            "打听",
+            "打发",
+            "扎实",
+            "扁担",
+            "戒指",
+            "懒得",
+            "意识",
+            "意思",
+            "情形",
+            "悟性",
+            "怪物",
+            "思量",
+            "怎么",
+            "念头",
+            "念叨",
+            "快活",
+            "忙活",
+            "志气",
+            "心思",
+            "得罪",
+            "张罗",
+            "弟兄",
+            "开通",
+            "应酬",
+            "庄稼",
+            "干事",
+            "帮手",
+            "帐篷",
+            "希罕",
+            "师父",
+            "师傅",
+            "巴结",
+            "巴掌",
+            "差事",
+            "工夫",
+            "岁数",
+            "屁股",
+            "尾巴",
+            "少爷",
+            "小气",
+            "小伙",
+            "将就",
+            "对头",
+            "对付",
+            "寡妇",
+            "家伙",
+            "客气",
+            "实在",
+            "官司",
+            "学问",
+            "学生",
+            "字号",
+            "嫁妆",
+            "媳妇",
+            "媒人",
+            "婆家",
+            "娘家",
+            "委屈",
+            "姑娘",
+            "姐夫",
+            "妯娌",
+            "妥当",
+            "妖精",
+            "奴才",
+            "女婿",
+            "头发",
+            "太阳",
+            "大爷",
+            "大方",
+            "大意",
+            "大夫",
+            "多少",
+            "多么",
+            "外甥",
+            "壮实",
+            "地道",
+            "地方",
+            "在乎",
+            "困难",
+            "嘴巴",
+            "嘱咐",
+            "嘟囔",
+            "嘀咕",
+            "喜欢",
+            "喇嘛",
+            "喇叭",
+            "商量",
+            "唾沫",
+            "哑巴",
+            "哈欠",
+            "哆嗦",
+            "咳嗽",
+            "和尚",
+            "告诉",
+            "告示",
+            "含糊",
+            "吓唬",
+            "后头",
+            "名字",
+            "名堂",
+            "合同",
+            "吆喝",
+            "叫唤",
+            "口袋",
+            "厚道",
+            "厉害",
+            "千斤",
+            "包袱",
+            "包涵",
+            "匀称",
+            "勤快",
+            "动静",
+            "动弹",
+            "功夫",
+            "力气",
+            "前头",
+            "刺猬",
+            "刺激",
+            "别扭",
+            "利落",
+            "利索",
+            "利害",
+            "分析",
+            "出息",
+            "凑合",
+            "凉快",
+            "冷战",
+            "冤枉",
+            "冒失",
+            "养活",
+            "关系",
+            "先生",
+            "兄弟",
+            "便宜",
+            "使唤",
+            "佩服",
+            "作坊",
+            "体面",
+            "位置",
+            "似的",
+            "伙计",
+            "休息",
+            "什么",
+            "人家",
+            "亲戚",
+            "亲家",
+            "交情",
+            "云彩",
+            "事情",
+            "买卖",
+            "主意",
+            "丫头",
+            "丧气",
+            "两口",
+            "东西",
+            "东家",
+            "世故",
+            "不由",
+            "不在",
+            "下水",
+            "下巴",
+            "上头",
+            "上司",
+            "丈夫",
+            "丈人",
+            "一辈",
+            "那个",
+            "菩萨",
+            "父亲",
+            "母亲",
+            "咕噜",
+            "邋遢",
+            "费用",
+            "冤家",
+            "甜头",
+            "介绍",
+            "荒唐",
+            "大人",
+            "泥鳅",
+            "幸福",
+            "熟悉",
+            "计划",
+            "扑腾",
+            "蜡烛",
+            "姥爷",
+            "照顾",
+            "喉咙",
+            "吉他",
+            "弄堂",
+            "蚂蚱",
+            "凤凰",
+            "拖沓",
+            "寒碜",
+            "糟蹋",
+            "倒腾",
+            "报复",
+            "逻辑",
+            "盘缠",
+            "喽啰",
+            "牢骚",
+            "咖喱",
+            "扫把",
+            "惦记",
+        }
+        self.must_not_neural_tone_words = {
+            "男子",
+            "女子",
+            "分子",
+            "原子",
+            "量子",
+            "莲子",
+            "石子",
+            "瓜子",
+            "电子",
+            "人人",
+            "虎虎",
+        }
+        self.punc = "：，；。？！“”‘’':,;.?!"
+
+    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
+    # e.g.
+    # word: "家里"
+    # pos: "s"
+    # finals: ['ia1', 'i3']
+    def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
+        for j, item in enumerate(word):
+            if (
+                j - 1 >= 0
+                and item == word[j - 1]
+                and pos[0] in {"n", "v", "a"}
+                and word not in self.must_not_neural_tone_words
+            ):
+                finals[j] = finals[j][:-1] + "5"
+        ge_idx = word.find("个")
+        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":
+            finals[-1] = finals[-1][:-1] + "5"
+        elif len(word) >= 1 and word[-1] in "的地得":
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 走了, 看着, 去过
+        # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
+        #     finals[-1] = finals[-1][:-1] + "5"
+        elif (
+            len(word) > 1
+            and word[-1] in "们子"
+            and pos in {"r", "n"}
+            and word not in self.must_not_neural_tone_words
+        ):
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 桌上, 地下, 家里
+        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 上来, 下去
+        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
+            finals[-1] = finals[-1][:-1] + "5"
+        # 个做量词
+        elif (
+            ge_idx >= 1
+            and (
+                word[ge_idx - 1].isnumeric()
+                or word[ge_idx - 1] in "几有两半多各整每做是"
+            )
+        ) or word == "个":
+            finals[ge_idx] = finals[ge_idx][:-1] + "5"
+        else:
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals[-1] = finals[-1][:-1] + "5"
+
+        word_list = self._split_word(word)
+        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+        for i, word in enumerate(word_list):
+            # conventional neural in Chinese
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
+        finals = sum(finals_list, [])
+        return finals
+
+    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # e.g. 看不懂
+        if len(word) == 3 and word[1] == "不":
+            finals[1] = finals[1][:-1] + "5"
+        else:
+            for i, char in enumerate(word):
+                # "不" before tone4 should be bu2, e.g. 不怕
+                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
+                    finals[i] = finals[i][:-1] + "2"
+        return finals
+
+    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # "一" in number sequences, e.g. 一零零, 二一零
+        if word.find("一") != -1 and all(
+            [item.isnumeric() for item in word if item != "一"]
+        ):
+            return finals
+        # "一" between reduplication words should be yi5, e.g. 看一看
+        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
+            finals[1] = finals[1][:-1] + "5"
+        # when "一" is ordinal word, it should be yi1
+        elif word.startswith("第一"):
+            finals[1] = finals[1][:-1] + "1"
+        else:
+            for i, char in enumerate(word):
+                if char == "一" and i + 1 < len(word):
+                    # "一" before tone4 should be yi2, e.g. 一段
+                    if finals[i + 1][-1] == "4":
+                        finals[i] = finals[i][:-1] + "2"
+                    # "一" before non-tone4 should be yi4, e.g. 一天
+                    else:
+                        # "一" 后面如果是标点，还读一声
+                        if word[i + 1] not in self.punc:
+                            finals[i] = finals[i][:-1] + "4"
+        return finals
+
+    def _split_word(self, word: str) -> List[str]:
+        word_list = jieba.cut_for_search(word)
+        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
+        first_subword = word_list[0]
+        first_begin_idx = word.find(first_subword)
+        if first_begin_idx == 0:
+            second_subword = word[len(first_subword) :]
+            new_word_list = [first_subword, second_subword]
+        else:
+            second_subword = word[: -len(first_subword)]
+            new_word_list = [second_subword, first_subword]
+        return new_word_list
+
+    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        if len(word) == 2 and self._all_tone_three(finals):
+            finals[0] = finals[0][:-1] + "2"
+        elif len(word) == 3:
+            word_list = self._split_word(word)
+            if self._all_tone_three(finals):
+                #  disyllabic + monosyllabic, e.g. 蒙古/包
+                if len(word_list[0]) == 2:
+                    finals[0] = finals[0][:-1] + "2"
+                    finals[1] = finals[1][:-1] + "2"
+                #  monosyllabic + disyllabic, e.g. 纸/老虎
+                elif len(word_list[0]) == 1:
+                    finals[1] = finals[1][:-1] + "2"
+            else:
+                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+                if len(finals_list) == 2:
+                    for i, sub in enumerate(finals_list):
+                        # e.g. 所有/人
+                        if self._all_tone_three(sub) and len(sub) == 2:
+                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
+                        # e.g. 好/喜欢
+                        elif (
+                            i == 1
+                            and not self._all_tone_three(sub)
+                            and finals_list[i][0][-1] == "3"
+                            and finals_list[0][-1][-1] == "3"
+                        ):
+                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
+                        finals = sum(finals_list, [])
+        # split idiom into two words who's length is 2
+        elif len(word) == 4:
+            finals_list = [finals[:2], finals[2:]]
+            finals = []
+            for sub in finals_list:
+                if self._all_tone_three(sub):
+                    sub[0] = sub[0][:-1] + "2"
+                finals += sub
+
+        return finals
+
+    def _all_tone_three(self, finals: List[str]) -> bool:
+        return all(x[-1] == "3" for x in finals)
+
+    # merge "不" and the word behind it
+    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
+    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        last_word = ""
+        for word, pos in seg:
+            if last_word == "不":
+                word = last_word + word
+            if word != "不":
+                new_seg.append((word, pos))
+            last_word = word[:]
+        if last_word == "不":
+            new_seg.append((last_word, "d"))
+            last_word = ""
+        return new_seg
+
+    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
+    # function 2: merge single  "一" and the word behind it
+    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
+    # e.g.
+    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
+    # output seg: [['听一听', 'v']]
+    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        # function 1
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and word == "一"
+                and i + 1 < len(seg)
+                and seg[i - 1][0] == seg[i + 1][0]
+                and seg[i - 1][1] == "v"
+            ):
+                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
+            else:
+                if (
+                    i - 2 >= 0
+                    and seg[i - 1][0] == "一"
+                    and seg[i - 2][0] == word
+                    and pos == "v"
+                ):
+                    continue
+                else:
+                    new_seg.append([word, pos])
+        seg = new_seg
+        new_seg = []
+        # function 2
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and new_seg[-1][0] == "一":
+                new_seg[-1][0] = new_seg[-1][0] + word
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    # the first and the second words are all_tone_three
+    def _merge_continuous_three_tones(
+        self, seg: List[Tuple[str, str]]
+    ) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and self._all_tone_three(sub_finals_list[i - 1])
+                and self._all_tone_three(sub_finals_list[i])
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+
+        return new_seg
+
+    def _is_reduplication(self, word: str) -> bool:
+        return len(word) == 2 and word[0] == word[1]
+
+    # the last char of first word and the first char of second word is tone_three
+    def _merge_continuous_three_tones_2(
+        self, seg: List[Tuple[str, str]]
+    ) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and sub_finals_list[i - 1][-1][-1] == "3"
+                and sub_finals_list[i][0][-1] == "3"
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and word == new_seg[-1][0]:
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        seg = self._merge_bu(seg)
+        try:
+            seg = self._merge_yi(seg)
+        except:
+            print("_merge_yi failed")
+        seg = self._merge_reduplication(seg)
+        seg = self._merge_continuous_three_tones(seg)
+        seg = self._merge_continuous_three_tones_2(seg)
+        seg = self._merge_er(seg)
+        return seg
+
+    def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        finals = self._bu_sandhi(word, finals)
+        finals = self._yi_sandhi(word, finals)
+        finals = self._neural_sandhi(word, pos, finals)
+        finals = self._three_sandhi(word, finals)
+        return finals
diff --git a/onnx_modules/V200_OnnxInference/__init__.py b/onnx_modules/V200_OnnxInference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..327dd1284d7beb3347521b81d4c3ed2ff37a0dbb
--- /dev/null
+++ b/onnx_modules/V200_OnnxInference/__init__.py
@@ -0,0 +1,126 @@
+import numpy as np
+import onnxruntime as ort
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = np.arange(max_length, dtype=length.dtype)
+    return np.expand_dims(x, 0) < np.expand_dims(length, 1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = np.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.reshape(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y)
+    path = path.reshape(b, t_x, t_y)
+    path = path ^ np.pad(path, ((0, 0), (1, 0), (0, 0)))[:, :-1]
+    path = np.expand_dims(path, 1).transpose(0, 1, 3, 2)
+    return path
+
+
+class OnnxInferenceSession:
+    def __init__(self, path, Providers=["CPUExecutionProvider"]):
+        self.enc = ort.InferenceSession(path["enc"], providers=Providers)
+        self.emb_g = ort.InferenceSession(path["emb_g"], providers=Providers)
+        self.dp = ort.InferenceSession(path["dp"], providers=Providers)
+        self.sdp = ort.InferenceSession(path["sdp"], providers=Providers)
+        self.flow = ort.InferenceSession(path["flow"], providers=Providers)
+        self.dec = ort.InferenceSession(path["dec"], providers=Providers)
+
+    def __call__(
+        self,
+        seq,
+        tone,
+        language,
+        bert_zh,
+        bert_jp,
+        bert_en,
+        sid,
+        seed=114514,
+        seq_noise_scale=0.8,
+        sdp_noise_scale=0.6,
+        length_scale=1.0,
+        sdp_ratio=0.0,
+    ):
+        if seq.ndim == 1:
+            seq = np.expand_dims(seq, 0)
+        if tone.ndim == 1:
+            tone = np.expand_dims(tone, 0)
+        if language.ndim == 1:
+            language = np.expand_dims(language, 0)
+        assert (seq.ndim == 2, tone.ndim == 2, language.ndim == 2)
+        g = self.emb_g.run(
+            None,
+            {
+                "sid": sid.astype(np.int64),
+            },
+        )[0]
+        g = np.expand_dims(g, -1)
+        enc_rtn = self.enc.run(
+            None,
+            {
+                "x": seq.astype(np.int64),
+                "t": tone.astype(np.int64),
+                "language": language.astype(np.int64),
+                "bert_0": bert_zh.astype(np.float32),
+                "bert_1": bert_jp.astype(np.float32),
+                "bert_2": bert_en.astype(np.float32),
+                "g": g.astype(np.float32),
+            },
+        )
+        x, m_p, logs_p, x_mask = enc_rtn[0], enc_rtn[1], enc_rtn[2], enc_rtn[3]
+        np.random.seed(seed)
+        zinput = np.random.randn(x.shape[0], 2, x.shape[2]) * sdp_noise_scale
+        logw = self.sdp.run(
+            None, {"x": x, "x_mask": x_mask, "zin": zinput.astype(np.float32), "g": g}
+        )[0] * (sdp_ratio) + self.dp.run(None, {"x": x, "x_mask": x_mask, "g": g})[
+            0
+        ] * (
+            1 - sdp_ratio
+        )
+        w = np.exp(logw) * x_mask * length_scale
+        w_ceil = np.ceil(w)
+        y_lengths = np.clip(np.sum(w_ceil, (1, 2)), a_min=1.0, a_max=100000).astype(
+            np.int64
+        )
+        y_mask = np.expand_dims(sequence_mask(y_lengths, None), 1)
+        attn_mask = np.expand_dims(x_mask, 2) * np.expand_dims(y_mask, -1)
+        attn = generate_path(w_ceil, attn_mask)
+        m_p = np.matmul(attn.squeeze(1), m_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = (
+            m_p
+            + np.random.randn(m_p.shape[0], m_p.shape[1], m_p.shape[2])
+            * np.exp(logs_p)
+            * seq_noise_scale
+        )
+
+        z = self.flow.run(
+            None,
+            {
+                "z_p": z_p.astype(np.float32),
+                "y_mask": y_mask.astype(np.float32),
+                "g": g,
+            },
+        )[0]
+
+        return self.dec.run(None, {"z_in": z.astype(np.float32), "g": g})[0]
diff --git a/onnx_modules/V210/__init__.py b/onnx_modules/V210/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V210/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V210/attentions_onnx.py b/onnx_modules/V210/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V210/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V210/models_onnx.py b/onnx_modules/V210/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..6be05f1a0892c877a6b0b150de0db061fa023183
--- /dev/null
+++ b/onnx_modules/V210/models_onnx.py
@@ -0,0 +1,1044 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+from vector_quantize_pytorch import VectorQuantize
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(
+            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
+
+    def forward_probability(self, x, x_mask, dur, g=None):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_1(x)
+        x = self.drop(x)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_2(x)
+        x = self.drop(x)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_speakers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.emo_proj = nn.Linear(1024, 1024)
+        self.emo_quantizer = nn.ModuleList()
+        for i in range(0, n_speakers):
+            self.emo_quantizer.append(
+                VectorQuantize(
+                    dim=1024,
+                    codebook_size=10,
+                    decay=0.8,
+                    commitment_weight=1.0,
+                    learnable_codebook=True,
+                    ema_update=False,
+                )
+            )
+        self.emo_q_proj = nn.Linear(1024, hidden_channels)
+        self.n_speakers = n_speakers
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def init_vq(self):
+        self.emb_vq = nn.Embedding(10 * self.n_speakers, 1024)
+        self.emb_vq_weight = torch.zeros(10 * self.n_speakers, 1024).float()
+        for i in range(self.n_speakers):
+            for j in range(10):
+                self.emb_vq_weight[i * 10 + j] = self.emo_quantizer[
+                    i
+                ].get_output_from_indices(torch.LongTensor([j]))
+        self.emb_vq.weight = nn.Parameter(self.emb_vq_weight.clone())
+
+    def forward(
+        self,
+        x,
+        x_lengths,
+        tone,
+        language,
+        bert,
+        ja_bert,
+        en_bert,
+        g=None,
+        vqidx=None,
+        sid=None,
+    ):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+
+        emb_vq_idx = torch.clamp(
+            (sid * 10) + vqidx, min=0, max=(self.n_speakers * 10) - 1
+        )
+
+        vqval = self.emb_vq(emb_vq_idx)
+
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+            + self.emo_q_proj(vqval)
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=4,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            n_speakers,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        ja_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        en_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        self.enc_p.init_vq()
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, ja_bert, en_bert, g, sid, sid),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "bert_1",
+                "bert_2",
+                "g",
+                "vqidx",
+                "sid",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "bert_1": [0],
+                "bert_2": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, g, sid, sid
+        )
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V210/text/__init__.py b/onnx_modules/V210/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V210/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V210/text/symbols.py b/onnx_modules/V210/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V210/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V210_OnnxInference/__init__.py b/onnx_modules/V210_OnnxInference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c51e03b5530083cf675464d51fe56acb7f5148fd
--- /dev/null
+++ b/onnx_modules/V210_OnnxInference/__init__.py
@@ -0,0 +1,129 @@
+import numpy as np
+import onnxruntime as ort
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = np.arange(max_length, dtype=length.dtype)
+    return np.expand_dims(x, 0) < np.expand_dims(length, 1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = np.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.reshape(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y)
+    path = path.reshape(b, t_x, t_y)
+    path = path ^ np.pad(path, ((0, 0), (1, 0), (0, 0)))[:, :-1]
+    path = np.expand_dims(path, 1).transpose(0, 1, 3, 2)
+    return path
+
+
+class OnnxInferenceSession:
+    def __init__(self, path, Providers=["CPUExecutionProvider"]):
+        self.enc = ort.InferenceSession(path["enc"], providers=Providers)
+        self.emb_g = ort.InferenceSession(path["emb_g"], providers=Providers)
+        self.dp = ort.InferenceSession(path["dp"], providers=Providers)
+        self.sdp = ort.InferenceSession(path["sdp"], providers=Providers)
+        self.flow = ort.InferenceSession(path["flow"], providers=Providers)
+        self.dec = ort.InferenceSession(path["dec"], providers=Providers)
+
+    def __call__(
+        self,
+        seq,
+        tone,
+        language,
+        bert_zh,
+        bert_jp,
+        bert_en,
+        vqidx,
+        sid,
+        seed=114514,
+        seq_noise_scale=0.8,
+        sdp_noise_scale=0.6,
+        length_scale=1.0,
+        sdp_ratio=0.0,
+    ):
+        if seq.ndim == 1:
+            seq = np.expand_dims(seq, 0)
+        if tone.ndim == 1:
+            tone = np.expand_dims(tone, 0)
+        if language.ndim == 1:
+            language = np.expand_dims(language, 0)
+        assert (seq.ndim == 2, tone.ndim == 2, language.ndim == 2)
+        g = self.emb_g.run(
+            None,
+            {
+                "sid": sid.astype(np.int64),
+            },
+        )[0]
+        g = np.expand_dims(g, -1)
+        enc_rtn = self.enc.run(
+            None,
+            {
+                "x": seq.astype(np.int64),
+                "t": tone.astype(np.int64),
+                "language": language.astype(np.int64),
+                "bert_0": bert_zh.astype(np.float32),
+                "bert_1": bert_jp.astype(np.float32),
+                "bert_2": bert_en.astype(np.float32),
+                "g": g.astype(np.float32),
+                "vqidx": vqidx.astype(np.int64),
+                "sid": sid.astype(np.int64),
+            },
+        )
+        x, m_p, logs_p, x_mask = enc_rtn[0], enc_rtn[1], enc_rtn[2], enc_rtn[3]
+        np.random.seed(seed)
+        zinput = np.random.randn(x.shape[0], 2, x.shape[2]) * sdp_noise_scale
+        logw = self.sdp.run(
+            None, {"x": x, "x_mask": x_mask, "zin": zinput.astype(np.float32), "g": g}
+        )[0] * (sdp_ratio) + self.dp.run(None, {"x": x, "x_mask": x_mask, "g": g})[
+            0
+        ] * (
+            1 - sdp_ratio
+        )
+        w = np.exp(logw) * x_mask * length_scale
+        w_ceil = np.ceil(w)
+        y_lengths = np.clip(np.sum(w_ceil, (1, 2)), a_min=1.0, a_max=100000).astype(
+            np.int64
+        )
+        y_mask = np.expand_dims(sequence_mask(y_lengths, None), 1)
+        attn_mask = np.expand_dims(x_mask, 2) * np.expand_dims(y_mask, -1)
+        attn = generate_path(w_ceil, attn_mask)
+        m_p = np.matmul(attn.squeeze(1), m_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = (
+            m_p
+            + np.random.randn(m_p.shape[0], m_p.shape[1], m_p.shape[2])
+            * np.exp(logs_p)
+            * seq_noise_scale
+        )
+
+        z = self.flow.run(
+            None,
+            {
+                "z_p": z_p.astype(np.float32),
+                "y_mask": y_mask.astype(np.float32),
+                "g": g,
+            },
+        )[0]
+
+        return self.dec.run(None, {"z_in": z.astype(np.float32), "g": g})[0]
diff --git a/onnx_modules/V220/__init__.py b/onnx_modules/V220/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V220/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V220/attentions_onnx.py b/onnx_modules/V220/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V220/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V220/models_onnx.py b/onnx_modules/V220/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..d70814d95c358def14c2eb58a01e621c98f78dff
--- /dev/null
+++ b/onnx_modules/V220/models_onnx.py
@@ -0,0 +1,1076 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+from vector_quantize_pytorch import VectorQuantize
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(
+            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
+
+    def forward_probability(self, x, x_mask, dur, g=None):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_1(x)
+        x = self.drop(x)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_2(x)
+        x = self.drop(x)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_speakers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        # self.emo_proj = nn.Linear(1024, 1024)
+        # self.emo_quantizer = nn.ModuleList()
+        # for i in range(0, n_speakers):
+        #    self.emo_quantizer.append(
+        #        VectorQuantize(
+        #            dim=1024,
+        #            codebook_size=10,
+        #            decay=0.8,
+        #            commitment_weight=1.0,
+        #            learnable_codebook=True,
+        #            ema_update=False,
+        #        )
+        #    )
+        # self.emo_q_proj = nn.Linear(1024, hidden_channels)
+        self.n_speakers = n_speakers
+        self.in_feature_net = nn.Sequential(
+            # input is assumed to an already normalized embedding
+            nn.Linear(512, 1028, bias=False),
+            nn.GELU(),
+            nn.LayerNorm(1028),
+            *[Block(1028, 512) for _ in range(1)],
+            nn.Linear(1028, 512, bias=False),
+            # normalize before passing to VQ?
+            # nn.GELU(),
+            # nn.LayerNorm(512),
+        )
+        self.emo_vq = VectorQuantize(
+            dim=512,
+            codebook_size=64,
+            codebook_dim=32,
+            commitment_weight=0.1,
+            decay=0.85,
+            heads=32,
+            kmeans_iters=20,
+            separate_codebook_per_head=True,
+            stochastic_sample_codes=True,
+            threshold_ema_dead_code=2,
+        )
+        self.out_feature_net = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(
+        self, x, x_lengths, tone, language, bert, ja_bert, en_bert, emo, g=None
+    ):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        emo_emb = self.in_feature_net(emo.transpose(0, 1))
+        emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1))
+
+        emo_emb = self.out_feature_net(emo_emb)
+
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+            + emo_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=4,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            self.n_speakers,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        ja_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        en_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        emo = torch.randn(512, 1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, ja_bert, en_bert, emo, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "bert_1",
+                "bert_2",
+                "emo",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "bert_1": [0],
+                "bert_2": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, emo, g
+        )
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V220/text/__init__.py b/onnx_modules/V220/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V220/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V220/text/symbols.py b/onnx_modules/V220/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V220/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V220_OnnxInference/__init__.py b/onnx_modules/V220_OnnxInference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..175ee0d7218bf102aa56f8c497d2b4046b015dc9
--- /dev/null
+++ b/onnx_modules/V220_OnnxInference/__init__.py
@@ -0,0 +1,128 @@
+import numpy as np
+import onnxruntime as ort
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = np.arange(max_length, dtype=length.dtype)
+    return np.expand_dims(x, 0) < np.expand_dims(length, 1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = np.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.reshape(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y)
+    path = path.reshape(b, t_x, t_y)
+    path = path ^ np.pad(path, ((0, 0), (1, 0), (0, 0)))[:, :-1]
+    path = np.expand_dims(path, 1).transpose(0, 1, 3, 2)
+    return path
+
+
+class OnnxInferenceSession:
+    def __init__(self, path, Providers=["CPUExecutionProvider"]):
+        self.enc = ort.InferenceSession(path["enc"], providers=Providers)
+        self.emb_g = ort.InferenceSession(path["emb_g"], providers=Providers)
+        self.dp = ort.InferenceSession(path["dp"], providers=Providers)
+        self.sdp = ort.InferenceSession(path["sdp"], providers=Providers)
+        self.flow = ort.InferenceSession(path["flow"], providers=Providers)
+        self.dec = ort.InferenceSession(path["dec"], providers=Providers)
+
+    def __call__(
+        self,
+        seq,
+        tone,
+        language,
+        bert_zh,
+        bert_jp,
+        bert_en,
+        emo,
+        sid,
+        seed=114514,
+        seq_noise_scale=0.8,
+        sdp_noise_scale=0.6,
+        length_scale=1.0,
+        sdp_ratio=0.0,
+    ):
+        if seq.ndim == 1:
+            seq = np.expand_dims(seq, 0)
+        if tone.ndim == 1:
+            tone = np.expand_dims(tone, 0)
+        if language.ndim == 1:
+            language = np.expand_dims(language, 0)
+        assert (seq.ndim == 2, tone.ndim == 2, language.ndim == 2)
+        g = self.emb_g.run(
+            None,
+            {
+                "sid": sid.astype(np.int64),
+            },
+        )[0]
+        g = np.expand_dims(g, -1)
+        enc_rtn = self.enc.run(
+            None,
+            {
+                "x": seq.astype(np.int64),
+                "t": tone.astype(np.int64),
+                "language": language.astype(np.int64),
+                "bert_0": bert_zh.astype(np.float32),
+                "bert_1": bert_jp.astype(np.float32),
+                "bert_2": bert_en.astype(np.float32),
+                "emo": emo.astype(np.float32),
+                "g": g.astype(np.float32),
+            },
+        )
+        x, m_p, logs_p, x_mask = enc_rtn[0], enc_rtn[1], enc_rtn[2], enc_rtn[3]
+        np.random.seed(seed)
+        zinput = np.random.randn(x.shape[0], 2, x.shape[2]) * sdp_noise_scale
+        logw = self.sdp.run(
+            None, {"x": x, "x_mask": x_mask, "zin": zinput.astype(np.float32), "g": g}
+        )[0] * (sdp_ratio) + self.dp.run(None, {"x": x, "x_mask": x_mask, "g": g})[
+            0
+        ] * (
+            1 - sdp_ratio
+        )
+        w = np.exp(logw) * x_mask * length_scale
+        w_ceil = np.ceil(w)
+        y_lengths = np.clip(np.sum(w_ceil, (1, 2)), a_min=1.0, a_max=100000).astype(
+            np.int64
+        )
+        y_mask = np.expand_dims(sequence_mask(y_lengths, None), 1)
+        attn_mask = np.expand_dims(x_mask, 2) * np.expand_dims(y_mask, -1)
+        attn = generate_path(w_ceil, attn_mask)
+        m_p = np.matmul(attn.squeeze(1), m_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = (
+            m_p
+            + np.random.randn(m_p.shape[0], m_p.shape[1], m_p.shape[2])
+            * np.exp(logs_p)
+            * seq_noise_scale
+        )
+
+        z = self.flow.run(
+            None,
+            {
+                "z_p": z_p.astype(np.float32),
+                "y_mask": y_mask.astype(np.float32),
+                "g": g,
+            },
+        )[0]
+
+        return self.dec.run(None, {"z_in": z.astype(np.float32), "g": g})[0]
diff --git a/onnx_modules/V220_novq_dev/__init__.py b/onnx_modules/V220_novq_dev/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V220_novq_dev/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V220_novq_dev/attentions_onnx.py b/onnx_modules/V220_novq_dev/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V220_novq_dev/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V220_novq_dev/models_onnx.py b/onnx_modules/V220_novq_dev/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..0be615899edad1ba3f49da0ea0946be7443b2d25
--- /dev/null
+++ b/onnx_modules/V220_novq_dev/models_onnx.py
@@ -0,0 +1,1048 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(
+            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
+
+    def forward_probability(self, x, x_mask, dur, g=None):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_1(x)
+        x = self.drop(x)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_2(x)
+        x = self.drop(x)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_speakers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        # self.emo_proj = nn.Linear(1024, 1024)
+        # self.emo_quantizer = nn.ModuleList()
+        # for i in range(0, n_speakers):
+        #    self.emo_quantizer.append(
+        #        VectorQuantize(
+        #            dim=1024,
+        #            codebook_size=10,
+        #            decay=0.8,
+        #            commitment_weight=1.0,
+        #            learnable_codebook=True,
+        #            ema_update=False,
+        #        )
+        #    )
+        # self.emo_q_proj = nn.Linear(1024, hidden_channels)
+        self.n_speakers = n_speakers
+        self.emo_proj = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(
+        self, x, x_lengths, tone, language, bert, ja_bert, en_bert, emo, g=None
+    ):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+            + self.emo_proj(emo)
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=4,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            self.n_speakers,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        ja_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        en_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        emo = torch.randn(512, 1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, ja_bert, en_bert, emo, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "bert_1",
+                "bert_2",
+                "emo",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "bert_1": [0],
+                "bert_2": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, emo, g
+        )
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V220_novq_dev/text/__init__.py b/onnx_modules/V220_novq_dev/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V220_novq_dev/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V220_novq_dev/text/symbols.py b/onnx_modules/V220_novq_dev/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V220_novq_dev/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V230/__init__.py b/onnx_modules/V230/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V230/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V230/attentions_onnx.py b/onnx_modules/V230/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V230/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V230/models_onnx.py b/onnx_modules/V230/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..22712c3236e7f7e3887444fe5a3947788a41fdb4
--- /dev/null
+++ b/onnx_modules/V230/models_onnx.py
@@ -0,0 +1,1063 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, bert, ja_bert, en_bert, g=None):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        en_bert_emb = self.en_bert_proj(en_bert.transpose(0, 1).unsqueeze(0)).transpose(
+            1, 2
+        )
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+        self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=4,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        ja_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        en_bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, ja_bert, en_bert, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "bert_1",
+                "bert_2",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "bert_1": [0],
+                "bert_2": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, g
+        )
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V230/text/__init__.py b/onnx_modules/V230/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V230/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V230/text/symbols.py b/onnx_modules/V230/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..e489dd0daf8de5ca6529a69734c8e1ca35087ed7
--- /dev/null
+++ b/onnx_modules/V230/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V230_OnnxInference/__init__.py b/onnx_modules/V230_OnnxInference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..327dd1284d7beb3347521b81d4c3ed2ff37a0dbb
--- /dev/null
+++ b/onnx_modules/V230_OnnxInference/__init__.py
@@ -0,0 +1,126 @@
+import numpy as np
+import onnxruntime as ort
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = np.arange(max_length, dtype=length.dtype)
+    return np.expand_dims(x, 0) < np.expand_dims(length, 1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = np.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.reshape(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y)
+    path = path.reshape(b, t_x, t_y)
+    path = path ^ np.pad(path, ((0, 0), (1, 0), (0, 0)))[:, :-1]
+    path = np.expand_dims(path, 1).transpose(0, 1, 3, 2)
+    return path
+
+
+class OnnxInferenceSession:
+    def __init__(self, path, Providers=["CPUExecutionProvider"]):
+        self.enc = ort.InferenceSession(path["enc"], providers=Providers)
+        self.emb_g = ort.InferenceSession(path["emb_g"], providers=Providers)
+        self.dp = ort.InferenceSession(path["dp"], providers=Providers)
+        self.sdp = ort.InferenceSession(path["sdp"], providers=Providers)
+        self.flow = ort.InferenceSession(path["flow"], providers=Providers)
+        self.dec = ort.InferenceSession(path["dec"], providers=Providers)
+
+    def __call__(
+        self,
+        seq,
+        tone,
+        language,
+        bert_zh,
+        bert_jp,
+        bert_en,
+        sid,
+        seed=114514,
+        seq_noise_scale=0.8,
+        sdp_noise_scale=0.6,
+        length_scale=1.0,
+        sdp_ratio=0.0,
+    ):
+        if seq.ndim == 1:
+            seq = np.expand_dims(seq, 0)
+        if tone.ndim == 1:
+            tone = np.expand_dims(tone, 0)
+        if language.ndim == 1:
+            language = np.expand_dims(language, 0)
+        assert (seq.ndim == 2, tone.ndim == 2, language.ndim == 2)
+        g = self.emb_g.run(
+            None,
+            {
+                "sid": sid.astype(np.int64),
+            },
+        )[0]
+        g = np.expand_dims(g, -1)
+        enc_rtn = self.enc.run(
+            None,
+            {
+                "x": seq.astype(np.int64),
+                "t": tone.astype(np.int64),
+                "language": language.astype(np.int64),
+                "bert_0": bert_zh.astype(np.float32),
+                "bert_1": bert_jp.astype(np.float32),
+                "bert_2": bert_en.astype(np.float32),
+                "g": g.astype(np.float32),
+            },
+        )
+        x, m_p, logs_p, x_mask = enc_rtn[0], enc_rtn[1], enc_rtn[2], enc_rtn[3]
+        np.random.seed(seed)
+        zinput = np.random.randn(x.shape[0], 2, x.shape[2]) * sdp_noise_scale
+        logw = self.sdp.run(
+            None, {"x": x, "x_mask": x_mask, "zin": zinput.astype(np.float32), "g": g}
+        )[0] * (sdp_ratio) + self.dp.run(None, {"x": x, "x_mask": x_mask, "g": g})[
+            0
+        ] * (
+            1 - sdp_ratio
+        )
+        w = np.exp(logw) * x_mask * length_scale
+        w_ceil = np.ceil(w)
+        y_lengths = np.clip(np.sum(w_ceil, (1, 2)), a_min=1.0, a_max=100000).astype(
+            np.int64
+        )
+        y_mask = np.expand_dims(sequence_mask(y_lengths, None), 1)
+        attn_mask = np.expand_dims(x_mask, 2) * np.expand_dims(y_mask, -1)
+        attn = generate_path(w_ceil, attn_mask)
+        m_p = np.matmul(attn.squeeze(1), m_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = (
+            m_p
+            + np.random.randn(m_p.shape[0], m_p.shape[1], m_p.shape[2])
+            * np.exp(logs_p)
+            * seq_noise_scale
+        )
+
+        z = self.flow.run(
+            None,
+            {
+                "z_p": z_p.astype(np.float32),
+                "y_mask": y_mask.astype(np.float32),
+                "g": g,
+            },
+        )[0]
+
+        return self.dec.run(None, {"z_in": z.astype(np.float32), "g": g})[0]
diff --git a/onnx_modules/V240/__init__.py b/onnx_modules/V240/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V240/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V240/attentions_onnx.py b/onnx_modules/V240/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V240/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V240/models_onnx.py b/onnx_modules/V240/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..d485d7c8b6861c3fb62e22a7a8d89653ceae2ffe
--- /dev/null
+++ b/onnx_modules/V240/models_onnx.py
@@ -0,0 +1,1078 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.bert_pre_proj = nn.Conv1d(2048, 1024, 1)
+        self.in_feature_net = nn.Sequential(
+            # input is assumed to an already normalized embedding
+            nn.Linear(512, 1028, bias=False),
+            nn.GELU(),
+            nn.LayerNorm(1028),
+            *[Block(1028, 512) for _ in range(1)],
+            nn.Linear(1028, 512, bias=False),
+            # normalize before passing to VQ?
+            # nn.GELU(),
+            # nn.LayerNorm(512),
+        )
+        self.emo_vq = VectorQuantize(
+            dim=512,
+            codebook_size=64,
+            codebook_dim=32,
+            commitment_weight=0.1,
+            decay=0.85,
+            heads=32,
+            kmeans_iters=20,
+            separate_codebook_per_head=True,
+            stochastic_sample_codes=True,
+            threshold_ema_dead_code=2,
+        )
+        self.out_feature_net = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, bert, emo, g=None):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(
+            self.bert_pre_proj(bert.transpose(0, 1).unsqueeze(0))
+        ).transpose(1, 2)
+        emo_emb = self.in_feature_net(emo)
+        emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1))
+        emo_emb = self.out_feature_net(emo_emb)
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + emo_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+        self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=6,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        emo = torch.randn(512, 1)
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, emo, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "emo",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, emo, g)
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V240/text/__init__.py b/onnx_modules/V240/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V240/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V240/text/symbols.py b/onnx_modules/V240/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V240/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V240_JP/__init__.py b/onnx_modules/V240_JP/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V240_JP/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V240_JP/attentions_onnx.py b/onnx_modules/V240_JP/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V240_JP/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V240_JP/models_onnx.py b/onnx_modules/V240_JP/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7b4c783555c926f5b4e61ed5f4511ad54fcf66e
--- /dev/null
+++ b/onnx_modules/V240_JP/models_onnx.py
@@ -0,0 +1,1081 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+from vector_quantize_pytorch import VectorQuantize
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        # self.bert_pre_proj = nn.Conv1d(2048, 1024, 1)
+        self.in_feature_net = nn.Sequential(
+            # input is assumed to an already normalized embedding
+            nn.Linear(512, 1028, bias=False),
+            nn.GELU(),
+            nn.LayerNorm(1028),
+            *[Block(1028, 512) for _ in range(1)],
+            nn.Linear(1028, 512, bias=False),
+            # normalize before passing to VQ?
+            # nn.GELU(),
+            # nn.LayerNorm(512),
+        )
+        self.emo_vq = VectorQuantize(
+            dim=512,
+            # codebook_size=128,
+            codebook_size=256,
+            codebook_dim=16,
+            # codebook_dim=32,
+            commitment_weight=0.1,
+            decay=0.99,
+            heads=32,
+            kmeans_iters=20,
+            separate_codebook_per_head=True,
+            stochastic_sample_codes=True,
+            threshold_ema_dead_code=2,
+            use_cosine_sim=True,
+        )
+        self.out_feature_net = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, bert, emo, g=None):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(bert.transpose(0, 1).unsqueeze(0)).transpose(1, 2)
+        emo_emb = self.in_feature_net(emo.transpose(0, 1))
+        emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1))
+        emo_emb = self.out_feature_net(emo_emb)
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + emo_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+        self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=6,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 1024)).cpu()
+        emo = torch.randn(512, 1)
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, emo, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "emo",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, emo, g)
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V240_JP/text/__init__.py b/onnx_modules/V240_JP/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V240_JP/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V240_JP/text/symbols.py b/onnx_modules/V240_JP/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V240_JP/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/V240_OnnxInference/__init__.py b/onnx_modules/V240_OnnxInference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4daf871620eb60136864888c1acb986a547648d4
--- /dev/null
+++ b/onnx_modules/V240_OnnxInference/__init__.py
@@ -0,0 +1,124 @@
+import numpy as np
+import onnxruntime as ort
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = np.arange(max_length, dtype=length.dtype)
+    return np.expand_dims(x, 0) < np.expand_dims(length, 1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = np.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.reshape(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y)
+    path = path.reshape(b, t_x, t_y)
+    path = path ^ np.pad(path, ((0, 0), (1, 0), (0, 0)))[:, :-1]
+    path = np.expand_dims(path, 1).transpose(0, 1, 3, 2)
+    return path
+
+
+class OnnxInferenceSession:
+    def __init__(self, path, Providers=["CPUExecutionProvider"]):
+        self.enc = ort.InferenceSession(path["enc"], providers=Providers)
+        self.emb_g = ort.InferenceSession(path["emb_g"], providers=Providers)
+        self.dp = ort.InferenceSession(path["dp"], providers=Providers)
+        self.sdp = ort.InferenceSession(path["sdp"], providers=Providers)
+        self.flow = ort.InferenceSession(path["flow"], providers=Providers)
+        self.dec = ort.InferenceSession(path["dec"], providers=Providers)
+
+    def __call__(
+        self,
+        seq,
+        tone,
+        language,
+        bert_zh,
+        emo,
+        sid,
+        seed=114514,
+        seq_noise_scale=0.8,
+        sdp_noise_scale=0.6,
+        length_scale=1.0,
+        sdp_ratio=0.0,
+    ):
+        if seq.ndim == 1:
+            seq = np.expand_dims(seq, 0)
+        if tone.ndim == 1:
+            tone = np.expand_dims(tone, 0)
+        if language.ndim == 1:
+            language = np.expand_dims(language, 0)
+        assert (seq.ndim == 2, tone.ndim == 2, language.ndim == 2)
+        g = self.emb_g.run(
+            None,
+            {
+                "sid": sid.astype(np.int64),
+            },
+        )[0]
+        g = np.expand_dims(g, -1)
+        enc_rtn = self.enc.run(
+            None,
+            {
+                "x": seq.astype(np.int64),
+                "t": tone.astype(np.int64),
+                "language": language.astype(np.int64),
+                "bert_0": bert_zh.astype(np.float32),
+                "emo": emo.astype(np.float32),
+                "g": g.astype(np.float32),
+            },
+        )
+        x, m_p, logs_p, x_mask = enc_rtn[0], enc_rtn[1], enc_rtn[2], enc_rtn[3]
+        np.random.seed(seed)
+        zinput = np.random.randn(x.shape[0], 2, x.shape[2]) * sdp_noise_scale
+        logw = self.sdp.run(
+            None, {"x": x, "x_mask": x_mask, "zin": zinput.astype(np.float32), "g": g}
+        )[0] * (sdp_ratio) + self.dp.run(None, {"x": x, "x_mask": x_mask, "g": g})[
+            0
+        ] * (
+            1 - sdp_ratio
+        )
+        w = np.exp(logw) * x_mask * length_scale
+        w_ceil = np.ceil(w)
+        y_lengths = np.clip(np.sum(w_ceil, (1, 2)), a_min=1.0, a_max=100000).astype(
+            np.int64
+        )
+        y_mask = np.expand_dims(sequence_mask(y_lengths, None), 1)
+        attn_mask = np.expand_dims(x_mask, 2) * np.expand_dims(y_mask, -1)
+        attn = generate_path(w_ceil, attn_mask)
+        m_p = np.matmul(attn.squeeze(1), m_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = np.matmul(attn.squeeze(1), logs_p.transpose(0, 2, 1)).transpose(
+            0, 2, 1
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = (
+            m_p
+            + np.random.randn(m_p.shape[0], m_p.shape[1], m_p.shape[2])
+            * np.exp(logs_p)
+            * seq_noise_scale
+        )
+
+        z = self.flow.run(
+            None,
+            {
+                "z_p": z_p.astype(np.float32),
+                "y_mask": y_mask.astype(np.float32),
+                "g": g,
+            },
+        )[0]
+
+        return self.dec.run(None, {"z_in": z.astype(np.float32), "g": g})[0]
diff --git a/onnx_modules/V240_ZH/__init__.py b/onnx_modules/V240_ZH/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e90089ffbadbc73e96e38a8f2eae9542e184c2d5
--- /dev/null
+++ b/onnx_modules/V240_ZH/__init__.py
@@ -0,0 +1,4 @@
+from .text.symbols import symbols
+from .models_onnx import SynthesizerTrn
+
+__all__ = ["symbols", "SynthesizerTrn"]
diff --git a/onnx_modules/V240_ZH/attentions_onnx.py b/onnx_modules/V240_ZH/attentions_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6243cd22de6d9e70f069f991c835283cb6c501
--- /dev/null
+++ b/onnx_modules/V240_ZH/attentions_onnx.py
@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/onnx_modules/V240_ZH/models_onnx.py b/onnx_modules/V240_ZH/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa35907f484240e329f71b4eb2d4911f1f48f769
--- /dev/null
+++ b/onnx_modules/V240_ZH/models_onnx.py
@@ -0,0 +1,1080 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from . import attentions_onnx
+
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from commons import init_weights, get_padding
+from .text import symbols, num_tones, num_languages
+
+from vector_quantize_pytorch import VectorQuantize
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+        share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions_onnx.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, z, g=None):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        flows = list(reversed(self.flows))
+        flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+        for flow in flows:
+            z = flow(z, x_mask, g=x, reverse=True)
+        z0, z1 = torch.split(z, [1, 1], 1)
+        logw = z0
+        return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.bert_pre_proj = nn.Conv1d(2048, 1024, 1)
+        self.in_feature_net = nn.Sequential(
+            # input is assumed to an already normalized embedding
+            nn.Linear(512, 1028, bias=False),
+            nn.GELU(),
+            nn.LayerNorm(1028),
+            *[Block(1028, 512) for _ in range(1)],
+            nn.Linear(1028, 512, bias=False),
+            # normalize before passing to VQ?
+            # nn.GELU(),
+            # nn.LayerNorm(512),
+        )
+        self.emo_vq = VectorQuantize(
+            dim=512,
+            codebook_size=64,
+            codebook_dim=32,
+            commitment_weight=0.1,
+            decay=0.85,
+            heads=32,
+            kmeans_iters=20,
+            separate_codebook_per_head=True,
+            stochastic_sample_codes=True,
+            threshold_ema_dead_code=2,
+        )
+        self.out_feature_net = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions_onnx.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, bert, emo, g=None):
+        x_mask = torch.ones_like(x).unsqueeze(0)
+        bert_emb = self.bert_proj(
+            self.bert_pre_proj(bert.transpose(0, 1).unsqueeze(0))
+        ).transpose(1, 2)
+        emo_emb = self.in_feature_net(emo.transpose(0, 1))
+        emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1))
+        emo_emb = self.out_feature_net(emo_emb)
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + emo_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = x_mask.to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=True):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+        self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=256,
+        gin_channels=256,
+        use_sdp=True,
+        n_flow_layer=4,
+        n_layers_trans_flow=6,
+        flow_share_parameter=False,
+        use_transformer_flow=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def export_onnx(
+        self,
+        path,
+        max_len=None,
+        sdp_ratio=0,
+        y=None,
+    ):
+        noise_scale = 0.667
+        length_scale = 1
+        noise_scale_w = 0.8
+        x = (
+            torch.LongTensor(
+                [
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    78,
+                    0,
+                    8,
+                    0,
+                    76,
+                    0,
+                    37,
+                    0,
+                    40,
+                    0,
+                    97,
+                    0,
+                    8,
+                    0,
+                    23,
+                    0,
+                    8,
+                    0,
+                    74,
+                    0,
+                    26,
+                    0,
+                    104,
+                    0,
+                ]
+            )
+            .unsqueeze(0)
+            .cpu()
+        )
+        tone = torch.zeros_like(x).cpu()
+        language = torch.zeros_like(x).cpu()
+        x_lengths = torch.LongTensor([x.shape[1]]).cpu()
+        sid = torch.LongTensor([0]).cpu()
+        bert = torch.randn(size=(x.shape[1], 2048)).cpu()
+        emo = torch.randn(512, 1)
+
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+            torch.onnx.export(
+                self.emb_g,
+                (sid),
+                f"onnx/{path}/{path}_emb.onnx",
+                input_names=["sid"],
+                output_names=["g"],
+                verbose=True,
+            )
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+
+        torch.onnx.export(
+            self.enc_p,
+            (x, x_lengths, tone, language, bert, emo, g),
+            f"onnx/{path}/{path}_enc_p.onnx",
+            input_names=[
+                "x",
+                "x_lengths",
+                "t",
+                "language",
+                "bert_0",
+                "emo",
+                "g",
+            ],
+            output_names=["xout", "m_p", "logs_p", "x_mask"],
+            dynamic_axes={
+                "x": [0, 1],
+                "t": [0, 1],
+                "language": [0, 1],
+                "bert_0": [0],
+                "xout": [0, 2],
+                "m_p": [0, 2],
+                "logs_p": [0, 2],
+                "x_mask": [0, 2],
+            },
+            verbose=True,
+            opset_version=16,
+        )
+
+        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, emo, g)
+
+        zinput = (
+            torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+            * noise_scale_w
+        )
+        torch.onnx.export(
+            self.sdp,
+            (x, x_mask, zinput, g),
+            f"onnx/{path}/{path}_sdp.onnx",
+            input_names=["x", "x_mask", "zin", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        torch.onnx.export(
+            self.dp,
+            (x, x_mask, g),
+            f"onnx/{path}/{path}_dp.onnx",
+            input_names=["x", "x_mask", "g"],
+            output_names=["logw"],
+            dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]},
+            verbose=True,
+        )
+        logw = self.sdp(x, x_mask, zinput, g=g) * (sdp_ratio) + self.dp(
+            x, x_mask, g=g
+        ) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        torch.onnx.export(
+            self.flow,
+            (z_p, y_mask, g),
+            f"onnx/{path}/{path}_flow.onnx",
+            input_names=["z_p", "y_mask", "g"],
+            output_names=["z"],
+            dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]},
+            verbose=True,
+        )
+
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        z_in = (z * y_mask)[:, :, :max_len]
+
+        torch.onnx.export(
+            self.dec,
+            (z_in, g),
+            f"onnx/{path}/{path}_dec.onnx",
+            input_names=["z_in", "g"],
+            output_names=["o"],
+            dynamic_axes={"z_in": [0, 2], "o": [0, 2]},
+            verbose=True,
+        )
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
diff --git a/onnx_modules/V240_ZH/text/__init__.py b/onnx_modules/V240_ZH/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e670aedef137ce1ddcebd447524fe3834211abc
--- /dev/null
+++ b/onnx_modules/V240_ZH/text/__init__.py
@@ -0,0 +1 @@
+from .symbols import *
diff --git a/onnx_modules/V240_ZH/text/symbols.py b/onnx_modules/V240_ZH/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/onnx_modules/V240_ZH/text/symbols.py
@@ -0,0 +1,187 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+
+# chinese
+zh_symbols = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+num_zh_tones = 6
+
+# japanese
+ja_symbols = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+num_ja_tones = 2
+
+# English
+en_symbols = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+num_en_tones = 4
+
+# combine all symbols
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
+symbols = [pad] + normal_symbols + pu_symbols
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_zh_tones + num_ja_tones + num_en_tones
+
+# language maps
+language_id_map = {"ZH": 0, "JP": 1, "EN": 2}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "ZH": 0,
+    "JP": num_zh_tones,
+    "EN": num_zh_tones + num_ja_tones,
+}
+
+if __name__ == "__main__":
+    a = set(zh_symbols)
+    b = set(en_symbols)
+    print(sorted(a & b))
diff --git a/onnx_modules/__init__.py b/onnx_modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..665e1740aef757ec9bab728d9399d9a3e7fa3da6
--- /dev/null
+++ b/onnx_modules/__init__.py
@@ -0,0 +1,79 @@
+from utils import get_hparams_from_file, load_checkpoint
+import json
+
+
+def export_onnx(export_path, model_path, config_path, novq, dev, Extra):
+    hps = get_hparams_from_file(config_path)
+    version = hps.version[0:3]
+    enable_emo = False
+    BertPaths = [
+        "chinese-roberta-wwm-ext-large",
+        "deberta-v2-large-japanese",
+        "bert-base-japanese-v3",
+        "bert-large-cantonese"
+    ]
+    if version == "2.0" or (version == "2.1" and novq):
+        from .V200 import SynthesizerTrn, symbols
+    elif version == "2.1" and (not novq):
+        from .V210 import SynthesizerTrn, symbols
+    elif version == "2.2":
+        enable_emo = True
+        if novq and dev:
+            from .V220_novq_dev import SynthesizerTrn, symbols
+        else:
+            from .V220 import SynthesizerTrn, symbols
+    elif version == "2.3":
+        from .V230 import SynthesizerTrn, symbols
+
+        BertPaths[1] = "deberta-v2-large-japanese-char-wwm"
+    elif version == "2.4":
+        enable_emo = True
+        if Extra == "chinese":
+            from .V240_ZH import SynthesizerTrn, symbols
+        if Extra == "japanese":
+            from .V240_JP import SynthesizerTrn, symbols
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    )
+    _ = net_g.eval()
+    _ = load_checkpoint(model_path, net_g, None, skip_optimizer=True)
+    net_g.cpu()
+    net_g.export_onnx(export_path)
+
+    spklist = []
+    for key in hps.data.spk2id.keys():
+        spklist.append(key)
+
+    LangDict = {"ZH": [0, 0], "JP": [1, 6], "EN": [2, 8]}
+    BertSize = 1024
+    if version == "2.4":
+        BertPaths = (
+            ["Erlangshen-MegatronBert-1.3B-Chinese"]
+            if Extra == "chinese"
+            else ["deberta-v2-large-japanese-char-wwm"]
+        )
+        if Extra == "chinese":
+            BertSize = 2048
+
+    MoeVSConf = {
+        "Folder": f"{export_path}",
+        "Name": f"{export_path}",
+        "Type": "BertVits",
+        "Symbol": symbols,
+        "Cleaner": "",
+        "Rate": hps.data.sampling_rate,
+        "CharaMix": True,
+        "Characters": spklist,
+        "LanguageMap": LangDict,
+        "Dict": "BasicDict",
+        "BertPath": BertPaths,
+        "Clap": ("clap-htsat-fused" if enable_emo else False),
+        "BertSize": BertSize,
+    }
+
+    with open(f"onnx/{export_path}.json", "w") as MoeVsConfFile:
+        json.dump(MoeVSConf, MoeVsConfFile, indent=4)
diff --git a/re_matching.py b/re_matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd464a5cca8fd00d2da35dfddf9a03295dd5b627
--- /dev/null
+++ b/re_matching.py
@@ -0,0 +1,81 @@
+import re
+
+
+def extract_language_and_text_updated(speaker, dialogue):
+    # 使用正则表达式匹配<语言>标签和其后的文本
+    pattern_language_text = r"<(\S+?)>([^<]+)"
+    matches = re.findall(pattern_language_text, dialogue, re.DOTALL)
+    speaker = speaker[1:-1]
+    # 清理文本：去除两边的空白字符
+    matches_cleaned = [(lang.upper(), text.strip()) for lang, text in matches]
+    matches_cleaned.append(speaker)
+    return matches_cleaned
+
+
+def validate_text(input_text):
+    # 验证说话人的正则表达式
+    pattern_speaker = r"(\[\S+?\])((?:\s*<\S+?>[^<\[\]]+?)+)"
+
+    # 使用re.DOTALL标志使.匹配包括换行符在内的所有字符
+    matches = re.findall(pattern_speaker, input_text, re.DOTALL)
+
+    # 对每个匹配到的说话人内容进行进一步验证
+    for _, dialogue in matches:
+        language_text_matches = extract_language_and_text_updated(_, dialogue)
+        if not language_text_matches:
+            return (
+                False,
+                "Error: Invalid format detected in dialogue content. Please check your input.",
+            )
+
+    # 如果输入的文本中没有找到任何匹配项
+    if not matches:
+        return (
+            False,
+            "Error: No valid speaker format detected. Please check your input.",
+        )
+
+    return True, "Input is valid."
+
+
+def text_matching(text: str) -> list:
+    speaker_pattern = r"(\[\S+?\])(.+?)(?=\[\S+?\]|$)"
+    matches = re.findall(speaker_pattern, text, re.DOTALL)
+    result = []
+    for speaker, dialogue in matches:
+        result.append(extract_language_and_text_updated(speaker, dialogue))
+    return result
+
+
+def cut_para(text):
+    splitted_para = re.split("[\n]", text)  # 按段分
+    splitted_para = [
+        sentence.strip() for sentence in splitted_para if sentence.strip()
+    ]  # 删除空字符串
+    return splitted_para
+
+
+def cut_sent(para):
+    para = re.sub("([。！;？\?])([^”’])", r"\1\n\2", para)  # 单字符断句符
+    para = re.sub("(\.{6})([^”’])", r"\1\n\2", para)  # 英文省略号
+    para = re.sub("(\…{2})([^”’])", r"\1\n\2", para)  # 中文省略号
+    para = re.sub("([。！？\?][”’])([^，。！？\?])", r"\1\n\2", para)
+    para = para.rstrip()  # 段尾如果有多余的\n就去掉它
+    return para.split("\n")
+
+
+if __name__ == "__main__":
+    text = """
+    [说话人1]
+    [说话人2]<zh>你好吗？<jp>元気ですか？<jp>こんにちは，世界。<zh>你好吗？
+    [说话人3]<zh>谢谢。<jp>どういたしまして。
+    """
+    text_matching(text)
+    # 测试函数
+    test_text = """
+    [说话人1]<zh>你好，こんにちは！<jp>こんにちは，世界。
+    [说话人2]<zh>你好吗？
+    """
+    text_matching(test_text)
+    res = validate_text(test_text)
+    print(res)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9b31d036a41bf329a65d99c60c3c311f249126a8
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,35 @@
+librosa==0.9.2
+matplotlib
+numpy
+numba
+phonemizer
+scipy
+tensorboard
+Unidecode
+amfm_decompy
+jieba
+transformers
+pypinyin
+pinyin_jyutping
+pycantonese
+cn2an
+gradio==3.50.2
+av
+mecab-python3
+loguru
+unidic-lite
+cmudict
+fugashi
+num2words
+PyYAML
+requests
+pyopenjtalk-prebuilt
+jaconv
+psutil
+GPUtil
+vector_quantize_pytorch
+g2p_en
+sentencepiece
+pykakasi
+langid
+WeTextProcessing>=0.1.10; platform_system != "Windows"
diff --git a/resample.py b/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..678c352518a269aa2b4e1fd8acada6e2a00e1de1
--- /dev/null
+++ b/resample.py
@@ -0,0 +1,75 @@
+import os
+import argparse
+import librosa
+from multiprocessing import Pool, cpu_count
+
+import soundfile
+from tqdm import tqdm
+
+from config import config
+
+
+def process(item):
+    spkdir, wav_name, args = item
+    wav_path = os.path.join(args.in_dir, spkdir, wav_name)
+    if os.path.exists(wav_path) and wav_path.lower().endswith(".wav"):
+        wav, sr = librosa.load(wav_path, sr=args.sr)
+        soundfile.write(os.path.join(args.out_dir, spkdir, wav_name), wav, sr)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--sr",
+        type=int,
+        default=config.resample_config.sampling_rate,
+        help="sampling rate",
+    )
+    parser.add_argument(
+        "--in_dir",
+        type=str,
+        default=config.resample_config.in_dir,
+        help="path to source dir",
+    )
+    parser.add_argument(
+        "--out_dir",
+        type=str,
+        default=config.resample_config.out_dir,
+        help="path to target dir",
+    )
+    parser.add_argument(
+        "--processes",
+        type=int,
+        default=0,
+        help="cpu_processes",
+    )
+    args, _ = parser.parse_known_args()
+    # autodl 无卡模式会识别出46个cpu
+    if args.processes == 0:
+        processes = cpu_count() - 2 if cpu_count() > 4 else 1
+    else:
+        processes = args.processes
+    pool = Pool(processes=processes)
+
+    tasks = []
+
+    for dirpath, _, filenames in os.walk(args.in_dir):
+        # 子级目录
+        spk_dir = os.path.relpath(dirpath, args.in_dir)
+        spk_dir_out = os.path.join(args.out_dir, spk_dir)
+        if not os.path.isdir(spk_dir_out):
+            os.makedirs(spk_dir_out, exist_ok=True)
+        for filename in filenames:
+            if filename.lower().endswith(".wav"):
+                twople = (spk_dir, filename, args)
+                tasks.append(twople)
+
+    for _ in tqdm(
+        pool.imap_unordered(process, tasks),
+    ):
+        pass
+
+    pool.close()
+    pool.join()
+
+    print("音频重采样完毕!")
diff --git a/resample_legacy.py b/resample_legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d617558755f1e875be79c4ea5d5696d6bcf106
--- /dev/null
+++ b/resample_legacy.py
@@ -0,0 +1,71 @@
+import os
+import argparse
+import librosa
+from multiprocessing import Pool, cpu_count
+
+import soundfile
+from tqdm import tqdm
+
+from config import config
+
+
+def process(item):
+    wav_name, args = item
+    wav_path = os.path.join(args.in_dir, wav_name)
+    if os.path.exists(wav_path) and wav_path.lower().endswith(".wav"):
+        wav, sr = librosa.load(wav_path, sr=args.sr)
+        soundfile.write(os.path.join(args.out_dir, wav_name), wav, sr)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--sr",
+        type=int,
+        default=config.resample_config.sampling_rate,
+        help="sampling rate",
+    )
+    parser.add_argument(
+        "--in_dir",
+        type=str,
+        default=config.resample_config.in_dir,
+        help="path to source dir",
+    )
+    parser.add_argument(
+        "--out_dir",
+        type=str,
+        default=config.resample_config.out_dir,
+        help="path to target dir",
+    )
+    parser.add_argument(
+        "--processes",
+        type=int,
+        default=0,
+        help="cpu_processes",
+    )
+    args, _ = parser.parse_known_args()
+    # autodl 无卡模式会识别出46个cpu
+    if args.processes == 0:
+        processes = cpu_count() - 2 if cpu_count() > 4 else 1
+    else:
+        processes = args.processes
+    pool = Pool(processes=processes)
+
+    tasks = []
+
+    for dirpath, _, filenames in os.walk(args.in_dir):
+        if not os.path.isdir(args.out_dir):
+            os.makedirs(args.out_dir, exist_ok=True)
+        for filename in filenames:
+            if filename.lower().endswith(".wav"):
+                tasks.append((filename, args))
+
+    for _ in tqdm(
+        pool.imap_unordered(process, tasks),
+    ):
+        pass
+
+    pool.close()
+    pool.join()
+
+    print("音频重采样完毕!")
diff --git a/slm/wavlm-base-plus/.gitattributes b/slm/wavlm-base-plus/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..6d34772f5ca361021038b404fb913ec8dc0b1a5a
--- /dev/null
+++ b/slm/wavlm-base-plus/.gitattributes
@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text 
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/slm/wavlm-base-plus/README.md b/slm/wavlm-base-plus/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1192671605727eff94113d3b599161892688b9d5
--- /dev/null
+++ b/slm/wavlm-base-plus/README.md
@@ -0,0 +1,65 @@
+---
+language:
+- en
+datasets:
+tags:
+- speech
+inference: false
+---
+
+# WavLM-Base-Plus
+
+[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
+
+The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. 
+
+**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
+
+The model was pre-trained on:
+
+- 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875)
+- 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909)
+- 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390)
+
+[Paper: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900)
+
+Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei
+
+**Abstract**
+*Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.*
+
+The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm.
+
+# Usage
+
+This is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be 
+used in inference. The model was pre-trained in English and should therefore perform well only in English. The model has been shown to work well on the [SUPERB benchmark](https://superbbenchmark.org/).
+
+**Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence 
+of phonemes before fine-tuning.
+
+## Speech Recognition
+
+To fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition).
+
+## Speech Classification
+
+To fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification).
+
+## Speaker Verification
+
+TODO
+
+## Speaker Diarization
+
+TODO
+
+# Contribution
+
+The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+
+# License
+
+The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
+
+![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wavlm.png)
\ No newline at end of file
diff --git a/slm/wavlm-base-plus/config.json b/slm/wavlm-base-plus/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b7b4e5f6c410f71283a59e26250e40855ca99310
--- /dev/null
+++ b/slm/wavlm-base-plus/config.json
@@ -0,0 +1,99 @@
+{
+  "_name_or_path": "wavlm-base-plus",
+  "activation_dropout": 0.0,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "WavLMModel"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": false,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "freeze_feat_extract_train": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.05,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wavlm",
+  "no_mask_channel_overlap": false,
+  "no_mask_time_overlap": false,
+  "num_adapter_layers": 3,
+  "num_attention_heads": 12,
+  "num_buckets": 320,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_ctc_classes": 80,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 768,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 256,
+  "replace_prob": 0.5,
+  "torch_dtype": "float32",
+  "transformers_version": "4.13.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32,
+  "tokenizer_class": "Wav2Vec2CTCTokenizer"
+}
diff --git a/slm/wavlm-base-plus/preprocessor_config.json b/slm/wavlm-base-plus/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..10f6def8c83d70a2b087a567dcf523b75152a80b
--- /dev/null
+++ b/slm/wavlm-base-plus/preprocessor_config.json
@@ -0,0 +1,9 @@
+{
+  "do_normalize": false,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
diff --git a/spec_gen.py b/spec_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6715faff6fd9abdae8197fcc0e0aac1d50fde4a
--- /dev/null
+++ b/spec_gen.py
@@ -0,0 +1,87 @@
+import torch
+from tqdm import tqdm
+from multiprocessing import Pool
+from mel_processing import spectrogram_torch, mel_spectrogram_torch
+from utils import load_wav_to_torch
+
+
+class AudioProcessor:
+    def __init__(
+        self,
+        max_wav_value,
+        use_mel_spec_posterior,
+        filter_length,
+        n_mel_channels,
+        sampling_rate,
+        hop_length,
+        win_length,
+        mel_fmin,
+        mel_fmax,
+    ):
+        self.max_wav_value = max_wav_value
+        self.use_mel_spec_posterior = use_mel_spec_posterior
+        self.filter_length = filter_length
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.mel_fmin = mel_fmin
+        self.mel_fmax = mel_fmax
+
+    def process_audio(self, filename):
+        audio, sampling_rate = load_wav_to_torch(filename)
+        audio_norm = audio / self.max_wav_value
+        audio_norm = audio_norm.unsqueeze(0)
+        spec_filename = filename.replace(".wav", ".spec.pt")
+        if self.use_mel_spec_posterior:
+            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
+        try:
+            spec = torch.load(spec_filename)
+        except:
+            if self.use_mel_spec_posterior:
+                spec = mel_spectrogram_torch(
+                    audio_norm,
+                    self.filter_length,
+                    self.n_mel_channels,
+                    self.sampling_rate,
+                    self.hop_length,
+                    self.win_length,
+                    self.mel_fmin,
+                    self.mel_fmax,
+                    center=False,
+                )
+            else:
+                spec = spectrogram_torch(
+                    audio_norm,
+                    self.filter_length,
+                    self.sampling_rate,
+                    self.hop_length,
+                    self.win_length,
+                    center=False,
+                )
+            spec = torch.squeeze(spec, 0)
+            torch.save(spec, spec_filename)
+        return spec, audio_norm
+
+
+# 使用示例
+processor = AudioProcessor(
+    max_wav_value=32768.0,
+    use_mel_spec_posterior=False,
+    filter_length=2048,
+    n_mel_channels=128,
+    sampling_rate=44100,
+    hop_length=512,
+    win_length=2048,
+    mel_fmin=0.0,
+    mel_fmax="null",
+)
+
+with open("filelists/train.list", "r") as f:
+    filepaths = [line.split("|")[0] for line in f]  # 取每一行的第一部分作为audiopath
+
+# 使用多进程处理
+with Pool(processes=32) as pool:  # 使用4个进程
+    with tqdm(total=len(filepaths)) as pbar:
+        for i, _ in enumerate(pool.imap_unordered(processor.process_audio, filepaths)):
+            pbar.update()
diff --git a/text/__init__.py b/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8673656981e9804f1f70d073836380df40fedf62
--- /dev/null
+++ b/text/__init__.py
@@ -0,0 +1,61 @@
+from text.symbols import *
+
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+
+
+def cleaned_text_to_sequence(cleaned_text, tones, language):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    tone_start = language_tone_start_map[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = language_id_map[language]
+    lang_ids = [lang_id for i in phones]
+    return phones, tones, lang_ids
+
+
+def get_bert(norm_text, word2ph, language, device, style_text=None, style_weight=0.7):
+    from .cantonese_bert import get_bert_feature as yue_bert
+
+    lang_bert_func_map = {"HAKKA": yue_bert}
+    bert = lang_bert_func_map[language](
+        norm_text, word2ph, device, style_text, style_weight
+    )
+    return bert
+
+
+# def check_bert_models():
+#     import json
+#     from pathlib import Path
+# 
+#     from config import config
+#     from .bert_utils import _check_bert
+# 
+#     if config.mirror.lower() == "openi":
+#         import openi
+# 
+#         kwargs = {"token": config.openi_token} if config.openi_token else {}
+#         openi.login(**kwargs)
+# 
+#     with open("./bert/bert_models.json", "r") as fp:
+#         models = json.load(fp)
+#         for k, v in models.items():
+#             local_path = Path("./bert").joinpath(k)
+#             _check_bert(v["repo_id"], v["files"], local_path)
+
+
+def init_openjtalk():
+    import platform
+
+    if platform.platform() == "Linux":
+        import pyopenjtalk
+
+        pyopenjtalk.g2p("こんにちは，世界。")
+
+
+init_openjtalk()
+# check_bert_models()
diff --git a/text/bert_utils.py b/text/bert_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e0c4a95e5d086726a1c684231cfe51ebd60aef4
--- /dev/null
+++ b/text/bert_utils.py
@@ -0,0 +1,23 @@
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+from config import config
+
+
+MIRROR: str = config.mirror
+
+
+def _check_bert(repo_id, files, local_path):
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            if MIRROR.lower() == "openi":
+                import openi
+
+                openi.model.download_model(
+                    "Stardust_minus/Bert-VITS2", repo_id.split("/")[-1], "./bert"
+                )
+            else:
+                hf_hub_download(
+                    repo_id, file, local_dir=local_path, local_dir_use_symlinks=False
+                )
diff --git a/text/cantonese_bert.py b/text/cantonese_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8f1027a48c0f6e250958553beb45ab5c6337bad
--- /dev/null
+++ b/text/cantonese_bert.py
@@ -0,0 +1,122 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from config import config
+
+LOCAL_PATH = "./bert/bert-large-cantonese"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(
+    text,
+    word2ph,
+    device=config.bert_gen_config.device,
+    style_text=None,
+    style_weight=0.7,
+):
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device not in models.keys():
+        models[device] = AutoModelForMaskedLM.from_pretrained(
+            LOCAL_PATH).to(device)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = models[device](**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = models[device](
+                **style_inputs, output_hidden_states=True)
+            style_res = torch.cat(
+                style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
+
+
+if __name__ == "__main__":
+    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
+    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,
+        2,
+        2,
+        2,
+        1,
+    ]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 1024])
diff --git a/text/cleaner.py b/text/cleaner.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4c0420f0b953c824458af4fbcf5c5ece260c810
--- /dev/null
+++ b/text/cleaner.py
@@ -0,0 +1,34 @@
+from text import hakka, cleaned_text_to_sequence
+
+
+language_module_map = {"HAKKA": hakka}
+
+
+def clean_text(text, language, g2p_bypass=False):
+    language_module = language_module_map[language]
+    # if not g2p_bypass:
+    #     norm_text = language_module.text_normalize(text)
+    # else:
+    norm_text = text.split()
+    phones, tones, word2ph = language_module.g2p(norm_text, g2p_bypass)
+    return norm_text, phones, tones, word2ph
+
+
+def clean_text_bert(text, language, g2p_bypass=False):
+    language_module = language_module_map[language]
+    if not g2p_bypass:
+        norm_text = language_module.text_normalize(text)
+    else:
+        norm_text = text.split()
+    phones, tones, word2ph = language_module.g2p(norm_text, g2p_bypass)
+    bert = language_module.get_bert_feature(norm_text, word2ph)
+    return phones, tones, bert
+
+
+def text_to_sequence(text, language):
+    norm_text, phones, tones, word2ph = clean_text(text, language)
+    return cleaned_text_to_sequence(phones, tones, language)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/text/hakka.py b/text/hakka.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc83ddd7a5bb5a905084e237c6c745893970a5ae
--- /dev/null
+++ b/text/hakka.py
@@ -0,0 +1,363 @@
+import re
+import unicodedata
+import cn2an
+import pinyin_jyutping
+import pycantonese
+
+from text.symbols import punctuation
+
+# from symbols import punctuation
+
+from functools import reduce
+
+
+def normalizer(x):
+    return cn2an.transform(x, "an2cn")
+
+
+j = pinyin_jyutping.PinyinJyutping()
+
+INITIALS = [
+    "aa",
+    "aai",
+    "aak",
+    "aap",
+    "aat",
+    "aau",
+    "ai",
+    "au",
+    "ap",
+    "at",
+    "ak",
+    "a",
+    "p",
+    "b",
+    "e",
+    "ts",
+    "t",
+    "dz",
+    "d",
+    "kw",
+    "k",
+    "gw",
+    "g",
+    "f",
+    "h",
+    "l",
+    "m",
+    "ng",
+    "n",
+    "s",
+    "y",
+    "w",
+    "c",
+    "v",
+    "z",
+    "y",
+    "ong",
+    "on",
+    "ou",
+    "oi",
+    "ok",
+    "o",
+    "uk",
+    "ung",
+]
+
+med_rhymes = [
+    "ia",
+    "iau",
+    "iam",
+    "ien",
+    "iang",
+    "iap",
+    "iet",
+    "iak",
+    "io",
+    "ioi",
+    "ion",
+    "iong",
+    "iok",
+    "iun",
+    "iung",
+    "iut",
+    "iuk",
+]
+
+rep_map = {
+    "": ",",
+    "：": ",",
+    "︰": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "﹖": "?",
+    "﹗": "!",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "丶": ",",
+    "...": "…",
+    "⋯": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+    "_": "-",
+}
+
+replacement_chars = {
+    "ㄧ": "一",
+    "—": "一",
+    "更": "更",
+    "不": "不",
+    "料": "料",
+    "聯": "聯",
+    "行": "行",
+    "利": "利",
+    "謢": "護",
+    "岀": "出",
+    "鎭": "鎮",
+    "戯": "戲",
+    "旣": "既",
+    "立": "立",
+    "來": "來",
+    "年": "年",
+    "㗇": "蝦",
+}
+
+
+def replace_punctuation(text):
+    # text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = "".join(
+        c
+        for c in replaced_text
+        if unicodedata.name(c, "").startswith("CJK UNIFIED IDEOGRAPH")
+        or c in punctuation
+    )
+
+    return replaced_text
+
+
+def replace_chars(text):
+    for k, v in replacement_chars.items():
+        text = text.replace(k, v)
+    return text
+
+
+def text_normalize(text):
+    text = normalizer(text)
+    text = replace_punctuation(text)
+    text = replace_chars(text)
+    return text
+
+
+def rom_to_initials_medials_finals_tones(jyutping_syllables):
+    initials_medials_finals = []
+    tones = []
+    word2ph = []
+
+    for syllable in jyutping_syllables:
+        if syllable in punctuation:
+            initials_medials_finals.append(syllable)
+            tones.append(0)
+            word2ph.append(1)
+        else:
+            try:
+                tone = int(syllable[-1])
+                syllable_without_tone = syllable[:-1]
+            except ValueError:
+                tone = 0
+                syllable_without_tone = syllable
+
+            assert str(tone) in "1234560"
+
+            for initial in INITIALS:
+                if syllable_without_tone.startswith(initial):
+                    final = syllable_without_tone[len(initial) :] or initial[-1]
+
+                    if initial == "j":
+                        medial = "i"
+                    elif any(final.startswith(f) for f in med_rhymes):
+                        medial = "i"
+                        final = final[1:]
+                    else:
+                        medial = "#"
+
+                    initials_medials_finals.extend([initial, medial, final])
+                    tones.extend([tone, 0 if medial == "#" else tone, tone])
+                    word2ph.append(3)
+                    break
+            else:
+                initial = syllable_without_tone
+                medial = "#"
+                final = initial[-1] if initial else initial
+
+                initials_medials_finals.extend([initial, medial, final])
+                tones.extend([tone, 0, tone])
+                word2ph.append(3)
+
+    print(initials_medials_finals)
+    assert len(initials_medials_finals) == len(tones)
+    assert sum(word2ph) == len(initials_medials_finals)
+    return initials_medials_finals, tones, word2ph
+
+
+def get_jyutping(text):
+    converted_text = j.jyutping(text, tone_numbers=True, spaces=True)
+    converted_words = converted_text.split()
+
+    # # replace ... with …
+    # converted_text = re.sub(r"\.{2,}", "…", converted_text)
+    # # replace -- with -
+    # converted_text = re.sub(r"-{2,}", "-", converted_text)
+
+    for i, word in enumerate(converted_words):
+        if set(word) <= set(text) - set(punctuation):
+            converted_word = pycantonese.characters_to_jyutping(word)[0][1]
+            converted_words[i] = converted_word
+
+        if (
+            converted_words[i] not in punctuation
+            and re.search(r"^[a-zA-Z]+[1-6]$", converted_words[i]) is None
+        ):
+            raise ValueError(
+                f"Failed to convert {converted_words[i]}, {converted_text}"
+            )
+
+    jyutping_sentence = " ".join(converted_words)
+
+    for symbol in punctuation:
+        jyutping_sentence = jyutping_sentence.replace(symbol, " " + symbol + " ")
+    jyutping_array = jyutping_sentence.split()
+
+    return jyutping_array
+
+
+def jyutping2hakka(j):
+    ROM_MAPPING = {
+        "aa": "a",
+        "j": "y",
+    }
+
+    return reduce(lambda pron, rule: pron.replace(*rule), ROM_MAPPING.items(), j)
+
+
+def get_bert_feature(text, word2ph):
+    from text import cantonese_bert
+
+    return cantonese_bert.get_bert_feature(text, word2ph)
+
+
+def g2p(text, g2p_bypass=False):
+    word2ph = []
+    # if not g2p_bypass:
+    #     jyuping = get_jyutping(text)
+    #     rom = [jyutping2hakka(j) for j in jyuping]
+    #     phones, tones, word2ph = rom_to_initials_medials_finals_tones(rom)
+    # else:
+    phones, tones, word2ph = rom_to_initials_medials_finals_tones(text)
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def test_dataset(dataset, metadata):
+    import csv
+    import tqdm
+
+    with open(metadata, "r", encoding="utf-8") as _file_:
+        if dataset == "ciugo":
+            reader = list(csv.reader(_file_, delimiter="|"))
+            for row in tqdm.tqdm(reader, desc="Processing dataset"):
+                _, _, rom_text = row
+                rom_syllables = rom_text.split()
+                try:
+                    phones, tones, word2ph = rom_to_initials_medials_finals_tones(
+                        rom_syllables
+                    )
+                    if not len(word2ph) == len(text):
+                        print(f"word2ph not fit!: {rom_text}")
+                        print(f"phones: {phones}")
+                        print(f"tones: {tones}")
+                        print(f"word2ph: {word2ph}")
+                    assert len(word2ph) == len(text)
+                    # print(phones)
+                except Exception as e:
+                    # print(f"Error converting line: {row}")
+                    # print(f"Exception: {e}")
+                    print("")
+        else:
+            with open(metadata, "r", encoding="utf-8") as _file_:
+                for line in _file_:
+                    text = line.strip().split("|")[-1]
+                    text = text_normalize(text)
+                    try:
+                        phones, tones, word2ph = g2p(text)
+                        if not len(word2ph) == len(text) + 2:
+                            print(f"word2ph not fit!: {text}")
+                            print(f"phones: {phones}")
+                            print(f"tones: {tones}")
+                            print(f"word2ph: {word2ph}")
+                        assert len(word2ph) == len(text) + 2
+                        # print(phones)
+                    except Exception as e:
+                        # print(f"Error converting text: {text}")
+                        # print(f"Exception: {e}")
+                        print("")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=str, choices=["ciugo", "list"])
+    parser.add_argument("--metadata", type=str)
+    args = parser.parse_args()
+
+    if args.dataset:
+        if args.metadata is None:
+            args.metadata = "./metadata.csv"
+        test_dataset(args.dataset, args.metadata)
+    else:
+        g2p_bypass = True
+        # from text.cantonese_bert import get_bert_feature
+
+        # text = "你點解會咁柒㗎？我真係唔該晒你呀！"
+        text = "gin1 ngit5 tien1 hi4 hau3"
+
+        if not g2p_bypass:
+            text = text_normalize(text)
+            print(text)
+        else:
+            text = text.split()  # text: list
+            print(text)
+        phones, tones, word2ph = g2p(text, g2p_bypass)
+        # bert = get_bert_feature(text, word2ph)
+
+        # print(phones, tones, word2ph, bert.shape)
+        print(phones, tones, word2ph)
diff --git a/text/symbols.py b/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..04b5b5fc71f36d92748c666c02cf7bed06678ddf
--- /dev/null
+++ b/text/symbols.py
@@ -0,0 +1,111 @@
+punctuation = ["!", "?", "…", ",", ".", "'", "-"]
+pu_symbols = punctuation + ["SP", "UNK"]
+pad = "_"
+silent = "#"
+
+# hakka
+
+hakka_symbols = [
+    "a",
+    "aa",
+    "aai",
+    "aak",
+    "aam",
+    "aan",
+    "aang",
+    "aap",
+    "aat",
+    "aau",
+    "ai",
+    "ak",
+    "am",
+    "an",
+    "ang",
+    "ap",
+    "at",
+    "au",
+    "b",
+    "c",
+    "d",
+    "e",
+    "ei",
+    "ek",
+    "eng",
+    "eoi",
+    "eon",
+    "eot",
+    "eu",
+    "em",
+    "en",
+    "ep",
+    "et",
+    "f",
+    "g",
+    "gw",
+    "h",
+    "i",
+    "ik",
+    "im",
+    "in",
+    "ing",
+    "ip",
+    "it",
+    "iu",
+    "y",
+    "k",
+    "kw",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "o",
+    "oe",
+    "oek",
+    "oeng",
+    "oi",
+    "ok",
+    "on",
+    "ong",
+    "ot",
+    "ou",
+    "p",
+    "s",
+    "sil",
+    "sp",
+    "spl",
+    "t",
+    "u",
+    "ui",
+    "uk",
+    "un",
+    "ung",
+    "ut",
+    "v",
+    "w",
+    "yu",
+    "yun",
+    "yut",
+    "z",
+]
+num_hakka_tones = 7
+
+# combine all symbols
+normal_symbols = sorted(set(hakka_symbols))
+symbols = [pad] + normal_symbols + pu_symbols + [silent]
+symbols = symbols + sorted((set(hakka_symbols) - set(symbols)))
+sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
+
+# combine all tones
+num_tones = num_hakka_tones
+
+# language maps
+language_id_map = {"HAKKA": 0}
+num_languages = len(language_id_map.keys())
+
+language_tone_start_map = {
+    "HAKKA": 0,
+}
+
+if __name__ == "__main__":
+    a = set(hakka_symbols)
+    print(sorted(a))
diff --git a/tools/__init__.py b/tools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68d332953ea3f5d49df8a76e23f1fafbbdcb695
--- /dev/null
+++ b/tools/__init__.py
@@ -0,0 +1,3 @@
+"""
+工具包
+"""
diff --git a/tools/classify_language.py b/tools/classify_language.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b8a7ab420b7010776be0218ed87c675d4821a34
--- /dev/null
+++ b/tools/classify_language.py
@@ -0,0 +1,197 @@
+import regex as re
+
+try:
+    from config import config
+
+    LANGUAGE_IDENTIFICATION_LIBRARY = (
+        config.webui_config.language_identification_library
+    )
+except:
+    LANGUAGE_IDENTIFICATION_LIBRARY = "langid"
+
+module = LANGUAGE_IDENTIFICATION_LIBRARY.lower()
+
+langid_languages = [
+    "af",
+    "am",
+    "an",
+    "ar",
+    "as",
+    "az",
+    "be",
+    "bg",
+    "bn",
+    "br",
+    "bs",
+    "ca",
+    "cs",
+    "cy",
+    "da",
+    "de",
+    "dz",
+    "el",
+    "en",
+    "eo",
+    "es",
+    "et",
+    "eu",
+    "fa",
+    "fi",
+    "fo",
+    "fr",
+    "ga",
+    "gl",
+    "gu",
+    "he",
+    "hi",
+    "hr",
+    "ht",
+    "hu",
+    "hy",
+    "id",
+    "is",
+    "it",
+    "ja",
+    "jv",
+    "ka",
+    "kk",
+    "km",
+    "kn",
+    "ko",
+    "ku",
+    "ky",
+    "la",
+    "lb",
+    "lo",
+    "lt",
+    "lv",
+    "mg",
+    "mk",
+    "ml",
+    "mn",
+    "mr",
+    "ms",
+    "mt",
+    "nb",
+    "ne",
+    "nl",
+    "nn",
+    "no",
+    "oc",
+    "or",
+    "pa",
+    "pl",
+    "ps",
+    "pt",
+    "qu",
+    "ro",
+    "ru",
+    "rw",
+    "se",
+    "si",
+    "sk",
+    "sl",
+    "sq",
+    "sr",
+    "sv",
+    "sw",
+    "ta",
+    "te",
+    "th",
+    "tl",
+    "tr",
+    "ug",
+    "uk",
+    "ur",
+    "vi",
+    "vo",
+    "wa",
+    "xh",
+    "zh",
+    "zu",
+]
+
+
+def classify_language(text: str, target_languages: list = None) -> str:
+    if module == "fastlid" or module == "fasttext":
+        from fastlid import fastlid, supported_langs
+
+        classifier = fastlid
+        if target_languages != None:
+            target_languages = [
+                lang for lang in target_languages if lang in supported_langs
+            ]
+            fastlid.set_languages = target_languages
+    elif module == "langid":
+        import langid
+
+        classifier = langid.classify
+        if target_languages != None:
+            target_languages = [
+                lang for lang in target_languages if lang in langid_languages
+            ]
+            langid.set_languages(target_languages)
+    else:
+        raise ValueError(f"Wrong module {module}")
+
+    lang = classifier(text)[0]
+
+    return lang
+
+
+def classify_zh_ja(text: str) -> str:
+    for idx, char in enumerate(text):
+        unicode_val = ord(char)
+
+        # 检测日语字符
+        if 0x3040 <= unicode_val <= 0x309F or 0x30A0 <= unicode_val <= 0x30FF:
+            return "ja"
+
+        # 检测汉字字符
+        if 0x4E00 <= unicode_val <= 0x9FFF:
+            # 检查周围的字符
+            next_char = text[idx + 1] if idx + 1 < len(text) else None
+
+            if next_char and (
+                0x3040 <= ord(next_char) <= 0x309F or 0x30A0 <= ord(next_char) <= 0x30FF
+            ):
+                return "ja"
+
+    return "zh"
+
+
+def split_alpha_nonalpha(text, mode=1):
+    if mode == 1:
+        pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF\d\s])(?=[\p{Latin}])|(?<=[\p{Latin}\s])(?=[\u4e00-\u9fff\u3040-\u30FF\d])"
+    elif mode == 2:
+        pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF\s])(?=[\p{Latin}\d])|(?<=[\p{Latin}\d\s])(?=[\u4e00-\u9fff\u3040-\u30FF])"
+    else:
+        raise ValueError("Invalid mode. Supported modes are 1 and 2.")
+
+    return re.split(pattern, text)
+
+
+if __name__ == "__main__":
+    text = "这是一个测试文本"
+    print(classify_language(text))
+    print(classify_zh_ja(text))  # "zh"
+
+    text = "これはテストテキストです"
+    print(classify_language(text))
+    print(classify_zh_ja(text))  # "ja"
+
+    text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"
+
+    print(split_alpha_nonalpha(text, mode=1))
+    # output: ['vits', '和', 'Bert-VITS', '2是', 'tts', '模型。花费3', 'days.花费3天。Take 3 days']
+
+    print(split_alpha_nonalpha(text, mode=2))
+    # output: ['vits', '和', 'Bert-VITS2', '是', 'tts', '模型。花费', '3days.花费', '3', '天。Take 3 days']
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费3days.花费3天。Take 3 days"
+    print(split_alpha_nonalpha(text, mode=1))
+    # output: ['vits ', '和 ', 'Bert-VITS', '2 ', '是 ', 'tts ', '模型。花费3', 'days.花费3天。Take ', '3 ', 'days']
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费3days.花费3天。Take 3 days"
+    print(split_alpha_nonalpha(text, mode=2))
+    # output: ['vits ', '和 ', 'Bert-VITS2 ', '是 ', 'tts ', '模型。花费', '3days.花费', '3', '天。Take ', '3 ', 'days']
diff --git a/tools/log.py b/tools/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b57df08770821f4dd5aa91fd002242975142ca0
--- /dev/null
+++ b/tools/log.py
@@ -0,0 +1,17 @@
+"""
+logger封装
+"""
+
+from loguru import logger
+import sys
+
+
+# 移除所有默认的处理器
+logger.remove()
+
+# 自定义格式并添加到标准输出
+log_format = (
+    "<g>{time:MM-DD HH:mm:ss}</g> <lvl>{level:<9}</lvl>| {file}:{line} | {message}"
+)
+
+logger.add(sys.stdout, format=log_format, backtrace=True, diagnose=True)
diff --git a/tools/sentence.py b/tools/sentence.py
new file mode 100644
index 0000000000000000000000000000000000000000..b66864ca0d183d54c935586acc529de41b9a911e
--- /dev/null
+++ b/tools/sentence.py
@@ -0,0 +1,173 @@
+import logging
+
+import regex as re
+
+from tools.classify_language import classify_language, split_alpha_nonalpha
+
+
+def check_is_none(item) -> bool:
+    """none -> True, not none -> False"""
+    return (
+        item is None
+        or (isinstance(item, str) and str(item).isspace())
+        or str(item) == ""
+    )
+
+
+def markup_language(text: str, target_languages: list = None) -> str:
+    pattern = (
+        r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
+        r"\！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」"
+        r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
+    )
+    sentences = re.split(pattern, text)
+
+    pre_lang = ""
+    p = 0
+
+    if target_languages is not None:
+        sorted_target_languages = sorted(target_languages)
+        if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
+            new_sentences = []
+            for sentence in sentences:
+                new_sentences.extend(split_alpha_nonalpha(sentence))
+            sentences = new_sentences
+
+    for sentence in sentences:
+        if check_is_none(sentence):
+            continue
+
+        lang = classify_language(sentence, target_languages)
+
+        if pre_lang == "":
+            text = text[:p] + text[p:].replace(
+                sentence, f"[{lang.upper()}]{sentence}", 1
+            )
+            p += len(f"[{lang.upper()}]")
+        elif pre_lang != lang:
+            text = text[:p] + text[p:].replace(
+                sentence, f"[{pre_lang.upper()}][{lang.upper()}]{sentence}", 1
+            )
+            p += len(f"[{pre_lang.upper()}][{lang.upper()}]")
+        pre_lang = lang
+        p += text[p:].index(sentence) + len(sentence)
+    text += f"[{pre_lang.upper()}]"
+
+    return text
+
+
+def split_by_language(text: str, target_languages: list = None) -> list:
+    pattern = (
+        r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
+        r"\！？\。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」"
+        r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
+    )
+    sentences = re.split(pattern, text)
+
+    pre_lang = ""
+    start = 0
+    end = 0
+    sentences_list = []
+
+    if target_languages is not None:
+        sorted_target_languages = sorted(target_languages)
+        if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
+            new_sentences = []
+            for sentence in sentences:
+                new_sentences.extend(split_alpha_nonalpha(sentence))
+            sentences = new_sentences
+
+    for sentence in sentences:
+        if check_is_none(sentence):
+            continue
+
+        lang = classify_language(sentence, target_languages)
+
+        end += text[end:].index(sentence)
+        if pre_lang != "" and pre_lang != lang:
+            sentences_list.append((text[start:end], pre_lang))
+            start = end
+        end += len(sentence)
+        pre_lang = lang
+    sentences_list.append((text[start:], pre_lang))
+
+    return sentences_list
+
+
+def sentence_split(text: str, max: int) -> list:
+    pattern = r"[!(),—+\-.:;?？。，、；：]+"
+    sentences = re.split(pattern, text)
+    discarded_chars = re.findall(pattern, text)
+
+    sentences_list, count, p = [], 0, 0
+
+    # 按被分割的符号遍历
+    for i, discarded_chars in enumerate(discarded_chars):
+        count += len(sentences[i]) + len(discarded_chars)
+        if count >= max:
+            sentences_list.append(text[p : p + count].strip())
+            p += count
+            count = 0
+
+    # 加入最后剩余的文本
+    if p < len(text):
+        sentences_list.append(text[p:])
+
+    return sentences_list
+
+
+def sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None):
+    # 如果该speaker只支持一种语言
+    if speaker_lang is not None and len(speaker_lang) == 1:
+        if lang.upper() not in ["AUTO", "MIX"] and lang.lower() != speaker_lang[0]:
+            logging.debug(
+                f'lang "{lang}" is not in speaker_lang {speaker_lang},automatically set lang={speaker_lang[0]}'
+            )
+        lang = speaker_lang[0]
+
+    sentences_list = []
+    if lang.upper() != "MIX":
+        if max <= 0:
+            sentences_list.append(
+                markup_language(text, speaker_lang)
+                if lang.upper() == "AUTO"
+                else f"[{lang.upper()}]{text}[{lang.upper()}]"
+            )
+        else:
+            for i in sentence_split(text, max):
+                if check_is_none(i):
+                    continue
+                sentences_list.append(
+                    markup_language(i, speaker_lang)
+                    if lang.upper() == "AUTO"
+                    else f"[{lang.upper()}]{i}[{lang.upper()}]"
+                )
+    else:
+        sentences_list.append(text)
+
+    for i in sentences_list:
+        logging.debug(i)
+
+    return sentences_list
+
+
+if __name__ == "__main__":
+    text = "这几天心里颇不宁静。今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。我悄悄地披了大衫，带上门出去。"
+    print(markup_language(text, target_languages=None))
+    print(sentence_split(text, max=50))
+    print(sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None))
+
+    text = "你好，这是一段用来测试自动标注的文本。こんにちは,これは自動ラベリングのテスト用テキストです.Hello, this is a piece of text to test autotagging.你好！今天我们要介绍VITS项目，其重点是使用了GAN Duration predictor和transformer flow,并且接入了Bert模型来提升韵律。Bert embedding会在稍后介绍。"
+    print(split_by_language(text, ["zh", "ja", "en"]))
+
+    text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"
+
+    print(split_by_language(text, ["zh", "ja", "en"]))
+    # output: [('vits', 'en'), ('和', 'ja'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]
+
+    print(split_by_language(text, ["zh", "en"]))
+    # output: [('vits', 'en'), ('和', 'zh'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费 3 days. 花费 3天。Take 3 days"
+    print(split_by_language(text, ["zh", "en"]))
+    # output: [('vits ', 'en'), ('和 ', 'zh'), ('Bert-VITS2 ', 'en'), ('是 ', 'zh'), ('tts ', 'en'), ('模型。花费 ', 'zh'), ('3 days. ', 'en'), ('花费 3天。', 'zh'), ('Take 3 days', 'en')]
diff --git a/tools/translate.py b/tools/translate.py
new file mode 100644
index 0000000000000000000000000000000000000000..be0f7ea459b8a56f298bbb6dab12941795f82e32
--- /dev/null
+++ b/tools/translate.py
@@ -0,0 +1,62 @@
+"""
+翻译api
+"""
+
+from config import config
+
+import random
+import hashlib
+import requests
+
+
+def translate(Sentence: str, to_Language: str = "jp", from_Language: str = ""):
+    """
+    :param Sentence: 待翻译语句
+    :param from_Language: 待翻译语句语言
+    :param to_Language: 目标语言
+    :return: 翻译后语句 出错时返回None
+
+    常见语言代码：中文 zh 英语 en 日语 jp
+    """
+    appid = config.translate_config.app_key
+    key = config.translate_config.secret_key
+    if appid == "" or key == "":
+        return "请开发者在config.yml中配置app_key与secret_key"
+    url = "https://fanyi-api.baidu.com/api/trans/vip/translate"
+    texts = Sentence.splitlines()
+    outTexts = []
+    for t in texts:
+        if t != "":
+            # 签名计算 参考文档 https://api.fanyi.baidu.com/product/113
+            salt = str(random.randint(1, 100000))
+            signString = appid + t + salt + key
+            hs = hashlib.md5()
+            hs.update(signString.encode("utf-8"))
+            signString = hs.hexdigest()
+            if from_Language == "":
+                from_Language = "auto"
+            headers = {"Content-Type": "application/x-www-form-urlencoded"}
+            payload = {
+                "q": t,
+                "from": from_Language,
+                "to": to_Language,
+                "appid": appid,
+                "salt": salt,
+                "sign": signString,
+            }
+            # 发送请求
+            try:
+                response = requests.post(
+                    url=url, data=payload, headers=headers, timeout=3
+                )
+                response = response.json()
+                if "trans_result" in response.keys():
+                    result = response["trans_result"][0]
+                    if "dst" in result.keys():
+                        dst = result["dst"]
+                        outTexts.append(dst)
+            except Exception:
+                return Sentence
+        else:
+            outTexts.append(t)
+    return "\n".join(outTexts)
diff --git a/transforms.py b/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..a11f799e023864ff7082c1f49c0cc18351a13b47
--- /dev/null
+++ b/transforms.py
@@ -0,0 +1,209 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails=None,
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
+
+    outputs, logabsdet = spline_fn(
+        inputs=inputs,
+        unnormalized_widths=unnormalized_widths,
+        unnormalized_heights=unnormalized_heights,
+        unnormalized_derivatives=unnormalized_derivatives,
+        inverse=inverse,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+        **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
+
+
+def unconstrained_rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails="linear",
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == "linear":
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError("{} tails are not implemented.".format(tails))
+
+    (
+        outputs[inside_interval_mask],
+        logabsdet[inside_interval_mask],
+    ) = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound,
+        right=tail_bound,
+        bottom=-tail_bound,
+        top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+    )
+
+    return outputs, logabsdet
+
+
+def rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    left=0.0,
+    right=1.0,
+    bottom=0.0,
+    top=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError("Input to a transform is not within its domain")
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError("Minimal bin width too large for the number of bins")
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError("Minimal bin height too large for the number of bins")
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        ) + input_heights * (input_delta - input_derivatives)
+        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        )
+        c = -input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * root.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - root).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (
+            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
+        )
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * theta.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - theta).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet
diff --git a/update_status.py b/update_status.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ee18f1826d8895e78dce9fdd4ae2cecd34e23b8
--- /dev/null
+++ b/update_status.py
@@ -0,0 +1,93 @@
+import os
+import gradio as gr
+
+lang_dict = {"EN(英文)": "_en", "ZH(中文)": "_zh", "JP(日语)": "_jp"}
+
+
+def raw_dir_convert_to_path(target_dir: str, lang):
+    res = target_dir.rstrip("/").rstrip("\\")
+    if (not target_dir.startswith("raw")) and (not target_dir.startswith("./raw")):
+        res = os.path.join("./raw", res)
+    if (
+        (not res.endswith("_zh"))
+        and (not res.endswith("_jp"))
+        and (not res.endswith("_en"))
+    ):
+        res += lang_dict[lang]
+    return res
+
+
+def update_g_files():
+    g_files = []
+    cnt = 0
+    for root, dirs, files in os.walk(os.path.abspath("./logs")):
+        for file in files:
+            if file.startswith("G_") and file.endswith(".pth"):
+                g_files.append(os.path.join(root, file))
+                cnt += 1
+    print(g_files)
+    return f"更新模型列表完成, 共找到{cnt}个模型", gr.Dropdown.update(choices=g_files)
+
+
+def update_c_files():
+    c_files = []
+    cnt = 0
+    for root, dirs, files in os.walk(os.path.abspath("./logs")):
+        for file in files:
+            if file.startswith("config.json"):
+                c_files.append(os.path.join(root, file))
+                cnt += 1
+    print(c_files)
+    return f"更新模型列表完成, 共找到{cnt}个配置文件", gr.Dropdown.update(
+        choices=c_files
+    )
+
+
+def update_model_folders():
+    subdirs = []
+    cnt = 0
+    for root, dirs, files in os.walk(os.path.abspath("./logs")):
+        for dir_name in dirs:
+            if os.path.basename(dir_name) != "eval":
+                subdirs.append(os.path.join(root, dir_name))
+                cnt += 1
+    print(subdirs)
+    return f"更新模型文件夹列表完成, 共找到{cnt}个文件夹", gr.Dropdown.update(
+        choices=subdirs
+    )
+
+
+def update_wav_lab_pairs():
+    wav_count = tot_count = 0
+    for root, _, files in os.walk("./raw"):
+        for file in files:
+            # print(file)
+            file_path = os.path.join(root, file)
+            if file.lower().endswith(".wav"):
+                lab_file = os.path.splitext(file_path)[0] + ".lab"
+                if os.path.exists(lab_file):
+                    wav_count += 1
+                tot_count += 1
+    return f"{wav_count} / {tot_count}"
+
+
+def update_raw_folders():
+    subdirs = []
+    cnt = 0
+    script_path = os.path.dirname(os.path.abspath(__file__))  # 获取当前脚本的绝对路径
+    raw_path = os.path.join(script_path, "raw")
+    print(raw_path)
+    os.makedirs(raw_path, exist_ok=True)
+    for root, dirs, files in os.walk(raw_path):
+        for dir_name in dirs:
+            relative_path = os.path.relpath(
+                os.path.join(root, dir_name), script_path
+            )  # 获取相对路径
+            subdirs.append(relative_path)
+            cnt += 1
+    print(subdirs)
+    return (
+        f"更新raw音频文件夹列表完成, 共找到{cnt}个文件夹",
+        gr.Dropdown.update(choices=subdirs),
+        gr.Textbox.update(value=update_wav_lab_pairs()),
+    )
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c302bd3be8f7a16f4555343e906bbaa5d1ff3ec
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,471 @@
+import os
+import glob
+import argparse
+import logging
+import json
+import shutil
+import subprocess
+import numpy as np
+from huggingface_hub import hf_hub_download
+from scipy.io.wavfile import read
+import torch
+import re
+
+MATPLOTLIB_FLAG = False
+
+logger = logging.getLogger(__name__)
+
+
+def download_emo_models(mirror, repo_id, model_name):
+    if mirror == "openi":
+        import openi
+
+        openi.model.download_model(
+            "Stardust_minus/Bert-VITS2",
+            repo_id.split("/")[-1],
+            "./emotional",
+        )
+    else:
+        hf_hub_download(
+            repo_id,
+            "pytorch_model.bin",
+            local_dir=model_name,
+            local_dir_use_symlinks=False,
+        )
+
+
+def download_checkpoint(
+    dir_path, repo_config, token=None, regex="G_*.pth", mirror="openi"
+):
+    repo_id = repo_config["repo_id"]
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    if f_list:
+        print("Use existed model, skip downloading.")
+        return
+    if mirror.lower() == "openi":
+        import openi
+
+        kwargs = {"token": token} if token else {}
+        openi.login(**kwargs)
+
+        model_image = repo_config["model_image"]
+        openi.model.download_model(repo_id, model_image, dir_path)
+
+        fs = glob.glob(os.path.join(dir_path, model_image, "*.pth"))
+        for file in fs:
+            shutil.move(file, dir_path)
+        shutil.rmtree(os.path.join(dir_path, model_image))
+    else:
+        for file in ["DUR_0.pth", "D_0.pth", "G_0.pth"]:
+            hf_hub_download(
+                repo_id, file, local_dir=dir_path, local_dir_use_symlinks=False
+            )
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
+    assert os.path.isfile(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+    iteration = checkpoint_dict["iteration"]
+    learning_rate = checkpoint_dict["learning_rate"]
+    if (
+        optimizer is not None
+        and not skip_optimizer
+        and checkpoint_dict["optimizer"] is not None
+    ):
+        optimizer.load_state_dict(checkpoint_dict["optimizer"])
+    elif optimizer is None and not skip_optimizer:
+        # else:      Disable this line if Infer and resume checkpoint,then enable the line upper
+        new_opt_dict = optimizer.state_dict()
+        new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
+        new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
+        new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
+        optimizer.load_state_dict(new_opt_dict)
+
+    saved_state_dict = checkpoint_dict["model"]
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        try:
+            # assert "emb_g" not in k
+            new_state_dict[k] = saved_state_dict[k]
+            assert saved_state_dict[k].shape == v.shape, (
+                saved_state_dict[k].shape,
+                v.shape,
+            )
+        except:
+            # For upgrading from the old version
+            if "ja_bert_proj" in k:
+                v = torch.zeros_like(v)
+                logger.warn(
+                    f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
+                )
+            else:
+                logger.error(f"{k} is not in the checkpoint")
+
+            new_state_dict[k] = v
+
+    if hasattr(model, "module"):
+        model.module.load_state_dict(new_state_dict, strict=False)
+    else:
+        model.load_state_dict(new_state_dict, strict=False)
+
+    logger.info(
+        "Loaded checkpoint '{}' (iteration {})".format(
+            checkpoint_path, iteration)
+    )
+
+    return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+    logger.info(
+        "Saving model and optimizer state at iteration {} to {}".format(
+            iteration, checkpoint_path
+        )
+    )
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save(
+        {
+            "model": state_dict,
+            "iteration": iteration,
+            "optimizer": optimizer.state_dict(),
+            "learning_rate": learning_rate,
+        },
+        checkpoint_path,
+    )
+
+
+def summarize(
+    writer,
+    global_step,
+    scalars={},
+    histograms={},
+    images={},
+    audios={},
+    audio_sampling_rate=22050,
+):
+    for k, v in scalars.items():
+        writer.add_scalar(k, v, global_step)
+    for k, v in histograms.items():
+        writer.add_histogram(k, v, global_step)
+    for k, v in images.items():
+        writer.add_image(k, v, global_step, dataformats="HWC")
+    for k, v in audios.items():
+        writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+    x = f_list[-1]
+    return x
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto",
+                   origin="lower", interpolation="none")
+    plt.colorbar(im, ax=ax)
+    plt.xlabel("Frames")
+    plt.ylabel("Channels")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+    im = ax.imshow(
+        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
+    )
+    fig.colorbar(im, ax=ax)
+    xlabel = "Decoder timestep"
+    if info is not None:
+        xlabel += "\n\n" + info
+    plt.xlabel(xlabel)
+    plt.ylabel("Encoder timestep")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def load_wav_to_torch(full_path):
+    try:
+        sampling_rate, data = read(full_path)
+        return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+    except Exception as e:
+        print(full_path)
+        raise e
+
+
+def load_filepaths_and_text(filename, split="|"):
+    with open(filename, encoding="utf-8") as f:
+        filepaths_and_text = [line.strip().split(split) for line in f]
+    return filepaths_and_text
+
+
+def get_hparams(init=True):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        default="./configs/base.json",
+        help="JSON file for configuration",
+    )
+    parser.add_argument("-m", "--model", type=str,
+                        required=True, help="Model name")
+
+    args = parser.parse_args()
+    model_dir = os.path.join("./logs", args.model)
+
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+
+    config_path = args.config
+    config_save_path = os.path.join(model_dir, "config.json")
+    if init:
+        with open(config_path, "r", encoding="utf-8") as f:
+            data = f.read()
+        with open(config_save_path, "w", encoding="utf-8") as f:
+            f.write(data)
+    else:
+        with open(config_save_path, "r", vencoding="utf-8") as f:
+            data = f.read()
+    config = json.loads(data)
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    return hparams
+
+
+def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
+    """Freeing up space by deleting saved ckpts
+
+    Arguments:
+    path_to_models    --  Path to the model directory
+    n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
+    sort_by_time      --  True -> chronologically delete ckpts
+                          False -> lexicographically delete ckpts
+    """
+    import re
+
+    ckpts_files = [
+        f
+        for f in os.listdir(path_to_models)
+        if os.path.isfile(os.path.join(path_to_models, f))
+    ]
+
+    def name_key(_f):
+        return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
+
+    def time_key(_f):
+        return os.path.getmtime(os.path.join(path_to_models, _f))
+
+    sort_key = time_key if sort_by_time else name_key
+
+    def x_sorted(_x):
+        return sorted(
+            [f for f in ckpts_files if f.startswith(
+                _x) and not f.endswith("_0.pth")],
+            key=sort_key,
+        )
+
+    to_del = [
+        os.path.join(path_to_models, fn)
+        for fn in (
+            x_sorted("G")[:-n_ckpts_to_keep]
+            + x_sorted("D")[:-n_ckpts_to_keep]
+            + x_sorted("WD")[:-n_ckpts_to_keep]
+        )
+    ]
+
+    def del_info(fn):
+        return logger.info(f".. Free up space by deleting ckpt {fn}")
+
+    def del_routine(x):
+        return [os.remove(x), del_info(x)]
+
+    [del_routine(fn) for fn in to_del]
+
+
+def get_hparams_from_dir(model_dir):
+    config_save_path = os.path.join(model_dir, "config.json")
+    with open(config_save_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    return hparams
+
+
+def get_hparams_from_file(config_path):
+    # print("config_path: ", config_path)
+    with open(config_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    return hparams
+
+
+def check_git_hash(model_dir):
+    source_dir = os.path.dirname(os.path.realpath(__file__))
+    if not os.path.exists(os.path.join(source_dir, ".git")):
+        logger.warn(
+            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
+                source_dir
+            )
+        )
+        return
+
+    cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+    path = os.path.join(model_dir, "githash")
+    if os.path.exists(path):
+        saved_hash = open(path).read()
+        if saved_hash != cur_hash:
+            logger.warn(
+                "git hash values are different. {}(saved) != {}(current)".format(
+                    saved_hash[:8], cur_hash[:8]
+                )
+            )
+    else:
+        open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+    global logger
+    logger = logging.getLogger(os.path.basename(model_dir))
+    logger.setLevel(logging.DEBUG)
+
+    formatter = logging.Formatter(
+        "%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    h = logging.FileHandler(os.path.join(model_dir, filename))
+    h.setLevel(logging.DEBUG)
+    h.setFormatter(formatter)
+    logger.addHandler(h)
+    return logger
+
+
+class HParams:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()
+
+
+def load_model(model_path, config_path):
+    hps = get_hparams_from_file(config_path)
+    net = SynthesizerTrn(
+        # len(symbols),
+        108,
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    ).to("cpu")
+    _ = net.eval()
+    _ = load_checkpoint(model_path, net, None, skip_optimizer=True)
+    return net
+
+
+def mix_model(
+    network1, network2, output_path, voice_ratio=(0.5, 0.5), tone_ratio=(0.5, 0.5)
+):
+    if hasattr(network1, "module"):
+        state_dict1 = network1.module.state_dict()
+        state_dict2 = network2.module.state_dict()
+    else:
+        state_dict1 = network1.state_dict()
+        state_dict2 = network2.state_dict()
+    for k in state_dict1.keys():
+        if k not in state_dict2.keys():
+            continue
+        if "enc_p" in k:
+            state_dict1[k] = (
+                state_dict1[k].clone() * tone_ratio[0]
+                + state_dict2[k].clone() * tone_ratio[1]
+            )
+        else:
+            state_dict1[k] = (
+                state_dict1[k].clone() * voice_ratio[0]
+                + state_dict2[k].clone() * voice_ratio[1]
+            )
+    for k in state_dict2.keys():
+        if k not in state_dict1.keys():
+            state_dict1[k] = state_dict2[k].clone()
+    torch.save(
+        {"model": state_dict1, "iteration": 0,
+            "optimizer": None, "learning_rate": 0},
+        output_path,
+    )
+
+
+def get_steps(model_path):
+    matches = re.findall(r"\d+", model_path)
+    return matches[-1] if matches else None
diff --git a/webui.py b/webui.py
new file mode 100644
index 0000000000000000000000000000000000000000..c71dd0e8c72ce01808302f4a42d5a4e6c0b1b7b4
--- /dev/null
+++ b/webui.py
@@ -0,0 +1,572 @@
+# flake8: noqa: E402
+import librosa
+from tools.translate import translate
+from config import config
+import numpy as np
+import webbrowser
+import gradio as gr
+from infer import infer, latest_version, get_net_g, infer_multilang
+import utils
+import torch
+import gc
+import os
+import logging
+import re_matching
+from tools.sentence import split_by_language
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+logging.getLogger("markdown_it").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+
+logging.basicConfig(
+    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
+)
+
+logger = logging.getLogger(__name__)
+
+
+net_g = None
+
+device = config.webui_config.device
+if device == "mps":
+    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
+
+def free_up_memory():
+    # Prior inference run might have large variables not cleaned up due to exception during the run.
+    # Free up as much memory as possible to allow this run to be successful.
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+
+def generate_audio(
+    slices,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    speaker,
+    language,
+    reference_audio,
+    emotion,
+    style_text,
+    style_weight,
+    skip_start=False,
+    skip_end=False,
+):
+    audio_list = []
+    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
+
+    free_up_memory()
+
+    with torch.no_grad():
+        for idx, piece in enumerate(slices):
+            skip_start = idx != 0
+            skip_end = idx != len(slices) - 1
+            audio = infer(
+                piece,
+                reference_audio=reference_audio,
+                emotion=emotion,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                sid=speaker,
+                language=language,
+                hps=hps,
+                net_g=net_g,
+                device=device,
+                skip_start=skip_start,
+                skip_end=skip_end,
+                style_text=style_text,
+                style_weight=style_weight,
+            )
+            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
+            audio_list.append(audio16bit)
+    return audio_list
+
+
+def generate_audio_multilang(
+    slices,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    speaker,
+    language,
+    reference_audio,
+    emotion,
+    skip_start=False,
+    skip_end=False,
+):
+    audio_list = []
+    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
+
+    free_up_memory()
+
+    with torch.no_grad():
+        for idx, piece in enumerate(slices):
+            skip_start = idx != 0
+            skip_end = idx != len(slices) - 1
+            audio = infer_multilang(
+                piece,
+                reference_audio=reference_audio,
+                emotion=emotion,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                sid=speaker,
+                language=language[idx],
+                hps=hps,
+                net_g=net_g,
+                device=device,
+                skip_start=skip_start,
+                skip_end=skip_end,
+            )
+            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
+            audio_list.append(audio16bit)
+    return audio_list
+
+
+def tts_split(
+    text: str,
+    speaker,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    language,
+    cut_by_sent,
+    interval_between_para,
+    interval_between_sent,
+    reference_audio,
+    emotion,
+    style_text,
+    style_weight,
+):
+    while text.find("\n\n") != -1:
+        text = text.replace("\n\n", "\n")
+    text = text.replace("|", "")
+    para_list = re_matching.cut_para(text)
+    para_list = [p for p in para_list if p != ""]
+    audio_list = []
+    for p in para_list:
+        if not cut_by_sent:
+            audio_list += process_text(
+                p,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                reference_audio,
+                emotion,
+                style_text,
+                style_weight,
+            )
+            silence = np.zeros(
+                (int)(44100 * interval_between_para), dtype=np.int16)
+            audio_list.append(silence)
+        else:
+            audio_list_sent = []
+            sent_list = re_matching.cut_sent(p)
+            sent_list = [s for s in sent_list if s != ""]
+            for s in sent_list:
+                audio_list_sent += process_text(
+                    s,
+                    speaker,
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    language,
+                    reference_audio,
+                    emotion,
+                    style_text,
+                    style_weight,
+                )
+                silence = np.zeros((int)(44100 * interval_between_sent))
+                audio_list_sent.append(silence)
+            if (interval_between_para - interval_between_sent) > 0:
+                silence = np.zeros(
+                    (int)(44100 * (interval_between_para - interval_between_sent))
+                )
+                audio_list_sent.append(silence)
+            audio16bit = gr.processing_utils.convert_to_16_bit_wav(
+                np.concatenate(audio_list_sent)
+            )  # 对完整句子做音量归一
+            audio_list.append(audio16bit)
+    audio_concat = np.concatenate(audio_list)
+    return ("Success", (hps.data.sampling_rate, audio_concat))
+
+
+def process_mix(slice):
+    _speaker = slice.pop()
+    _text, _lang = [], []
+    for lang, content in slice:
+        content = content.split("|")
+        content = [part for part in content if part != ""]
+        if len(content) == 0:
+            continue
+        if len(_text) == 0:
+            _text = [[part] for part in content]
+            _lang = [[lang] for part in content]
+        else:
+            _text[-1].append(content[0])
+            _lang[-1].append(lang)
+            if len(content) > 1:
+                _text += [[part] for part in content[1:]]
+                _lang += [[lang] for part in content[1:]]
+    return _text, _lang, _speaker
+
+
+def process_auto(text):
+    _text, _lang = [], []
+    for slice in text.split("|"):
+        if slice == "":
+            continue
+        temp_text, temp_lang = [], []
+        sentences_list = split_by_language(
+            slice, target_languages=["zh", "ja", "en"])
+        for sentence, lang in sentences_list:
+            if sentence == "":
+                continue
+            temp_text.append(sentence)
+            if lang == "ja":
+                lang = "jp"
+            temp_lang.append(lang.upper())
+        _text.append(temp_text)
+        _lang.append(temp_lang)
+    return _text, _lang
+
+
+def process_text(
+    text: str,
+    speaker,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    language,
+    reference_audio,
+    emotion,
+    style_text=None,
+    style_weight=0,
+):
+    audio_list = []
+    if language == "mix":
+        bool_valid, str_valid = re_matching.validate_text(text)
+        if not bool_valid:
+            return str_valid, (
+                hps.data.sampling_rate,
+                np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
+            )
+        for slice in re_matching.text_matching(text):
+            _text, _lang, _speaker = process_mix(slice)
+            if _speaker is None:
+                continue
+            print(f"Text: {_text}\nLang: {_lang}")
+            audio_list.extend(
+                generate_audio_multilang(
+                    _text,
+                    sdp_ratio,
+                    noise_scale,
+                    noise_scale_w,
+                    length_scale,
+                    _speaker,
+                    _lang,
+                    reference_audio,
+                    emotion,
+                )
+            )
+    elif language.lower() == "auto":
+        _text, _lang = process_auto(text)
+        print(f"Text: {_text}\nLang: {_lang}")
+        audio_list.extend(
+            generate_audio_multilang(
+                _text,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                speaker,
+                _lang,
+                reference_audio,
+                emotion,
+            )
+        )
+    else:
+        audio_list.extend(
+            generate_audio(
+                text.split("|"),
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                speaker,
+                language,
+                reference_audio,
+                emotion,
+                style_text,
+                style_weight,
+            )
+        )
+    return audio_list
+
+
+def tts_fn(
+    text: str,
+    speaker,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    language,
+    reference_audio,
+    emotion,
+    prompt_mode,
+    style_text=None,
+    style_weight=0,
+):
+    if style_text == "":
+        style_text = None
+    if prompt_mode == "Audio prompt":
+        if reference_audio == None:
+            return ("Invalid audio prompt", None)
+        else:
+            reference_audio = load_audio(reference_audio)[1]
+    else:
+        reference_audio = None
+
+    audio_list = process_text(
+        text,
+        speaker,
+        sdp_ratio,
+        noise_scale,
+        noise_scale_w,
+        length_scale,
+        language,
+        reference_audio,
+        emotion,
+        style_text,
+        style_weight,
+    )
+
+    audio_concat = np.concatenate(audio_list)
+    return "Success", (hps.data.sampling_rate, audio_concat)
+
+
+def format_utils(text, speaker):
+    _text, _lang = process_auto(text)
+    res = f"[{speaker}]"
+    for lang_s, content_s in zip(_lang, _text):
+        for lang, content in zip(lang_s, content_s):
+            res += f"<{lang.lower()}>{content}"
+        res += "|"
+    return "mix", res[:-1]
+
+
+def load_audio(path):
+    audio, sr = librosa.load(path, 48000)
+    # audio = librosa.resample(audio, 44100, 48000)
+    return sr, audio
+
+
+def gr_util(item):
+    if item == "Text prompt":
+        return {"visible": True, "__type__": "update"}, {
+            "visible": False,
+            "__type__": "update",
+        }
+    else:
+        return {"visible": False, "__type__": "update"}, {
+            "visible": True,
+            "__type__": "update",
+        }
+
+
+if __name__ == "__main__":
+    if config.webui_config.debug:
+        logger.info("Enable DEBUG-LEVEL log")
+        logging.basicConfig(level=logging.DEBUG)
+    hps = utils.get_hparams_from_file(config.webui_config.config_path)
+    # 若config.json中未指定版本则默认为最新版本
+    version = hps.version if hasattr(hps, "version") else latest_version
+    net_g = get_net_g(
+        model_path=config.webui_config.model, device=device, hps=hps
+    )
+    speaker_ids = hps.data.spk2id
+    speakers = list(speaker_ids.keys())
+    languages = ["HAKKA"]
+    with gr.Blocks() as app:
+        with gr.Row():
+            with gr.Column():
+                text = gr.TextArea(
+                    label="输入文本内容",
+                    placeholder="""
+                    如果你选择语言为\'mix\'，必须按照格式输入，否则报错:
+                        格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi):
+                         [说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。
+                         [说话人2]<zh>你好吗？<jp>元気ですか？
+                         [说话人3]<zh>谢谢。<jp>どういたしまして。
+                         ...
+                    另外，所有的语言选项都可以用'|'分割长段实现分句生成。
+                    """,
+                )
+                trans = gr.Button("中翻日", variant="primary")
+                slicer = gr.Button("快速切分", variant="primary")
+                formatter = gr.Button("检测语言，并整理为 MIX 格式", variant="primary")
+                speaker = gr.Dropdown(
+                    choices=speakers, value=speakers[0], label="Speaker"
+                )
+                _ = gr.Markdown(
+                    value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n",
+                    visible=False,
+                )
+                prompt_mode = gr.Radio(
+                    ["Text prompt", "Audio prompt"],
+                    label="Prompt Mode",
+                    value="Text prompt",
+                    visible=False,
+                )
+                text_prompt = gr.Textbox(
+                    label="Text prompt",
+                    placeholder="用文字描述生成风格。如：Happy",
+                    value="Happy",
+                    visible=False,
+                )
+                audio_prompt = gr.Audio(
+                    label="Audio prompt", type="filepath", visible=False
+                )
+                sdp_ratio = gr.Slider(
+                    minimum=0, maximum=1, value=1.0, step=0.1, label="SDP Ratio"
+                )
+                noise_scale = gr.Slider(
+                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise"
+                )
+                noise_scale_w = gr.Slider(
+                    minimum=0.1, maximum=2, value=0.9, step=0.1, label="Noise_W"
+                )
+                length_scale = gr.Slider(
+                    minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
+                )
+                language = gr.Dropdown(
+                    choices=languages, value=languages[0], label="Language"
+                )
+                btn = gr.Button("生成音频！", variant="primary")
+            with gr.Column():
+                with gr.Accordion("融合文本语义", open=False):
+                    gr.Markdown(
+                        value="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
+                        "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
+                        "效果较不明确，留空即为不使用该功能"
+                    )
+                    style_text = gr.Textbox(label="辅助文本")
+                    style_weight = gr.Slider(
+                        minimum=0,
+                        maximum=1,
+                        value=0.7,
+                        step=0.1,
+                        label="Weight",
+                        info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
+                    )
+                with gr.Row():
+                    with gr.Column():
+                        interval_between_sent = gr.Slider(
+                            minimum=0,
+                            maximum=5,
+                            value=0.2,
+                            step=0.1,
+                            label="句间停顿(秒)，勾选按句切分才生效",
+                        )
+                        interval_between_para = gr.Slider(
+                            minimum=0,
+                            maximum=10,
+                            value=1,
+                            step=0.1,
+                            label="段间停顿(秒)，需要大于句间停顿才有效",
+                        )
+                        opt_cut_by_sent = gr.Checkbox(
+                            label="按句切分    在按段落切分的基础上再按句子切分文本"
+                        )
+                        slicer = gr.Button("切分生成", variant="primary")
+                text_output = gr.Textbox(label="状态信息")
+                audio_output = gr.Audio(label="输出音频")
+                # explain_image = gr.Image(
+                #     label="参数解释信息",
+                #     show_label=True,
+                #     show_share_button=False,
+                #     show_download_button=False,
+                #     value=os.path.abspath("./img/参数说明.png"),
+                # )
+        btn.click(
+            tts_fn,
+            inputs=[
+                text,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                audio_prompt,
+                text_prompt,
+                prompt_mode,
+                style_text,
+                style_weight,
+            ],
+            outputs=[text_output, audio_output],
+        )
+
+        trans.click(
+            translate,
+            inputs=[text],
+            outputs=[text],
+        )
+        slicer.click(
+            tts_split,
+            inputs=[
+                text,
+                speaker,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                language,
+                opt_cut_by_sent,
+                interval_between_para,
+                interval_between_sent,
+                audio_prompt,
+                text_prompt,
+                style_text,
+                style_weight,
+            ],
+            outputs=[text_output, audio_output],
+        )
+
+        prompt_mode.change(
+            lambda x: gr_util(x),
+            inputs=[prompt_mode],
+            outputs=[text_prompt, audio_prompt],
+        )
+
+        audio_prompt.upload(
+            lambda x: load_audio(x),
+            inputs=[audio_prompt],
+            outputs=[audio_prompt],
+        )
+
+        formatter.click(
+            format_utils,
+            inputs=[text, speaker],
+            outputs=[language, text],
+        )
+
+    print("推理页面已开启!")
+    webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
+    app.launch(share=config.webui_config.share,
+               server_port=config.webui_config.port)