diff --git a/App.bat b/App.bat
new file mode 100644
index 0000000000000000000000000000000000000000..2f2b6534bdb03d42eac717940cc35f431207db3c
--- /dev/null
+++ b/App.bat
@@ -0,0 +1,11 @@
+chcp 65001 > NUL
+@echo off
+
+pushd %~dp0
+echo Running app.py...
+venv\Scripts\python app.py
+
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+popd
+pause
\ No newline at end of file
diff --git a/Data/.gitignore b/Data/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..c96a04f008ee21e260b28f7701595ed59e2839e3
--- /dev/null
+++ b/Data/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
\ No newline at end of file
diff --git a/Dataset.bat b/Dataset.bat
new file mode 100644
index 0000000000000000000000000000000000000000..03d3850a0db1cb60a4b9aadd56d3993fe9a77092
--- /dev/null
+++ b/Dataset.bat
@@ -0,0 +1,11 @@
+chcp 65001 > NUL
+@echo off
+
+pushd %~dp0
+echo Running webui_dataset.py...
+venv\Scripts\python webui_dataset.py
+
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+popd
+pause
\ No newline at end of file
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
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+want it, that you can change the software or use pieces of it in new
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+
+  Developers that use our General Public Licenses protect your rights
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+SUCH DAMAGES.
+
+  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
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
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+
+  You should also get your employer (if you work as a programmer) or school,
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+<https://www.gnu.org/licenses/>.
diff --git a/Merge.bat b/Merge.bat
new file mode 100644
index 0000000000000000000000000000000000000000..8e1dedba6dc343f9f69084b6b1e700b9b06032ce
--- /dev/null
+++ b/Merge.bat
@@ -0,0 +1,13 @@
+chcp 65001 > NUL
+
+@echo off
+
+pushd %~dp0
+
+echo Running webui_merge.py...
+venv\Scripts\python webui_merge.py
+
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+popd
+pause
\ No newline at end of file
diff --git a/README.md b/README.md
index ff5405f6c0b7a06caaa97a1dfb42f42f8e2e7cdd..5601d39a369a828a468c00f43a43636c0ae76a46 100644
--- a/README.md
+++ b/README.md
@@ -10,4 +10,216 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Style-Bert-VITS2
+
+Bert-VITS2 with more controllable voice styles.
+
+https://github.com/litagin02/Style-Bert-VITS2/assets/139731664/e853f9a2-db4a-4202-a1dd-56ded3c562a0
+
+- [English README](docs/README_en.md)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)
+- [🤗 オンラインデモはこちらから](https://huggingface.co/spaces/litagin/Style-Bert-VITS2-JVNV)
+- [Zennの解説記事](https://zenn.dev/litagin/articles/034819a5256ff4)
+
+- [**リリースページ**](https://github.com/litagin02/Style-Bert-VITS2/releases/)、[更新履歴](docs/CHANGELOG.md)
+
+  - 2024-02-09: ver 2.2
+  - 2024-02-07: ver 2.1
+  - 2024-02-03: ver 2.0
+  - 2024-01-09: ver 1.3
+  - 2023-12-31: ver 1.2
+  - 2023-12-29: ver 1.1
+  - 2023-12-27: ver 1.0
+
+This repository is based on [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2) v2.1 and Japanese-Extra, so many thanks to the original author!
+
+**概要**
+
+- 入力されたテキストの内容をもとに感情豊かな音声を生成する[Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)のv2.1とJapanese-Extraを元に、感情や発話スタイルを強弱込みで自由に制御できるようにしたものです。
+- GitやPythonがない人でも（Windowsユーザーなら）簡単にインストールでき、学習もできます (多くを[EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2/)からお借りしました)。またGoogle Colabでの学習もサポートしています: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)
+- 音声合成のみに使う場合は、グラボがなくてもCPUで動作します。
+- 他との連携に使えるAPIサーバーも同梱しています ([@darai0512](https://github.com/darai0512) 様によるPRです、ありがとうございます)。
+- 元々「楽しそうな文章は楽しそうに、悲しそうな文章は悲しそうに」読むのがBert-VITS2の強みですので、スタイル指定がデフォルトでも感情豊かな音声を生成することができます。
+
+
+## 使い方
+
+<!-- 詳しくは[こちら](docs/tutorial.md)を参照してください。 -->
+
+### 動作環境
+
+各UIとAPI Serverにおいて、Windows コマンドプロンプト・WSL2・Linux(Ubuntu Desktop)での動作を確認しています(WSLでのパス指定は相対パスなど工夫ください)。NVidiaのGPUが無い場合は学習はできませんが音声合成とマージは可能です。
+
+### インストール
+
+#### GitやPythonに馴染みが無い方
+
+Windowsを前提としています。
+
+1. [このzipファイル](https://github.com/litagin02/Style-Bert-VITS2/releases/download/2.2/Style-Bert-VITS2.zip)を**パスに日本語や空白が含まれない場所に**ダウンロードして展開します。
+  - グラボがある方は、`Install-Style-Bert-VITS2.bat`をダブルクリックします。
+  - グラボがない方は、`Install-Style-Bert-VITS2-CPU.bat`をダブルクリックします。CPU版では学習はできませんが、音声合成とマージは可能です。
+2. 待つと自動で必要な環境がインストールされます。
+3. その後、自動的に音声合成するためのWebUIが起動したらインストール成功です。デフォルトのモデルがダウンロードされるているので、そのまま遊ぶことができます。
+
+またアップデートをしたい場合は、`Update-Style-Bert-VITS2.bat`をダブルクリックしてください。ただし**1.x**から**2.x**へアップデートする場合は、[このbatファイル](https://github.com/litagin02/Style-Bert-VITS2/releases/download/2.2/Update-to-JP-Extra.bat)を`Style-Bert-VITS2`フォルダがあるフォルダ（`Update-Style-Bert-VITS2.bat`等があるフォルダ）へ保存してからダブルクリックしてください。
+
+#### GitやPython使える人
+
+```bash
+git clone https://github.com/litagin02/Style-Bert-VITS2.git
+cd Style-Bert-VITS2
+python -m venv venv
+venv\Scripts\activate
+# PyTorch 2.2.x系は今のところは学習エラーが出るので前のバージョンを使う
+pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+python initialize.py  # 必要なモデルとデフォルトTTSモデルをダウンロード
+```
+最後を忘れずに。
+
+### 音声合成
+
+`App.bat`をダブルクリックか、`python app.py`するとWebUIが起動します（`python app.py --cpu`でCPUモードで起動、学習中チェックに便利です）。インストール時にデフォルトのモデルがダウンロードされているので、学習していなくてもそれを使うことができます。
+
+音声合成に必要なモデルファイルたちの構造は以下の通りです（手動で配置する必要はありません）。
+```
+model_assets
+├── your_model
+│   ├── config.json
+│   ├── your_model_file1.safetensors
+│   ├── your_model_file2.safetensors
+│   ├── ...
+│   └── style_vectors.npy
+└── another_model
+    ├── ...
+```
+このように、推論には`config.json`と`*.safetensors`と`style_vectors.npy`が必要です。モデルを共有する場合は、この3つのファイルを共有してください。
+
+このうち`style_vectors.npy`はスタイルを制御するために必要なファイルで、学習の時にデフォルトで平均スタイル「Neutral」が生成されます。
+複数スタイルを使ってより詳しくスタイルを制御したい方は、下の「スタイルの生成」を参照してください（平均スタイルのみでも、学習データが感情豊かならば十分感情豊かな音声が生成されます）。
+
+### 学習
+
+学習には2-14秒程度の音声ファイルが複数と、それらの書き起こしデータが必要です。
+
+- 既存コーパスなどですでに分割された音声ファイルと書き起こしデータがある場合はそのまま（必要に応じて書き起こしファイルを修正して）使えます。下の「学習WebUI」を参照してください。
+- そうでない場合、（長さは問わない）音声ファイルのみがあれば、そこから学習にすぐに使えるようにデータセットを作るためのツールを同梱しています。
+
+#### データセット作り
+
+- `Dataset.bat`をダブルクリックか`python webui_dataset.py`すると、音声ファイルからデータセットを作るためのWebUIが起動します（音声ファイルを適切な長さにスライスし、その後に文字の書き起こしを自動で行います）。
+- 指示に従った後、閉じて下の「学習WebUI」でそのまま学習を行うことができます。
+
+注意: データセットの手動修正やノイズ除去等、細かい修正を行いたい場合は[Aivis](https://github.com/tsukumijima/Aivis)や、そのデータセット部分のWindows対応版 [Aivis Dataset](https://github.com/litagin02/Aivis-Dataset) を使うといいかもしれません。ですがファイル数が多い場合などは、このツールで簡易的に切り出してデータセットを作るだけでも十分という気もしています。
+
+データセットがどのようなものがいいかは各自試行錯誤中してください。
+
+#### 学習WebUI
+
+- `Train.bat`をダブルクリックか`python webui_train.py`するとWebUIが起動するので指示に従ってください。
+
+### スタイルの生成
+
+- デフォルトスタイル「Neutral」以外のスタイルを使いたい人向けです。
+- `Style.bat`をダブルクリックか`python webui_style_vectors.py`するとWebUIが起動します。
+- 学習とは独立しているので、学習中でもできるし、学習が終わっても何度もやりなおせます（前処理は終わらせている必要があります）。
+- スタイルについての仕様の詳細は[clustering.ipynb](clustering.ipynb)を参照してください。
+
+### API Server
+
+構築した環境下で`python server_fastapi.py`するとAPIサーバーが起動します。
+API仕様は起動後に`/docs`にて確認ください。
+
+- 入力文字数はデフォルトで100文字が上限となっています。これは`config.yml`の`server.limit`で変更できます。
+- デフォルトではCORS設定を全てのドメインで許可しています。できる限り、`config.yml`の`server.origins`の値を変更し、信頼できるドメインに制限ください(キーを消せばCORS設定を無効にできます)。
+
+### マージ
+
+2つのモデルを、「声質」「声の高さ」「感情表現」「テンポ」の4点で混ぜ合わせて、新しいモデルを作ることが出来ます。
+`Merge.bat`をダブルクリックか`python webui_merge.py`するとWebUIが起動します。
+
+### 自然性評価
+
+学習結果のうちどのステップ数がいいかの「一つの」指標として、[SpeechMOS](https://github.com/tarepan/SpeechMOS) を使うスクリプトを用意しています:
+```bash
+python speech_mos.py -m <model_name>
+```
+ステップごとの自然性評価が表示され、`mos_results`フォルダの`mos_{model_name}.csv`と`mos_{model_name}.png`に結果が保存される。読み上げさせたい文章を変えたかったら中のファイルを弄って各自調整してください。またあくまでアクセントや感情表現や抑揚を全く考えない基準での評価で、目安のひとつなので、実際に読み上げさせて選別するのが一番だと思います。
+
+## Bert-VITS2との関係
+
+基本的にはBert-VITS2のモデル構造を少し改造しただけです。[旧事前学習モデル](https://huggingface.co/litagin/Style-Bert-VITS2-1.0-base)も[JP-Extraの事前学習モデル](https://huggingface.co/litagin/Style-Bert-VITS2-2.0-base-JP-Extra)も、実質Bert-VITS2 v2.1 or JP-Extraと同じものを使用しています（不要な重みを削ってsafetensorsに変換したもの）。
+
+具体的には以下の点が異なります。
+
+- [EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2)のように、PythonやGitを知らない人でも簡単に使える。
+- 感情埋め込みのモデルを変更（256次元の[wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM)へ、感情埋め込みというよりは話者識別のための埋め込み）
+- 感情埋め込みもベクトル量子化を取り払い、単なる全結合層に。
+- スタイルベクトルファイル`style_vectors.npy`を作ることで、そのスタイルを使って効果の強さも連続的に指定しつつ音声を生成することができる。
+- 各種WebUIを作成
+- bf16での学習のサポート
+- safetensors形式のサポート、デフォルトでsafetensorsを使用するように
+- その他軽微なbugfixやリファクタリング
+
+## TODO
+- [x] デフォルトのJVNVモデルにJP-Extra版のものを追加
+- [x] LinuxやWSL等、Windowsの通常環境以外でのサポート ← おそらく問題ないとの報告あり
+- [x] 複数話者学習での音声合成対応（学習は現在でも可能）
+- [x] `server_fastapi.py`の対応、とくにAPIで使えるようになると嬉しい人が増えるのかもしれない
+- [x] モデルのマージで声音と感情表現を混ぜる機能の実装
+- [ ] 英語等多言語対応？
+
+## References
+In addition to the original reference (written below), I used the following repositories:
+- [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)
+- [EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2)
+
+[The pretrained model](https://huggingface.co/litagin/Style-Bert-VITS2-1.0-base) and [JP-Extra version](https://huggingface.co/litagin/Style-Bert-VITS2-2.0-base-JP-Extra) is essentially taken from [the original base model of Bert-VITS2 v2.1](https://huggingface.co/Garydesu/bert-vits2_base_model-2.1) and [JP-Extra pretrained model of Bert-VITS2](https://huggingface.co/Stardust-minus/Bert-VITS2-Japanese-Extra), so all the credits go to the original author ([Fish Audio](https://github.com/fishaudio)):
+
+
+Below is the original README.md.
+---
+
+<div align="center">
+
+<img alt="LOGO" src="https://cdn.jsdelivr.net/gh/fishaudio/fish-diffusion@main/images/logo_512x512.png" width="256" height="256" />
+
+# Bert-VITS2
+
+VITS2 Backbone with multilingual bert
+
+For quick guide, please refer to `webui_preprocess.py`.
+
+简易教程请参见 `webui_preprocess.py`。
+
+## 请注意，本项目核心思路来源于[anyvoiceai/MassTTS](https://github.com/anyvoiceai/MassTTS) 一个非常好的tts项目
+## MassTTS的演示demo为[ai版峰哥锐评峰哥本人,并找回了在金三角失落的腰子](https://www.bilibili.com/video/BV1w24y1c7z9)
+
+[//]: # (## 本项目与[PlayVoice/vits_chinese]&#40;https://github.com/PlayVoice/vits_chinese&#41; 没有任何关系)
+
+[//]: # ()
+[//]: # (本仓库来源于之前朋友分享了ai峰哥的视频，本人被其中的效果惊艳，在自己尝试MassTTS以后发现fs在音质方面与vits有一定差距，并且training的pipeline比vits更复杂，因此按照其思路将bert)
+
+## 成熟的旅行者/开拓者/舰长/博士/sensei/猎魔人/喵喵露/V应当参阅代码自己学习如何训练。
+
+### 严禁将此项目用于一切违反《中华人民共和国宪法》，《中华人民共和国刑法》，《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。
+### 严禁用于任何政治相关用途。
+#### Video:https://www.bilibili.com/video/BV1hp4y1K78E
+#### Demo:https://www.bilibili.com/video/BV1TF411k78w
+#### QQ Group：815818430
+## References
++ [anyvoiceai/MassTTS](https://github.com/anyvoiceai/MassTTS)
++ [jaywalnut310/vits](https://github.com/jaywalnut310/vits)
++ [p0p4k/vits2_pytorch](https://github.com/p0p4k/vits2_pytorch)
++ [svc-develop-team/so-vits-svc](https://github.com/svc-develop-team/so-vits-svc)
++ [PaddlePaddle/PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech)
++ [emotional-vits](https://github.com/innnky/emotional-vits)
++ [fish-speech](https://github.com/fishaudio/fish-speech)
++ [Bert-VITS2-UI](https://github.com/jiangyuxiaoxiao/Bert-VITS2-UI)
+## 感谢所有贡献者作出的努力
+<a href="https://github.com/fishaudio/Bert-VITS2/graphs/contributors" target="_blank">
+  <img src="https://contrib.rocks/image?repo=fishaudio/Bert-VITS2"/>
+</a>
+
+[//]: # (# 本项目所有代码引用均已写明，bert部分代码思路来源于[AI峰哥]&#40;https://www.bilibili.com/video/BV1w24y1c7z9&#41;，与[vits_chinese]&#40;https://github.com/PlayVoice/vits_chinese&#41;无任何关系。欢迎各位查阅代码。同时，我们也对该开发者的[碰瓷，乃至开盒开发者的行为]&#40;https://www.bilibili.com/read/cv27101514/&#41;表示强烈谴责。)
diff --git a/Style.bat b/Style.bat
new file mode 100644
index 0000000000000000000000000000000000000000..409cf9105b376d4526bc62c2c36d970f7b116533
--- /dev/null
+++ b/Style.bat
@@ -0,0 +1,12 @@
+chcp 65001 > NUL
+
+@echo off
+
+pushd %~dp0
+echo Running webui_style_vectors.py...
+venv\Scripts\python webui_style_vectors.py
+
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+popd
+pause
\ No newline at end of file
diff --git a/Train.bat b/Train.bat
new file mode 100644
index 0000000000000000000000000000000000000000..5a93d026fc2995233c193fc0b81e812d2c4f7601
--- /dev/null
+++ b/Train.bat
@@ -0,0 +1,13 @@
+chcp 65001 > NUL
+
+@echo off
+
+pushd %~dp0
+
+echo Running webui_train.py...
+venv\Scripts\python webui_train.py
+
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+popd
+pause
\ No newline at end of file
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diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9ceb5edcca25732f54ef64050697c0747a91eef
--- /dev/null
+++ b/app.py
@@ -0,0 +1,475 @@
+import argparse
+import datetime
+import json
+import os
+import sys
+from typing import Optional
+
+import gradio as gr
+import torch
+import yaml
+
+from common.constants import (
+    DEFAULT_ASSIST_TEXT_WEIGHT,
+    DEFAULT_LENGTH,
+    DEFAULT_LINE_SPLIT,
+    DEFAULT_NOISE,
+    DEFAULT_NOISEW,
+    DEFAULT_SDP_RATIO,
+    DEFAULT_SPLIT_INTERVAL,
+    DEFAULT_STYLE,
+    DEFAULT_STYLE_WEIGHT,
+    GRADIO_THEME,
+    LATEST_VERSION,
+    Languages,
+)
+from common.log import logger
+from common.tts_model import ModelHolder
+from infer import InvalidToneError
+from text.japanese import g2kata_tone, kata_tone2phone_tone, text_normalize
+
+# Get path settings
+with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
+    path_config: dict[str, str] = yaml.safe_load(f.read())
+    # dataset_root = path_config["dataset_root"]
+    assets_root = path_config["assets_root"]
+
+languages = [l.value for l in Languages]
+
+
+def tts_fn(
+    model_name,
+    model_path,
+    text,
+    language,
+    reference_audio_path,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    line_split,
+    split_interval,
+    assist_text,
+    assist_text_weight,
+    use_assist_text,
+    style,
+    style_weight,
+    kata_tone_json_str,
+    use_tone,
+    speaker,
+):
+    model_holder.load_model_gr(model_name, model_path)
+
+    wrong_tone_message = ""
+    kata_tone: Optional[list[tuple[str, int]]] = None
+    if use_tone and kata_tone_json_str != "":
+        if language != "JP":
+            logger.warning("Only Japanese is supported for tone generation.")
+            wrong_tone_message = "アクセント指定は現在日本語のみ対応しています。"
+        if line_split:
+            logger.warning("Tone generation is not supported for line split.")
+            wrong_tone_message = (
+                "アクセント指定は改行で分けて生成を使わない場合のみ対応しています。"
+            )
+        try:
+            kata_tone = []
+            json_data = json.loads(kata_tone_json_str)
+            # tupleを使うように変換
+            for kana, tone in json_data:
+                assert isinstance(kana, str) and tone in (0, 1), f"{kana}, {tone}"
+                kata_tone.append((kana, tone))
+        except Exception as e:
+            logger.warning(f"Error occurred when parsing kana_tone_json: {e}")
+            wrong_tone_message = f"アクセント指定が不正です: {e}"
+            kata_tone = None
+
+    # toneは実際に音声合成に代入される際のみnot Noneになる
+    tone: Optional[list[int]] = None
+    if kata_tone is not None:
+        phone_tone = kata_tone2phone_tone(kata_tone)
+        tone = [t for _, t in phone_tone]
+
+    speaker_id = model_holder.current_model.spk2id[speaker]
+
+    start_time = datetime.datetime.now()
+
+    try:
+        sr, audio = model_holder.current_model.infer(
+            text=text,
+            language=language,
+            reference_audio_path=reference_audio_path,
+            sdp_ratio=sdp_ratio,
+            noise=noise_scale,
+            noisew=noise_scale_w,
+            length=length_scale,
+            line_split=line_split,
+            split_interval=split_interval,
+            assist_text=assist_text,
+            assist_text_weight=assist_text_weight,
+            use_assist_text=use_assist_text,
+            style=style,
+            style_weight=style_weight,
+            given_tone=tone,
+            sid=speaker_id,
+        )
+    except InvalidToneError as e:
+        logger.error(f"Tone error: {e}")
+        return f"Error: アクセント指定が不正です:\n{e}", None, kata_tone_json_str
+    except ValueError as e:
+        logger.error(f"Value error: {e}")
+        return f"Error: {e}", None, kata_tone_json_str
+
+    end_time = datetime.datetime.now()
+    duration = (end_time - start_time).total_seconds()
+
+    if tone is None and language == "JP":
+        # アクセント指定に使えるようにアクセント情報を返す
+        norm_text = text_normalize(text)
+        kata_tone = g2kata_tone(norm_text)
+        kata_tone_json_str = json.dumps(kata_tone, ensure_ascii=False)
+    elif tone is None:
+        kata_tone_json_str = ""
+    message = f"Success, time: {duration} seconds."
+    if wrong_tone_message != "":
+        message = wrong_tone_message + "\n" + message
+    return message, (sr, audio), kata_tone_json_str
+
+
+initial_text = "こんにちは、初めまして。あなたの名前はなんていうの？"
+
+examples = [
+    [initial_text, "JP"],
+    [
+        """あなたがそんなこと言うなんて、私はとっても嬉しい。
+あなたがそんなこと言うなんて、私はとっても怒ってる。
+あなたがそんなこと言うなんて、私はとっても驚いてる。
+あなたがそんなこと言うなんて、私はとっても辛い。""",
+        "JP",
+    ],
+    [  # ChatGPTに考えてもらった告白セリフ
+        """私、ずっと前からあなたのことを見てきました。あなたの笑顔、優しさ、強さに、心惹かれていたんです。
+友達として過ごす中で、あなたのことがだんだんと特別な存在になっていくのがわかりました。
+えっと、私、あなたのことが好きです！もしよければ、私と付き合ってくれませんか？""",
+        "JP",
+    ],
+    [  # 夏目漱石『吾輩は猫である』
+        """吾輩は猫である。名前はまだ無い。
+どこで生れたかとんと見当がつかぬ。なんでも薄暗いじめじめした所でニャーニャー泣いていた事だけは記憶している。
+吾輩はここで初めて人間というものを見た。しかもあとで聞くと、それは書生という、人間中で一番獰悪な種族であったそうだ。
+この書生というのは時々我々を捕まえて煮て食うという話である。""",
+        "JP",
+    ],
+    [  # 梶井基次郎『桜の樹の下には』
+        """桜の樹の下には屍体が埋まっている！これは信じていいことなんだよ。
+何故って、桜の花があんなにも見事に咲くなんて信じられないことじゃないか。俺はあの美しさが信じられないので、このにさんにち不安だった。
+しかしいま、やっとわかるときが来た。桜の樹の下には屍体が埋まっている。これは信じていいことだ。""",
+        "JP",
+    ],
+    [  # ChatGPTと考えた、感情を表すセリフ
+        """やったー！テストで満点取れた！私とっても嬉しいな！
+どうして私の意見を無視するの？許せない！ムカつく！あんたなんか死ねばいいのに。
+あはははっ！この漫画めっちゃ笑える、見てよこれ、ふふふ、あはは。
+あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しい。""",
+        "JP",
+    ],
+    [  # 上の丁寧語バージョン
+        """やりました！テストで満点取れましたよ！私とっても嬉しいです！
+どうして私の意見を無視するんですか？許せません！ムカつきます！あんたなんか死んでください。
+あはははっ！この漫画めっちゃ笑えます、見てくださいこれ、ふふふ、あはは。
+あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しいです。""",
+        "JP",
+    ],
+    [  # ChatGPTに考えてもらった音声合成の説明文章
+        """音声合成は、機械学習を活用して、テキストから人の声を再現する技術です。この技術は、言語の構造を解析し、それに基づいて音声を生成します。
+この分野の最新の研究成果を使うと、より自然で表現豊かな音声の生成が可能である。深層学習の応用により、感情やアクセントを含む声質の微妙な変化も再現することが出来る。""",
+        "JP",
+    ],
+    [
+        "Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products.",
+        "EN",
+    ],
+    [
+        "语音合成是人工制造人类语音。用于此目的的计算机系统称为语音合成器，可以通过软件或硬件产品实现。",
+        "ZH",
+    ],
+]
+
+initial_md = f"""
+# Style-Bert-VITS2 ver {LATEST_VERSION} 音声合成
+
+注意: 初期からある[jvnvのモデル](https://huggingface.co/litagin/style_bert_vits2_jvnv)は、[JVNVコーパス（言語音声と非言語音声を持つ日本語感情音声コーパス）](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus)で学習されたモデルです。ライセンスは[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja)です。
+"""
+
+how_to_md = """
+下のように`model_assets`ディレクトリの中にモデルファイルたちを置いてください。
+```
+model_assets
+├── your_model
+│   ├── config.json
+│   ├── your_model_file1.safetensors
+│   ├── your_model_file2.safetensors
+│   ├── ...
+│   └── style_vectors.npy
+└── another_model
+    ├── ...
+```
+各モデルにはファイルたちが必要です：
+- `config.json`：学習時の設定ファイル
+- `*.safetensors`：学習済みモデルファイル（1つ以上が必要、複数可）
+- `style_vectors.npy`：スタイルベクトルファイル
+
+上2つは`Train.bat`による学習で自動的に正しい位置に保存されます。`style_vectors.npy`は`Style.bat`を実行して指示に従って生成してください。
+"""
+
+style_md = f"""
+- プリセットまたは音声ファイルから読み上げの声音・感情・スタイルのようなものを制御できます。
+- デフォルトの{DEFAULT_STYLE}でも、十分に読み上げる文に応じた感情で感情豊かに読み上げられます。このスタイル制御は、それを重み付きで上書きするような感じです。
+- 強さを大きくしすぎると発音が変になったり声にならなかったりと崩壊することがあります。
+- どのくらいに強さがいいかはモデルやスタイルによって異なるようです。
+- 音声ファイルを入力する場合は、学習データと似た声音の話者（特に同じ性別）でないとよい効果が出ないかもしれません。
+"""
+
+
+def make_interactive():
+    return gr.update(interactive=True, value="音声合成")
+
+
+def make_non_interactive():
+    return gr.update(interactive=False, value="音声合成（モデルをロードしてください）")
+
+
+def gr_util(item):
+    if item == "プリセットから選ぶ":
+        return (gr.update(visible=True), gr.Audio(visible=False, value=None))
+    else:
+        return (gr.update(visible=False), gr.update(visible=True))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cpu", action="store_true", help="Use CPU instead of GPU")
+    parser.add_argument(
+        "--dir", "-d", type=str, help="Model directory", default=assets_root
+    )
+    parser.add_argument(
+        "--share", action="store_true", help="Share this app publicly", default=False
+    )
+    parser.add_argument(
+        "--server-name",
+        type=str,
+        default=None,
+        help="Server name for Gradio app",
+    )
+    parser.add_argument(
+        "--no-autolaunch",
+        action="store_true",
+        default=False,
+        help="Do not launch app automatically",
+    )
+    args = parser.parse_args()
+    model_dir = args.dir
+
+    if args.cpu:
+        device = "cpu"
+    else:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    model_holder = ModelHolder(model_dir, device)
+
+    model_names = model_holder.model_names
+    if len(model_names) == 0:
+        logger.error(
+            f"モデルが見つかりませんでした。{model_dir}にモデルを置いてください。"
+        )
+        sys.exit(1)
+    initial_id = 0
+    initial_pth_files = model_holder.model_files_dict[model_names[initial_id]]
+
+    with gr.Blocks(theme=GRADIO_THEME) as app:
+        gr.Markdown(initial_md)
+        with gr.Accordion(label="使い方", open=False):
+            gr.Markdown(how_to_md)
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        model_name = gr.Dropdown(
+                            label="モデル一覧",
+                            choices=model_names,
+                            value=model_names[initial_id],
+                        )
+                        model_path = gr.Dropdown(
+                            label="モデルファイル",
+                            choices=initial_pth_files,
+                            value=initial_pth_files[0],
+                        )
+                    refresh_button = gr.Button("更新", scale=1, visible=True)
+                    load_button = gr.Button("ロード", scale=1, variant="primary")
+                text_input = gr.TextArea(label="テキスト", value=initial_text)
+
+                line_split = gr.Checkbox(
+                    label="改行で分けて生成（分けたほうが感情が乗ります）",
+                    value=DEFAULT_LINE_SPLIT,
+                )
+                split_interval = gr.Slider(
+                    minimum=0.0,
+                    maximum=2,
+                    value=DEFAULT_SPLIT_INTERVAL,
+                    step=0.1,
+                    label="改行ごとに挟む無音の長さ（秒）",
+                )
+                line_split.change(
+                    lambda x: (gr.Slider(visible=x)),
+                    inputs=[line_split],
+                    outputs=[split_interval],
+                )
+                tone = gr.Textbox(
+                    label="アクセント調整（数値は 0=低 か1=高 のみ）",
+                    info="改行で分けない場合のみ使えます。万能ではありません。",
+                )
+                use_tone = gr.Checkbox(label="アクセント調整を使う", value=False)
+                use_tone.change(
+                    lambda x: (gr.Checkbox(value=False) if x else gr.Checkbox()),
+                    inputs=[use_tone],
+                    outputs=[line_split],
+                )
+                language = gr.Dropdown(choices=languages, value="JP", label="Language")
+                speaker = gr.Dropdown(label="話者")
+                with gr.Accordion(label="詳細設定", open=False):
+                    sdp_ratio = gr.Slider(
+                        minimum=0,
+                        maximum=1,
+                        value=DEFAULT_SDP_RATIO,
+                        step=0.1,
+                        label="SDP Ratio",
+                    )
+                    noise_scale = gr.Slider(
+                        minimum=0.1,
+                        maximum=2,
+                        value=DEFAULT_NOISE,
+                        step=0.1,
+                        label="Noise",
+                    )
+                    noise_scale_w = gr.Slider(
+                        minimum=0.1,
+                        maximum=2,
+                        value=DEFAULT_NOISEW,
+                        step=0.1,
+                        label="Noise_W",
+                    )
+                    length_scale = gr.Slider(
+                        minimum=0.1,
+                        maximum=2,
+                        value=DEFAULT_LENGTH,
+                        step=0.1,
+                        label="Length",
+                    )
+                    use_assist_text = gr.Checkbox(
+                        label="Assist textを使う", value=False
+                    )
+                    assist_text = gr.Textbox(
+                        label="Assist text",
+                        placeholder="どうして私の意見を無視するの？許せない、ムカつく！死ねばいいのに。",
+                        info="このテキストの読み上げと似た声音・感情になりやすくなります。ただ抑揚やテンポ等が犠牲になる傾向があります。",
+                        visible=False,
+                    )
+                    assist_text_weight = gr.Slider(
+                        minimum=0,
+                        maximum=1,
+                        value=DEFAULT_ASSIST_TEXT_WEIGHT,
+                        step=0.1,
+                        label="Assist textの強さ",
+                        visible=False,
+                    )
+                    use_assist_text.change(
+                        lambda x: (gr.Textbox(visible=x), gr.Slider(visible=x)),
+                        inputs=[use_assist_text],
+                        outputs=[assist_text, assist_text_weight],
+                    )
+            with gr.Column():
+                with gr.Accordion("スタイルについて詳細", open=False):
+                    gr.Markdown(style_md)
+                style_mode = gr.Radio(
+                    ["プリセットから選ぶ", "音声ファイルを入力"],
+                    label="スタイルの指定方法",
+                    value="プリセットから選ぶ",
+                )
+                style = gr.Dropdown(
+                    label=f"スタイル（{DEFAULT_STYLE}が平均スタイル）",
+                    choices=["モデルをロードしてください"],
+                    value="モデルをロードしてください",
+                )
+                style_weight = gr.Slider(
+                    minimum=0,
+                    maximum=50,
+                    value=DEFAULT_STYLE_WEIGHT,
+                    step=0.1,
+                    label="スタイルの強さ",
+                )
+                ref_audio_path = gr.Audio(
+                    label="参照音声", type="filepath", visible=False
+                )
+                tts_button = gr.Button(
+                    "音声合成（モデルをロードしてください）",
+                    variant="primary",
+                    interactive=False,
+                )
+                text_output = gr.Textbox(label="情報")
+                audio_output = gr.Audio(label="結果")
+                with gr.Accordion("テキスト例", open=False):
+                    gr.Examples(examples, inputs=[text_input, language])
+
+        tts_button.click(
+            tts_fn,
+            inputs=[
+                model_name,
+                model_path,
+                text_input,
+                language,
+                ref_audio_path,
+                sdp_ratio,
+                noise_scale,
+                noise_scale_w,
+                length_scale,
+                line_split,
+                split_interval,
+                assist_text,
+                assist_text_weight,
+                use_assist_text,
+                style,
+                style_weight,
+                tone,
+                use_tone,
+                speaker,
+            ],
+            outputs=[text_output, audio_output, tone],
+        )
+
+        model_name.change(
+            model_holder.update_model_files_gr,
+            inputs=[model_name],
+            outputs=[model_path],
+        )
+
+        model_path.change(make_non_interactive, outputs=[tts_button])
+
+        refresh_button.click(
+            model_holder.update_model_names_gr,
+            outputs=[model_name, model_path, tts_button],
+        )
+
+        load_button.click(
+            model_holder.load_model_gr,
+            inputs=[model_name, model_path],
+            outputs=[style, tts_button, speaker],
+        )
+
+        style_mode.change(
+            gr_util,
+            inputs=[style_mode],
+            outputs=[style, ref_audio_path],
+        )
+
+    app.launch(
+        inbrowser=not args.no_autolaunch, share=args.share, server_name=args.server_name
+    )
diff --git a/attentions.py b/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a4bba9cbfe4443f839db598d97a239595a07269
--- /dev/null
+++ b/attentions.py
@@ -0,0 +1,462 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+from common.log import logger as logging
+
+
+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/bert_models.json b/bert/bert_models.json
new file mode 100644
index 0000000000000000000000000000000000000000..cd5906d116d51629aaf94a85cf3d2df7e8032941
--- /dev/null
+++ b/bert/bert_models.json
@@ -0,0 +1,14 @@
+{
+    "deberta-v2-large-japanese-char-wwm": {
+        "repo_id": "ku-nlp/deberta-v2-large-japanese-char-wwm",
+        "files": ["pytorch_model.bin"]
+    },
+    "chinese-roberta-wwm-ext-large": {
+        "repo_id": "hfl/chinese-roberta-wwm-ext-large",
+        "files": ["pytorch_model.bin"]
+    },
+    "deberta-v3-large": {
+        "repo_id": "microsoft/deberta-v3-large",
+        "files": ["spm.model", "pytorch_model.bin"]
+    }
+}
diff --git a/bert/chinese-roberta-wwm-ext-large/.gitattributes b/bert/chinese-roberta-wwm-ext-large/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..ae8c63daedbd4206d7d40126955d4e6ab1c80f8f
--- /dev/null
+++ b/bert/chinese-roberta-wwm-ext-large/.gitattributes
@@ -0,0 +1,9 @@
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
diff --git a/bert/chinese-roberta-wwm-ext-large/README.md b/bert/chinese-roberta-wwm-ext-large/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ebc4b2e6fb6b95ddc5f678b4a7f829466799f2da
--- /dev/null
+++ b/bert/chinese-roberta-wwm-ext-large/README.md
@@ -0,0 +1,57 @@
+---
+language:
+- zh
+tags:
+- bert
+license: "apache-2.0"
+---
+
+# Please use 'Bert' related functions to load this model!
+
+## Chinese BERT with Whole Word Masking
+For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
+
+**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
+Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
+
+This repository is developed based on：https://github.com/google-research/bert
+
+You may also interested in,
+- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
+- Chinese MacBERT: https://github.com/ymcui/MacBERT
+- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
+- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
+- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
+
+More resources by HFL: https://github.com/ymcui/HFL-Anthology
+
+## Citation
+If you find the technical report or resource is useful, please cite the following technical report in your paper.
+- Primary: https://arxiv.org/abs/2004.13922
+```
+@inproceedings{cui-etal-2020-revisiting,
+    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
+    author = "Cui, Yiming  and
+      Che, Wanxiang  and
+      Liu, Ting  and
+      Qin, Bing  and
+      Wang, Shijin  and
+      Hu, Guoping",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
+    pages = "657--668",
+}
+```
+- Secondary: https://arxiv.org/abs/1906.08101
+```
+@article{chinese-bert-wwm,
+  title={Pre-Training with Whole Word Masking for Chinese BERT},
+  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
+  journal={arXiv preprint arXiv:1906.08101},
+  year={2019}
+ }
+```
diff --git a/bert/chinese-roberta-wwm-ext-large/added_tokens.json b/bert/chinese-roberta-wwm-ext-large/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..0967ef424bce6791893e9a57bb952f80fd536e93
--- /dev/null
+++ b/bert/chinese-roberta-wwm-ext-large/added_tokens.json
@@ -0,0 +1 @@
+{}
diff --git a/bert/chinese-roberta-wwm-ext-large/config.json b/bert/chinese-roberta-wwm-ext-large/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..920741798717df58aa766c378de136df3053b609
--- /dev/null
+++ b/bert/chinese-roberta-wwm-ext-large/config.json
@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
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+##￥
+##👍
+##🔥
+##😂
+##😎
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diff --git a/bert/deberta-v2-large-japanese-char-wwm/README.md b/bert/deberta-v2-large-japanese-char-wwm/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b7317110ce981c229ebd7b3b975545de5f35fa30
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@@ -0,0 +1,89 @@
+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+  - deberta
+  - deberta-v2
+  - fill-mask
+  - character
+  - wwm
+datasets:
+  - wikipedia
+  - cc100
+  - oscar
+metrics:
+  - accuracy
+mask_token: "[MASK]"
+widget:
+    - text: "京都大学で自然言語処理を[MASK][MASK]する。"
+---
+
+# Model Card for Japanese character-level DeBERTa V2 large
+
+## Model description
+
+This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
+This model is trained with character-level tokenization and whole word masking.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+
+sentence = '京都大学で自然言語処理を[MASK][MASK]する。'
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer.
+The texts are tokenized into character-level tokens by [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training data
+
+We used the following corpora for pre-training:
+
+- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
+- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
+- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
+
+Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
+Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
+
+## Training procedure
+
+We first segmented texts in the corpora into words using [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) for whole word masking.
+Then, we built a sentencepiece model with 22,012 tokens including all characters that appear in the training corpus.
+
+We tokenized raw corpora into character-level subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
+The training took 26 days using 16 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 26
+- distributed_type: multi-GPU
+- num_devices: 16
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 3,328
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup (lr = 0 at 300k steps)
+- training_steps: 260,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.795.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
+For training models, we used the mdx: a platform for the data-driven future.
diff --git a/bert/deberta-v2-large-japanese-char-wwm/config.json b/bert/deberta-v2-large-japanese-char-wwm/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9fb6b0ac2ec49b6556e58b5ed9492eb33166714d
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/config.json
@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "DebertaV2ForMaskedLM"
+  ],
+  "attention_head_size": 64,
+  "attention_probs_dropout_prob": 0.1,
+  "conv_act": "gelu",
+  "conv_kernel_size": 3,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 0,
+  "vocab_size": 22012
+}
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+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
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diff --git a/bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json b/bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json
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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "do_subword_tokenize": true,
+  "do_word_tokenize": true,
+  "jumanpp_kwargs": null,
+  "mask_token": "[MASK]",
+  "mecab_kwargs": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "subword_tokenizer_type": "character",
+  "sudachi_kwargs": null,
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "unk_token": "[UNK]",
+  "word_tokenizer_type": "basic"
+}
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+𲄠
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+񂪑
+񣘠
+񤭥
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+񻋆
+򂢃
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diff --git a/bert/deberta-v3-large/.gitattributes b/bert/deberta-v3-large/.gitattributes
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index 0000000000000000000000000000000000000000..957b2579c6ef20995a09efd9a17f8fd90606f5ed
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@@ -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/bert/deberta-v3-large/README.md b/bert/deberta-v3-large/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e2ff251e2b40e3405e2dbc2e0687c0adcafe5305
--- /dev/null
+++ b/bert/deberta-v3-large/README.md
@@ -0,0 +1,93 @@
+---
+language: en
+tags:
+  - deberta
+  - deberta-v3
+  - fill-mask
+thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
+license: mit
+---
+
+## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
+
+[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
+
+In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa,  our V3 version significantly improves the model performance on downstream tasks.  You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
+
+Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
+
+The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters  with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
+
+
+#### Fine-tuning on NLU tasks
+
+We present the dev results on SQuAD 2.0 and MNLI tasks.
+
+| Model             |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
+|-------------------|----------|-------------------|-----------|----------|
+| RoBERTa-large     |50     |304                | 89.4/86.5 | 90.2   |
+| XLNet-large       |32     |-                  | 90.6/87.9 | 90.8   |
+| DeBERTa-large     |50     |-                  | 90.7/88.0 | 91.3   |
+| **DeBERTa-v3-large**|128|304                  |  **91.5/89.0**| **91.8/91.9**|
+
+
+#### Fine-tuning with HF transformers
+
+```bash
+#!/bin/bash
+
+cd transformers/examples/pytorch/text-classification/
+
+pip install datasets
+export TASK_NAME=mnli
+
+output_dir="ds_results"
+
+num_gpus=8
+
+batch_size=8
+
+python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
+  run_glue.py \
+  --model_name_or_path microsoft/deberta-v3-large \
+  --task_name $TASK_NAME \
+  --do_train \
+  --do_eval \
+  --evaluation_strategy steps \
+  --max_seq_length 256 \
+  --warmup_steps 50 \
+  --per_device_train_batch_size ${batch_size} \
+  --learning_rate 6e-6 \
+  --num_train_epochs 2 \
+  --output_dir $output_dir \
+  --overwrite_output_dir \
+  --logging_steps 1000 \
+  --logging_dir $output_dir
+
+```
+
+### Citation
+
+If you find DeBERTa useful for your work, please cite the following papers:
+
+``` latex
+@misc{he2021debertav3,
+      title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
+      author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
+      year={2021},
+      eprint={2111.09543},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+``` latex
+@inproceedings{
+he2021deberta,
+title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
+author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
+booktitle={International Conference on Learning Representations},
+year={2021},
+url={https://openreview.net/forum?id=XPZIaotutsD}
+}
+```
diff --git a/bert/deberta-v3-large/config.json b/bert/deberta-v3-large/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9d95ab0632a030f501c2e15336123041c556b8a7
--- /dev/null
+++ b/bert/deberta-v3-large/config.json
@@ -0,0 +1,22 @@
+{
+		"model_type": "deberta-v2",
+		"attention_probs_dropout_prob": 0.1,
+		"hidden_act": "gelu",
+		"hidden_dropout_prob": 0.1,
+		"hidden_size": 1024,
+		"initializer_range": 0.02,
+		"intermediate_size": 4096,
+		"max_position_embeddings": 512,
+		"relative_attention": true,
+		"position_buckets": 256,
+		"norm_rel_ebd": "layer_norm",
+		"share_att_key": true,
+		"pos_att_type": "p2c|c2p",
+		"layer_norm_eps": 1e-7,
+		"max_relative_positions": -1,
+		"position_biased_input": false,
+		"num_attention_heads": 16,
+		"num_hidden_layers": 24,
+		"type_vocab_size": 0,
+		"vocab_size": 128100
+}
diff --git a/bert/deberta-v3-large/generator_config.json b/bert/deberta-v3-large/generator_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..97bd497a506de3bf306f52506fab62cdb954a262
--- /dev/null
+++ b/bert/deberta-v3-large/generator_config.json
@@ -0,0 +1,22 @@
+{
+	"model_type": "deberta-v2",
+	"attention_probs_dropout_prob": 0.1,
+	"hidden_act": "gelu",
+	"hidden_dropout_prob": 0.1,
+	"hidden_size": 1024,
+	"initializer_range": 0.02,
+	"intermediate_size": 4096,
+	"max_position_embeddings": 512,
+	"relative_attention": true,
+	"position_buckets": 256,
+	"norm_rel_ebd": "layer_norm",
+	"share_att_key": true,
+	"pos_att_type": "p2c|c2p",
+	"layer_norm_eps": 1e-7,
+	"max_relative_positions": -1,
+	"position_biased_input": false,
+	"num_attention_heads": 16,
+	"num_hidden_layers": 12,
+	"type_vocab_size": 0,
+	"vocab_size": 128100
+}
diff --git a/bert/deberta-v3-large/pytorch_model.bin b/bert/deberta-v3-large/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..58c54570f69df07db5d2ec51a8bd4c9572be61d4
--- /dev/null
+++ b/bert/deberta-v3-large/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd5b5d93e2db101aaf281df0ea1216c07ad73620ff59c5b42dccac4bf2eef5b5
+size 873673253
diff --git a/bert/deberta-v3-large/spm.model b/bert/deberta-v3-large/spm.model
new file mode 100644
index 0000000000000000000000000000000000000000..b1b95e5b0fef33623979511f423eaeee465c46f0
--- /dev/null
+++ b/bert/deberta-v3-large/spm.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616
diff --git a/bert/deberta-v3-large/tokenizer_config.json b/bert/deberta-v3-large/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..acfd94e399c5659e4bed75f91b4ee24b111fc7a6
--- /dev/null
+++ b/bert/deberta-v3-large/tokenizer_config.json
@@ -0,0 +1,4 @@
+{
+  "do_lower_case": false,
+  "vocab_type": "spm"
+}
diff --git a/bert_gen.py b/bert_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5f7c258f3aa2fa5973baa58d73100a038146bad
--- /dev/null
+++ b/bert_gen.py
@@ -0,0 +1,85 @@
+import argparse
+from concurrent.futures import ThreadPoolExecutor
+
+import torch
+import torch.multiprocessing as mp
+from tqdm import tqdm
+
+import commons
+import utils
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+from config import config
+from text import cleaned_text_to_sequence, get_bert
+
+
+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[-1] == len(phone)
+    except Exception:
+        bert = get_bert(text, word2ph, language_str, device)
+        assert bert.shape[-1] == len(phone)
+        torch.save(bert, bert_path)
+
+
+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)
+    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 ThreadPoolExecutor(max_workers=num_processes) as executor:
+            _ = list(
+                tqdm(
+                    executor.map(process_line, zip(lines, add_blank)),
+                    total=len(lines),
+                    file=SAFE_STDOUT,
+                )
+            )
+
+    logger.info(f"bert.pt is generated! total: {len(lines)} bert.pt files.")
diff --git a/clustering.ipynb b/clustering.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..12e728ef6b35c8fb62f7860eed62fe0d3080bdd0
--- /dev/null
+++ b/clustering.ipynb
@@ -0,0 +1,316 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# スタイルベクトルをもっと詳しく作りたい人向け\n",
+        "\n",
+        "JVNVコーパスの例を使いながらクラスタリングをいろいろいじったり、また正解ラベルからベクトルを作りたい人向けです。\n",
+        "もっといろいろ足したりいろんなスタイルベクトルを作って遊べると思います。\n",
+        "ある程度慣れている人向けです。\n",
+        "\n",
+        "JVNVコーパスのように**スタイルが既にファイル名等で分かれている場合は、最後の方のセルを使えばそれを利用してスタイルを作ることができます。**\n",
+        "\n",
+        "例では[JVNVコーパス](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus)のjvnv-M1を使います。\n",
+        "\n",
+        "## そもそもスタイルベクトルとは\n",
+        "[この話者識別モデル](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM)を使って生成された、1つの音声ファイルにつき256次元のベクトルです。話者識別用のものですが、感情や声音の特徴も含まれているので、スタイルベクトルとして使えます。\n",
+        "\n",
+        "このStyle-Bert-VITS2では、この256次元のベクトルをエンコーダに注入して学習しているので、推論時にそのベクトルを入れてあげる必要があります。ある感情を強く表していると思われるベクトルを入れると、その感情を強く表現した音声が生成される、という仕組みです。\n",
+        "\n",
+        "もともとが話者識別用なので、「この感情はこのベクトル」のような普遍的なスタイルベクトルは使えません。なのでこのように、いちいちデータセットごとにベクトルを作る必要があります。\n",
+        "\n",
+        "## モデルを使うために必要なもの\n",
+        "- `model_assets/{model_name}/model_name.safetensors`: 学習の結果出力されるモデルファイル。これは自動的にこの場所に置かれ、スタイルベクトルとは全く独立。\n",
+        "- `model_assets/{model_name}/style_vectors.npy`: スタイルベクトルのnumpyファイル。ベクトルをいくつかいれる。\n",
+        "- `model_assets/{model_name}/config.json`: モデルの設定ファイル（学習前準備で自動的に生成されるはず）。これの以下の項目を設定。\n",
+        "```json\n",
+        "{\n",
+        "    \"data\": {\n",
+        "        \"num_styles\": 4, // スタイルベクトルの数\n",
+        "        \"style2id\": { // スタイルベクトルの名前とidの対応\n",
+        "            \"Neutral\": 0,\n",
+        "            \"Angry\": 1,\n",
+        "            \"Happy\": 2,\n",
+        "            \"Sad\": 3\n",
+        "        }\n",
+        "    }\n",
+        "}\n",
+        "```\n",
+        "ここでidは0から始まる整数で、スタイルベクトルのnumpyファイルの何番目のベクトルかを指定します。最初のNeutralは含めたほうがよさそうで、WebUIや下では全スタイルベクトルの平均を入れています。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "\n",
+        "wav_dir = \"Data/jvnv-M1/wavs\"\n",
+        "\n",
+        "embs = []\n",
+        "names= []\n",
+        "for file in os.listdir(wav_dir):\n",
+        "    if file.endswith(\".npy\"):\n",
+        "        xvec = np.load(os.path.join(wav_dir, file))\n",
+        "        embs.append(np.expand_dims(xvec, axis=0))\n",
+        "        names.append(file)\n",
+        "\n",
+        "x = np.concatenate(embs, axis=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x700 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# TSNEで可視化\n",
+        "from sklearn.manifold import TSNE\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "# 特徴量の関係か、コサイン距離が良さげ\n",
+        "tsne = TSNE(n_components=2, random_state=42, metric=\"cosine\")\n",
+        "\n",
+        "x_tsne = tsne.fit_transform(x)\n",
+        "\n",
+        "plt.figure(figsize=(7, 7))\n",
+        "plt.scatter(x_tsne[:, 0], x_tsne[:, 1])\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
+        "\n",
+        "method = \"k\"\n",
+        "n_clusters = 5\n",
+        "\n",
+        "if method == \"k\":\n",
+        "    model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n",
+        "elif method == \"a\":\n",
+        "    model = AgglomerativeClustering(n_clusters=n_clusters)\n",
+        "\n",
+        "y_predict = model.fit_predict(x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "上は直接生のベクトルでクラスタリングしてますが、次のようにt-SNEで図のように2次元に削減したものをクラスタリングしたほうが、識別がきれいに分かれる、ことが多いような気がします、が詳しくは分かりません:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_predict = model.fit_predict(x_tsne)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "centroids = []\n",
+        "for i in range(n_clusters):\n",
+        "    centroids.append(x[y_predict == i].mean(axis=0))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x700 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# TSNEで可視化\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(7, 7))\n",
+        "plt.scatter(x_tsne[:, 0], x_tsne[:, 1], c=y_predict)\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from scipy.spatial.distance import cdist\n",
+        "from IPython.display import Audio, display\n",
+        "\n",
+        "# 各クラスターのセントロイドに最も近い点に対応するファイル名を取得\n",
+        "closest_files = []\n",
+        "for center_idx in range(len(centroids)):\n",
+        "    closest_idx = np.argmin(\n",
+        "        cdist(centroids[center_idx : center_idx + 1], x, metric=\"cosine\")\n",
+        "    )\n",
+        "    closest_files.append(names[closest_idx])\n",
+        "\n",
+        "# 対応する音声ファイルをJupyterノートブック上で再生\n",
+        "for file_name in closest_files:\n",
+        "    wav_path = os.path.join(wav_dir, file_name.replace(\".npy\", \"\"))\n",
+        "    if os.path.exists(wav_path):\n",
+        "        print(wav_path)\n",
+        "        display(Audio(wav_path))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# meanとcentroidを保存\n",
+        "mean = x.mean(axis=0)\n",
+        "save_vectors = np.vstack([mean, centroids])\n",
+        "os.makedirs(\"model_assets/jvnv-M1\", exist_ok=True)\n",
+        "np.save(\"model_assets/jvnv-M1/style_vectors.npy\", save_vectors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 正解ラベルがファイル名から分かる場合の作り方\n",
+        "JVNVコーパス等でファイル名によってスタイルラベルが分かる場合、以下のようにしてスタイルベクトルを作ることができます（デフォルトのJVNVモデルのスタイルはこれで作成しています）。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# JVNVコーパスの場合は正解はファイル名の最初の方から分かる。それを使って正解ラベルを作成\n",
+        "label_dict = {\"ang\": 0, \"dis\": 1, \"fea\": 2, \"hap\": 3, \"sad\": 4, \"sur\": 5}\n",
+        "\n",
+        "y_true = [label_dict[name[3:6]] for name in names]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 正解のセントロイドを計算\n",
+        "y_true = np.array(y_true)\n",
+        "true_centroids = []\n",
+        "for i in range(6):\n",
+        "    true_centroids.append(np.mean(x[y_true == i], axis=0))\n",
+        "\n",
+        "true_centroids = np.array(true_centroids)\n",
+        "\n",
+        "# すべてのベクトルの平均を計算\n",
+        "mean = np.mean(x, axis=0)\n",
+        "\n",
+        "# 保存\n",
+        "os.makedirs(\"model_assets/jvnv-M1\", exist_ok=True)\n",
+        "np.save(\"model_assets/jvnv-M1/style_vectors.npy\", np.vstack([mean, true_centroids]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x700 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# TSNEで正解ラベルを可視化\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "cmap = plt.get_cmap(\"tab10\")\n",
+        "\n",
+        "true_label_dict = {0: \"ang\", 1: \"dis\", 2: \"fea\", 3: \"hap\", 4: \"sad\", 5: \"sur\"}\n",
+        "\n",
+        "plt.figure(figsize=(7, 7))\n",
+        "for i in true_label_dict:\n",
+        "    plt.scatter(\n",
+        "        x_tsne[y_true == i, 0],\n",
+        "        x_tsne[y_true == i, 1],\n",
+        "        color=cmap(i),\n",
+        "        label=f\"{true_label_dict[i]}\",\n",
+        "    )\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "手動でconfig.jsonのstyle2idとnum_stylesを設定するのを忘れずに。"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
diff --git a/colab.ipynb b/colab.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f5cab3e7972fcb79995f89cb342e5dc44ddd04ee
--- /dev/null
+++ b/colab.ipynb
@@ -0,0 +1,406 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Style-Bert-VITS2 (ver 2.2) のGoogle Colabでの学習\n",
+        "\n",
+        "Google Colab上でStyle-Bert-VITS2の学習を行うことができます。\n",
+        "\n",
+        "このnotebookでは、通常使用ではあなたのGoogle Driveにフォルダ`Style-Bert-VITS2`を作り、その内部での作業を行います。他のフォルダには触れません。\n",
+        "Google Driveを使わない場合は、初期設定のところで適切なパスを指定してください。\n",
+        "\n",
+        "## 流れ\n",
+        "\n",
+        "### 学習を最初からやりたいとき\n",
+        "上から順に実行していけばいいです。音声合成に必要なファイルはGoogle Driveの`Style-Bert-VITS2/model_assets/`に保存されます。また、途中経過も`Style-Bert-VITS2/Data/`に保存されるので、学習を中断したり、途中から再開することもできます。\n",
+        "\n",
+        "### 学習を途中から再開したいとき\n",
+        "0と1を行い、3の前処理は飛ばして、4から始めてください。スタイル分け5は、学習が終わったら必要なら行ってください。\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 0. 環境構築\n",
+        "\n",
+        "Style-Bert-VITS2の環境をcolab上に構築します。グラボモードが有効になっていることを確認し、以下のセルを順に実行してください。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#@title このセルを実行して環境構築してください。\n",
+        "#@markdown 最後に赤文字でエラーや警告が出ても何故かうまくいくみたいです。\n",
+        "\n",
+        "!git clone https://github.com/litagin02/Style-Bert-VITS2.git\n",
+        "%cd Style-Bert-VITS2/\n",
+        "!pip install -r requirements.txt\n",
+        "!apt install libcublas11\n",
+        "!python initialize.py --skip_jvnv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Google driveを使う方はこちらを実行してください。\n",
+        "\n",
+        "from google.colab import drive\n",
+        "drive.mount(\"/content/drive\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. 初期設定\n",
+        "\n",
+        "学習とその結果を保存するディレクトリ名を指定します。\n",
+        "Google driveの場合はそのまま実行、カスタマイズしたい方は変更して実行してください。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 学習に必要なファイルや途中経過が保存されるディレクトリ\n",
+        "dataset_root = \"/content/drive/MyDrive/Style-Bert-VITS2/Data\"\n",
+        "\n",
+        "# 学習結果（音声合成に必要なファイルたち）が保存されるディレクトリ\n",
+        "assets_root = \"/content/drive/MyDrive/Style-Bert-VITS2/model_assets\"\n",
+        "\n",
+        "import yaml\n",
+        "\n",
+        "\n",
+        "with open(\"configs/paths.yml\", \"w\", encoding=\"utf-8\") as f:\n",
+        "    yaml.dump({\"dataset_root\": dataset_root, \"assets_root\": assets_root}, f)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. 学習に使うデータ準備\n",
+        "\n",
+        "すでに音声ファイル（1ファイル2-12秒程度）とその書き起こしデータがある場合は2.2を、ない場合は2.1を実行してください。"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2.1 音声ファイルからのデータセットの作成（ある人はスキップ可）\n",
+        "\n",
+        "音声ファイル（1ファイル2-12秒程度）とその書き起こしのデータセットを持っていない方は、（日本語の）音声ファイルのみから以下の手順でデータセットを作成することができます。Google drive上の`Style-Bert-VITS2/inputs/`フォルダに音声ファイル（wavファイル形式、1ファイルでも複数ファイルでも可）を置いて、下を実行すると、データセットが作られ、自動的に正しい場所へ配置されます。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 元となる音声ファイル（wav形式）を入れるディレクトリ\n",
+        "input_dir = \"/content/drive/MyDrive/Style-Bert-VITS2/inputs\"\n",
+        "# モデル名（話者名）を入力\n",
+        "model_name = \"your_model_name\"\n",
+        "\n",
+        "!python slice.py -i {input_dir} -o {dataset_root}/{model_name}/raw\n",
+        "!python transcribe.py -i {dataset_root}/{model_name}/raw -o {dataset_root}/{model_name}/esd.list --speaker_name {model_name} --compute_type float16"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "成功したらそのまま3へ進んでください"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2.2 音声ファイルと書き起こしデータがすでにある場合\n",
+        "\n",
+        "指示に従って適切にデータセットを配置してください。\n",
+        "\n",
+        "次のセルを実行して、学習データをいれるフォルダ（1で設定した`dataset_root`）を作成します。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "esCNJl704h52"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.makedirs(dataset_root, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "次に、学習に必要なデータを、Google driveに作成された`Style-Bert-VITS2/Data`フォルダに配置します。\n",
+        "\n",
+        "まず音声データ（wavファイルで1ファイルが2-12秒程度の、長すぎず短すぎない発話のものをいくつか）と、書き起こしテキストを用意してください。wavファイル名やモデルの名前は空白を含まない半角で、wavファイルの拡張子は小文字`.wav`である必要があります。\n",
+        "\n",
+        "書き起こしテキストは、次の形式で記述してください。\n",
+        "```\n",
+        "****.wav|{話者名}|{言語ID、ZHかJPかEN}|{書き起こしテキスト}\n",
+        "```\n",
+        "\n",
+        "例：\n",
+        "```\n",
+        "wav_number1.wav|hanako|JP|こんにちは、聞こえて、いますか？\n",
+        "wav_next.wav|taro|JP|はい、聞こえています……。\n",
+        "english_teacher.wav|Mary|EN|How are you? I'm fine, thank you, and you?\n",
+        "...\n",
+        "```\n",
+        "日本語話者の単一話者データセットで構いません。\n",
+        "\n",
+        "### データセットの配置\n",
+        "\n",
+        "次にモデルの名前を適当に決めてください（空白を含まない半角英数字がよいです）。\n",
+        "そして、書き起こしファイルを`esd.list`という名前で保存し、またwavファイルも`raw`というフォルダを作成し、あなたのGoogle Driveの中の（上で自動的に作られるはずの）`Data`フォルダのなかに、次のように配置します。\n",
+        "```\n",
+        "├── Data\n",
+        "│   ├── {モデルの名前}\n",
+        "│   │   ├── esd.list\n",
+        "│   │   ├── raw\n",
+        "│   │   │   ├── ****.wav\n",
+        "│   │   │   ├── ****.wav\n",
+        "│   │   │   ├── ...\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5r85-W20ECcr"
+      },
+      "source": [
+        "## 3. 学習の前処理\n",
+        "\n",
+        "次に学習の前処理を行います。必要なパラメータをここで指定します。次のセルに設定等を入力して実行してください。「～～かどうか」は`True`もしくは`False`を指定してください。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "CXR7kjuF5GlE"
+      },
+      "outputs": [],
+      "source": [
+        "# 上でつけたフォルダの名前`Data/{model_name}/`\n",
+        "model_name = \"your_model_name\"\n",
+        "\n",
+        "# JP-Extra （日本語特化版）を使うかどうか。日本語の能力が向上する代わりに英語と中国語は使えなくなります。\n",
+        "use_jp_extra = True\n",
+        "\n",
+        "# 学習のバッチサイズ。VRAMのはみ出具合に応じて調整してください。\n",
+        "batch_size = 4\n",
+        "\n",
+        "# 学習のエポック数（データセットを合計何周するか）。\n",
+        "# 100ぐらいで十分かもしれませんが、もっと多くやると質が上がるのかもしれません。\n",
+        "epochs = 100\n",
+        "\n",
+        "# 保存頻度。何ステップごとにモデルを保存するか。分からなければデフォルトのままで。\n",
+        "save_every_steps = 1000\n",
+        "\n",
+        "# 音声ファイルの音量を正規化するかどうか\n",
+        "normalize = False\n",
+        "\n",
+        "# 音声ファイルの開始・終了にある無音区間を削除するかどうか\n",
+        "trim = False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "上のセルが実行されたら、次のセルを実行して学習の前処理を行います。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xMVaOIPLabV5",
+        "outputId": "15fac868-9132-45d9-9f5f-365b6aeb67b0"
+      },
+      "outputs": [],
+      "source": [
+        "from webui_train import preprocess_all\n",
+        "\n",
+        "preprocess_all(\n",
+        "    model_name=model_name,\n",
+        "    batch_size=batch_size,\n",
+        "    epochs=epochs,\n",
+        "    save_every_steps=save_every_steps,\n",
+        "    num_processes=2,\n",
+        "    normalize=normalize,\n",
+        "    trim=trim,\n",
+        "    freeze_EN_bert=False,\n",
+        "    freeze_JP_bert=False,\n",
+        "    freeze_ZH_bert=False,\n",
+        "    freeze_style=False,\n",
+        "    use_jp_extra=use_jp_extra,\n",
+        "    val_per_lang=0,\n",
+        "    log_interval=200,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. 学習\n",
+        "\n",
+        "前処理が正常に終わったら、学習を行います。次のセルを実行すると学習が始まります。\n",
+        "\n",
+        "学習の結果は、上で指定した`save_every_steps`の間隔で、Google Driveの中の`Style-Bert-VITS2/Data/{モデルの名前}/model_assets/`フォルダに保存されます。\n",
+        "\n",
+        "このフォルダをダウンロードし、ローカルのStyle-Bert-VITS2の`model_assets`フォルダに上書きすれば、学習結果を使うことができます。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "laieKrbEb6Ij",
+        "outputId": "72238c88-f294-4ed9-84f6-84c1c17999ca"
+      },
+      "outputs": [],
+      "source": [
+        "# 上でつけたモデル名を入力。学習を途中からする場合はきちんとモデルが保存されているフォルダ名を入力。\n",
+        "model_name = \"your_model_name\"\n",
+        "\n",
+        "\n",
+        "import yaml\n",
+        "from webui_train import get_path\n",
+        "\n",
+        "dataset_path, _, _, _, config_path = get_path(model_name)\n",
+        "\n",
+        "with open(\"default_config.yml\", \"r\", encoding=\"utf-8\") as f:\n",
+        "    yml_data = yaml.safe_load(f)\n",
+        "yml_data[\"model_name\"] = model_name\n",
+        "with open(\"config.yml\", \"w\", encoding=\"utf-8\") as f:\n",
+        "    yaml.dump(yml_data, f, allow_unicode=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 日本語特化版を「使う」場合\n",
+        "!python train_ms_jp_extra.py --config {config_path} --model {dataset_path} --assets_root {assets_root}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 日本語特化版を「使わない」場合\n",
+        "!python train_ms.py --config {config_path} --model {dataset_path} --assets_root {assets_root}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c7g0hrdeP1Tl",
+        "outputId": "94f9a6f6-027f-4554-ce0c-60ac56251c22"
+      },
+      "outputs": [],
+      "source": [
+        "#@title 学習結果を試すならここから\n",
+        "!python app.py --share --dir {assets_root}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. スタイル分け"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!python webui_style_vectors.py --share"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. マージ"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!python webui_merge.py --share"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
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diff --git a/common/__pycache__/tts_model.cpython-310.pyc b/common/__pycache__/tts_model.cpython-310.pyc
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diff --git a/common/constants.py b/common/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2d6352c085448314937e0e9ad457151e3a1745e
--- /dev/null
+++ b/common/constants.py
@@ -0,0 +1,25 @@
+import enum
+
+# Built-in theme: "default", "base", "monochrome", "soft", "glass"
+# See https://huggingface.co/spaces/gradio/theme-gallery for more themes
+GRADIO_THEME: str = "NoCrypt/miku"
+
+LATEST_VERSION: str = "2.2"
+DEFAULT_STYLE: str = "Neutral"
+DEFAULT_STYLE_WEIGHT: float = 5.0
+
+
+class Languages(str, enum.Enum):
+    JP = "JP"
+    EN = "EN"
+    ZH = "ZH"
+
+
+DEFAULT_SDP_RATIO: float = 0.2
+DEFAULT_NOISE: float = 0.6
+DEFAULT_NOISEW: float = 0.8
+DEFAULT_LENGTH: float = 1.0
+DEFAULT_LINE_SPLIT: bool = True
+DEFAULT_SPLIT_INTERVAL: float = 0.5
+DEFAULT_ASSIST_TEXT_WEIGHT: float = 0.7
+DEFAULT_ASSIST_TEXT_WEIGHT: float = 1.0
diff --git a/common/log.py b/common/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..51dca5f3f39047ed9fb58f59d765ca0a332bc49f
--- /dev/null
+++ b/common/log.py
@@ -0,0 +1,16 @@
+"""
+logger封装
+"""
+from loguru import logger
+
+from .stdout_wrapper import SAFE_STDOUT
+
+# 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:^8}</lvl>| {file}:{line} | {message}"
+)
+
+logger.add(SAFE_STDOUT, format=log_format, backtrace=True, diagnose=True)
diff --git a/common/stdout_wrapper.py b/common/stdout_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..af5eaaedbe163824792474c68540cef8bccefd9d
--- /dev/null
+++ b/common/stdout_wrapper.py
@@ -0,0 +1,38 @@
+"""
+`sys.stdout` wrapper for both Google Colab and local environment.
+"""
+
+import sys
+import tempfile
+
+
+class StdoutWrapper:
+    def __init__(self):
+        self.temp_file = tempfile.NamedTemporaryFile(mode="w+", delete=False)
+        self.original_stdout = sys.stdout
+
+    def write(self, message: str):
+        self.temp_file.write(message)
+        self.temp_file.flush()
+        print(message, end="", file=self.original_stdout)
+
+    def flush(self):
+        self.temp_file.flush()
+
+    def read(self):
+        self.temp_file.seek(0)
+        return self.temp_file.read()
+
+    def close(self):
+        self.temp_file.close()
+
+    def fileno(self):
+        return self.temp_file.fileno()
+
+
+try:
+    import google.colab
+
+    SAFE_STDOUT = StdoutWrapper()
+except ImportError:
+    SAFE_STDOUT = sys.stdout
diff --git a/common/subprocess_utils.py b/common/subprocess_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a0e3237b49ed3fe6f92584942575125c32451ea
--- /dev/null
+++ b/common/subprocess_utils.py
@@ -0,0 +1,32 @@
+import subprocess
+import sys
+
+from .log import logger
+from .stdout_wrapper import SAFE_STDOUT
+
+python = sys.executable
+
+
+def run_script_with_log(cmd: list[str], ignore_warning=False) -> tuple[bool, str]:
+    logger.info(f"Running: {' '.join(cmd)}")
+    result = subprocess.run(
+        [python] + cmd,
+        stdout=SAFE_STDOUT,  # type: ignore
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+    if result.returncode != 0:
+        logger.error(f"Error: {' '.join(cmd)}\n{result.stderr}")
+        return False, result.stderr
+    elif result.stderr and not ignore_warning:
+        logger.warning(f"Warning: {' '.join(cmd)}\n{result.stderr}")
+        return True, result.stderr
+    logger.success(f"Success: {' '.join(cmd)}")
+    return True, ""
+
+
+def second_elem_of(original_function):
+    def inner_function(*args, **kwargs):
+        return original_function(*args, **kwargs)[1]
+
+    return inner_function
diff --git a/common/tts_model.py b/common/tts_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c14859813f482d09059ee8a48a3e736c7ba778cc
--- /dev/null
+++ b/common/tts_model.py
@@ -0,0 +1,250 @@
+import numpy as np
+import gradio as gr
+import torch
+import os
+import warnings
+from gradio.processing_utils import convert_to_16_bit_wav
+from typing import Dict, List, Optional, Union
+
+import utils
+from infer import get_net_g, infer
+from models import SynthesizerTrn
+from models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
+
+from .log import logger
+from .constants import (
+    DEFAULT_ASSIST_TEXT_WEIGHT,
+    DEFAULT_LENGTH,
+    DEFAULT_LINE_SPLIT,
+    DEFAULT_NOISE,
+    DEFAULT_NOISEW,
+    DEFAULT_SDP_RATIO,
+    DEFAULT_SPLIT_INTERVAL,
+    DEFAULT_STYLE,
+    DEFAULT_STYLE_WEIGHT,
+)
+
+
+class Model:
+    def __init__(
+        self, model_path: str, config_path: str, style_vec_path: str, device: str
+    ):
+        self.model_path: str = model_path
+        self.config_path: str = config_path
+        self.device: str = device
+        self.style_vec_path: str = style_vec_path
+        self.hps: utils.HParams = utils.get_hparams_from_file(self.config_path)
+        self.spk2id: Dict[str, int] = self.hps.data.spk2id
+        self.id2spk: Dict[int, str] = {v: k for k, v in self.spk2id.items()}
+
+        self.num_styles: int = self.hps.data.num_styles
+        if hasattr(self.hps.data, "style2id"):
+            self.style2id: Dict[str, int] = self.hps.data.style2id
+        else:
+            self.style2id: Dict[str, int] = {str(i): i for i in range(self.num_styles)}
+        if len(self.style2id) != self.num_styles:
+            raise ValueError(
+                f"Number of styles ({self.num_styles}) does not match the number of style2id ({len(self.style2id)})"
+            )
+
+        self.style_vectors: np.ndarray = np.load(self.style_vec_path)
+        if self.style_vectors.shape[0] != self.num_styles:
+            raise ValueError(
+                f"The number of styles ({self.num_styles}) does not match the number of style vectors ({self.style_vectors.shape[0]})"
+            )
+
+        self.net_g: Union[SynthesizerTrn, SynthesizerTrnJPExtra, None] = None
+
+    def load_net_g(self):
+        self.net_g = get_net_g(
+            model_path=self.model_path,
+            version=self.hps.version,
+            device=self.device,
+            hps=self.hps,
+        )
+
+    def get_style_vector(self, style_id: int, weight: float = 1.0) -> np.ndarray:
+        mean = self.style_vectors[0]
+        style_vec = self.style_vectors[style_id]
+        style_vec = mean + (style_vec - mean) * weight
+        return style_vec
+
+    def get_style_vector_from_audio(
+        self, audio_path: str, weight: float = 1.0
+    ) -> np.ndarray:
+        from style_gen import get_style_vector
+
+        xvec = get_style_vector(audio_path)
+        mean = self.style_vectors[0]
+        xvec = mean + (xvec - mean) * weight
+        return xvec
+
+    def infer(
+        self,
+        text: str,
+        language: str = "JP",
+        sid: int = 0,
+        reference_audio_path: Optional[str] = None,
+        sdp_ratio: float = DEFAULT_SDP_RATIO,
+        noise: float = DEFAULT_NOISE,
+        noisew: float = DEFAULT_NOISEW,
+        length: float = DEFAULT_LENGTH,
+        line_split: bool = DEFAULT_LINE_SPLIT,
+        split_interval: float = DEFAULT_SPLIT_INTERVAL,
+        assist_text: Optional[str] = None,
+        assist_text_weight: float = DEFAULT_ASSIST_TEXT_WEIGHT,
+        use_assist_text: bool = False,
+        style: str = DEFAULT_STYLE,
+        style_weight: float = DEFAULT_STYLE_WEIGHT,
+        given_tone: Optional[list[int]] = None,
+    ) -> tuple[int, np.ndarray]:
+        logger.info(f"Start generating audio data from text:\n{text}")
+        if language != "JP" and self.hps.version.endswith("JP-Extra"):
+            raise ValueError(
+                "The model is trained with JP-Extra, but the language is not JP"
+            )
+        if reference_audio_path == "":
+            reference_audio_path = None
+        if assist_text == "" or not use_assist_text:
+            assist_text = None
+
+        if self.net_g is None:
+            self.load_net_g()
+        if reference_audio_path is None:
+            style_id = self.style2id[style]
+            style_vector = self.get_style_vector(style_id, style_weight)
+        else:
+            style_vector = self.get_style_vector_from_audio(
+                reference_audio_path, style_weight
+            )
+        if not line_split:
+            with torch.no_grad():
+                audio = infer(
+                    text=text,
+                    sdp_ratio=sdp_ratio,
+                    noise_scale=noise,
+                    noise_scale_w=noisew,
+                    length_scale=length,
+                    sid=sid,
+                    language=language,
+                    hps=self.hps,
+                    net_g=self.net_g,
+                    device=self.device,
+                    assist_text=assist_text,
+                    assist_text_weight=assist_text_weight,
+                    style_vec=style_vector,
+                    given_tone=given_tone,
+                )
+        else:
+            texts = text.split("\n")
+            texts = [t for t in texts if t != ""]
+            audios = []
+            with torch.no_grad():
+                for i, t in enumerate(texts):
+                    audios.append(
+                        infer(
+                            text=t,
+                            sdp_ratio=sdp_ratio,
+                            noise_scale=noise,
+                            noise_scale_w=noisew,
+                            length_scale=length,
+                            sid=sid,
+                            language=language,
+                            hps=self.hps,
+                            net_g=self.net_g,
+                            device=self.device,
+                            assist_text=assist_text,
+                            assist_text_weight=assist_text_weight,
+                            style_vec=style_vector,
+                        )
+                    )
+                    if i != len(texts) - 1:
+                        audios.append(np.zeros(int(44100 * split_interval)))
+                audio = np.concatenate(audios)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                audio = convert_to_16_bit_wav(audio)
+        logger.info("Audio data generated successfully")
+        return (self.hps.data.sampling_rate, audio)
+
+
+class ModelHolder:
+    def __init__(self, root_dir: str, device: str):
+        self.root_dir: str = root_dir
+        self.device: str = device
+        self.model_files_dict: Dict[str, List[str]] = {}
+        self.current_model: Optional[Model] = None
+        self.model_names: List[str] = []
+        self.models: List[Model] = []
+        self.refresh()
+
+    def refresh(self):
+        self.model_files_dict = {}
+        self.model_names = []
+        self.current_model = None
+        model_dirs = [
+            d
+            for d in os.listdir(self.root_dir)
+            if os.path.isdir(os.path.join(self.root_dir, d))
+        ]
+        for model_name in model_dirs:
+            model_dir = os.path.join(self.root_dir, model_name)
+            model_files = [
+                os.path.join(model_dir, f)
+                for f in os.listdir(model_dir)
+                if f.endswith(".pth") or f.endswith(".pt") or f.endswith(".safetensors")
+            ]
+            if len(model_files) == 0:
+                logger.warning(
+                    f"No model files found in {self.root_dir}/{model_name}, so skip it"
+                )
+                continue
+            self.model_files_dict[model_name] = model_files
+            self.model_names.append(model_name)
+
+    def load_model_gr(
+        self, model_name: str, model_path: str
+    ) -> tuple[gr.Dropdown, gr.Button, gr.Dropdown]:
+        if model_name not in self.model_files_dict:
+            raise ValueError(f"Model `{model_name}` is not found")
+        if model_path not in self.model_files_dict[model_name]:
+            raise ValueError(f"Model file `{model_path}` is not found")
+        if (
+            self.current_model is not None
+            and self.current_model.model_path == model_path
+        ):
+            # Already loaded
+            speakers = list(self.current_model.spk2id.keys())
+            styles = list(self.current_model.style2id.keys())
+            return (
+                gr.Dropdown(choices=styles, value=styles[0]),
+                gr.Button(interactive=True, value="音声合成"),
+                gr.Dropdown(choices=speakers, value=speakers[0]),
+            )
+        self.current_model = Model(
+            model_path=model_path,
+            config_path=os.path.join(self.root_dir, model_name, "config.json"),
+            style_vec_path=os.path.join(self.root_dir, model_name, "style_vectors.npy"),
+            device=self.device,
+        )
+        speakers = list(self.current_model.spk2id.keys())
+        styles = list(self.current_model.style2id.keys())
+        return (
+            gr.Dropdown(choices=styles, value=styles[0]),
+            gr.Button(interactive=True, value="音声合成"),
+            gr.Dropdown(choices=speakers, value=speakers[0]),
+        )
+
+    def update_model_files_gr(self, model_name: str) -> gr.Dropdown:
+        model_files = self.model_files_dict[model_name]
+        return gr.Dropdown(choices=model_files, value=model_files[0])
+
+    def update_model_names_gr(self) -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]:
+        self.refresh()
+        initial_model_name = self.model_names[0]
+        initial_model_files = self.model_files_dict[initial_model_name]
+        return (
+            gr.Dropdown(choices=self.model_names, value=initial_model_name),
+            gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]),
+            gr.Button(interactive=False),  # For tts_button
+        )
diff --git a/commons.py b/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..081b8a061286150f5a6f449601488f3e3f7b0207
--- /dev/null
+++ b/commons.py
@@ -0,0 +1,152 @@
+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 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/config.py b/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbd764e7b96650b2407c0670ab2cfd5f842f6f9a
--- /dev/null
+++ b/config.py
@@ -0,0 +1,291 @@
+"""
+@Desc: 全局配置文件读取
+"""
+
+import os
+import shutil
+from typing import Dict, List
+
+import torch
+import yaml
+
+from common.log import logger
+
+# If not cuda available, set possible devices to cpu
+cuda_available = torch.cuda.is_available()
+
+
+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
+        if not cuda_available:
+            device = "cpu"
+        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 Style_gen_config:
+    """style_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 4,
+        device: str = "cuda",
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        if not cuda_available:
+            device = "cpu"
+        self.device = 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_dir: 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_dir = model_dir  # 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 (for webui.py, not supported now)"""
+
+    def __init__(
+        self,
+        device: str,
+        model: str,
+        config_path: str,
+        language_identification_library: str,
+        port: int = 7860,
+        share: bool = False,
+        debug: bool = False,
+    ):
+        if not cuda_available:
+            device = "cpu"
+        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,
+        port: int = 5000,
+        device: str = "cuda",
+        limit: int = 100,
+        language: str = "JP",
+        origins: List[str] = None,
+    ):
+        self.port: int = port
+        if not cuda_available:
+            device = "cpu"
+        self.device: str = device
+        self.language: str = language
+        self.limit: int = limit
+        self.origins: List[str] = origins
+
+    @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, path_config: dict[str, str]):
+        if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
+            shutil.copy(src="default_config.yml", dst=config_path)
+            logger.info(
+                f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml."
+            )
+            logger.info(
+                "If you have no special needs, please do not modify default_config.yml."
+            )
+            # 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())
+            model_name: str = yaml_config["model_name"]
+            self.model_name: str = model_name
+            if "dataset_path" in yaml_config:
+                dataset_path = yaml_config["dataset_path"]
+            else:
+                dataset_path = os.path.join(path_config["dataset_root"], model_name)
+            self.dataset_path: str = dataset_path
+            self.assets_root: str = path_config["assets_root"]
+            self.out_dir = os.path.join(self.assets_root, model_name)
+            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.style_gen_config: Style_gen_config = Style_gen_config.from_dict(
+                dataset_path, yaml_config["style_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"]
+            # )
+
+
+with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
+    path_config: dict[str, str] = yaml.safe_load(f.read())
+    # Should contain the following keys:
+    # - dataset_root: the root directory of the dataset, default to "Data"
+    # - assets_root: the root directory of the assets, default to "model_assets"
+
+
+try:
+    config = Config("config.yml", path_config)
+except (TypeError, KeyError):
+    logger.warning("Old config.yml found. Replace it with default_config.yml.")
+    shutil.copy(src="default_config.yml", dst="config.yml")
+    config = Config("config.yml", path_config)
diff --git a/config.yml b/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..c7cc19ea8a81049c01168ffa119af143907061b8
--- /dev/null
+++ b/config.yml
@@ -0,0 +1,70 @@
+model_name: "model_name"
+
+# If you want to use a specific dataset path, uncomment the following line.
+# Otherwise, the dataset path is `{dataset_root}/{model_name}`.
+
+# dataset_path: "your/dataset/path"
+
+resample:
+  sampling_rate: 44100
+  in_dir: "raw"
+  out_dir: "wavs"
+
+preprocess_text:
+  transcription_path: "esd.list"
+  cleaned_path: ""
+  train_path: "train.list"
+  val_path: "val.list"
+  config_path: "config.json"
+  val_per_lang: 0
+  max_val_total: 12
+  clean: true
+
+bert_gen:
+  config_path: "config.json"
+  num_processes: 2
+  device: "cuda"
+  use_multi_device: false
+
+style_gen:
+  config_path: "config.json"
+  num_processes: 4
+  device: "cuda"
+
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+  model_dir: "models"  # The directory to save the model (for training), relative to `{dataset_root}/{model_name}`.
+  config_path: "config.json"
+  num_workers: 16
+  spec_cache: True
+  keep_ckpts: 1 # Set this to 0 to keep all checkpoints
+
+webui:  # For `webui.py`, which is not supported yet in Style-Bert-VITS2.
+  # 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's config
+server:
+  port: 5000
+  device: "cuda"
+  language: "JP"
+  limit: 100
+  origins:
+    - "*"
diff --git a/configs/config.json b/configs/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6aa647ccf406e1b3605d1755aba006aba5944d6b
--- /dev/null
+++ b/configs/config.json
@@ -0,0 +1,71 @@
+{
+  "model_name": "your_model_name",
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.0002,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 2,
+    "bf16_run": false,
+    "lr_decay": 0.99995,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "skip_optimizer": false,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_style": false
+  },
+  "data": {
+    "training_files": "Data/your_model_name/filelists/train.list",
+    "validation_files": "Data/your_model_name/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": 1,
+    "cleaned_text": true,
+    "num_styles": 1,
+    "style2id": {
+      "Neutral": 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": 256
+  },
+  "version": "2.2"
+}
diff --git a/configs/configs_jp_extra.json b/configs/configs_jp_extra.json
new file mode 100644
index 0000000000000000000000000000000000000000..7e2698ea51d2c3fc46cc57270934d166164a34ef
--- /dev/null
+++ b/configs/configs_jp_extra.json
@@ -0,0 +1,78 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.0001,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 2,
+    "bf16_run": false,
+    "fp16_run": false,
+    "lr_decay": 0.99996,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": false,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_emo": false,
+    "freeze_style": false
+  },
+  "data": {
+    "use_jp_extra": true,
+    "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": 512,
+    "cleaned_text": true
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": false,
+    "use_wavlm_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": "2.2-JP-Extra"
+}
diff --git a/configs/paths.yml b/configs/paths.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d743748f7f52d5c41f1487b53872143c2f010222
--- /dev/null
+++ b/configs/paths.yml
@@ -0,0 +1,8 @@
+# Root directory of the training dataset.
+# The training dataset of {model_name} should be placed in {dataset_root}/{model_name}.
+dataset_root: Data
+
+# Root directory of the model assets (for inference).
+# In training, the model assets will be saved to {assets_root}/{model_name},
+# and in inference, we load all the models from {assets_root}.
+assets_root: model_assets
diff --git a/data_utils.py b/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac038c22fb542d70500002ae120fa56605e9b03e
--- /dev/null
+++ b/data_utils.py
@@ -0,0 +1,456 @@
+import os
+import random
+import sys
+
+import numpy as np
+import torch
+import torch.utils.data
+from tqdm import tqdm
+
+import commons
+from config import config
+from mel_processing import mel_spectrogram_torch, spectrogram_torch
+from text import cleaned_text_to_sequence
+from common.log import logger
+from utils import load_filepaths_and_text, load_wav_to_torch
+
+"""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_jp_extra = getattr(hparams, "use_jp_extra", False)
+
+        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, file=sys.stdout
+        ):
+            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
+
+        bert, ja_bert, en_bert, 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])])
+        style_vec = torch.FloatTensor(np.load(f"{audiopath}.npy"))
+        if self.use_jp_extra:
+            return (phones, spec, wav, sid, tone, language, ja_bert, style_vec)
+        else:
+            return (
+                phones,
+                spec,
+                wav,
+                sid,
+                tone,
+                language,
+                bert,
+                ja_bert,
+                en_bert,
+                style_vec,
+            )
+
+    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
+        bert_path = wav_path.replace(".wav", ".bert.pt")
+        try:
+            bert_ori = torch.load(bert_path)
+            assert bert_ori.shape[-1] == len(phone)
+        except Exception as e:
+            logger.warning("Bert load Failed")
+            logger.warning(e)
+
+        if language_str == "ZH":
+            bert = bert_ori
+            ja_bert = torch.zeros(1024, len(phone))
+            en_bert = torch.zeros(1024, len(phone))
+        elif language_str == "JP":
+            bert = torch.zeros(1024, len(phone))
+            ja_bert = bert_ori
+            en_bert = torch.zeros(1024, len(phone))
+        elif language_str == "EN":
+            bert = torch.zeros(1024, len(phone))
+            ja_bert = torch.zeros(1024, len(phone))
+            en_bert = bert_ori
+        phone = torch.LongTensor(phone)
+        tone = torch.LongTensor(tone)
+        language = torch.LongTensor(language)
+        return bert, ja_bert, en_bert, 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, use_jp_extra=False):
+        self.return_ids = return_ids
+        self.use_jp_extra = use_jp_extra
+
+    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)
+        # This is ZH bert if not use_jp_extra, JA bert if use_jp_extra
+        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
+        if not self.use_jp_extra:
+            ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
+            en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
+        style_vec = torch.FloatTensor(len(batch), 256)
+
+        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_()
+        bert_padded.zero_()
+        if not self.use_jp_extra:
+            ja_bert_padded.zero_()
+            en_bert_padded.zero_()
+        style_vec.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
+
+            bert = row[6]
+            bert_padded[i, :, : bert.size(1)] = bert
+
+            if self.use_jp_extra:
+                style_vec[i, :] = row[7]
+            else:
+                ja_bert = row[7]
+                ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
+
+                en_bert = row[8]
+                en_bert_padded[i, :, : en_bert.size(1)] = en_bert
+                style_vec[i, :] = row[9]
+
+        if self.use_jp_extra:
+            return (
+                text_padded,
+                text_lengths,
+                spec_padded,
+                spec_lengths,
+                wav_padded,
+                wav_lengths,
+                sid,
+                tone_padded,
+                language_padded,
+                bert_padded,
+                style_vec,
+            )
+        else:
+            return (
+                text_padded,
+                text_lengths,
+                spec_padded,
+                spec_lengths,
+                wav_padded,
+                wav_lengths,
+                sid,
+                tone_padded,
+                language_padded,
+                bert_padded,
+                ja_bert_padded,
+                en_bert_padded,
+                style_vec,
+            )
+
+
+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()
+        logger.info(f"Bucket info: {self.num_samples_per_bucket}")
+        # logger.info(
+        #     f"Unused samples: {len(self.lengths) - sum(self.num_samples_per_bucket)}"
+        # )
+        # ↑マイナスになることあるし、別にこれは使われないサンプル数ではないようだ……
+        # バケットの仕組みはよく分からない
+
+        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:
+            logger.info("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..c7cc19ea8a81049c01168ffa119af143907061b8
--- /dev/null
+++ b/default_config.yml
@@ -0,0 +1,70 @@
+model_name: "model_name"
+
+# If you want to use a specific dataset path, uncomment the following line.
+# Otherwise, the dataset path is `{dataset_root}/{model_name}`.
+
+# dataset_path: "your/dataset/path"
+
+resample:
+  sampling_rate: 44100
+  in_dir: "raw"
+  out_dir: "wavs"
+
+preprocess_text:
+  transcription_path: "esd.list"
+  cleaned_path: ""
+  train_path: "train.list"
+  val_path: "val.list"
+  config_path: "config.json"
+  val_per_lang: 0
+  max_val_total: 12
+  clean: true
+
+bert_gen:
+  config_path: "config.json"
+  num_processes: 2
+  device: "cuda"
+  use_multi_device: false
+
+style_gen:
+  config_path: "config.json"
+  num_processes: 4
+  device: "cuda"
+
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+  model_dir: "models"  # The directory to save the model (for training), relative to `{dataset_root}/{model_name}`.
+  config_path: "config.json"
+  num_workers: 16
+  spec_cache: True
+  keep_ckpts: 1 # Set this to 0 to keep all checkpoints
+
+webui:  # For `webui.py`, which is not supported yet in Style-Bert-VITS2.
+  # 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's config
+server:
+  port: 5000
+  device: "cuda"
+  language: "JP"
+  limit: 100
+  origins:
+    - "*"
diff --git a/default_style.py b/default_style.py
new file mode 100644
index 0000000000000000000000000000000000000000..9198ca8b2f69a3386ec384b96170df4753110b99
--- /dev/null
+++ b/default_style.py
@@ -0,0 +1,30 @@
+import os
+from common.log import logger
+from common.constants import DEFAULT_STYLE
+
+import numpy as np
+import json
+
+
+def set_style_config(json_path, output_path):
+    with open(json_path, "r", encoding="utf-8") as f:
+        json_dict = json.load(f)
+    json_dict["data"]["num_styles"] = 1
+    json_dict["data"]["style2id"] = {DEFAULT_STYLE: 0}
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(json_dict, f, indent=2, ensure_ascii=False)
+    logger.info(f"Save style config (only {DEFAULT_STYLE}) to {output_path}")
+
+
+def save_mean_vector(wav_dir, output_path):
+    embs = []
+    for file in os.listdir(wav_dir):
+        if file.endswith(".npy"):
+            xvec = np.load(os.path.join(wav_dir, file))
+            embs.append(np.expand_dims(xvec, axis=0))
+
+    x = np.concatenate(embs, axis=0)  # (N, 256)
+    mean = np.mean(x, axis=0)  # (256,)
+    only_mean = np.stack([mean])  # (1, 256)
+    np.save(output_path, only_mean)
+    logger.info(f"Saved mean style vector to {output_path}")
diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..d9a3015454ee9c65fbe94a5106b53fa702a0e251
--- /dev/null
+++ b/docs/CHANGELOG.md
@@ -0,0 +1,107 @@
+# Changelog
+
+## v2.2 (2024-02-09)
+
+### 変更・機能追加
+- bfloat16オプションはデメリットしか無さそうなので、常にオフで学習するよう変更
+- バッチサイズのデフォルトを4から2に変更。学習が遅い場合はバッチサイズを下げて試してみて、VRAMに余裕があれば上げてください。JP-Extra使用時でのバッチサイズごとのVRAM使用量目安は、1: 6GB, 2: 8GB, 3: 10GB, 4: 12GB くらいのようです。
+- 学習の際の検証データ数をデフォルトで0に変更し、また検証データ数を学習用WebUIで指定できるようにした
+- Tensorboardのログ間隔を学習用WebUIで指定できるようにした
+- UIのテーマを`common/constants.py`の`GRADIO_THEME`で指定できるようにした
+
+### バグ修正
+- JP-Extra使用時にバッチサイズが1だと学習中にエラーが発生するバグを修正
+- 「こんにちは!?!?!?!?」等、感嘆符等の記号が連続すると学習・音声合成でエラーになるバグを修正
+- `—` (em dash, U+2014) や `―` (quotation dash, U+2015) 等のダッシュやハイフンの各種変種が、種類によって`-`（通常の半角ハイフン）に正規化されたりされていなかったりする処理を、全て正規化するように修正
+
+## v2.1 (2024-02-07)
+
+### 変更
+- 学習の際、デフォルトではbfloat16オプションを使わないよう変更（学習が発散したり質が下がることがある模様）
+- 学習の際のメモリ使用量を削減しようと頑張った
+
+### バグ修正や改善
+- 学習WebUIからTensorboardのログを見れるように
+- 音声合成（やそのAPI）において、同時に別の話者が選択され音声合成がリクエストされた場合に発生するエラーを修正
+- モデルマージ時に、そのレシピを`recipe.json`ファイルへ保存するように変更
+- 「改行で分けて生成」がより感情が乗る旨の明記等、軽微な説明文の改善
+- 「`ーーそれは面白い`」や「`なるほど。ーーーそういうことか。`」等、長音記号の前が母音でない場合、長音記号`ー`でなくダッシュ`―`の勘違いだと思われるので、ダッシュ記号として処理するように変更
+
+## v2.0.1 (2024-02-05)
+
+軽微なバグ修正や改善
+- スタイルベクトルに`NaN`が含まれていた場合（主に音声ファイルが極端に短い場合に発生）、それを学習リストから除外するように修正
+- colabにマージの追加
+- 学習時のプログレスバーの表示がおかしかったのを修正
+- デフォルトのjvnvモデルをJP-Extra版にアップデート。新しいモデルを使いたい方は手動で[こちら](https://huggingface.co/litagin/style_bert_vits2_jvnv/tree/main)からダウンロードするか、`python initialize.py`をするか、[このbatファイル](https://github.com/litagin02/Style-Bert-VITS2/releases/download/2.0.1/Update-to-JP-Extra.bat)を`Style-Bert-VITS2`フォルダがある場所（インストールbatファイルとかがあったところ）においてダブルクリックしてください。
+
+## v2.0 (2024-02-03)
+
+### 大きい変更
+モデル構造に [Bert-VITS2の日本語特化モデル JP-Extra](https://github.com/fishaudio/Bert-VITS2/releases/tag/JP-Exta) を取り込んだものを使えるように変更、[事前学習モデル](https://huggingface.co/litagin/Style-Bert-VITS2-2.0-base-JP-Extra)も[Bert-VITS2 JP-Extra](https://huggingface.co/Stardust-minus/Bert-VITS2-Japanese-Extra)のものを改造してStyle-Bert-VITS2で使えるようにしました (モデル構造を見直して日本語での学習をしていただいた [@Stardust-minus](https://github.com/Stardust-minus) 様に感謝します)
+- これにより、日本語の発音やアクセントや抑揚や自然性が向上する傾向があります
+- スタイルベクトルを使ったスタイルの操作は変わらず使えます
+- ただしJP-Extraでは英語と中国語の音声合成は（現状は）できません
+- 旧モデルも引き続き使うことができ、また旧モデルで学習することもできます
+- デフォルトのJVNVモデルは現在は旧verのままです
+
+### 改善
+- `Merge.bat`で、声音マージを、より細かく「声質」と「声の高さ」の点でマージできるように。
+
+### バグ修正
+- PyTorchのバージョンに由来するバグを修正（torchのバージョンを2.1.2に固定）
+- `―`（ダッシュ、長音記号ではない）が2連続すると学習・音声合成でエラーになるバグを修正
+- 「三円」等「ん＋母音」のアクセントの仮名表記が「サネン」等になり、また偶にエラーが発生する問題を修正（「ん」の音素表記を内部的には「N」で統一）
+
+## v1.3 (2024-01-09)
+
+### 大きい変更
+- 元々のBert-VITS2に存在した、日本語の発音・アクセント処理部分のバグを修正・リファクタリング
+    - `車両`が`シャリヨオ`、`思う`が`オモオ`、`見つける`が`ミッケル`等に発音・学習されており、その単語以降のアクセント情報が全て死んでいた
+    - `私はそれを見る`のアクセントが`ワ➚タシ➘ワ　ソ➚レ➘オ　ミ➘ル`だったのを`ワ➚タシワ　ソ➚レオ　ミ➘ル`に修正
+    - 学習・音声合成で無視されていたアルファベット・ギリシャ文字を無視しないように変更（基本はアルファベット読みだけど簡単な単語は読めるらしい、学習の際は念のためカタカナ等にしたほうがよいです）
+    - 修正の影響で、前処理時に（今まで無視されていた）読めない漢字等で引っかかるようになりました。その場合は書き起こしを確認して修正するようにしてください。
+- アクセントを調整して音声合成できるように（完全に制御できるわけではないが改善される場合がある）。
+
+これまでのモデルもこれまで通り使え、アクセントや発音等が改善される可能性があります。新しいバージョンで学習し直すとより良くなる可能性もあります。が劇的に良くなるかは分かりません。
+
+### 改善
+- `Dataset.bat`の音声スライスと書き起こしをよりカスタマイズできるように（スライスの秒数設定や書き起こしのWhisperモデル指定や言語指定等）
+- `Style.bat`のスタイル分けで、スタイルごとのサンプル音声を指定した数だけ複数再生できるように。また新しい次元削減方法（UMAP）と新しいスタイル分けの方法（DBSCAN）を追加（UMAPのほうがよくスタイルが分かれるかもしれません）
+- `App.bat`での音声合成時に複数話者モデルの場合に話者を指定できるように
+- colabの[ノートブック](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)で、音声ファイルのみからデータセットを作成するオプション部分を追加
+- クラウド実行等の際にパスの指定をこちらでできるように、パスの設定を`configs/paths.yml`にまとめた（colabの[ノートブック](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)もそれに伴って更新）。デフォルトは`dataset_root: Data`と`assets_root: model_assets`なので、クラウド等でやる方はここを変更してください。
+- どのステップ数の出力がよいかの「一つの」指標として [SpeechMOS](https://github.com/tarepan/SpeechMOS) を使うスクリプトを追加：
+```bash
+python speech_mos.py -m <model_name>
+```
+ステップごとの自然性評価が表示され、`mos_results`フォルダの`mos_{model_name}.csv`と`mos_{model_name}.png`に結果が保存される。読み上げさせたい文章を変えたかったら中のファイルを弄って各自調整してください。あくまでアクセントや感情表現や抑揚を全く考えない基準での評価で、目安のひとつなので、実際に読み上げさせて選別するのが一番だと思います。
+- 学習時のウォームアップオプションを機能するように（ [@kale4eat](https://github.com/kale4eat) 様によるPRです、ありがとうございます！）。前処理時に生成される`config.json`の`train`の`warmup_epochs`を変更することで、ウォームアップのエポック数を変更できます。デフォルトは`0`で今までと同じ学習率の挙動です。
+
+### その他
+- `Dataset.bat`の音声スライスでノーマライズ機能を削除（学習前処理で行えるため）
+- `Train.bat`の音量ノーマライズと無音切り詰めをデフォルトでオフに変更
+- 学習時の進捗を全体エポック数で表示し、学習全体の進捗を見やすいように( [@RedRayz](https://github.com/RedRayz) 様によるPRです、ありがとうございます！)
+- その他バグ修正等（ [@tinjyuu](https://github.com/@tinjyuu) 様、 [@darai0512](https://github.com/darai0512) 様ありがとうございます！）
+- `config.json`にスタイル埋め込み部分を学習しない`freeze_style`オプションを追加（デフォルトは`false`）
+
+### TIPS
+- 日本語学習の場合、`config.json`の`freeze_bert`と`freeze_en_bert`を`true`にしておくと、英語と中国語の発話能力が学習の過程で落ちないかもしれませんが、あまり比較していなので分かりません。
+
+## v1.2 (2023-12-31)
+
+- グラボがないユーザーでの音声合成をサポート、`Install-Style-Bert-VITS2-CPU.bat`でインストール。
+- Google Colabでの学習をサポート、[ノートブック](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)を追加
+- 音声合成のAPIサーバーを追加、`python server_fastapi.py`で起動します。API仕様は起動後に`/docs`にて確認ください。（ [@darai0512](https://github.com/darai0512) 様によるPRです、ありがとうございます！）
+- 学習時に自動的にデフォルトスタイル Neutral を生成するように。特にスタイル指定が必要のない方は、学習したらそのまま音声合成を試せます。これまで通りスタイルを自分で作ることもできます。
+- マージ機能の新規追加: `Merge.bat`, `webui_merge.py`
+- 前処理のリサンプリング時に音声ファイルの開始・終了部分の無音を削除するオプションを追加（デフォルトでオン）
+- `スタイルテキスト (style text)`がスタイル指定と紛らわしかったので、`アシストテキスト (assist text)`に変更
+- その他コードのリファクタリング
+
+## v1.1 (2023-12-29)
+- TrainとDatasetのWebUIの改良・調整（一括事前処理ボタン等）
+- 前処理のリサンプリング時に音量を正規化するオプションを追加（デフォルトでオン）
+
+## v1.0 (2023-12-27)
+- 初版
diff --git a/docs/CLI.md b/docs/CLI.md
new file mode 100644
index 0000000000000000000000000000000000000000..ca955e169c998e858de996d09fc8089c85c74156
--- /dev/null
+++ b/docs/CLI.md
@@ -0,0 +1,56 @@
+# CLI
+
+**WIP**
+
+## Dataset
+
+`Dataset.bat` webui (`python webui_dataset.py`) consists of **slice audio** and **transcribe wavs**.
+
+### Slice audio
+
+```bash
+python slice.py -i <input_dir> -o <output_dir> -m <min_sec> -M <max_sec>
+```
+
+Required:
+- `input_dir`: Path to the directory containing the audio files to slice.
+- `output_dir`: Path to the directory where the sliced audio files will be saved.
+
+Optional:
+- `min_sec`: Minimum duration of the sliced audio files in seconds (default 2).
+- `max_sec`: Maximum duration of the sliced audio files in seconds (default 12).
+
+### Transcribe wavs
+
+```bash
+python transcribe.py -i <input_dir> -o <output_file> --speaker_name <speaker_name>
+```
+
+Required:
+- `input_dir`: Path to the directory containing the audio files to transcribe.
+- `output_file`: Path to the file where the transcriptions will be saved.
+- `speaker_name`: Name of the speaker.
+
+Optional
+- `--initial_prompt`: Initial prompt to use for the transcription (default value is specific to Japanese).
+- `--device`: `cuda` or `cpu` (default: `cuda`).
+- `--language`: `jp`, `en`, or `en` (default: `jp`).
+- `--model`: Whisper model, default: `large-v3`
+- `--compute_type`: default: `bfloat16`
+
+## Train
+
+`Train.bat` webui (`python webui_train.py`) consists of the following.
+
+### Preprocess audio
+```bash
+python resample.py -i <input_dir> -o <output_dir> [--normalize] [--trim]
+```
+
+Required:
+- `input_dir`: Path to the directory containing the audio files to preprocess.
+- `output_dir`: Path to the directory where the preprocessed audio files will be saved.
+
+TO BE WRITTEN (WIP)
+
+これいる？
diff --git a/docs/README_en.md b/docs/README_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..7dd53ae0d3b9331a3612280816c491dc1f3913b0
--- /dev/null
+++ b/docs/README_en.md
@@ -0,0 +1,125 @@
+# This English README is for 1.x versions. WIP for 2.x versions.
+
+# Style-Bert-VITS2
+
+Bert-VITS2 with more controllable voice styles.
+
+https://github.com/litagin02/Style-Bert-VITS2/assets/139731664/b907c1b8-43aa-46e6-b03f-f6362f5a5a1e
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)
+
+Online demo: https://huggingface.co/spaces/litagin/Style-Bert-VITS2-JVNV
+
+This repository is based on [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2) v2.1, so many thanks to the original author!
+
+- [Update History](docs/CHANGELOG.md)
+
+**Overview**
+
+- Based on Bert-VITS2 v2.1, which generates emotionally rich voices from entered text, this version allows free control of emotions and speaking styles, including intensity.
+- Easy to install and train for people without Git or Python (for Windows users), much is borrowed from [EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2/). Training on Google Colab is also supported: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)
+- If used only for voice synthesis, it can operate on CPU without a graphics card.
+- Also includes an API server for integration with others (PR by [@darai0512](https://github.com/darai0512), thank you).
+- Originally, Bert-VITS2's strength was to read "happy text happily, sad text sadly", so even without using the added style specification in this fork, you can generate emotionally rich voices.
+
+
+## How to Use
+
+<!-- For more details, please refer to [here](docs/tutorial.md). -->
+
+### Operating Environment
+
+We have confirmed the operation in Windows Command Prompt, WSL2, and Linux (Ubuntu Desktop) for each UI and API Server (please be creative with path specifications in WSL).
+
+### Installation
+
+#### For Those Unfamiliar with Git or Python
+
+Assuming Windows:
+
+1. Download and unzip [this zip file](https://github.com/litagin02/Style-Bert-VITS2/releases/download/1.3/Style-Bert-VITS2.zip).
+   - If you have a graphics card, double-click `Install-Style-Bert-VITS2.bat`.
+   - If you don't have a graphics card, double-click `Install-Style-Bert-VITS2-CPU.bat`.
+2. Wait for the necessary environment to install automatically.
+3. After that, if the WebUI for voice synthesis launches automatically, the installation is successful. The default model will be downloaded, so you can play with it immediately.
+
+For updates, please double-click `Update-Style-Bert-VITS2.bat`.
+
+#### For Those Familiar with Git and Python
+
+```bash
+git clone https://github.com/litagin02/Style-Bert-VITS2.git
+cd Style-Bert-VITS2
+python -m venv venv
+venv\Scripts\activate
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+python initialize.py  # Download necessary models and default TTS model
+```
+Don't forget the last step.
+
+### Voice Synthesis
+Double-click `App.bat` or run `python app.py` to launch the WebUI. The default model is downloaded during installation, so you can use it even if you haven't trained it.
+
+The structure of the model files required for voice synthesis is as follows (you don't need to place them manually):
+
+```
+model_assets
+├── your_model
+│   ├── config.json
+│   ├── your_model_file1.safetensors
+│   ├── your_model_file2.safetensors
+│   ├── ...
+│   └── style_vectors.npy
+└── another_model
+    ├── ...
+```
+
+For inference, `config.json`, `*.safetensors`, and `style_vectors.npy` are necessary. If you want to share a model, please share these three files.
+
+Among them, `style_vectors.npy` is a file necessary to control the style. By default, the average style "Neutral" is generated during training.
+If you want to use multiple styles for more detailed control, please refer to "Generating Styles" below (even with only the average style, if the training data is emotionally rich, sufficiently emotionally rich voices can be generated).
+
+### Training
+
+Double-click Train.bat or run `python webui_train.py` to launch the WebUI.
+
+### Generating Styles
+For those who want to use styles other than the default "Neutral".
+
+- Double-click `Style.bat` or run `python webui_style_vectors.py` to launch the WebUI.
+- It is independent of training, so you can do it even during training, and you can redo it any number of times after training is complete (preprocessing must be finished).
+- For more details on the specifications of the style, please refer to [clustering.ipynb](../clustering.ipynb).
+
+### Dataset Creation
+
+- Double-click `Dataset.bat` or run `python webui_dataset.py` to launch the WebUI for creating datasets from audio files. You can use this tool to learn from audio files only.
+
+Note: If you want to manually correct the dataset, remove noise, etc., you may find [Aivis](https://github.com/tsukumijima/Aivis) or its Windows-compatible dataset part [Aivis Dataset](https://github.com/litagin02/Aivis-Dataset) useful. However, if there are many files, etc., it may be sufficient to simply cut out and create a dataset with this tool.
+
+Please experiment to see what kind of dataset is best.
+
+### API Server
+Run `python server_fastapi.py` in the constructed environment to launch the API server.
+Please check the API specification after launching at `/docs`.
+
+By default, CORS settings are allowed for all domains.
+As much as possible, change the value of server.origins in `config.yml` and limit it to trusted domains (if you delete the key, you can disable the CORS settings).
+
+### Merging
+You can create a new model by mixing two models in terms of "voice", "emotional expression", and "tempo".
+Double-click `Merge.bat` or run `python webui_merge.py` to launch the WebUI.
+
+## Relation to Bert-VITS2 v2.1
+Basically, it's just a slight modification of the Bert-VITS2 v2.1 model structure. The [pre-trained model](https://huggingface.co/litagin/Style-Bert-VITS2-1.0-base) is also essentially the same as Bert-VITS2 v2.1 (unnecessary weights have been removed and converted to safetensors).
+
+The differences are as follows:
+
+- Like [EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2), it is easy to use even for people who do not know Python or Git.
+- Changed the model for emotional embedding (from 1024-dimensional [wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim) to 256-dimensional [wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM), which is more for speaker identification than emotional embedding)
+- Removed vector quantization from embeddings and replaced it with just a fully connected layer.
+- By creating a style vector file `style_vectors.npy`, you can generate voices using that style and continuously specify the strength of the effect.
+- Various WebUIs created
+- Support for bf16 training
+- Support for safetensors format, defaulting to using safetensors
+- Other minor bug fixes and refactoring
\ No newline at end of file
diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e88bfa31a2b3e4d81b0605e0094d612b5fd2de97
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,306 @@
+import torch
+
+import commons
+import utils
+from models import SynthesizerTrn
+from models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
+from text import cleaned_text_to_sequence, get_bert
+from text.cleaner import clean_text
+from text.symbols import symbols
+from common.log import logger
+
+
+class InvalidToneError(ValueError):
+    pass
+
+
+def get_net_g(model_path: str, version: str, device: str, hps):
+    if version.endswith("JP-Extra"):
+        logger.info("Using JP-Extra model")
+        net_g = SynthesizerTrnJPExtra(
+            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)
+    else:
+        logger.info("Using normal model")
+        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.state_dict()
+    _ = net_g.eval()
+    if model_path.endswith(".pth") or model_path.endswith(".pt"):
+        _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
+    elif model_path.endswith(".safetensors"):
+        _ = utils.load_safetensors(model_path, net_g, True)
+    else:
+        raise ValueError(f"Unknown model format: {model_path}")
+    return net_g
+
+
+def get_text(
+    text,
+    language_str,
+    hps,
+    device,
+    assist_text=None,
+    assist_text_weight=0.7,
+    given_tone=None,
+):
+    use_jp_extra = hps.version.endswith("JP-Extra")
+    norm_text, phone, tone, word2ph = clean_text(text, language_str, use_jp_extra)
+    if given_tone is not None:
+        if len(given_tone) != len(phone):
+            raise InvalidToneError(
+                f"Length of given_tone ({len(given_tone)}) != length of phone ({len(phone)})"
+            )
+        tone = given_tone
+    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
+    bert_ori = get_bert(
+        norm_text, word2ph, language_str, device, assist_text, assist_text_weight
+    )
+    del word2ph
+    assert bert_ori.shape[-1] == len(phone), phone
+
+    if language_str == "ZH":
+        bert = bert_ori
+        ja_bert = torch.zeros(1024, len(phone))
+        en_bert = torch.zeros(1024, len(phone))
+    elif language_str == "JP":
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = bert_ori
+        en_bert = torch.zeros(1024, len(phone))
+    elif language_str == "EN":
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = torch.zeros(1024, len(phone))
+        en_bert = bert_ori
+    else:
+        raise ValueError("language_str should be ZH, JP or EN")
+
+    assert bert.shape[-1] == len(
+        phone
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, en_bert, phone, tone, language
+
+
+def infer(
+    text,
+    style_vec,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid: int,  # In the original Bert-VITS2, its speaker_name: str, but here it's id
+    language,
+    hps,
+    net_g,
+    device,
+    skip_start=False,
+    skip_end=False,
+    assist_text=None,
+    assist_text_weight=0.7,
+    given_tone=None,
+):
+    is_jp_extra = hps.version.endswith("JP-Extra")
+    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
+        text,
+        language,
+        hps,
+        device,
+        assist_text=assist_text,
+        assist_text_weight=assist_text_weight,
+        given_tone=given_tone,
+    )
+    if skip_start:
+        phones = phones[3:]
+        tones = tones[3:]
+        lang_ids = lang_ids[3:]
+        bert = bert[:, 3:]
+        ja_bert = ja_bert[:, 3:]
+        en_bert = en_bert[:, 3:]
+    if skip_end:
+        phones = phones[:-2]
+        tones = tones[:-2]
+        lang_ids = lang_ids[:-2]
+        bert = bert[:, :-2]
+        ja_bert = ja_bert[:, :-2]
+        en_bert = en_bert[:, :-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)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        en_bert = en_bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        style_vec = torch.from_numpy(style_vec).to(device).unsqueeze(0)
+        del phones
+        sid_tensor = torch.LongTensor([sid]).to(device)
+        if is_jp_extra:
+            output = net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                sid_tensor,
+                tones,
+                lang_ids,
+                ja_bert,
+                style_vec=style_vec,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )
+        else:
+            output = net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                sid_tensor,
+                tones,
+                lang_ids,
+                bert,
+                ja_bert,
+                en_bert,
+                style_vec=style_vec,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )
+        audio = output[0][0, 0].data.cpu().float().numpy()
+        del (
+            x_tst,
+            tones,
+            lang_ids,
+            bert,
+            x_tst_lengths,
+            sid_tensor,
+            ja_bert,
+            en_bert,
+            style_vec,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
+
+
+def infer_multilang(
+    text,
+    style_vec,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+    skip_start=False,
+    skip_end=False,
+):
+    bert, ja_bert, en_bert, 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_bert,
+            temp_ja_bert,
+            temp_en_bert,
+            temp_phones,
+            temp_tones,
+            temp_lang_ids,
+        ) = get_text(txt, lang, hps, device)
+        if _skip_start:
+            temp_bert = temp_bert[:, 3:]
+            temp_ja_bert = temp_ja_bert[:, 3:]
+            temp_en_bert = temp_en_bert[:, 3:]
+            temp_phones = temp_phones[3:]
+            temp_tones = temp_tones[3:]
+            temp_lang_ids = temp_lang_ids[3:]
+        if _skip_end:
+            temp_bert = temp_bert[:, :-2]
+            temp_ja_bert = temp_ja_bert[:, :-2]
+            temp_en_bert = temp_en_bert[:, :-2]
+            temp_phones = temp_phones[:-2]
+            temp_tones = temp_tones[:-2]
+            temp_lang_ids = temp_lang_ids[:-2]
+        bert.append(temp_bert)
+        ja_bert.append(temp_ja_bert)
+        en_bert.append(temp_en_bert)
+        phones.append(temp_phones)
+        tones.append(temp_tones)
+        lang_ids.append(temp_lang_ids)
+    bert = torch.concatenate(bert, dim=1)
+    ja_bert = torch.concatenate(ja_bert, dim=1)
+    en_bert = torch.concatenate(en_bert, dim=1)
+    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)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        en_bert = en_bert.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,
+                bert,
+                ja_bert,
+                en_bert,
+                style_vec=style_vec,
+                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,
+            bert,
+            x_tst_lengths,
+            speakers,
+            ja_bert,
+            en_bert,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
diff --git a/initialize.py b/initialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..c163ef9f6ccedd79cc0f3ad19a177d0f64d208b1
--- /dev/null
+++ b/initialize.py
@@ -0,0 +1,107 @@
+import argparse
+import json
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+from common.log import logger
+
+
+def download_bert_models():
+    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)
+        for file in v["files"]:
+            if not Path(local_path).joinpath(file).exists():
+                logger.info(f"Downloading {k} {file}")
+                hf_hub_download(
+                    v["repo_id"],
+                    file,
+                    local_dir=local_path,
+                    local_dir_use_symlinks=False,
+                )
+
+
+def download_slm_model():
+    local_path = Path("slm/wavlm-base-plus/")
+    file = "pytorch_model.bin"
+    if not Path(local_path).joinpath(file).exists():
+        logger.info(f"Downloading wavlm-base-plus {file}")
+        hf_hub_download(
+            "microsoft/wavlm-base-plus",
+            file,
+            local_dir=local_path,
+            local_dir_use_symlinks=False,
+        )
+
+
+def download_pretrained_models():
+    files = ["G_0.safetensors", "D_0.safetensors", "DUR_0.safetensors"]
+    local_path = Path("pretrained")
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            logger.info(f"Downloading pretrained {file}")
+            hf_hub_download(
+                "litagin/Style-Bert-VITS2-1.0-base",
+                file,
+                local_dir=local_path,
+                local_dir_use_symlinks=False,
+            )
+
+
+def download_jp_extra_pretrained_models():
+    files = ["G_0.safetensors", "D_0.safetensors", "WD_0.safetensors"]
+    local_path = Path("pretrained_jp_extra")
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            logger.info(f"Downloading JP-Extra pretrained {file}")
+            hf_hub_download(
+                "litagin/Style-Bert-VITS2-2.0-base-JP-Extra",
+                file,
+                local_dir=local_path,
+                local_dir_use_symlinks=False,
+            )
+
+
+def download_jvnv_models():
+    files = [
+        "jvnv-F1-jp/config.json",
+        "jvnv-F1-jp/jvnv-F1-jp_e160_s14000.safetensors",
+        "jvnv-F1-jp/style_vectors.npy",
+        "jvnv-F2-jp/config.json",
+        "jvnv-F2-jp/jvnv-F2_e166_s20000.safetensors",
+        "jvnv-F2-jp/style_vectors.npy",
+        "jvnv-M1-jp/config.json",
+        "jvnv-M1-jp/jvnv-M1-jp_e158_s14000.safetensors",
+        "jvnv-M1-jp/style_vectors.npy",
+        "jvnv-M2-jp/config.json",
+        "jvnv-M2-jp/jvnv-M2-jp_e159_s17000.safetensors",
+        "jvnv-M2-jp/style_vectors.npy",
+    ]
+    for file in files:
+        if not Path(f"model_assets/{file}").exists():
+            logger.info(f"Downloading {file}")
+            hf_hub_download(
+                "litagin/style_bert_vits2_jvnv",
+                file,
+                local_dir="model_assets",
+                local_dir_use_symlinks=False,
+            )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--skip_jvnv", action="store_true")
+    args = parser.parse_args()
+
+    download_bert_models()
+
+    download_slm_model()
+
+    download_pretrained_models()
+
+    download_jp_extra_pretrained_models()
+
+    if not args.skip_jvnv:
+        download_jvnv_models()
diff --git a/inputs/.gitignore b/inputs/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..d6b7ef32c8478a48c3994dcadc86837f4371184d
--- /dev/null
+++ b/inputs/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
diff --git a/losses.py b/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..763cc028511570a69924eb434bb648536171ebfc
--- /dev/null
+++ b/losses.py
@@ -0,0 +1,155 @@
+import torch
+import torchaudio
+from transformers import AutoModel
+
+from common.log import logger
+
+
+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, 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..e9e7ec3622a0ee940e238f29c3f0d7509abfe59b
--- /dev/null
+++ b/mel_processing.py
@@ -0,0 +1,146 @@
+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(
+            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=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
diff --git a/model_assets/fi-epoch100-1.0.0/config.json b/model_assets/fi-epoch100-1.0.0/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ef6b7b3159e471e36fbfc63a84d6615a61f03f7b
--- /dev/null
+++ b/model_assets/fi-epoch100-1.0.0/config.json
@@ -0,0 +1,117 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 100,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 4,
+    "bf16_run": false,
+    "fp16_run": false,
+    "lr_decay": 0.99996,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": true,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_emo": false,
+    "freeze_style": false
+  },
+  "data": {
+    "use_jp_extra": true,
+    "training_files": "Data/fi-epoch100-1.0.0/train.list",
+    "validation_files": "Data/fi-epoch100-1.0.0/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": {
+      "fi-epoch100-1.0.0": 0
+    },
+    "num_styles": 1,
+    "style2id": {
+      "Neutral": 0
+    }
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": false,
+    "use_wavlm_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": "2.1-JP-Extra",
+  "model_name": "fi-epoch100-1.0.0"
+}
\ No newline at end of file
diff --git a/model_assets/fi-epoch100-1.0.0/fi-epoch100-1.0.0_e100_s27200.safetensors b/model_assets/fi-epoch100-1.0.0/fi-epoch100-1.0.0_e100_s27200.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..b32697498f9e646b20202c64260a560270c2f6ab
--- /dev/null
+++ b/model_assets/fi-epoch100-1.0.0/fi-epoch100-1.0.0_e100_s27200.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fe14a3e18d1727caeed54f093536c2a4bba6b0f1e77f64734bbed4be64de4b6
+size 251150980
diff --git a/model_assets/fi-epoch100-1.0.0/style_vectors.npy b/model_assets/fi-epoch100-1.0.0/style_vectors.npy
new file mode 100644
index 0000000000000000000000000000000000000000..2282f2a8b2fb83c36133a505356f0f691cba0c94
--- /dev/null
+++ b/model_assets/fi-epoch100-1.0.0/style_vectors.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f26f5e0321401494273d76b9318bf4408ae6aa43cdb1d19a6aaa2ac6b46a7012
+size 1152
diff --git a/models.py b/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb706fd664012029e0e5c68c94c350e158b5a912
--- /dev/null
+++ b/models.py
@@ -0,0 +1,1024 @@
+import math
+import warnings
+
+import torch
+from torch import nn
+from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+import attentions
+import commons
+import modules
+import monotonic_align
+from commons import get_padding, init_weights
+from text import num_languages, num_tones, symbols
+
+
+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.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__()
+        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, 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 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.style_proj = nn.Linear(256, hidden_channels)
+
+        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,
+        bert,
+        ja_bert,
+        en_bert,
+        style_vec,
+        sid,
+        g=None,
+    ):
+        bert_emb = self.bert_proj(bert).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert).transpose(1, 2)
+        en_bert_emb = self.en_bert_proj(en_bert).transpose(1, 2)
+        style_emb = self.style_proj(style_vec.unsqueeze(1))
+
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+            + style_emb
+        ) * 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 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 forward(
+        self,
+        x,
+        x_lengths,
+        y,
+        y_lengths,
+        sid,
+        tone,
+        language,
+        bert,
+        ja_bert,
+        en_bert,
+        style_vec,
+    ):
+        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, bert, ja_bert, en_bert, style_vec, sid, 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_),
+        )
+
+    def infer(
+        self,
+        x,
+        x_lengths,
+        sid,
+        tone,
+        language,
+        bert,
+        ja_bert,
+        en_bert,
+        style_vec,
+        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, bert, ja_bert, en_bert, style_vec, sid, 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/models_jp_extra.py b/models_jp_extra.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bb2dd2e3b76a3d1978cd591eb4d4415c4c8a9ba
--- /dev/null
+++ b/models_jp_extra.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__()
+        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, 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.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+
+        # Remove emo_vq since it's not working well.
+        self.style_proj = nn.Linear(256, hidden_channels)
+
+        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, bert, style_vec, g=None):
+        bert_emb = self.bert_proj(bert).transpose(1, 2)
+        style_emb = self.style_proj(style_vec.unsqueeze(1))
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + style_emb
+        ) * 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=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 forward(
+        self,
+        x,
+        x_lengths,
+        y,
+        y_lengths,
+        sid,
+        tone,
+        language,
+        bert,
+        style_vec,
+    ):
+        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, bert, style_vec, 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,
+        bert,
+        style_vec,
+        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, bert, style_vec, 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..86b93b50a56b047bb46ee6b6160c1d0b40e1101d
--- /dev/null
+++ b/modules.py
@@ -0,0 +1,581 @@
+import math
+import warnings
+
+import torch
+from torch import nn
+from torch.nn import Conv1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+import commons
+from attentions import Encoder
+from commons import get_padding, init_weights
+from transforms import piecewise_rational_quadratic_transform
+
+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):
+    """
+    Dialted 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/__pycache__/__init__.cpython-310.pyc b/monotonic_align/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..75ee1ed28ed7734b80354d8e8ea75326460b2bcc
Binary files /dev/null and b/monotonic_align/__pycache__/__init__.cpython-310.pyc differ
diff --git a/monotonic_align/__pycache__/core.cpython-310.pyc b/monotonic_align/__pycache__/core.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..16688521c877de768a421b42ce5e9a533c5537dc
Binary files /dev/null and b/monotonic_align/__pycache__/core.cpython-310.pyc differ
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/preprocess_text.py b/preprocess_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fba5ebd0ab2353828fe30c77e503068b8bb65dd
--- /dev/null
+++ b/preprocess_text.py
@@ -0,0 +1,146 @@
+import json
+import os
+from collections import defaultdict
+from random import shuffle
+from typing import Optional
+
+import click
+from tqdm import tqdm
+
+from config import config
+from text.cleaner import clean_text
+from common.stdout_wrapper import SAFE_STDOUT
+from common.log import logger
+
+preprocess_text_config = config.preprocess_text_config
+
+
+@click.command()
+@click.option(
+    "--transcription-path",
+    default=preprocess_text_config.transcription_path,
+    type=click.Path(exists=True, file_okay=True, dir_okay=False),
+)
+@click.option("--cleaned-path", default=preprocess_text_config.cleaned_path)
+@click.option("--train-path", default=preprocess_text_config.train_path)
+@click.option("--val-path", default=preprocess_text_config.val_path)
+@click.option(
+    "--config-path",
+    default=preprocess_text_config.config_path,
+    type=click.Path(exists=True, file_okay=True, dir_okay=False),
+)
+@click.option("--val-per-lang", default=preprocess_text_config.val_per_lang)
+@click.option("--max-val-total", default=preprocess_text_config.max_val_total)
+@click.option("--clean/--no-clean", default=preprocess_text_config.clean)
+@click.option("-y", "--yml_config")
+@click.option("--use_jp_extra", is_flag=True)
+def preprocess(
+    transcription_path: str,
+    cleaned_path: Optional[str],
+    train_path: str,
+    val_path: str,
+    config_path: str,
+    val_per_lang: int,
+    max_val_total: int,
+    clean: bool,
+    yml_config: str,  # 这个不要删
+    use_jp_extra: bool,
+):
+    if cleaned_path == "" or cleaned_path is None:
+        cleaned_path = transcription_path + ".cleaned"
+
+    if clean:
+        with open(cleaned_path, "w", encoding="utf-8") as out_file:
+            with open(transcription_path, "r", encoding="utf-8") as trans_file:
+                for line in tqdm(trans_file, file=SAFE_STDOUT):
+                    try:
+                        utt, spk, language, text = line.strip().split("|")
+                        norm_text, phones, tones, word2ph = clean_text(
+                            text, language, use_jp_extra
+                        )
+                        out_file.write(
+                            "{}|{}|{}|{}|{}|{}|{}\n".format(
+                                utt,
+                                spk,
+                                language,
+                                norm_text,
+                                " ".join(phones),
+                                " ".join([str(i) for i in tones]),
+                                " ".join([str(i) for i in word2ph]),
+                            )
+                        )
+                    except Exception as e:
+                        logger.error(
+                            f"An error occurred while generating the training set and validation set, at line:\n{line}\nDetails:\n{e}"
+                        )
+                        raise
+
+    transcription_path = cleaned_path
+    spk_utt_map = defaultdict(list)
+    spk_id_map = {}
+    current_sid = 0
+
+    with open(transcription_path, "r", encoding="utf-8") as f:
+        audioPaths = set()
+        countSame = 0
+        countNotFound = 0
+        for line in f.readlines():
+            utt, spk, language, text, phones, tones, word2ph = line.strip().split("|")
+            if utt in audioPaths:
+                # 过滤数据集错误：相同的音频匹配多个文本，导致后续bert出问题
+                logger.warning(f"Same audio matches multiple texts: {line}")
+                countSame += 1
+                continue
+            if not os.path.isfile(utt):
+                # 过滤数据集错误：不存在对应音频
+                logger.warning(f"Audio not found: {utt}")
+                countNotFound += 1
+                continue
+            audioPaths.add(utt)
+            spk_utt_map[language].append(line)
+            if spk not in spk_id_map.keys():
+                spk_id_map[spk] = current_sid
+                current_sid += 1
+        logger.info(
+            f"Total repeated audios: {countSame}, Total number of audio not found: {countNotFound}"
+        )
+
+    train_list = []
+    val_list = []
+
+    for spk, utts in spk_utt_map.items():
+        shuffle(utts)
+        val_list += utts[:val_per_lang]
+        train_list += utts[val_per_lang:]
+
+    shuffle(val_list)
+    if len(val_list) > max_val_total:
+        train_list += val_list[max_val_total:]
+        val_list = val_list[:max_val_total]
+
+    with open(train_path, "w", encoding="utf-8") as f:
+        for line in train_list:
+            f.write(line)
+
+    with open(val_path, "w", encoding="utf-8") as f:
+        for line in val_list:
+            f.write(line)
+
+    json_config = json.load(open(config_path, encoding="utf-8"))
+    json_config["data"]["spk2id"] = spk_id_map
+    json_config["data"]["n_speakers"] = len(spk_id_map)
+    # 新增写入：写入训练版本、数据集路径
+    # json_config["version"] = latest_version
+    json_config["data"]["training_files"] = os.path.normpath(train_path).replace(
+        "\\", "/"
+    )
+    json_config["data"]["validation_files"] = os.path.normpath(val_path).replace(
+        "\\", "/"
+    )
+    with open(config_path, "w", encoding="utf-8") as f:
+        json.dump(json_config, f, indent=2, ensure_ascii=False)
+    logger.info("Training set and validation set generation from texts is complete!")
+
+
+if __name__ == "__main__":
+    preprocess()
diff --git a/pretrained/DUR_0.safetensors b/pretrained/DUR_0.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..a111187b83e3c5678e4838cd672cfd89558ceb68
--- /dev/null
+++ b/pretrained/DUR_0.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:802e99864e072bead2630f5622733620d489175e14f34fe70a09405d7d55f08d
+size 2422556
diff --git a/pretrained/D_0.safetensors b/pretrained/D_0.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..6fa961bd8401b18cf6ffc7e3e614118c60638d93
--- /dev/null
+++ b/pretrained/D_0.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc27022d712fcc030db11ebb6d023c95310d2d909d32126de9abfa92d00e8dfc
+size 187000064
diff --git a/pretrained/G_0.safetensors b/pretrained/G_0.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..897dbacc449fc199547a4a5cf59a244e3b6262d9
--- /dev/null
+++ b/pretrained/G_0.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee9718a5f3ef1a4ce47fedaad8504e67bfd68b536493ee2d8642be42b450470c
+size 234266180
diff --git a/pretrained_jp_extra/D_0.safetensors b/pretrained_jp_extra/D_0.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..256ab500339946ccce6e19161168e06200820578
--- /dev/null
+++ b/pretrained_jp_extra/D_0.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d8de5a29a00c4bbe682d27cd882b2837ac9a16cc9025d7be76b67954b743ef3
+size 187000064
diff --git a/pretrained_jp_extra/G_0.safetensors b/pretrained_jp_extra/G_0.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..f356694174b44603a5706cb6d4b3253d5b81b654
--- /dev/null
+++ b/pretrained_jp_extra/G_0.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90c26f3dc5f6678a695c5308fdf9441fb5be15cdcb87f27e811084f25e87c92d
+size 292939468
diff --git a/pretrained_jp_extra/WD_0.safetensors b/pretrained_jp_extra/WD_0.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..227f366ce5f3f15cb677e268cd09621ca6c0b75d
--- /dev/null
+++ b/pretrained_jp_extra/WD_0.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba4a0fb0d611f05ad2656c8cfe8282d53f42c61aeeb5aafcd1daf5c05ece6523
+size 4695736
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..3bc57ac2df35c45e1a46c45ab0606c13836113f8
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,29 @@
+cmudict
+cn2an
+faster-whisper
+g2p_en
+GPUtil
+gradio
+jaconv
+jieba
+langid
+librosa
+loguru
+matplotlib
+mecab-python3
+num2words
+numba
+numpy
+psutil
+pyannote.audio
+pyloudnorm
+pyopenjtalk-prebuilt
+pypinyin
+PyYAML
+requests
+safetensors
+scipy
+tensorboard
+torch
+transformers
+umap-learn
\ No newline at end of file
diff --git a/resample.py b/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..5aff5ef4907ede6a8ba3c27d0ec676884691206e
--- /dev/null
+++ b/resample.py
@@ -0,0 +1,131 @@
+import argparse
+import os
+from concurrent.futures import ThreadPoolExecutor
+
+import librosa
+import pyloudnorm as pyln
+import soundfile
+from tqdm import tqdm
+
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+from config import config
+
+DEFAULT_BLOCK_SIZE: float = 0.400  # seconds
+
+
+class BlockSizeException(Exception):
+    pass
+
+
+def normalize_audio(data, sr):
+    meter = pyln.Meter(sr, block_size=DEFAULT_BLOCK_SIZE)  # create BS.1770 meter
+    try:
+        loudness = meter.integrated_loudness(data)
+    except ValueError as e:
+        raise BlockSizeException(e)
+    # logger.info(f"loudness: {loudness}")
+    data = pyln.normalize.loudness(data, loudness, -23.0)
+    return data
+
+
+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)
+        if args.normalize:
+            try:
+                wav = normalize_audio(wav, sr)
+            except BlockSizeException:
+                logger.info(
+                    f"Skip normalize due to less than {DEFAULT_BLOCK_SIZE} second audio: {wav_path}"
+                )
+        if args.trim:
+            wav, _ = librosa.effects.trim(wav, top_db=30)
+        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",
+        "-i",
+        type=str,
+        default=config.resample_config.in_dir,
+        help="path to source dir",
+    )
+    parser.add_argument(
+        "--out_dir",
+        "-o",
+        type=str,
+        default=config.resample_config.out_dir,
+        help="path to target dir",
+    )
+    parser.add_argument(
+        "--num_processes",
+        type=int,
+        default=4,
+        help="cpu_processes",
+    )
+    parser.add_argument(
+        "--normalize",
+        action="store_true",
+        default=False,
+        help="loudness normalize audio",
+    )
+    parser.add_argument(
+        "--trim",
+        action="store_true",
+        default=False,
+        help="trim silence (start and end only)",
+    )
+    args, _ = parser.parse_known_args()
+    # autodl 无卡模式会识别出46个cpu
+    if args.num_processes == 0:
+        processes = cpu_count() - 2 if cpu_count() > 4 else 1
+    else:
+        processes = args.num_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)
+
+    if len(tasks) == 0:
+        logger.error(f"No wav files found in {args.in_dir}")
+        raise ValueError(f"No wav files found in {args.in_dir}")
+
+    # pool = Pool(processes=processes)
+    # for _ in tqdm(
+    #     pool.imap_unordered(process, tasks), file=SAFE_STDOUT, total=len(tasks)
+    # ):
+    #     pass
+
+    # pool.close()
+    # pool.join()
+
+    with ThreadPoolExecutor(max_workers=processes) as executor:
+        _ = list(
+            tqdm(
+                executor.map(process, tasks),
+                total=len(tasks),
+                file=SAFE_STDOUT,
+            )
+        )
+
+    logger.info("Resampling Done!")
diff --git a/scripts/Install-Style-Bert-VITS2-CPU.bat b/scripts/Install-Style-Bert-VITS2-CPU.bat
new file mode 100644
index 0000000000000000000000000000000000000000..e5d106fa8d6010be194e77a105fea856e3d2ab5f
--- /dev/null
+++ b/scripts/Install-Style-Bert-VITS2-CPU.bat
@@ -0,0 +1,70 @@
+chcp 65001 > NUL
+@echo off
+
+@REM https://github.com/Zuntan03/EasyBertVits2 より引用・改変
+
+pushd %~dp0
+set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass
+
+set CURL_CMD=C:\Windows\System32\curl.exe
+if not exist %CURL_CMD% (
+	echo [ERROR] %CURL_CMD% が見つかりません。
+	pause & popd & exit /b 1
+)
+
+@REM lib フォルダがなければ作成
+if not exist lib\ ( mkdir lib )
+
+@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード
+%CURL_CMD% -Lo Style-Bert-VITS2.zip^
+	https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる）
+%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM 元のzipを削除
+del Style-Bert-VITS2.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動
+xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\
+rmdir /s /q Style-Bert-VITS2-master
+
+echo ----------------------------------------
+echo Python環境の構築を開始します。
+echo ----------------------------------------
+
+@REM Pythonと仮想環境のセットアップを呼び出す（仮想環境が有効化されて戻ってくる）
+call Style-Bert-VITS2\scripts\Setup-Python.bat ..\..\lib\python ..\venv
+if %errorlevel% neq 0 ( popd & exit /b %errorlevel% )
+
+@REM 依存関係インストール
+pip install -r Style-Bert-VITS2\requirements.txt
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+echo ----------------------------------------
+echo 環境構築が完了しました。モデルのダウンロードを開始します。
+echo ----------------------------------------
+
+@REM Style-Bert-VITS2フォルダに移動
+pushd Style-Bert-VITS2
+
+@REM 初期化（必要なモデルのダウンロード）
+python initialize.py
+
+echo ----------------------------------------
+echo モデルのダウンロードが完了し、インストールが完了しました！
+echo 音声合成のWebUIを起動します。
+echo ----------------------------------------
+
+@REM 音声合成WebUIの起動
+python app.py
+
+pause
+
+popd
+
+popd
+
diff --git a/scripts/Install-Style-Bert-VITS2.bat b/scripts/Install-Style-Bert-VITS2.bat
new file mode 100644
index 0000000000000000000000000000000000000000..6e99230c3c40a4af3890ebe2fd1e5f753d5e5f00
--- /dev/null
+++ b/scripts/Install-Style-Bert-VITS2.bat
@@ -0,0 +1,72 @@
+chcp 65001 > NUL
+@echo off
+
+@REM https://github.com/Zuntan03/EasyBertVits2 より引用・改変
+
+pushd %~dp0
+set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass
+
+set CURL_CMD=C:\Windows\System32\curl.exe
+if not exist %CURL_CMD% (
+	echo [ERROR] %CURL_CMD% が見つかりません。
+	pause & popd & exit /b 1
+)
+
+@REM lib フォルダがなければ作成
+if not exist lib\ ( mkdir lib )
+
+@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード
+%CURL_CMD% -Lo Style-Bert-VITS2.zip^
+	https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる）
+%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM 元のzipを削除
+del Style-Bert-VITS2.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動
+xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\
+rmdir /s /q Style-Bert-VITS2-master
+
+echo ----------------------------------------
+echo Setup Python and Virtual Environment
+echo ----------------------------------------
+
+@REM Pythonと仮想環境のセットアップを呼び出す（仮想環境が有効化されて戻ってくる）
+call Style-Bert-VITS2\scripts\Setup-Python.bat ..\..\lib\python ..\venv
+if %errorlevel% neq 0 ( popd & exit /b %errorlevel% )
+
+@REM 依存関係インストール
+pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+pip install -r Style-Bert-VITS2\requirements.txt
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+echo ----------------------------------------
+echo Environment setup is complete. Start downloading the model.
+echo ----------------------------------------
+
+@REM Style-Bert-VITS2フォルダに移動
+pushd Style-Bert-VITS2
+
+@REM 初期化（必要なモデルのダウンロード）
+python initialize.py
+
+echo ----------------------------------------
+echo Model download is complete. Start the WebUI of the voice synthesis.
+echo ----------------------------------------
+
+@REM 音声合成WebUIの起動
+python app.py
+
+pause
+
+popd
+
+popd
+
diff --git a/scripts/Setup-Python.bat b/scripts/Setup-Python.bat
new file mode 100644
index 0000000000000000000000000000000000000000..17fd23f237a6e040382322519b8c222d4ca8966f
--- /dev/null
+++ b/scripts/Setup-Python.bat
@@ -0,0 +1,76 @@
+chcp 65001 > NUL
+
+@REM https://github.com/Zuntan03/EasyBertVits2 より引用・改変
+
+@echo off
+set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass
+set CURL_CMD=C:\Windows\System32\curl.exe
+
+if not exist %CURL_CMD% (
+	echo [ERROR] %CURL_CMD% が見つかりません。
+	pause & exit /b 1
+)
+
+if "%1" neq "" (
+	set PYTHON_DIR=%~dp0%~1
+) else (
+	set PYTHON_DIR=%~dp0python
+)
+set PYTHON_CMD=%PYTHON_DIR%\python.exe
+
+if "%2" neq "" (
+	set VENV_DIR=%~dp0%~2
+) else (
+	set VENV_DIR=%~dp0venv
+)
+
+echo PS_CMD: %PS_CMD%
+echo CURL_CMD: %CURL_CMD%
+echo PYTHON_CMD: %PYTHON_CMD%
+echo VENV_DIR: %VENV_DIR%
+
+if not exist %PYTHON_DIR%\ (
+	echo https://www.python.org/
+	echo %CURL_CMD% -o python.zip https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip
+	%CURL_CMD% -o python.zip https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+	echo %PS_CMD% Expand-Archive -Path python.zip -DestinationPath %PYTHON_DIR%
+	%PS_CMD% Expand-Archive -Path python.zip -DestinationPath %PYTHON_DIR%
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+	echo del python.zip
+	del python.zip
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+	echo %PS_CMD% "&{(Get-Content '%PYTHON_DIR%/python310._pth') -creplace '#import site', 'import site' | Set-Content '%PYTHON_DIR%/python310._pth' }"
+	%PS_CMD% "&{(Get-Content '%PYTHON_DIR%/python310._pth') -creplace '#import site', 'import site' | Set-Content '%PYTHON_DIR%/python310._pth' }"
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+	echo https://github.com/pypa/get-pip
+	echo %CURL_CMD% -o %PYTHON_DIR%\get-pip.py https://bootstrap.pypa.io/get-pip.py
+	%CURL_CMD% -o %PYTHON_DIR%\get-pip.py https://bootstrap.pypa.io/get-pip.py
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+	echo %PYTHON_CMD% %PYTHON_DIR%\get-pip.py --no-warn-script-location
+	%PYTHON_CMD% %PYTHON_DIR%\get-pip.py --no-warn-script-location
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+	echo %PYTHON_CMD% -m pip install virtualenv --no-warn-script-location
+	%PYTHON_CMD% -m pip install virtualenv --no-warn-script-location
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+)
+
+if not exist %VENV_DIR%\ (
+	echo %PYTHON_CMD% -m virtualenv --copies %VENV_DIR%
+	%PYTHON_CMD% -m virtualenv --copies %VENV_DIR%
+	if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+)
+
+echo call %VENV_DIR%\Scripts\activate.bat
+call %VENV_DIR%\Scripts\activate.bat
+if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
+
+echo python -m pip install --upgrade pip
+python -m pip install --upgrade pip
+if %errorlevel% neq 0 ( pause & exit /b %errorlevel% )
diff --git a/scripts/Update-Style-Bert-VITS2.bat b/scripts/Update-Style-Bert-VITS2.bat
new file mode 100644
index 0000000000000000000000000000000000000000..782f2efe9ca447c8f018a75b5a6332e8a521e973
--- /dev/null
+++ b/scripts/Update-Style-Bert-VITS2.bat
@@ -0,0 +1,44 @@
+chcp 65001 > NUL
+@echo off
+
+pushd %~dp0
+set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass
+
+set CURL_CMD=C:\Windows\System32\curl.exe
+if not exist %CURL_CMD% (
+	echo [ERROR] %CURL_CMD% が見つかりません。
+	pause & popd & exit /b 1
+)
+
+@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード
+%CURL_CMD% -Lo Style-Bert-VITS2.zip^
+	https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる）
+%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM 元のzipを削除
+del Style-Bert-VITS2.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動
+xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\
+rmdir /s /q Style-Bert-VITS2-master
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM 仮想環境のpip requirements.txtを更新
+
+echo call .\Style-Bert-VITS2\scripts\activate.bat
+call .\Style-Bert-VITS2\venv\Scripts\activate.bat
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+pip install -U -r Style-Bert-VITS2\requirements.txt
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+echo Style-Bert-VITS2のアップデートが完了しました。
+
+pause
+
+popd
\ No newline at end of file
diff --git a/scripts/Update-to-JP-Extra.bat b/scripts/Update-to-JP-Extra.bat
new file mode 100644
index 0000000000000000000000000000000000000000..95738c698dc7f7a77441bed363db6d60097d689f
--- /dev/null
+++ b/scripts/Update-to-JP-Extra.bat
@@ -0,0 +1,51 @@
+chcp 65001 > NUL
+@echo off
+
+pushd %~dp0
+set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass
+
+set CURL_CMD=C:\Windows\System32\curl.exe
+if not exist %CURL_CMD% (
+	echo [ERROR] %CURL_CMD% が見つかりません。
+	pause & popd & exit /b 1
+)
+
+@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード
+%CURL_CMD% -Lo Style-Bert-VITS2.zip^
+	https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる）
+%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM 元のzipを削除
+del Style-Bert-VITS2.zip
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動
+xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\
+rmdir /s /q Style-Bert-VITS2-master
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+@REM 仮想環境のpip requirements.txtを更新
+
+echo call .\Style-Bert-VITS2\scripts\activate.bat
+call .\Style-Bert-VITS2\venv\Scripts\activate.bat
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+pip install -U -r Style-Bert-VITS2\requirements.txt
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+pushd Style-Bert-VITS2
+
+@REM 初期化（必要なモデルのダウンロード）
+python initialize.py
+
+echo Style-Bert-VITS2の2.xへのアップデートが完了しました。
+
+pause
+
+popd
+
+popd
\ No newline at end of file
diff --git a/server_fastapi.py b/server_fastapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..40877fd98047ece8d5a66289fbe129819fd5eab0
--- /dev/null
+++ b/server_fastapi.py
@@ -0,0 +1,263 @@
+"""
+API server for TTS
+"""
+import argparse
+import os
+import sys
+from io import BytesIO
+from typing import Dict, Optional, Union
+from urllib.parse import unquote
+
+import GPUtil
+import psutil
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException, Query, Request, status
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, Response
+from scipy.io import wavfile
+
+from common.constants import (
+    DEFAULT_ASSIST_TEXT_WEIGHT,
+    DEFAULT_LENGTH,
+    DEFAULT_LINE_SPLIT,
+    DEFAULT_NOISE,
+    DEFAULT_NOISEW,
+    DEFAULT_SDP_RATIO,
+    DEFAULT_SPLIT_INTERVAL,
+    DEFAULT_STYLE,
+    DEFAULT_STYLE_WEIGHT,
+    Languages,
+)
+from common.log import logger
+from common.tts_model import Model, ModelHolder
+from config import config
+
+ln = config.server_config.language
+
+
+def raise_validation_error(msg: str, param: str):
+    logger.warning(f"Validation error: {msg}")
+    raise HTTPException(
+        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        detail=[dict(type="invalid_params", msg=msg, loc=["query", param])],
+    )
+
+
+class AudioResponse(Response):
+    media_type = "audio/wav"
+
+
+def load_models(model_holder: ModelHolder):
+    model_holder.models = []
+    for model_name, model_paths in model_holder.model_files_dict.items():
+        model = Model(
+            model_path=model_paths[0],
+            config_path=os.path.join(model_holder.root_dir, model_name, "config.json"),
+            style_vec_path=os.path.join(
+                model_holder.root_dir, model_name, "style_vectors.npy"
+            ),
+            device=model_holder.device,
+        )
+        model.load_net_g()
+        model_holder.models.append(model)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--cpu", action="store_true", help="Use CPU instead of GPU")
+    parser.add_argument(
+        "--dir", "-d", type=str, help="Model directory", default=config.assets_root
+    )
+    args = parser.parse_args()
+
+    if args.cpu:
+        device = "cpu"
+    else:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    model_dir = args.dir
+    model_holder = ModelHolder(model_dir, device)
+    if len(model_holder.model_names) == 0:
+        logger.error(f"Models not found in {model_dir}.")
+        sys.exit(1)
+
+    logger.info("Loading models...")
+    load_models(model_holder)
+    limit = config.server_config.limit
+    app = FastAPI()
+    allow_origins = config.server_config.origins
+    if allow_origins:
+        logger.warning(
+            f"CORS allow_origins={config.server_config.origins}. If you don't want, modify config.yml"
+        )
+        app.add_middleware(
+            CORSMiddleware,
+            allow_origins=config.server_config.origins,
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+    app.logger = logger
+
+    @app.get("/voice", response_class=AudioResponse)
+    async def voice(
+        request: Request,
+        text: str = Query(..., min_length=1, max_length=limit, description=f"セリフ"),
+        encoding: str = Query(None, description="textをURLデコードする(ex, `utf-8`)"),
+        model_id: int = Query(0, description="モデルID。`GET /models/info`のkeyの値を指定ください"),
+        speaker_name: str = Query(
+            None, description="話者名(speaker_idより優先)。esd.listの2列目の文字列を指定"
+        ),
+        speaker_id: int = Query(
+            0, description="話者ID。model_assets>[model]>config.json内のspk2idを確認"
+        ),
+        sdp_ratio: float = Query(
+            DEFAULT_SDP_RATIO,
+            description="SDP(Stochastic Duration Predictor)/DP混合比。比率が高くなるほどトーンのばらつきが大きくなる",
+        ),
+        noise: float = Query(DEFAULT_NOISE, description="サンプルノイズの割合。大きくするほどランダム性が高まる"),
+        noisew: float = Query(
+            DEFAULT_NOISEW, description="SDPノイズ。大きくするほど発音の間隔にばらつきが出やすくなる"
+        ),
+        length: float = Query(
+            DEFAULT_LENGTH, description="話速。基準は1で大きくするほど音声は長くなり読み上げが遅まる"
+        ),
+        language: Languages = Query(ln, description=f"textの言語"),
+        auto_split: bool = Query(DEFAULT_LINE_SPLIT, description="改行で分けて生成"),
+        split_interval: float = Query(
+            DEFAULT_SPLIT_INTERVAL, description="分けた場合に挟む無音の長さ（秒）"
+        ),
+        assist_text: Optional[str] = Query(
+            None, description="このテキストの読み上げと似た声音・感情になりやすくなる。ただし抑揚やテンポ等が犠牲になる傾向がある"
+        ),
+        assist_text_weight: float = Query(
+            DEFAULT_ASSIST_TEXT_WEIGHT, description="assist_textの強さ"
+        ),
+        style: Optional[Union[int, str]] = Query(DEFAULT_STYLE, description="スタイル"),
+        style_weight: float = Query(DEFAULT_STYLE_WEIGHT, description="スタイルの強さ"),
+        reference_audio_path: Optional[str] = Query(None, description="スタイルを音声ファイルで行う"),
+    ):
+        """Infer text to speech(テキストから感情付き音声を生成する)"""
+        logger.info(
+            f"{request.client.host}:{request.client.port}/voice  { unquote(str(request.query_params) )}"
+        )
+        if model_id >= len(model_holder.models):  # /models/refresh があるためQuery(le)で表現不可
+            raise_validation_error(f"model_id={model_id} not found", "model_id")
+
+        model = model_holder.models[model_id]
+        if speaker_name is None:
+            if speaker_id not in model.id2spk.keys():
+                raise_validation_error(
+                    f"speaker_id={speaker_id} not found", "speaker_id"
+                )
+        else:
+            if speaker_name not in model.spk2id.keys():
+                raise_validation_error(
+                    f"speaker_name={speaker_name} not found", "speaker_name"
+                )
+            speaker_id = model.spk2id[speaker_name]
+        if style not in model.style2id.keys():
+            raise_validation_error(f"style={style} not found", "style")
+        if encoding is not None:
+            text = unquote(text, encoding=encoding)
+        sr, audio = model.infer(
+            text=text,
+            language=language,
+            sid=speaker_id,
+            reference_audio_path=reference_audio_path,
+            sdp_ratio=sdp_ratio,
+            noise=noise,
+            noisew=noisew,
+            length=length,
+            line_split=auto_split,
+            split_interval=split_interval,
+            assist_text=assist_text,
+            assist_text_weight=assist_text_weight,
+            use_assist_text=bool(assist_text),
+            style=style,
+            style_weight=style_weight,
+        )
+        logger.success("Audio data generated and sent successfully")
+        with BytesIO() as wavContent:
+            wavfile.write(wavContent, sr, audio)
+            return Response(content=wavContent.getvalue(), media_type="audio/wav")
+
+    @app.get("/models/info")
+    def get_loaded_models_info():
+        """ロードされたモデル情報の取得"""
+
+        result: Dict[str, Dict] = dict()
+        for model_id, model in enumerate(model_holder.models):
+            result[str(model_id)] = {
+                "config_path": model.config_path,
+                "model_path": model.model_path,
+                "device": model.device,
+                "spk2id": model.spk2id,
+                "id2spk": model.id2spk,
+                "style2id": model.style2id,
+            }
+        return result
+
+    @app.post("/models/refresh")
+    def refresh():
+        """モデルをパスに追加/削除した際などに読み込ませる"""
+        model_holder.refresh()
+        load_models(model_holder)
+        return get_loaded_models_info()
+
+    @app.get("/status")
+    def get_status():
+        """実行環境のステータスを取得"""
+        cpu_percent = psutil.cpu_percent(interval=1)
+        memory_info = psutil.virtual_memory()
+        memory_total = memory_info.total
+        memory_available = memory_info.available
+        memory_used = memory_info.used
+        memory_percent = memory_info.percent
+        gpuInfo = []
+        devices = ["cpu"]
+        for i in range(torch.cuda.device_count()):
+            devices.append(f"cuda:{i}")
+        gpus = GPUtil.getGPUs()
+        for gpu in gpus:
+            gpuInfo.append(
+                {
+                    "gpu_id": gpu.id,
+                    "gpu_load": gpu.load,
+                    "gpu_memory": {
+                        "total": gpu.memoryTotal,
+                        "used": gpu.memoryUsed,
+                        "free": gpu.memoryFree,
+                    },
+                }
+            )
+        return {
+            "devices": devices,
+            "cpu_percent": cpu_percent,
+            "memory_total": memory_total,
+            "memory_available": memory_available,
+            "memory_used": memory_used,
+            "memory_percent": memory_percent,
+            "gpu": gpuInfo,
+        }
+
+    @app.get("/tools/get_audio", response_class=AudioResponse)
+    def get_audio(
+        request: Request, path: str = Query(..., description="local wav path")
+    ):
+        """wavデータを取得する"""
+        logger.info(
+            f"{request.client.host}:{request.client.port}/tools/get_audio  { unquote(str(request.query_params) )}"
+        )
+        if not os.path.isfile(path):
+            raise_validation_error(f"path={path} not found", "path")
+        if not path.lower().endswith(".wav"):
+            raise_validation_error(f"wav file not found in {path}", "path")
+        return FileResponse(path=path, media_type="audio/wav")
+
+    logger.info(f"server listen: http://127.0.0.1:{config.server_config.port}")
+    logger.info(f"API docs: http://127.0.0.1:{config.server_config.port}/docs")
+    uvicorn.run(
+        app, port=config.server_config.port, host="0.0.0.0", log_level="warning"
+    )
diff --git a/slice.py b/slice.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cb12c65d6bfbe1d1cea9317dd01d3bf818c7806
--- /dev/null
+++ b/slice.py
@@ -0,0 +1,150 @@
+import argparse
+import os
+import shutil
+from pathlib import Path
+
+import soundfile as sf
+import torch
+from tqdm import tqdm
+
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+
+vad_model, utils = torch.hub.load(
+    repo_or_dir="snakers4/silero-vad",
+    model="silero_vad",
+    onnx=True,
+    trust_repo=True,
+)
+
+(get_speech_timestamps, _, read_audio, *_) = utils
+
+
+def get_stamps(
+    audio_file, min_silence_dur_ms: int = 700, min_sec: float = 2, max_sec: float = 12
+):
+    """
+    min_silence_dur_ms: int (ミリ秒):
+        このミリ秒数以上を無音だと判断する。
+        逆に、この秒数以下の無音区間では区切られない。
+        小さくすると、音声がぶつ切りに小さくなりすぎ、
+        大きくすると音声一つ一つが長くなりすぎる。
+        データセットによってたぶん要調整。
+    min_sec: float (秒):
+        この秒数より小さい発話は無視する。
+    max_sec: float (秒):
+        この秒数より大きい発話は無視する。
+    """
+
+    sampling_rate = 16000  # 16kHzか8kHzのみ対応
+
+    min_ms = int(min_sec * 1000)
+
+    wav = read_audio(audio_file, sampling_rate=sampling_rate)
+    speech_timestamps = get_speech_timestamps(
+        wav,
+        vad_model,
+        sampling_rate=sampling_rate,
+        min_silence_duration_ms=min_silence_dur_ms,
+        min_speech_duration_ms=min_ms,
+        max_speech_duration_s=max_sec,
+    )
+
+    return speech_timestamps
+
+
+def split_wav(
+    audio_file,
+    target_dir="raw",
+    min_sec=2,
+    max_sec=12,
+    min_silence_dur_ms=700,
+):
+    margin = 200  # ミリ秒単位で、音声の前後に余裕を持たせる
+    speech_timestamps = get_stamps(
+        audio_file,
+        min_silence_dur_ms=min_silence_dur_ms,
+        min_sec=min_sec,
+        max_sec=max_sec,
+    )
+
+    data, sr = sf.read(audio_file)
+
+    total_ms = len(data) / sr * 1000
+
+    file_name = os.path.basename(audio_file).split(".")[0]
+    os.makedirs(target_dir, exist_ok=True)
+
+    total_time_ms = 0
+
+    # タイムスタンプに従って分割し、ファイルに保存
+    for i, ts in enumerate(speech_timestamps):
+        start_ms = max(ts["start"] / 16 - margin, 0)
+        end_ms = min(ts["end"] / 16 + margin, total_ms)
+
+        start_sample = int(start_ms / 1000 * sr)
+        end_sample = int(end_ms / 1000 * sr)
+        segment = data[start_sample:end_sample]
+
+        sf.write(os.path.join(target_dir, f"{file_name}-{i}.wav"), segment, sr)
+        total_time_ms += end_ms - start_ms
+
+    return total_time_ms / 1000
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--min_sec", "-m", type=float, default=2, help="Minimum seconds of a slice"
+    )
+    parser.add_argument(
+        "--max_sec", "-M", type=float, default=12, help="Maximum seconds of a slice"
+    )
+    parser.add_argument(
+        "--input_dir",
+        "-i",
+        type=str,
+        default="inputs",
+        help="Directory of input wav files",
+    )
+    parser.add_argument(
+        "--output_dir",
+        "-o",
+        type=str,
+        default="raw",
+        help="Directory of output wav files",
+    )
+    parser.add_argument(
+        "--min_silence_dur_ms",
+        "-s",
+        type=int,
+        default=700,
+        help="Silence above this duration (ms) is considered as a split point.",
+    )
+    args = parser.parse_args()
+
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    min_sec = args.min_sec
+    max_sec = args.max_sec
+    min_silence_dur_ms = args.min_silence_dur_ms
+
+    wav_files = Path(input_dir).glob("**/*.wav")
+    wav_files = list(wav_files)
+    logger.info(f"Found {len(wav_files)} wav files.")
+    if os.path.exists(output_dir):
+        logger.warning(f"Output directory {output_dir} already exists, deleting...")
+        shutil.rmtree(output_dir)
+
+    total_sec = 0
+    for wav_file in tqdm(wav_files, file=SAFE_STDOUT):
+        time_sec = split_wav(
+            audio_file=str(wav_file),
+            target_dir=output_dir,
+            min_sec=min_sec,
+            max_sec=max_sec,
+            min_silence_dur_ms=min_silence_dur_ms,
+        )
+        total_sec += time_sec
+
+    logger.info(f"Slice done! Total time: {total_sec / 60:.2f} min.")
diff --git a/slm/wavlm-base-plus/.gitattributes b/slm/wavlm-base-plus/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..957b2579c6ef20995a09efd9a17f8fd90606f5ed
--- /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..541b2e0cc438f3bcb05aa363b09e62f722c87cab
--- /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)
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/slm/wavlm-base-plus/pytorch_model.bin b/slm/wavlm-base-plus/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..a8bf334ae0e26f3e95a5eb7e34cf45fe1369e753
--- /dev/null
+++ b/slm/wavlm-base-plus/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bb273a6ace99408b50cfc81afdbb7ef2de02da2eab0234e18db608ce692fe51
+size 377617425
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/speech_mos.py b/speech_mos.py
new file mode 100644
index 0000000000000000000000000000000000000000..8464223a581fdeb3f302f7aa04fe18cadb1ac8f4
--- /dev/null
+++ b/speech_mos.py
@@ -0,0 +1,158 @@
+import argparse
+import csv
+import re
+import warnings
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+from tqdm import tqdm
+
+from common.log import logger
+from common.tts_model import Model
+from config import config
+
+warnings.filterwarnings("ignore")
+
+mos_result_dir = Path("mos_results")
+mos_result_dir.mkdir(exist_ok=True)
+
+test_texts = [
+    # JVNVコーパスのテキスト
+    # https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus
+    # CC BY-SA 4.0
+    "ああ？どうしてこんなに荒々しい態度をとるんだ？落ち着いて話を聞けばいいのに。",
+    "いや、あんな醜い人間を見るのは本当に嫌だ。",
+    "うわ、不景気の影響で失業してしまうかもしれない。どうしよう、心配で眠れない。",
+    "今日の山登りは最高だった！山頂で見た景色は言葉に表せないほど美しかった！あはは、絶頂の喜びが胸に溢れるよ！",
+    "あーあ、昨日の事故で大切な車が全損になっちゃった。もうどうしようもないよ。",
+    "ああ、彼は本当に速い！ダッシュの速さは尋常じゃない！",
+    # 以下app.pyの説明文章
+    "音声合成は、機械学習を活用して、テキストから人の声を再現する技術です。この技術は、言語の構造を解析し、それに基づいて音声を生成します。",
+    "この分野の最新の研究成果を使うと、より自然で表現豊かな音声の生成が可能である。深層学習の応用により、感情やアクセントを含む声質の微妙な変化も再現することが出来る。",
+]
+
+predictor = torch.hub.load(
+    "tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True
+)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name", "-m", type=str, required=True)
+parser.add_argument("--device", "-d", type=str, default="cuda")
+
+args = parser.parse_args()
+
+model_name: str = args.model_name
+device: str = args.device
+
+model_path = Path(config.assets_root) / model_name
+
+# .safetensorsファイルを検索
+safetensors_files = model_path.glob("*.safetensors")
+
+
+def get_model(model_file: Path):
+    return Model(
+        model_path=str(model_file),
+        config_path=str(model_file.parent / "config.json"),
+        style_vec_path=str(model_file.parent / "style_vectors.npy"),
+        device=device,
+    )
+
+
+results = []
+
+safetensors_files = list(safetensors_files)
+
+logger.info(f"There are {len(safetensors_files)} models.")
+
+for model_file in tqdm(safetensors_files):
+    # `test_e10_s1000.safetensors`` -> 1000を取り出す
+    match = re.search(r"_s(\d+)\.safetensors$", model_file.name)
+    if match:
+        step = int(match.group(1))
+    else:
+        logger.warning(f"Step count not found in {model_file.name}, so skip it.")
+        continue
+    model = get_model(model_file)
+    scores = []
+    for i, text in enumerate(test_texts):
+        sr, audio = model.infer(text)
+        audio = audio.astype("float32")
+        score = predictor(torch.from_numpy(audio).unsqueeze(0), sr).item()
+        scores.append(score)
+        logger.info(f"score: {score}")
+    results.append((model_file.name, step, scores))
+    del model
+
+logger.success("All models have been evaluated:")
+# meanを計算
+results = [
+    (model_file, step, scores + [np.mean(scores)])
+    for model_file, step, scores in results
+]
+# meanでソートして表示
+results = sorted(results, key=lambda x: x[2][-1], reverse=True)
+for model_file, step, scores in results:
+    logger.info(f"{model_file}: {scores[-1]}")
+
+with open(
+    mos_result_dir / f"mos_{model_name}.csv", "w", encoding="utf-8", newline=""
+) as f:
+    writer = csv.writer(f)
+    writer.writerow(["model_path"] + ["step"] + test_texts + ["mean"])
+    for model_file, step, scores in results:
+        writer.writerow([model_file] + [step] + scores)
+
+logger.info(f"mos_{model_name}.csv has been saved.")
+
+# step countと各MOSの値を格納するリストを初期化
+steps = []
+mos_values = []
+
+# resultsからデータを抽出
+for _, step, scores in results:
+    steps.append(step)
+    mos_values.append(scores)  # scores は MOS1, MOS2, MOS3,..., mean のリスト
+
+# DataFrame形式に変換
+df = pd.DataFrame(mos_values, index=steps)
+# ステップ数でソート
+df = df.sort_index()
+
+plt.figure(figsize=(10, 5))
+
+# 各MOSについての折れ線グラフを描画（最後の平均値の列は除外）
+for col in range(len(df.columns) - 1):
+    plt.plot(df.index, df.iloc[:, col], label=f"MOS{col + 1}")
+
+# 既存の平均値の列を使用
+plt.plot(df.index, df.iloc[:, -1], label="Mean", color="black", linewidth=2)
+
+
+# グラフのタイトルと軸ラベルを設定
+plt.title("TTS Model Naturalness MOS")
+plt.xlabel("Step Count")
+plt.ylabel("MOS")
+
+# ステップ数の軸ラベルを1000単位で表示するように調整
+plt.xticks(
+    ticks=np.arange(0, max(steps) + 1000, 2000),
+    labels=[f"{int(x/1000)}" for x in np.arange(0, max(steps) + 1000, 2000)],
+)
+
+# 縦の補助線を追加
+plt.grid(True, axis="x")
+
+# 凡例をグラフの右外側に配置
+plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
+
+plt.tight_layout()
+
+# グラフを画像として保存（plt.show() の前に実行する）
+plt.savefig(mos_result_dir / f"mos_{model_name}.png")
+
+# グラフを表示
+plt.show()
diff --git a/style_gen.py b/style_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..97a0aee83421ac7cbbaa0c93621915b490930ba8
--- /dev/null
+++ b/style_gen.py
@@ -0,0 +1,128 @@
+import argparse
+from concurrent.futures import ThreadPoolExecutor
+import warnings
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+import utils
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+from config import config
+
+warnings.filterwarnings("ignore", category=UserWarning)
+from pyannote.audio import Inference, Model
+
+model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM")
+inference = Inference(model, window="whole")
+device = torch.device(config.style_gen_config.device)
+inference.to(device)
+
+
+class NaNValueError(ValueError):
+    """カスタム例外クラス。NaN値が見つかった場合に使用されます。"""
+
+    pass
+
+
+# 推論時にインポートするために短いが関数を書く
+def get_style_vector(wav_path):
+    return inference(wav_path)
+
+
+def save_style_vector(wav_path):
+    try:
+        style_vec = get_style_vector(wav_path)
+    except Exception as e:
+        print("\n")
+        logger.error(f"Error occurred with file: {wav_path}, Details:\n{e}\n")
+        raise
+    # 値にNaNが含まれていると悪影響なのでチェックする
+    if np.isnan(style_vec).any():
+        print("\n")
+        logger.warning(f"NaN value found in style vector: {wav_path}")
+        raise NaNValueError(f"NaN value found in style vector: {wav_path}")
+    np.save(f"{wav_path}.npy", style_vec)  # `test.wav` -> `test.wav.npy`
+
+
+def process_line(line):
+    wavname = line.split("|")[0]
+    try:
+        save_style_vector(wavname)
+        return line, None
+    except NaNValueError:
+        return line, "nan_error"
+
+
+def save_average_style_vector(style_vectors, filename="style_vectors.npy"):
+    average_vector = np.mean(style_vectors, axis=0)
+    np.save(filename, average_vector)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c", "--config", type=str, default=config.style_gen_config.config_path
+    )
+    parser.add_argument(
+        "--num_processes", type=int, default=config.style_gen_config.num_processes
+    )
+    args, _ = parser.parse_known_args()
+    config_path = args.config
+    num_processes = args.num_processes
+
+    hps = utils.get_hparams_from_file(config_path)
+
+    device = config.style_gen_config.device
+
+    training_lines = []
+    with open(hps.data.training_files, encoding="utf-8") as f:
+        training_lines.extend(f.readlines())
+    with ThreadPoolExecutor(max_workers=num_processes) as executor:
+        training_results = list(
+            tqdm(
+                executor.map(process_line, training_lines),
+                total=len(training_lines),
+                file=SAFE_STDOUT,
+            )
+        )
+    ok_training_lines = [line for line, error in training_results if error is None]
+    nan_training_lines = [
+        line for line, error in training_results if error == "nan_error"
+    ]
+    if nan_training_lines:
+        nan_files = [line.split("|")[0] for line in nan_training_lines]
+        logger.warning(
+            f"Found NaN value in {len(nan_training_lines)} files: {nan_files}, so they will be deleted from training data."
+        )
+
+    val_lines = []
+    with open(hps.data.validation_files, encoding="utf-8") as f:
+        val_lines.extend(f.readlines())
+
+    with ThreadPoolExecutor(max_workers=num_processes) as executor:
+        val_results = list(
+            tqdm(
+                executor.map(process_line, val_lines),
+                total=len(val_lines),
+                file=SAFE_STDOUT,
+            )
+        )
+    ok_val_lines = [line for line, error in val_results if error is None]
+    nan_val_lines = [line for line, error in val_results if error == "nan_error"]
+    if nan_val_lines:
+        nan_files = [line.split("|")[0] for line in nan_val_lines]
+        logger.warning(
+            f"Found NaN value in {len(nan_val_lines)} files: {nan_files}, so they will be deleted from validation data."
+        )
+
+    with open(hps.data.training_files, "w", encoding="utf-8") as f:
+        f.writelines(ok_training_lines)
+
+    with open(hps.data.validation_files, "w", encoding="utf-8") as f:
+        f.writelines(ok_val_lines)
+
+    ok_num = len(ok_training_lines) + len(ok_val_lines)
+
+    logger.info(f"Finished generating style vectors! total: {ok_num} npy files.")
diff --git a/text/__init__.py b/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..495e57b50d87a4ca3e8fe8dbaf003b4888581927
--- /dev/null
+++ b/text/__init__.py
@@ -0,0 +1,32 @@
+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, assist_text=None, assist_text_weight=0.7
+):
+    from .chinese_bert import get_bert_feature as zh_bert
+    from .english_bert_mock import get_bert_feature as en_bert
+    from .japanese_bert import get_bert_feature as jp_bert
+
+    lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
+    bert = lang_bert_func_map[language](
+        norm_text, word2ph, device, assist_text, assist_text_weight
+    )
+    return bert
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diff --git a/text/chinese.py b/text/chinese.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9174ee0e194497bdcace2e4d99977f8dce0a528
--- /dev/null
+++ b/text/chinese.py
@@ -0,0 +1,199 @@
+import os
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+
+from text.symbols import punctuation
+from text.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/text/chinese_bert.py b/text/chinese_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..94e1408e22affe9b5f12a9644ff01a14d9547e1e
--- /dev/null
+++ b/text/chinese_bert.py
@@ -0,0 +1,121 @@
+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,
+    assist_text=None,
+    assist_text_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 == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    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 assist_text:
+            style_inputs = tokenizer(assist_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 assist_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_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..ab59b4e1a84261d0cd3d98c84ada275803a76b0e
--- /dev/null
+++ b/text/cleaner.py
@@ -0,0 +1,31 @@
+from text import chinese, japanese, english, cleaned_text_to_sequence
+
+
+language_module_map = {"ZH": chinese, "JP": japanese, "EN": english}
+
+
+def clean_text(text, language, use_jp_extra=True):
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    if language == "JP":
+        phones, tones, word2ph = language_module.g2p(norm_text, use_jp_extra)
+    else:
+        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/text/cmudict.rep b/text/cmudict.rep
new file mode 100644
index 0000000000000000000000000000000000000000..9bc61ebd759c72458da28bb74e039298068fcc0b
--- /dev/null
+++ b/text/cmudict.rep
@@ -0,0 +1,129530 @@
+## Date:  August 8, 1998
+##
+## The Carnegie Mellon Pronouncing Dictionary [cmudict.0.6] is Copyright 1998
+## by Carnegie Mellon University. Use of this dictionary, for any research or
+## commercial purpose, is completely unrestricted.  If you make use of or
+## redistribute this material, we would appreciate acknowlegement of its
+## origin.
+##
+## cmudict.0.6 is the fifth release of cmudict, first released as cmudict.0.1
+## in September of 1993.  There was no generally available public release
+## of version 0.5.
+##
+## See the README in this directory before you use this dictionary.
+##
+## Thanks to Bill Huggins at BBN; Bill Fisher at NIST; Alex Hauptman,
+## Alex Rudnicky, Jack Mostow, Roni Rosenfeld, Richard Stern,
+## Matthew Siegler, Kevin Lenzo, Maxine Eskenazi, Mosur Ravishankar,
+## Eric Thayer, Kristie Seymore, and Raj Reddy at CMU; Lin Chase at
+## LIMSI; Doug Paul at MIT Lincoln Labs; Ben Serridge at MIT SLS; Murray
+## Spiegel at Bellcore; Tony Robinson at Cambridge UK; David Bowness of
+## CAE Electronics Ltd. and CRIM; Stephen Hocking; Jerry Quinn at BNR
+## Canada, and Marshal Midden for bringing to our attention problems and
+## inadequacies with the first releases. Most special thanks to Bob Weide
+## for all his work on prior versions of the dictionary.
+##
+## We welcome input from users and will continue to acknowledge such input
+## in subsequent releases. If I failed to acknowledge your input in this
+## release, please remind me and I will update these comments. If I failed to
+## fix things that you brought to my attention, please remind me and have
+## patience. If I actually fixed things that you brought to my attention and
+## you appreciate it, I wouldn't mind a pat on the back.
+##
+## This version differs from previous releases of cmudict most significantly
+## in the addition of new words from the common ARPA tasks for 1996 and 1997.
+##
+## There are undoubtedly still errors and inconsistencies in this dictionary
+## so keep your eyes open for problems and mail them to me.
+##
+## We hope this dictionary is an improvement over cmudict.0.4.
+##
+## email: cmudict@cs.cmu.edu
+## web:   http://www.speech.cs.cmu.edu/cgi-bin/cmudict
+## ftp:   ftp://ftp.cs.cmu.edu/project/speech/dict/
+##
+## Thank you for your continued interest in the CMU Pronouncing
+## Dictionary.  Further addictions and improvements are planned
+## for forthcoming releases.
+##
+!EXCLAMATION-POINT  EH2 K - S K L AH0 - M EY1 - SH AH0 N - P OY2 N T
+"CLOSE-QUOTE  K L OW1 Z - K W OW1 T
+"DOUBLE-QUOTE  D AH1 - B AH0 L - K W OW1 T
+"END-OF-QUOTE  EH1 N - D AH0 V - K W OW1 T
+"END-QUOTE  EH1 N D - K W OW1 T
+"IN-QUOTES  IH1 N - K W OW1 T S
+"QUOTE  K W OW1 T
+"UNQUOTE  AH1 N - K W OW1 T
+#SHARP-SIGN  SH AA1 R P - S AY1 N
+%PERCENT  P ER0 - S EH1 N T
+&AMPERSAND  AE1 M - P ER0 - S AE2 N D
+'CAUSE  K AH0 Z
+'COURSE  K AO1 R S
+'EM  AH0 M
+'END-INNER-QUOTE  EH1 N - D IH1 - N ER0 - K W OW1 T
+'END-QUOTE  EH1 N D - K W OW1 T
+'INNER-QUOTE  IH1 - N ER0 - K W OW1 T
+'M  AH0 M
+'N  AH0 N
+'QUOTE  K W OW1 T
+'S  EH1 S
+'SINGLE-QUOTE  S IH1 NG - G AH0 L - K W OW1 T
+'TIL  T IH1 L
+'TIS  T IH1 Z
+'TWAS  T W AH1 Z
+(BEGIN-PARENS  B IH0 - G IH1 N - P ER0 - EH1 N Z
+(IN-PARENTHESES  IH1 N - P ER0 - EH1 N - TH AH0 - S IY2 Z
+(LEFT-PAREN  L EH1 F T - P ER0 - EH1 N
+(OPEN-PARENTHESES  OW1 - P AH0 N - P ER0 - EH1 N - TH AH0 - S IY2 Z
+(PAREN  P ER0 - EH1 N
+(PARENS  P ER0 - EH1 N Z
+(PARENTHESES  P ER0 - EH1 N - TH AH0 - S IY2 Z
+)CLOSE-PAREN  K L OW1 Z - P ER0 - EH1 N
+)CLOSE-PARENTHESES  K L OW1 Z - P ER0 - EH1 N - TH AH0 - S IY2 Z
+)END-PAREN  EH1 N D - P ER0 - EH1 N
+)END-PARENS  EH1 N D - P ER0 - EH1 N Z
+)END-PARENTHESES  EH1 N D - P ER0 - EH1 N - TH AH0 - S IY2 Z
+)END-THE-PAREN  EH1 N D - DH AH0 - P ER0 - EH1 N
+)PAREN  P ER0 - EH1 N
+)PARENS  P ER0 - EH1 N Z
+)RIGHT-PAREN  R AY1 T - P ER0 - EH1 N
+)RIGHT-PAREN(2)  R AY1 T - P EH1 - R AH0 N
+)UN-PARENTHESES  AH1 N - P ER0 - EH1 N - TH AH0 - S IY1 Z
+,COMMA  K AA1 - M AH0
+-DASH  D AE1 SH
+-HYPHEN  HH AY1 - F AH0 N
+...ELLIPSIS  IH0 - L IH1 P - S IH0 S
+.DECIMAL  D EH1 - S AH0 - M AH0 L
+.DOT  D AA1 T
+.FULL-STOP  F UH1 L - S T AA1 P
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+ACCOR  AE1 - K AO2 R
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+ALIENATION  EY2 - L IY0 - AH0 - N EY1 - SH AH0 N
+ALIENS  EY1 - L IY0 - AH0 N Z
+ALIFF  AE1 - L IH0 F
+ALIG  AE1 - L IH0 G
+ALIGHT  AH0 - L AY1 T
+ALIGN  AH0 - L AY1 N
+ALIGNED  AH0 - L AY1 N D
+ALIGNING  AH0 - L AY1 - N IH0 NG
+ALIGNMENT  AH0 - L AY1 N - M AH0 N T
+ALIGNMENTS  AH0 - L AY1 N - M AH0 N T S
+ALIGNS  AH0 - L AY1 N Z
+ALIJA  AH0 - L AY1 - JH AH0
+ALIJA'S  AH0 - L AY1 - JH AH0 Z
+ALIKE  AH0 - L AY1 K
+ALIKES  AH0 - L AY1 K S
+ALIMA  AA0 - L IY1 - M AH0
+ALIMENIES  AE1 - L IH0 - M EH2 - N IY0 Z
+ALIMENTARY  AE2 - L AH0 - M EH1 N - T ER0 - IY0
+ALIMONY  AE1 - L AH0 - M OW2 - N IY0
+ALINA  AH0 - L IY1 - N AH0
+ALINE  AH0 - L AY1 N
+ALINES  AH0 - L AY1 N Z
+ALIOTO  AA0 - L IY0 - OW1 - T OW0
+ALIQUIPPA  AE2 - L AH0 - K W IH1 - P AH0
+ALIQUIPPA'S  AE2 - L AH0 - K W IH1 - P AH0 Z
+ALIQUIPPAS  AE2 - L AH0 - K W IH1 - P AH0 Z
+ALIRE  AA0 - L IH1 - R EY0
+ALISKY  AH0 - L IH1 S - K IY0
+ALISON  AE1 - L IH0 - S AH0 N
+ALISSA  AH0 - L IH1 - S AH0
+ALISTAIR  AE1 - L IH0 - S T EH2 R
+ALISTER  AE1 - L IH0 - S T ER0
+ALITA  AA0 - L IY1 - T AH0
+ALITALIA  AE2 - L IH0 - T EY1 - L IY0 - AH0
+ALITALIA'S  AE2 - L IH0 - T EY1 - L IY0 - AH0 Z
+ALITALIA'S(2)  AE2 - L IH0 - T AE1 - L IY0 - AH0 Z
+ALITALIA(2)  AE2 - L IH0 - T AE1 - L IY0 - AH0
+ALITHIA  AH0 - L IH1 - TH IY0 - AH0
+ALITO  AH0 - L IY1 - T OW0
+ALIVE  AH0 - L AY1 V
+ALIX  AE1 - L IH0 K S
+ALIZAC  AE1 - L IH0 - Z AE0 K
+ALKA  AE1 L - K AH0
+ALKAHEST  AE1 L - K AH0 - HH EH2 S T
+ALKALI  AE1 L - K AH0 - L AY2
+ALKALIES  AE1 L - K AH0 - L AY2 Z
+ALKALINE  AE1 L - K AH0 - L AY2 N
+ALKALINITY  AE2 L - K AH0 - L IH1 - N AH0 - T IY0
+ALKALOID  AE1 L - K AH0 - L OY2 D
+ALKALOIDAL  AE0 L - K AH0 - L OY1 - D AH0 L
+ALKALOIDS  AE1 L - K AH0 - L OY2 D Z
+ALKANES  AE1 L - K EY2 N Z
+ALKEMA  AE1 L - K IH0 - M AH0
+ALKENE  AE1 L - K IY2 N
+ALKENES  AE1 L - K IY2 N Z
+ALKIRE  AH0 L - K AY1 R
+ALL  AO1 L
+ALL'S  AO1 L Z
+ALL-OUT  AO1 L - AW1 T
+ALL-PURPOSE  AO1 L - P ER1 - P AH0 S
+ALLA  AA1 - L AH0
+ALLAH  AA1 - L AH0
+ALLAIN  AH0 - L EY1 N
+ALLAIRE  AA0 - L EH1 R
+ALLAIS  AH0 - L EY1
+ALLAIS(2)  EY1 - L IY0 - AH0 S
+ALLAN  AE1 - L AH0 N
+ALLANTE  AE2 - L AA1 N - T EY0
+ALLAR  AH0 - L AA1 R
+ALLARD  AE1 - L ER0 D
+ALLAY  AH0 - L EY1
+ALLAYED  AH0 - L EY1 D
+ALLAYING  AH0 - L EY1 - IH0 NG
+ALLAYS  AH0 - L EY1 Z
+ALLBAUGH  AH0 L - B AO1
+ALLBEE  AO1 L - B IY2
+ALLBRIGHT  AO1 L - B R AY2 T
+ALLBRITTEN  AE1 L - B R IH0 - T AH0 N
+ALLBRITTON  AE1 L - B R IH0 - T AA0 N
+ALLCOCK  AO1 L - K AA2 K
+ALLCORN  AH0 L - K AO1 R N
+ALLDAY  AO1 L - D EY2
+ALLDERDICE  AA1 L - D ER0 - D AY2 S
+ALLDERDICE(2)  AE1 L - D ER0 - D AY2 S
+ALLDREDGE  AH0 L - D R EH1 JH
+ALLEBACH  AE1 - L IH0 - B AA0 K
+ALLEBACH(2)  AE1 L - B AA0 K
+ALLECO  AE2 - L EH1 - K OW0
+ALLEE  AH0 - L IY1
+ALLEGATION  AE2 - L AH0 - G EY1 - SH AH0 N
+ALLEGATIONS  AE2 - L AH0 - G EY1 - SH AH0 N Z
+ALLEGE  AH0 - L EH1 JH
+ALLEGED  AH0 - L EH1 JH D
+ALLEGEDLY  AH0 - L EH1 - JH AH0 D - L IY0
+ALLEGES  AH0 - L EH1 - JH AH0 Z
+ALLEGES(2)  AH0 - L EH1 - JH IH0 Z
+ALLEGHANY  AE1 - L AH0 - G EY2 - N IY0
+ALLEGHENY  AE1 - L AH0 - G EY2 - N IY0
+ALLEGHENY'S  AE1 - L AH0 - G EY2 - N IY0 Z
+ALLEGIANCE  AH0 - L IY1 - JH AH0 N S
+ALLEGIANCES  AE2 - L IY1 - JH IY0 - AE2 N - S IH0 Z
+ALLEGIANCES(2)  AH0 - L IY1 - JH AH0 N - S IH0 Z
+ALLEGING  AH0 - L EH1 - JH IH0 NG
+ALLEGIS  AE2 - L EY1 - JH IH0 S
+ALLEGIS'  AE2 - L EY1 - JH IH0 S
+ALLEGIS'S  AE2 - L EY1 - JH IH0 - S IH0 Z
+ALLEGORICAL  AE2 - L AH0 - G AO1 - R AH0 - K AH0 L
+ALLEGORIES  AE1 - L AH0 - G AO2 - R IY0 Z
+ALLEGORY  AE1 - L AH0 - G AO2 - R IY0
+ALLEGRA  AA0 - L EH1 - G R AH0
+ALLEGRETTI  AA0 - L EH0 - G R EH1 - T IY0
+ALLEGRO  AH0 - L EH1 - G R OW2
+ALLELE  AH0 - L EH1 - L IY0
+ALLELES  AH0 - L EH1 - L IY0 Z
+ALLELIC  AH0 - L EH1 - L IH0 K
+ALLEMAN  EY1 L - M AH0 N
+ALLEMAND  AE1 - L IH0 - M AH0 N D
+ALLEN  AE1 - L AH0 N
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+ALLENBAUGH  AH0 - L EH1 N - B AO0
+ALLENDALE  AE1 - L AH0 N - D EY2 L
+ALLENDE  AA2 - Y EH1 N - D EY0
+ALLENDER  AA0 - L Y EH1 N - D EY0 - ER0
+ALLENDER(2)  AA0 - L EH1 N - D ER0
+ALLENDORF  AE1 - L IH0 N - D AO0 R F
+ALLENE  AE1 - L IY2 N
+ALLENHURST  AE1 - L AH0 N - HH ER2 S T
+ALLENS  AE1 - L AH0 N Z
+ALLENSBACH  AE1 - L AH0 N Z - B AA2 K
+ALLENSWORTH  AE1 - L AH0 N Z - W ER0 TH
+ALLENTOWN  AE1 - L AH0 N - T AW2 N
+ALLENWOOD  AE1 - L AH0 N - W UH2 D
+ALLER  AO1 - L ER0
+ALLERGAN  AE1 - L ER0 - JH AH0 N
+ALLERGEN  AE1 - L ER0 - JH AH0 N
+ALLERGENS  AE1 - L ER0 - JH AH0 N Z
+ALLERGIC  AH0 - L ER1 - JH IH0 K
+ALLERGIES  AE1 - L ER0 - JH IY0 Z
+ALLERGIST  AE1 - L ER0 - JH AH0 S T
+ALLERGY  AE1 - L ER0 - JH IY0
+ALLERS  AO1 - L ER0 Z
+ALLERT  AE1 - L ER0 T
+ALLERTON  AE1 - L ER0 - T AH0 N
+ALLES  EY1 L Z
+ALLEVA  AA0 - L EY1 - V AH0
+ALLEVIATE  AH0 - L IY1 - V IY0 - EY2 T
+ALLEVIATED  AH0 - L IY1 - V IY0 - EY2 - T AH0 D
+ALLEVIATED(2)  AH0 - L IY1 - V IY0 - EY2 - T IH0 D
+ALLEVIATES  AH0 - L IY1 - V IY0 - EY0 T S
+ALLEVIATING  AH0 - L IY1 - V IY0 - EY2 - T IH0 NG
+ALLEVIATION  AH0 - L IY2 - V IY0 - EY1 - SH AH0 N
+ALLEY  AE1 - L IY0
+ALLEY'S  AE1 - L IY0 Z
+ALLEYNE  AE1 - L EY2 N
+ALLEYS  AE1 - L IY0 Z
+ALLEYWAY  AE1 - L IY0 - W EY2
+ALLEYWAYS  AE1 - L IY0 - W EY2 Z
+ALLGAIER  AE1 L - G AY0 - ER0
+ALLGEIER  AE1 L - G AY0 - ER0
+ALLGEMEINE  AO1 L - G AH0 - M AY2 N
+ALLGEYER  AE1 L - G IY0 - ER0
+ALLGOOD  AO1 L - G UH2 D
+ALLI  AE1 - L IY0
+ALLIANCE  AH0 - L AY1 - AH0 N S
+ALLIANCE'S  AH0 - L AY1 - AH0 N - S IH0 Z
+ALLIANCES  AH0 - L AY1 - AH0 N - S AH0 Z
+ALLIANCES(2)  AH0 - L AY1 - AH0 N - S IH0 Z
+ALLIANT  AH0 - L AY1 - AH0 N T
+ALLIANZ  AE1 - L IY0 - AH0 N Z
+ALLICK  AE1 - L IH0 K
+ALLIE  AE1 - L IY0
+ALLIED  AH0 - L AY1 D
+ALLIED'S  AE1 - L AY2 D Z
+ALLIED(2)  AE1 - L AY2 D
+ALLIEDSIGNAL  AE1 - L AY2 D - S IH1 G - N AH0 L
+ALLIEDSIGNAL'S  AE1 - L AY2 D - S IH1 G - N AH0 L Z
+ALLIES  AE1 - L AY0 Z
+ALLIES'  AE1 - L AY0 Z
+ALLIES(2)  AH0 - L AY1 Z
+ALLIGATOR  AE1 - L AH0 - G EY2 - T ER0
+ALLIGATORS  AE1 - L AH0 - G EY2 - T ER0 Z
+ALLIGOOD  AE1 - L IH0 - G UH2 D
+ALLIN  AH0 - L IH1 N
+ALLINDER  AE1 - L IH0 N - D ER0
+ALLING  AO1 - L IH0 NG
+ALLINGER  AO1 - L IH0 - NG ER0
+ALLINGHAM  AO1 - L IH0 NG - HH AE2 M
+ALLINGTON  AO1 - L IH0 NG - T AH0 N
+ALLINSON  AE1 - L IH0 N - S AH0 N
+ALLIS  AE1 - L IH0 S
+ALLISON  AE1 - L AH0 - S AH0 N
+ALLISON'S  AE1 - L IH0 - S AH0 N Z
+ALLISON(2)  AE1 - L IH0 - S AH0 N
+ALLISTER  AO1 - L IH0 - S T ER0
+ALLISTER(2)  AE1 - L IH0 - S T ER0
+ALLISTON  AE1 - L IH0 - S T AA0 N
+ALLITERATION  AH0 - L IH1 - T ER0 - EY2 - SH AH0 N
+ALLITERATIVE  AH0 - L IH1 - T ER0 - AH0 - T IH0 V
+ALLITERATIVE(2)  AH0 - L IH1 - T ER0 - EY2 - T IH0 V
+ALLMAN  AO1 L - M AH0 N
+ALLMENDINGER  AE1 L - M EH0 N - D IH0 - NG ER0
+ALLMON  AO1 L - M AH0 N
+ALLMOND  AH0 L - M AA1 N D
+ALLNET  AO1 L - N EH2 T
+ALLNUTT  AH0 L - N AH1 T
+ALLOCATE  AE1 - L AH0 - K EY2 T
+ALLOCATED  AE1 - L AH0 - K EY2 - T IH0 D
+ALLOCATES  AE1 - L AH0 - K EY2 T S
+ALLOCATING  AE1 - L AH0 - K EY2 - T IH0 NG
+ALLOCATION  AE2 - L AH0 - K EY1 - SH AH0 N
+ALLOCATIONS  AE2 - L AH0 - K EY1 - SH AH0 N Z
+ALLOCATOR  AE1 - L AH0 - K EY2 - T ER0
+ALLOCATORS  AE1 - L AH0 - K EY2 - T ER0 Z
+ALLOCCA  AE2 - L AA1 - K AH0
+ALLOCCO  AE2 - L AA1 - K OW0
+ALLOMORPH  AE1 - L AH0 - M AO0 R F
+ALLOMORPHS  AE1 - L AH0 - M AO0 R F S
+ALLOPHONE  AE1 - L AH0 - F OW2 N
+ALLOPHONES  AE1 - L AH0 - F OW2 N Z
+ALLOPHONIC  AE2 - L AH0 - F AA1 - N IH0 K
+ALLOR  AO1 - L ER0
+ALLOT  AH0 - L AA1 T
+ALLOTED  AH0 - L AA1 - T IH0 D
+ALLOTMENT  AH0 - L AA1 T - M AH0 N T
+ALLOTMENTS  AH0 - L AA1 T - M AH0 N T S
+ALLOTROPE  AE1 - L AH0 - T R OW2 P
+ALLOTROPES  AE1 - L AH0 - T R OW2 P S
+ALLOTS  AH0 - L AA1 T S
+ALLOTTED  AH0 - L AA1 - T IH0 D
+ALLOTTING  AH0 - L AA1 - T IH0 NG
+ALLOW  AH0 - L AW1
+ALLOWABLE  AH0 - L AW1 - AH0 - B AH0 L
+ALLOWANCE  AH0 - L AW1 - AH0 N S
+ALLOWANCES  AH0 - L AW1 - AH0 N - S IH0 Z
+ALLOWAY  AE1 - L OW0 - W EY2
+ALLOWED  AH0 - L AW1 D
+ALLOWING  AH0 - L AW1 - IH0 NG
+ALLOWS  AH0 - L AW1 Z
+ALLOY  AE1 - L OY2
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+ALLPHIN  AH0 L - F IH1 N
+ALLPORT  AO1 L - P AO2 R T
+ALLRED  AE1 L - R IH0 D
+ALLRIGHT  AA0 L - R AY1 T
+ALLS  AO1 L Z
+ALLSBROOK  AO1 L Z - B R UH2 K
+ALLSBROOK(2)  AO1 L T S - B R UH2 K
+ALLSHOUSE  AO1 L Z - HH AW2 S
+ALLSHOUSE(2)  AO1 L T S - HH AW2 S
+ALLSOP  AO1 L - S AA2 P
+ALLSOPP  AO1 L - S AA2 P
+ALLSPICE  AO1 L - S P AY2 S
+ALLSTATE  AO1 L - S T EY2 T
+ALLSTATE'S  AO1 L - S T EY2 T S
+ALLSTON  AO1 L - S T AH0 N
+ALLSUP  AE1 L - S AH0 P
+ALLTEL  AO1 L - T EH2 L
+ALLTIME  AO1 L - T AY2 M
+ALLTON  AO1 L - T AH0 N
+ALLTOP  AO1 L - T AA2 P
+ALLUDE  AH0 - L UW1 D
+ALLUDED  AH0 - L UW1 - D AH0 D
+ALLUDES  AH0 - L UW1 D Z
+ALLUDING  AH0 - L UW1 - D IH0 NG
+ALLUM  AE1 - L AH0 M
+ALLUMS  AE1 - L AH0 M Z
+ALLURE  AH0 - L UH1 R
+ALLURED  AH0 - L UH1 R D
+ALLURING  AH0 - L UH1 - R IH0 NG
+ALLUSION  AH0 - L UW1 - ZH AH0 N
+ALLUSIONS  AH0 - L UW1 - ZH AH0 N Z
+ALLUSIVE  AH0 - L UW1 - S IH0 V
+ALLUVIAL  AE2 - L UW1 - V IY0 - AH0 L
+ALLUVIUM  AH0 - L UW1 - V IY0 - AH0 M
+ALLWASTE  AO1 L - W EY2 S T
+ALLWEISS  AA1 L - W IY2 S
+ALLY  AE1 - L AY0
+ALLY'S  AH0 - L AY1 Z
+ALLY(2)  AH0 - L AY1
+ALLYING  AE1 - L AY0 - IH0 NG
+ALLYING(2)  AH0 - L AY1 - IH0 NG
+ALLYN  AE1 - L IH0 N
+ALLYS  AE1 - L AY0 Z
+ALLYSON  AE1 - L IH0 - S AH0 N
+ALM  AA1 M
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+ALMADA  AA0 L - M AA1 - D AH0
+ALMADANI  AO2 L - M AH0 - D AA1 - N IY0
+ALMADEN  AE1 L - M AH0 - D AH0 N
+ALMADEN(2)  AA1 L - M AH0 - D EH2 N
+ALMAGUER  AA0 L - M AA0 - G W EH1 R
+ALMAN  AE1 L - M AH0 N
+ALMANAC  AO1 L - M AH0 - N AE2 K
+ALMAND  AE1 L - M AH0 N D
+ALMANZA  AA0 L - M AA1 N - Z AH0
+ALMANZAR  AA0 L - M AA0 N - Z AA1 R
+ALMARAZ  AA0 L - M AA1 - R AA0 Z
+ALMAS  AE1 L - M AH0 Z
+ALMASY  AE1 L - M AH0 - S IY0
+ALMAZAN  AA0 L - M AA0 - Z AA1 N
+ALMEDA  AA0 L - M EY1 - D AH0
+ALMEIDA  AA0 L - M IY1 - D AH0
+ALMENDAREZ  AA0 L - M EY0 N - D AA1 - R EH0 Z
+ALMER  AO1 L - M ER0
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+ALMIRA  AA0 L - M IH1 - R AH0
+ALMO  AA1 L - M OW0
+ALMODOVAR  AA0 L - M OW0 - D OW0 - V AA1 R
+ALMON  AA1 L - M AH0 N
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+ALMOST  AO1 L - M OW2 S T
+ALMQUIST  AE1 L M - K W IH0 S T
+ALMS  AA1 L M Z
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+ALMY  AO1 - M IY0
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+ALODIE  AH0 - L AA1 - D IY0
+ALOE  AE1 - L OW2
+ALOFT  AH0 - L AO1 F T
+ALOHA  AH0 - L OW1 - HH AA0
+ALOI  AA1 - L OY0
+ALOIA  AA0 - L OW1 - Y AH0
+ALOIS  AA0 - L OY1 S
+ALOISA  AA0 - L OY1 - S AH0
+ALOISI  AA0 - L OY1 - S IY0
+ALOISIA  AA0 - L OY1 - S IY0 - AH0
+ALOISIO  AA0 - L OY1 - S IY0 - OW0
+ALOKA  AH0 - L OW1 - K AH0
+ALON  AH0 - L AA1 N
+ALONE  AH0 - L OW1 N
+ALONG  AH0 - L AO1 NG
+ALONGE  AE1 - L AH0 N JH
+ALONGI  AA0 - L OW1 NG - G IY0
+ALONGS  AH0 - L AO1 NG Z
+ALONGSIDE  AH0 - L AO1 NG - S AY1 D
+ALONSO  AH0 - L AA1 N - S OW0
+ALONZA  AH0 - L AA1 N - Z AH0
+ALONZO  AH0 - L AA1 N - Z OW0
+ALOOF  AH0 - L UW1 F
+ALOOFNESS  AH0 - L UW1 F - N AH0 S
+ALOT  AH0 - L AA1 T
+ALOUD  AH0 - L AW1 D
+ALOYS  AH0 - L OY1 Z
+ALOYSE  AH0 - L OY1 S
+ALOYSIA  AA0 - L OY1 - S IY0 - AH0
+ALOYSIUS  AE2 - L OW0 - IH1 - SH IH0 S
+ALPA  AE1 L - P AH0
+ALPACA  AE0 L - P AE1 - K AH0
+ALPAUGH  AH0 L - P AO1
+ALPER  AE1 L - P ER0
+ALPERIN  AE1 L - P ER0 - IH2 N
+ALPERN  AH0 L - P ER1 N
+ALPERS  AE1 L - P ER0 Z
+ALPERT  AE1 L - P ER0 T
+ALPEX  AE1 L - P EH0 K S
+ALPHA  AE1 L - F AH0
+ALPHA'S  AE1 L - F AH0 Z
+ALPHABET  AE1 L - F AH0 - B EH2 T
+ALPHABETIC  AE2 L - F AH0 - B EH1 - T IH0 K
+ALPHABETICAL  AE2 L - F AH0 - B EH1 - T IH0 - K AH0 L
+ALPHABETICALLY  AE2 L - F AH0 - B EH1 - T IH0 K - L IY0
+ALPHABETIZATION  AE2 L - F AH0 - B EH2 - T AH0 - Z EY1 - SH AH0 N
+ALPHABETIZE  AE1 L - F AH0 - B AH0 - T AY2 Z
+ALPHAMETRIC  AE1 L - F AH0 - M EH2 - T R IH0 K
+ALPHAMETRICS  AE1 L - F AH0 - M EH2 - T R IH0 K S
+ALPHANDERY  AE2 L - F AE1 - D ER0 - IY0
+ALPHANUMERIC  AE2 L - F AH0 - N UW0 - M EH1 - R IH0 K
+ALPHAREL  AE1 L - F ER0 - EH2 L
+ALPHARETTA  AE2 L - F ER0 - EH1 - T AH0
+ALPHIN  AE1 L - F IH0 N
+ALPHONSE  AE0 L - F AA1 N Z
+ALPHONSINE  AH0 L - F AA1 N - S IY0 N
+ALPHONSO  AE0 L - F AA1 N - S OW0
+ALPIN  AH0 L - P IH1 N
+ALPINE  AE1 L - P AY2 N
+ALPIREZ  AE1 L - P IH0 - R EH0 Z
+ALPO  AE1 L - P OW0
+ALPS  AE1 L P S
+ALQUIST  AE1 L - K W IH0 S T
+ALREADY  AO0 L - R EH1 - D IY0
+ALREADY(2)  AO0 - R EH1 - D IY0
+ALRED  AO1 L - R IH0 D
+ALRIGHT  AO2 L - R AY1 T
+ALROY  AH0 L - R OY1
+ALS  AE1 L Z
+ALSACE  AE0 L - S AA1 S
+ALSACE(2)  AE0 L - S AE1 S
+ALSATIAN  AE0 L - S EY1 - SH AH0 N
+ALSBROOK  AE1 L Z - B R UH0 K
+ALSBROOK'S  AE1 L Z - B R UH0 K S
+ALSBROOKS  AE1 L Z - B R UH0 K S
+ALSBROOKS'  AE1 L Z - B R UH0 K S
+ALSBURY  AO1 L Z - B EH0 - R IY0
+ALSBURY(2)  AE1 L Z - B EH0 - R IY0
+ALSDORF  AO1 L Z - D AO0 R F
+ALSDORF(2)  AE1 L Z - D AO0 R F
+ALSIP  AE1 L - S IH0 P
+ALSO  AO1 L - S OW0
+ALSOBROOK  AE1 L - S AH0 - B R UH0 K
+ALSOBROOKS  AE1 L - S AH0 - B R UH0 K S
+ALSOP  AE1 L - S AA0 P
+ALSPACH  AE1 L - S P AH0 K
+ALSPAUGH  AH0 L - S P AO1
+ALSTHOM  AE1 L - S TH AH0 M
+ALSTON  AO1 L - S T AH0 N
+ALSUP  AE1 L - S AH0 P
+ALT  AA1 L T
+ALTA  AA1 L - T AH0
+ALTADENA  AA2 L - T AH0 - D IY1 - N AH0
+ALTAI  AE0 L - T AY1
+ALTAIC  AE0 L - T EY1 - IH0 K
+ALTAMIRANO  AA0 L - T AA0 - M IH0 - R AA1 - N OW0
+ALTAMURO  AO2 L - T AH0 - M UH1 - R OW0
+ALTAR  AO1 L - T ER0
+ALTARPIECE  AO1 L - T ER0 - P IY2 S
+ALTARS  AO1 L - T ER0 Z
+ALTAVISTA  AO2 L - T AH0 - V IH1 - S T AH0
+ALTAY  AO1 L - T AY0
+ALTEMOSE  AE1 L - T IH0 - M OW0 S
+ALTEMUS  AE1 L - T IH0 - M IH0 S
+ALTENBURG  AO1 L - T AH0 N - B ER0 G
+ALTENHOFEN  AE1 L - T IH0 N - HH AH0 - F AH0 N
+ALTER  AO1 L - T ER0
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+ANTIGEN  AE1 N - T AH0 - JH AH0 N
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+APARTMENT  AH0 - P AA1 R T - M AH0 N T
+APARTMENTS  AH0 - P AA1 R T - M AH0 N T S
+APATHETIC  AE2 - P AH0 - TH EH1 - T IH0 K
+APATHY  AE1 - P AH0 - TH IY0
+APATITE  AE1 - P AH0 - T AY2 T
+APATITES  AE1 - P AH0 - T AY2 T S
+APC  EY1 - P IY1 - S IY1
+APC'S  EY1 - P IY1 - S IY1 Z
+APCAR  AE1 P - K AA0 R
+APCAR(2)  AE1 P - G AA0 R
+APE  EY1 P
+APEC  EY1 - P EH2 K
+APEC'S  EY1 - P EH2 K S
+APEL  AA0 - P EH1 L
+APELIKE  EY1 - P L AY2 K
+APENNINE  AE1 - P AH0 - N IY2 N
+APERTURE  AE1 - P ER0 - CH ER0
+APES  EY1 P S
+APEX  EY1 - P EH2 K S
+APEX'S  EY1 - P EH2 K - S IH0 Z
+APFEL  AE1 P - F AH0 L
+APFELBAUM  AE1 P - F AH0 L - B AW2 M
+APGAR  AE1 P - G ER0
+APHASIA  AH0 - F EY1 - ZH AH0
+APHID  AE1 - F AH0 D
+APHID(2)  EY1 - F AH0 D
+APHIDS  AE1 - F IH0 D Z
+APHIDS(2)  EY1 - F AH0 D Z
+APHORISM  AE1 - F ER0 - IH2 - Z AH0 M
+APHORISMS  AE1 - F ER0 - IH2 - Z AH0 M Z
+APHRODISIAC  AE2 - F R OW0 - D IY1 - Z IY0 - AE0 K
+APHRODITE  AE2 - F R AH0 - D AY1 - T IY0
+APHRODITE'S  AE2 - F R AH0 - D AY1 - T IY0 Z
+APHRODITES  AE2 - F R AH0 - D AY1 - T IY0 Z
+APICELLA  AE2 - P IH0 - S EH1 - L AH0
+APIECE  AH0 - P IY1 S
+APING  EY1 - P IH0 NG
+APLENTY  AH0 - P L EH1 N - T IY0
+APLIN  AE1 - P L IH0 N
+APLOMB  AH0 - P L AA1 M
+APNEA  AE1 P - N IY0 - AH0
+APOCALYPSE  AH0 - P AA1 - K AH0 - L IH2 P S
+APOCALYPTIC  AH0 - P AA2 - K AH0 - L IH1 P - T IH0 K
+APOCRYPHAL  AH0 - P AA1 - K R AH0 - F AH0 L
+APODACA  AA0 - P OW0 - D AA1 - K AH0
+APOGEE  AE1 - P AH0 - JH IY2
+APOLITICAL  EY2 - P AH0 - L IH1 - T IH0 - K AH0 L
+APOLLINE  AE1 - P AH0 - L AY2 N
+APOLLINIAN  AE2 - P AH0 - L IH1 - N IY0 - AH0 N
+APOLLO  AH0 - P AA1 - L OW0
+APOLLO'S  AH0 - P AA1 - L OW0 Z
+APOLLONIAN  AE2 - P AH0 - L OW1 - N IY0 - AH0 N
+APOLOGETIC  AH0 - P AA2 - L AH0 - JH EH1 - T IH0 K
+APOLOGETICALLY  AH0 - P AA2 - L AH0 - JH EH1 - T IH0 K - L IY0
+APOLOGIES  AH0 - P AA1 - L AH0 - JH IY0 Z
+APOLOGIST  AH0 - P AA1 - L AH0 - JH AH0 S T
+APOLOGISTS  AH0 - P AA1 - L AH0 - JH AH0 S T S
+APOLOGISTS(2)  AH0 - P AA1 - L AH0 - JH AH0 S S
+APOLOGISTS(3)  AH0 - P AA1 - L AH0 - JH AH0 S
+APOLOGIZE  AH0 - P AA1 - L AH0 - JH AY2 Z
+APOLOGIZED  AH0 - P AA1 - L AH0 - JH AY2 Z D
+APOLOGIZES  AH0 - P AA1 - L AH0 - JH AY2 - Z IH0 Z
+APOLOGIZING  AH0 - P AA1 - L AH0 - JH AY2 - Z IH0 NG
+APOLOGY  AH0 - P AA1 - L AH0 - JH IY0
+APONTE  AH0 - P AA1 N - T IY0
+APOPA  AH0 - P OW1 - P AH0
+APOPKA  AH0 - P AO1 P - K AH0
+APOPLECTIC  AE2 - P AH0 - P L EH1 K - T IH0 K
+APOPLEXY  AE1 - P AH0 - P L EH2 K - S IY0
+APOSTLE  AH0 - P AA1 - S AH0 L
+APOSTLES  AH0 - P AA1 - S AH0 L Z
+APOSTOL  AE1 - P AH0 - S T AH0 L
+APOSTOLOPOUL  AH0 - P AA2 - S T OW0 - L OW0 - P UW1 L
+APOSTROPHE  AH0 - P AA1 S - T R AH0 - F IY0
+APOTHECARY  AH0 - P AA1 - TH AH0 - K EH2 - R IY0
+APOTHEOSIS  AH0 - P AA2 - TH IY0 - OW1 - S AH0 S
+APP  AE1 P
+APPALACHIA  AE2 - P AH0 - L AE1 - CH IY0 - AH0
+APPALACHIAN  AE2 - P AH0 - L EY1 - CH AH0 N
+APPALACHIAN(2)  AE2 - P AH0 - L EY1 - SH AH0 N
+APPALACHIAN(3)  AE2 - P AH0 - L AE1 - CH AH0 N
+APPALACHIANS  AE2 - P AH0 - L EY1 - CH AH0 N Z
+APPALACHIANS(2)  AE2 - P AH0 - L EY1 - SH AH0 N Z
+APPALACHIANS(3)  AE1 - P AH0 - L AE1 - CH AH0 N Z
+APPALL  AH0 - P AO1 L
+APPALLED  AH0 - P AO1 L D
+APPALLING  AH0 - P AO1 - L IH0 NG
+APPALLINGLY  AH0 - P AO1 - L IH0 NG - L IY0
+APPALLS  AH0 - P AO1 L Z
+APPARATCHIK  AE2 - P ER0 - AE1 - CH IH0 K
+APPARATCHIKS  AE2 - P ER0 - AE1 - CH IH0 K S
+APPARATUS  AE2 - P ER0 - AE1 - T AH0 S
+APPARATUSES  AE2 - P ER0 - AE1 - T AH0 - S AH0 Z
+APPAREL  AH0 - P AE1 - R AH0 L
+APPAREL(2)  AH0 - P EH1 - R AH0 L
+APPARENT  AH0 - P EH1 - R AH0 N T
+APPARENTLY  AH0 - P EH1 - R AH0 N T - L IY0
+APPARITION  AE2 - P ER0 - IH1 - SH AH0 N
+APPARITIONS  AE2 - P ER0 - IH1 - SH AH0 N Z
+APPEAL  AH0 - P IY1 L
+APPEALABLE  AH0 - P IY1 - L AH0 - B AH0 L
+APPEALED  AH0 - P IY1 L D
+APPEALING  AH0 - P IY1 - L IH0 NG
+APPEALINGLY  AH0 - P IY1 - L IH0 NG - L IY0
+APPEALS  AH0 - P IY1 L Z
+APPEAR  AH0 - P IH1 R
+APPEARANCE  AH0 - P IH1 - R AH0 N S
+APPEARANCES  AH0 - P IH1 - R AH0 N - S AH0 Z
+APPEARED  AH0 - P IH1 R D
+APPEARING  AH0 - P IH1 - R IH0 NG
+APPEARS  AH0 - P IH1 R Z
+APPEASE  AH0 - P IY1 Z
+APPEASED  AH0 - P IY1 Z D
+APPEASEMENT  AH0 - P IY1 Z - M AH0 N T
+APPEASING  AH0 - P IY1 - Z IH0 NG
+APPEL  AE1 - P AH0 L
+APPELBAUM  AE1 - P AH0 L - B AW2 M
+APPELHANS  AE1 - P IH0 L - HH AH0 N Z
+APPELL  AE1 - P AH0 L
+APPELLATE  AH0 - P EH1 - L IH0 T
+APPELLATE(2)  AH0 - P EH1 - L EY2 T
+APPELLATION  AE2 - P AH0 - L EY1 - SH AH0 N
+APPELLATIONS  AE2 - P AH0 - L EY1 - SH AH0 N Z
+APPELMAN  AE1 - P AH0 L - M AH0 N
+APPELT  AE1 - P IH0 L T
+APPEND  AH0 - P EH1 N D
+APPENDAGE  AH0 - P EH1 N - D IH0 JH
+APPENDAGES  AH0 - P EH1 N - D IH0 - JH IH0 Z
+APPENDECTOMIES  AE2 - P AH0 N - D EH1 K - T AH0 - M IY0 Z
+APPENDECTOMY  AE2 - P IH0 N - D EH1 K - T AH0 - M IY0
+APPENDED  AH0 - P EH1 N - D IH0 D
+APPENDIX  AH0 - P EH1 N - D IH0 K S
+APPENDIXES  AH0 - P EH1 N - D IH0 K - S IH0 Z
+APPENZELLER  AE1 - P IH0 N - Z AH0 L - ER0
+APPERSON  AE1 - P ER0 - S AH0 N
+APPERT  AE1 - P ER0 T
+APPETITE  AE1 - P AH0 - T AY2 T
+APPETITES  AE1 - P AH0 - T AY2 T S
+APPETITO  AE2 - P AH0 - T IY1 - T OW2
+APPETIZER  AE1 - P AH0 - T AY2 - Z ER0
+APPETIZERS  AE1 - P AH0 - T AY2 - Z ER0 Z
+APPETIZING  AE1 - P IH0 - T AY2 - Z IH0 NG
+APPIA  AE1 - P IY0 - AH0
+APPIAN  AE1 - P IY0 - AH0 N
+APPLAUD  AH0 - P L AO1 D
+APPLAUDED  AH0 - P L AO1 - D AH0 D
+APPLAUDED(2)  AH0 - P L AO1 - D IH0 D
+APPLAUDING  AH0 - P L AO1 - D IH0 NG
+APPLAUDS  AH0 - P L AO1 D Z
+APPLAUSE  AH0 - P L AO1 Z
+APPLE  AE1 - P AH0 L
+APPLE'S  AE1 - P AH0 L Z
+APPLEBAUM  AE1 - P AH0 L - B AW2 M
+APPLEBEE  AE1 - P AH0 L - B IY2
+APPLEBEE'S  AE1 - P AH0 L - B IY2 Z
+APPLEBERRY  AE1 - P AH0 L - B EH2 - R IY0
+APPLEBY  AE1 - P IH0 L - B IY0
+APPLEGARTH  AE1 - P L IH0 - G AA0 R TH
+APPLEGATE  AE1 - P AH0 L - G EY2 T
+APPLEMAN  AE1 - P AH0 L - M AH0 N
+APPLEQUIST  AE1 - P AH0 L - K W IH0 S T
+APPLES  AE1 - P AH0 L Z
+APPLESAUCE  AE1 - P AH0 L - S AO2 S
+APPLESEED  AE1 - P AH0 L - S IY2 D
+APPLESHARE  AE1 - P AH0 L - SH EH2 R
+APPLETON  AE1 - P AH0 L - T AH0 N
+APPLEWHITE  AE1 - P AH0 L - W AY2 T
+APPLEYARD  AE1 - P AH0 L - Y AA2 R D
+APPLIANCE  AH0 - P L AY1 - AH0 N S
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+APPLIANCES(2)  AH0 - P L AY1 - AH0 N - S IH0 Z
+APPLICABILITY  AE2 - P L AH0 - K AH0 - B IH1 - L AH0 - T IY0
+APPLICABLE  AE1 - P L AH0 - K AH0 - B AH0 L
+APPLICANT  AE1 - P L IH0 - K AH0 N T
+APPLICANT'S  AE1 - P L IH0 - K AH0 N T S
+APPLICANTS  AE1 - P L IH0 - K AH0 N T S
+APPLICANTS'  AE1 - P L AH0 - K AH0 N T S
+APPLICATION  AE2 - P L AH0 - K EY1 - SH AH0 N
+APPLICATIONS  AE2 - P L AH0 - K EY1 - SH AH0 N Z
+APPLICATOR  AE1 - P L AH0 - K EY2 - T ER0
+APPLICATORS  AE1 - P L AH0 - K EY2 - T ER0 Z
+APPLIED  AH0 - P L AY1 D
+APPLIES  AH0 - P L AY1 Z
+APPLIN  AE1 - P L IH0 N
+APPLING  AE1 - P AH0 L - IH0 NG
+APPLING(2)  AE1 - P L IH0 NG
+APPLY  AH0 - P L AY1
+APPLYING  AH0 - P L AY1 - IH0 NG
+APPOINT  AH0 - P OY1 N T
+APPOINTED  AH0 - P OY1 N - T AH0 D
+APPOINTED(2)  AH0 - P OY1 N - T IH0 D
+APPOINTEE  AH0 - P OY0 N - T IY1
+APPOINTEE(2)  AH0 - P OY1 N - T IY0
+APPOINTEES  AH0 - P OY0 N - T IY1 Z
+APPOINTEES(2)  AH0 - P OY1 N - T IY0 Z
+APPOINTING  AH0 - P OY1 N - T IH0 NG
+APPOINTIVE  AH0 - P OY1 N - T IH0 V
+APPOINTMENT  AH0 - P OY1 N T - M AH0 N T
+APPOINTMENTS  AH0 - P OY1 N T - M AH0 N T S
+APPOINTS  AH0 - P OY1 N T S
+APPORTION  AH0 - P AO1 R - SH AH0 N
+APPORTIONED  AH0 - P AO1 R - SH AH0 N D
+APPORTIONING  AH0 - P AO1 R - SH AH0 N - IH0 NG
+APPORTIONMENT  AH0 - P AO1 R - SH AH0 N - M AH0 N T
+APPRAISAL  AH0 - P R EY1 - Z AH0 L
+APPRAISALS  AH0 - P R EY1 - Z AH0 L Z
+APPRAISE  AH0 - P R EY1 Z
+APPRAISED  AH0 - P R EY1 Z D
+APPRAISER  AH0 - P R EY1 - Z ER0
+APPRAISERS  AH0 - P R EY1 - Z ER0 Z
+APPRAISES  AH0 - P R EY1 - Z IH0 Z
+APPRAISING  AH0 - P R EY1 - Z IH0 NG
+APPRECIABLE  AH0 - P R IY1 - SH AH0 - B AH0 L
+APPRECIABLY  AH0 - P R IY1 - SH AH0 - B L IY0
+APPRECIATE  AH0 - P R IY1 - SH IY0 - EY2 T
+APPRECIATED  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 D
+APPRECIATES  AH0 - P R IY1 - SH IY0 - EY2 T S
+APPRECIATING  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 NG
+APPRECIATION  AH0 - P R IY2 - SH IY0 - EY1 - SH AH0 N
+APPRECIATIVE  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 V
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+APPRECIATIVELY  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 V - L IY0
+APPRECIATIVELY(2)  AH0 - P R IY1 - SH Y AH0 - T IH0 V - L IY0
+APPREHEND  AE2 - P R IH0 - HH EH1 N D
+APPREHENDED  AE2 - P R IH0 - HH EH1 N - D AH0 D
+APPREHENDING  AE2 - P R IH0 - HH EH1 N - D IH0 NG
+APPREHENSION  AE2 - P R IH0 - HH EH1 N - SH AH0 N
+APPREHENSIONS  AE2 - P R IH0 - HH EH1 N - SH AH0 N Z
+APPREHENSIVE  AE2 - P R IH0 - HH EH1 N - S IH0 V
+APPREHENSIVE(2)  AE2 - P R IY0 - HH EH1 N - S IH0 V
+APPRENTICE  AH0 - P R EH1 N - T AH0 S
+APPRENTICE(2)  AH0 - P R EH1 N - T IH0 S
+APPRENTICED  AH0 - P R EH1 N - T AH0 S T
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+APPRENTICESHIP  AH0 - P R EH1 N - T AH0 S - SH IH2 P
+APPRENTICESHIPS  AH0 - P R EH1 N - T AH0 S - SH IH2 P S
+APPRISE  AH0 - P R AY1 Z
+APPRISED  AH0 - P R AY1 Z D
+APPROACH  AH0 - P R OW1 CH
+APPROACHABLE  AH0 - P R OW1 - CH AH0 - B AH0 L
+APPROACHED  AH0 - P R OW1 CH T
+APPROACHES  AH0 - P R OW1 - CH AH0 Z
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+APPROACHING  AH0 - P R OW1 - CH IH0 NG
+APPROBATION  AE2 - P R AH0 - B EY1 - SH AH0 N
+APPROPRIATE  AH0 - P R OW1 - P R IY0 - AH0 T
+APPROPRIATE(2)  AH0 - P R OW1 - P R IY0 - EY2 T
+APPROPRIATED  AH0 - P R OW1 - P R IY0 - EY2 - T IH0 D
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+APPROPRIATES  AH0 - P R OW1 - P R IY0 - EY2 T S
+APPROPRIATING  AH0 - P R OW1 - P R IY0 - EY2 - T IH0 NG
+APPROPRIATION  AH0 - P R OW2 - P R IY0 - EY1 - SH AH0 N
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+APPROPRIATOR  AH0 - P R OW1 - P R IY0 - EY2 - T ER0
+APPROPRIATORS  AH0 - P R OW1 - P R IY0 - EY2 - T ER0 Z
+APPROVABLE  AH0 - P R UW1 - V AH0 - B AH0 L
+APPROVAL  AH0 - P R UW1 - V AH0 L
+APPROVALS  AH0 - P R UW1 - V AH0 L Z
+APPROVE  AH0 - P R UW1 V
+APPROVED  AH0 - P R UW1 V D
+APPROVES  AH0 - P R UW1 V Z
+APPROVING  AH0 - P R UW1 - V IH0 NG
+APPROVINGLY  AH0 - P R UW1 - V IH0 NG - L IY0
+APPROXIMATE  AH0 - P R AA1 K - S AH0 - M AH0 T
+APPROXIMATE(2)  AH0 - P R AA1 K - S AH0 - M EY2 T
+APPROXIMATED  AH0 - P R AA1 K - S AH0 - M EY2 - T AH0 D
+APPROXIMATELY  AH0 - P R AA1 K - S AH0 - M AH0 T - L IY0
+APPROXIMATES  AH0 - P R AA1 K - S AH0 - M AH0 T S
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+APPROXIMATING  AH0 - P R AA1 K - S IH0 - M EY2 - T IH0 NG
+APPROXIMATION  AH0 - P R AA2 K - S AH0 - M EY1 - SH AH0 N
+APPROXIMATIONS  AH0 - P R AA2 K - S AH0 - M EY1 - SH AH0 N Z
+APPS  AE1 P S
+APRA  EY1 - P R AH0
+APREA  AA1 - P R IY0 - AH0
+APRICOT  EY1 - P R AH0 - K AA2 T
+APRICOTS  AE1 - P R AH0 - K AA2 T S
+APRIL  EY1 - P R AH0 L
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+APRILE  AA1 - P R AH0 L
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+APROPOS  AE2 - P R AH0 - P OW1
+APSELL  AE1 P - S AH0 L
+APSEY  AE1 P - S IY0
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+APT  AE1 P T
+APTER  AE1 P - T ER0
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+AQUACULTURE  AE1 - K W AH0 - K AH2 L - CH ER0
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+AQUARIUS  AH0 - K W EH1 - R IY0 - AH0 S
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+ARABIAN  ER0 - EY1 - B IY0 - AH0 N
+ARABIANS  ER0 - EY1 - B IY0 - AH0 N Z
+ARABIC  AE1 - R AH0 - B IH0 K
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+ARABICA  ER0 - AE1 - B IH0 - K AH0
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+ARABIE  ER0 - AE1 - B IY0
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+AZIDOTHYMIDINE(4)  EY0 - Z AY2 - D AH0 - TH AY1 - M AH0 - D AY2 N
+AZINGER  EY1 - Z IH0 - NG ER0
+AZIZ  AH0 - Z IY1 Z
+AZMEER  AE1 Z - M IH2 R
+AZOFF  AE1 - Z AO0 F
+AZORES  AH0 - Z AO1 R Z
+AZPURUA  AE0 Z - P Y UH1 - R UW0 - AH0
+AZTAR  AE1 Z - T ER0
+AZTEC  AE1 Z - T EH2 K
+AZTECA  AE2 Z - T EH1 - K AH0
+AZTECS  AE1 Z - T EH2 K S
+AZURA  AH0 - Z UH1 - R AH0
+AZURE  AE1 - ZH ER0
+AZZARA  AA0 T - S AA1 - R AH0
+AZZARELLO  AA0 T - S AA0 - R EH1 - L OW0
+AZZARO  AA0 T - S AA1 - R OW0
+AZZATO  AH0 - Z AA1 - T OW0
+AZZOPARDI  AA0 T - S OW0 - P AA1 R - D IY0
+B  B IY1
+B'GOSH  B AH0 - G AO1 SH
+B'NAI  B AH0 - N EY1
+B'RITH  B R IH1 TH
+B'S  B IY1 Z
+B-J  B IY1 - JH EY1
+B-J'S  B IY1 - JH EY1 Z
+B.  B IY1
+B.'S  B IY1 Z
+B.S  B IY1 Z
+BA'ATH  B AA1 TH
+BA'ATH(2)  B AH0 - AA1 TH
+BAAB  B AA1 B
+BAACK  B AA1 K
+BAADE  B AA1 D
+BAALBEK  B AA1 L - B EH0 K
+BAALBEQ  B AA1 L - B EH0 K
+BAAR  B AA1 R
+BAARS  B AA1 R Z
+BAAS  B AA1 Z
+BAASCH  B AA1 SH
+BAATZ  B AA1 T S
+BAB  B AE1 B
+BABA  B AA1 - B AH0
+BABANGIDA  B AH0 - B AA1 NG - G IH0 - D AH0
+BABANGIDA(2)  B AH0 - B AE1 NG - G IH0 - D AH0
+BABB  B AE1 B
+BABBAGE  B AE1 - B IH0 JH
+BABBAGE'S  B AE1 - B IH0 - JH IH0 Z
+BABBIO  B AE1 - B IY0 - OW0
+BABBIT  B AE1 - B IH0 T
+BABBIT'S  B AE1 - B IH0 T S
+BABBITT  B AE1 - B IH0 T
+BABBITT'S  B AE1 - B AH0 T S
+BABBITTS  B AE1 - B AH0 T S
+BABBLE  B AE1 - B AH0 L
+BABBLED  B AE1 - B AH0 L D
+BABBLER  B AE1 - B L ER0
+BABBLERS  B AE1 - B L ER0 Z
+BABBLING  B AE1 - B AH0 L - IH0 NG
+BABBLING(2)  B AE1 - B L IH0 NG
+BABBS  B AE1 B Z
+BABCOCK  B AE1 B - K AO0 K
+BABE  B EY1 B
+BABEL  B AE1 - B AH0 L
+BABER  B EY1 - B ER0
+BABERS  B EY1 - B ER0 Z
+BABES  B EY1 B Z
+BABETTE  B AH0 - B EH1 T
+BABIAK  B AA1 - B IY0 - AE0 K
+BABIARZ  B AH0 - B IY1 - ER0 Z
+BABIC  B AA1 - B IH0 K
+BABICH  B AE1 - B IH0 CH
+BABIES  B EY1 - B IY0 Z
+BABIES'  B EY1 - B IY0 Z
+BABIK  B AA1 - B IH0 K
+BABIN  B AE1 - B IH0 N
+BABINEAU  B AE1 - B IH0 - N OW2
+BABINEAUX  B AE1 - B IH0 - N OW2
+BABINGTON  B AE1 - B IH0 NG - T AH0 N
+BABINO  B AA0 - B IY1 - N OW0
+BABINSKI  B AH0 - B IH1 N - S K IY0
+BABISH  B AE1 - B IH0 SH
+BABITA  B AA0 - B IY1 - T AH0
+BABKA  B AE1 B - K AH0
+BABLER  B EY1 - B AH0 L - ER0
+BABOON  B AH0 - B UW1 N
+BABOON'S  B AE0 - B UW1 N Z
+BABOON'S(2)  B AH0 - B UW1 N Z
+BABOONS  B AE0 - B UW1 N Z
+BABOONS(2)  B AH0 - B UW1 N Z
+BABS  B AE1 B Z
+BABSON  B AE1 B - S AH0 N
+BABU  B AA0 - B UW1
+BABULA  B AA0 - B UW1 - L AH0
+BABUSHKA  B AH0 - B UH1 SH - K AH0
+BABUSHKAS  B AH0 - B UH1 SH - K AH0 Z
+BABY  B EY1 - B IY0
+BABY'S  B EY1 - B IY0 Z
+BABYAK  B AE1 - B IY0 - AE0 K
+BABYHOOD  B EY1 - B IY0 - HH UH2 D
+BABYISH  B EY1 - B IY0 - IH0 SH
+BABYLON  B AE1 - B AH0 - L AA2 N
+BABYLONIAN  B AE2 - B AH0 - L OW1 - N IY0 - AH0 N
+BABYLONIANS  B AE2 - B AH0 - L OW1 - N IY0 - AH0 N Z
+BABYSAT  B EY1 - B IY0 - S AE2 T
+BABYSIT  B EY1 - B IY0 - S IH0 T
+BABYSITTER  B EY1 - B IY0 - S IH2 - T ER0
+BABYSITTERS  B EY1 - B IY0 - S IH2 - T ER0 Z
+BABYSITTING  B EY1 - B IY0 - S IH2 - T IH0 NG
+BACA  B AE1 - K AH0
+BACALL  B AH0 - K AO1 L
+BACARDI  B AH0 - K AA1 R - D IY0
+BACCALAUREATE  B AE2 - K AH0 - L AO1 - R IY0 - AH0 T
+BACCARAT  B AA2 - K ER0 - AA1
+BACCARI  B AA0 - K AA1 - R IY0
+BACCHANALIA  B AE2 - K AH0 - N EY1 - L Y AH0
+BACCHI  B AE1 - K IY0
+BACCHUS  B AE1 - K IH0 S
+BACCI  B AA1 - CH IY0
+BACCUS  B AE1 - K AH0 S
+BACH  B AA1 K
+BACHA  B AE1 - CH AH0
+BACHAND  B AE1 - CH AH0 N D
+BACHAR  B AA1 - K ER0
+BACHARACH  B AE1 - K ER0 - AE0 K
+BACHE  B AE1 CH
+BACHE'S  B AE1 - CH IH0 Z
+BACHE'S(2)  B EY1 - CH IH0 Z
+BACHE(2)  B EY1 CH
+BACHELDER  B AA1 - K IH0 L - D ER0
+BACHELLER  B AA1 - K AH0 - L ER0
+BACHELOR  B AE1 - CH AH0 - L ER0
+BACHELOR'S  B AE1 - CH AH0 - L ER0 Z
+BACHELOR'S(2)  B AE1 CH - L ER0 Z
+BACHELOR(2)  B AE1 CH - L ER0
+BACHELORS  B AE1 CH - L ER0 Z
+BACHER  B AA1 - K ER0
+BACHERA  B AA1 - K ER0 - AH0
+BACHERT  B AE1 - CH ER0 T
+BACHLER  B AE1 K - L ER0
+BACHMAN  B AA1 K - M AH0 N
+BACHMANN  B AA1 K - M AH0 N
+BACHMEIER  B AA1 K - M AY0 - ER0
+BACHNER  B AA1 K - N ER0
+BACHRACH  B AA1 - K R AH0 K
+BACHTEL  B AE1 K - T AH0 L
+BACHTELL  B AE1 K - T AH0 L
+BACHUS  B AE1 - CH AH0 S
+BACIGALUPI  B AA0 - CH IY0 - G AA0 - L UW1 - P IY0
+BACIGALUPO  B AA0 - CH IY0 - G AA0 - L UW1 - P OW0
+BACIK  B AA1 - CH IH0 K
+BACILLUS  B AH0 - S IH1 - L AH0 S
+BACINO  B AA0 - CH IY1 - N OW0
+BACK  B AE1 K
+BACKACHE  B AE1 K - EY2 K
+BACKACHES  B AE1 K - EY2 K S
+BACKBITE  B AE1 K - B AY2 T
+BACKBITING  B AE1 K - B AY2 - T IH0 NG
+BACKBOARD  B AE1 K - B AO2 D
+BACKBONE  B AE1 K - B OW2 N
+BACKDATE  B AE1 K - D EY2 T
+BACKDATED  B AE1 K - D EY2 - T IH0 D
+BACKDOOR  B AE1 K - D AO2 R
+BACKDROP  B AE1 K - D R AA2 P
+BACKDROPS  B AE1 K - D R AA2 P S
+BACKE  B AE1 K
+BACKED  B AE1 K T
+BACKER  B AE1 - K ER0
+BACKER'S  B AE1 - K ER0 Z
+BACKERS  B AE1 - K ER0 Z
+BACKES  B AE1 K S
+BACKFIELD  B AE1 K - F IY2 L D
+BACKFIRE  B AE1 K - F AY2 R
+BACKFIRED  B AE1 K - F AY2 - ER0 D
+BACKFIRES  B AE1 K - F AY2 - ER0 Z
+BACKFIRING  B AE1 K - F AY2 - ER0 - IH0 NG
+BACKGAMMON  B AE1 K - G AE2 - M AH0 N
+BACKGROUND  B AE1 K - G R AW2 N D
+BACKGROUNDS  B AE1 K - G R AW2 N D Z
+BACKHAND  B AE1 K - HH AE2 N D
+BACKHANDED  B AE1 K - HH AE2 N - D AH0 D
+BACKHANDED(2)  B AE1 K - HH AE2 N - D IH0 D
+BACKHAUS  B AE1 K - HH AW2 S
+BACKHOE  B AE1 K - HH OW2
+BACKING  B AE1 - K IH0 NG
+BACKLASH  B AE1 K - L AE2 SH
+BACKLESS  B AE1 K - L AH0 S
+BACKLIGHT  B AE1 K - L AY2 T
+BACKLIGHTS  B AE1 K - L AY2 T S
+BACKLIST  B AE1 K - L IH2 S T
+BACKLOG  B AE1 K - L AA2 G
+BACKLOG(2)  B AE1 K - L AO2 G
+BACKLOGGED  B AE1 K - L AA2 G D
+BACKLOGS  B AE1 K - L AO2 G Z
+BACKLUND  B AE1 K - L AH0 N D
+BACKMAN  B AE1 K - M AH0 N
+BACKPACK  B AE1 K - P AE2 K
+BACKPACKS  B AE1 K - P AE2 K S
+BACKPEDAL  B AE1 K - P EH2 - D AH0 L
+BACKPEDALED  B AE1 K - P EH2 - D AH0 L D
+BACKPEDALING  B AE1 K - P EH2 - D AH0 L - IH0 NG
+BACKPEDALING(2)  B AE1 K - P EH2 D - L IH0 NG
+BACKREST  B AE1 - K R EH2 S T
+BACKROOM  B AE1 K - R UW2 M
+BACKS  B AE1 K S
+BACKSAW  B AE1 K - S AA2
+BACKSAW(2)  B AE1 K - S AO2
+BACKSEAT  B AE0 K - S IY1 T
+BACKSET  B AE1 K - S EH2 T
+BACKSIDE  B AE1 K - S AY2 D
+BACKSLAP  B AE1 K - S L AE2 P
+BACKSLAPPING  B AE1 K - S L AE2 - P IH0 NG
+BACKSLASH  B AE1 K - S L AE2 SH
+BACKSLIDE  B AE1 K - S L AY2 D
+BACKSLIDING  B AE1 K - S L AY2 - D IH0 NG
+BACKSPIN  B AE1 K - S P IH0 N
+BACKSTAGE  B AE1 K - S T EY1 JH
+BACKSTAIRS  B AE1 K - S T EH2 R Z
+BACKSTITCH  B AE1 K - S T IH0 CH
+BACKSTOP  B AE1 K - S T AA2 P
+BACKSTROKE  B AE1 K - S T R OW2 K
+BACKSTROM  B AE1 K - S T R AH0 M
+BACKTRACK  B AE1 K - T R AE2 K
+BACKTRACKED  B AE1 K - T R AE2 K T
+BACKTRACKING  B AE1 K - T R AE2 - K IH0 NG
+BACKUP  B AE1 K - AH2 P
+BACKUPS  B AE1 K - AH2 P S
+BACKUS  B AE1 - K AH0 S
+BACKWARD  B AE1 K - W ER0 D
+BACKWARDATION  B AE2 K - W ER0 - D EY1 - SH AH0 N
+BACKWARDNESS  B AE1 - K W ER0 D - N AH0 S
+BACKWARDS  B AE1 K - W ER0 D Z
+BACKWASH  B AE1 K - W AA2 SH
+BACKWATER  B AE1 K - W AO2 - T ER0
+BACKWATERS  B AE1 K - W AO2 - T ER0 Z
+BACKWOODS  B AE1 K - W UH1 D Z
+BACKWOODSMAN  B AE1 K - W UH1 D Z - M AH0 N
+BACKYARD  B AE1 K - Y AA2 R D
+BACKYARDS  B AE1 K - Y AA2 R D Z
+BACON  B EY1 - K AH0 N
+BACON'S  B EY1 - K AH0 N Z
+BACORN  B AE1 - K ER0 N
+BACOT  B AE1 - K AH0 T
+BACOT(2)  B EY1 - K AE0 T
+BACOTE  B AH0 - K OW1 T
+BACTERIA  B AE0 K - T IH1 - R IY0 - AH0
+BACTERIAL  B AE0 K - T IH1 - R IY0 - AH0 L
+BACTERIOLOGY  B AE2 K - T IH0 - R IY2 - AA1 - L AH0 - JH IY0
+BACTERIUM  B AE0 K - T IH1 - R IY0 - AH0 M
+BACUS  B AE1 - K IH0 S
+BAD  B AE1 D
+BADA  B AA1 - D AH0
+BADAL  B EY1 - D AH0 L
+BADALAMENTI  B AA0 - D AA0 - L AA0 - M EH1 N - T IY0
+BADAMI  B AA0 - D AA1 - M IY0
+BADAVATZ  B AE1 - D AH0 - V AE2 T S
+BADDERS  B AE1 - D ER0 Z
+BADE  B EY1 D
+BADEAH  B AH0 - D IY1 - AH0
+BADEAU  B AH0 - D OW1
+BADEAUX  B AH0 - D OW1
+BADEN  B EY1 - D AH0 N
+BADEN'S  B AA1 - D AH0 N Z
+BADER  B AE1 - D ER0
+BADER(2)  B EY1 - D ER0
+BADERTSCHER  B AE1 - D ER0 - CH ER0
+BADGE  B AE1 JH
+BADGER  B AE1 - JH ER0
+BADGER'S  B AE1 - JH ER0 Z
+BADGERED  B AE1 - JH ER0 D
+BADGERING  B AE1 - JH ER0 - IH0 NG
+BADGERS  B AE1 - JH ER0 Z
+BADGES  B AE1 - JH IH0 Z
+BADGETT  B AE1 - JH IH0 T
+BADGLEY  B AE1 JH - L IY0
+BADIA  B AA1 - D IY0 - AH0
+BADILLO  B AH0 - D IH1 - L OW0
+BADLAND  B AE1 D - L AE2 N D
+BADLANDS  B AE1 D - L AE2 N D Z
+BADLEY  B AE1 D - L IY0
+BADLY  B AE1 D - L IY0
+BADMAN  B AE1 D - M AH0 N
+BADMINTON  B AE1 D - M IH2 N - T AH0 N
+BADMINTONS  B AE1 D - M IH2 N - T AH0 N Z
+BADNESS  B AE1 D - N AH0 S
+BADOLATO  B AA0 - D OW0 - L AA1 - T OW0
+BADOUR  B AH0 - D UH1 R
+BADURA  B AA0 - D UH1 - R AH0
+BADY  B EY1 - D IY0
+BAE  B AY1
+BAECHLE  B EH1 - K AH0 L
+BAECKER  B EH1 - K ER0
+BAEDEKER  B EY1 - D AH0 - K ER0
+BAEDEKER'S  B EY1 - D AH0 - K ER0 Z
+BAEHR  B IY1 R
+BAEHR(2)  B EH1 R
+BAEK  B IY1 K
+BAEK(2)  B EH1 K
+BAER  B EH1 R
+BAERWALD  B EH1 R - W AO0 L D
+BAETEN  B EH1 - T AH0 N
+BAETJER  B EH1 T - JH ER0
+BAETZ  B IY1 T S
+BAEZ  B AY0 - EH1 Z
+BAEZA  B IY1 - Z AH0
+BAFFA  B AE1 - F AH0
+BAFFLE  B AE1 - F AH0 L
+BAFFLED  B AE1 - F AH0 L D
+BAFFLES  B AE1 - F AH0 L Z
+BAFFLING  B AE1 - F AH0 L - IH0 NG
+BAFFLING(2)  B AE1 - F L IH0 NG
+BAFOKENG  B AE1 - F OW0 - K EH2 NG
+BAG  B AE1 G
+BAGAN  B EY1 - G AH0 N
+BAGBY  B AE1 G - B IY0
+BAGDAD  B AE1 G - D AE0 D
+BAGDASARIAN  B AE2 G - D AH0 - S EH1 - R IY0 - AH0 N
+BAGDIKIAN  B AE2 G - D IH1 - K IY0 - AH0 N
+BAGDON  B AE1 G - D AH0 N
+BAGDONAS  B AE1 G - D AH0 - N AH0 Z
+BAGEL  B EY1 - G AH0 L
+BAGELAND  B EY1 - G AH0 - L AE0 N D
+BAGELS  B EY1 - G AH0 L Z
+BAGENT  B EY1 - JH AH0 N T
+BAGFUL  B AE1 G - F UH2 L
+BAGG  B AE1 G
+BAGGAGE  B AE1 - G AH0 JH
+BAGGAGE(2)  B AE1 - G IH0 JH
+BAGGE  B AE1 G
+BAGGED  B AE1 G D
+BAGGERLY  B AE1 - G ER0 - L IY0
+BAGGETT  B AE1 - G IH0 T
+BAGGIE  B AE1 - G IY0
+BAGGINESS  B AE1 - G IY0 - N AH0 S
+BAGGING  B AE1 - G IH0 NG
+BAGGIO  B AA1 - JH IY0 - OW0
+BAGGIO'S  B AA1 - JH IY0 - OW0 Z
+BAGGOTT  B AE1 - G AH0 T
+BAGGS  B AE1 G Z
+BAGGY  B AE1 - G IY0
+BAGHDAD  B AE1 G - D AE0 D
+BAGHDAD'S  B AE1 G - D AE0 D Z
+BAGHLI  B AE1 G - L IY0
+BAGINSKI  B AH0 - G IH1 N - S K IY0
+BAGLEY  B AE1 G - L IY0
+BAGLIO  B AE1 G - L IY0 - OW0
+BAGMAN  B AE1 G - M AH0 N
+BAGNALL  B AE1 G - N AH0 L
+BAGNATO  B AA0 G - N AA1 - T OW0
+BAGNELL  B AE1 G - N AH0 L
+BAGPIPE  B AE1 G - P AY2 P
+BAGPIPER  B AE1 G - P AY2 - P ER0
+BAGPIPES  B AE1 G - P AY2 P S
+BAGS  B AE1 G Z
+BAGSBY  B AE1 G Z - B IY0
+BAGSHAW  B AE1 G - SH AO2
+BAGWELL  B AE1 - G W EH2 L
+BAH  B AA1
+BAHA'IS  B AA1 - HH AA0 - IH2 Z
+BAHAM  B AA1 - AH0 M
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+BAHAMIAN  B AH0 - HH EY1 - M IY0 - AH0 N
+BAHAN  B AE1 - HH AH0 N
+BAHE  B EY1 HH
+BAHENA  B AE1 - HH IH0 - N AH0
+BAHIA  B AA1 - HH IY0 - AH0
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+BAHM  B AE1 M
+BAHMAN  B AA1 - M AH0 N
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+BAHNSEN  B AA1 N - S AH0 N
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+BAHRAIN  B AA0 - R EY1 N
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+BAHRAIN'S(2)  B AY0 - R EY1 N Z
+BAHRAIN(2)  B AY0 - R EY1 N
+BAHRAINI  B ER0 - EY1 - N IY0
+BAHRAINI(2)  B AY0 - R AA1 - N IY0
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+BAHRAINIAN  B ER0 - EY1 - N IY0 - AH0 N
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+BAHRAINIAN(3)  B AA0 - R EY1 - N IY0 - AH0 N
+BAHRAINIS  B ER0 - EY1 - N IY0 Z
+BAHRE  B AA1 R
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+BAIE  B AY1 - IY0
+BAIER  B EY1 - ER0
+BAIG  B EY1 G
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+BAILIFF  B EY1 - L AH0 F
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+BAINE  B EY1 N
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+BAJA  B AA1 - HH AA2
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+BAJOREK  B AE1 - JH ER0 - EH0 K
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+BAKE  B EY1 K
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+BANKOWSKI  B AH0 NG - K AO1 F S - K IY0
+BANKROLL  B AE1 NG K - R OW2 L
+BANKROLLED  B AE1 NG K - R OW2 L D
+BANKROLLING  B AE1 NG K - R OW2 - L IH0 NG
+BANKROLLS  B AE1 NG K - R OW2 L Z
+BANKRUPCTY  B AE1 NG - K R AH0 P T - S IY0
+BANKRUPT  B AE1 NG - K R AH0 P T
+BANKRUPTCIES  B AE1 NG - K R AH0 P T - S IY0 Z
+BANKRUPTCY  B AE1 NG - K R AH0 P - S IY0
+BANKRUPTCY'S  B AE1 NG - K R AH0 P - S IY0 Z
+BANKRUPTCY(2)  B AE1 NG - K R AH0 P T - S IY0
+BANKRUPTED  B AE1 NG - K R AH0 P - T IH0 D
+BANKRUPTING  B AE1 NG - K R AH2 P - T IH0 NG
+BANKS  B AE1 NG K S
+BANKS'  B AE1 NG K S
+BANKS'S  B AE1 NG K - S IH0 Z
+BANKSHARE  B AE1 NG K - SH EH2 R
+BANKSHARES  B AE1 NG K - SH EH2 R Z
+BANKSON  B AE1 NG K - S AH0 N
+BANKSTON  B AE1 NG K - S T AH0 N
+BANKVEREIN  B AE1 NG K - V ER0 - AY2 N
+BANKVERMONT  B AE1 NG K - V ER0 - M AA1 N T
+BANKWORCESTER  B AE1 NG K - W ER1 - CH EH2 - S T ER0
+BANKWORCESTER(2)  B AE1 NG - K W UW1 - S T ER0
+BANN  B AE1 N
+BANNAN  B AE1 - N AH0 N
+BANNED  B AE1 N D
+BANNER  B AE1 - N ER0
+BANNER'S  B AE1 - N ER0 Z
+BANNERMAN  B AE1 - N ER0 - M AH0 N
+BANNERS  B AE1 - N ER0 Z
+BANNICK  B AE1 - N IH0 K
+BANNING  B AE1 - N IH0 NG
+BANNINGS  B AE1 - N IH0 NG Z
+BANNISTER  B AE1 - N AH0 - S T ER0
+BANNISTER(2)  B AE1 - N IH0 - S T ER0
+BANNON  B AE1 - N AH0 N
+BANOS  B AA1 - N OW0 Z
+BANOUN  B AH0 - N UW1 N
+BANPAIS  B AE2 N - P EY1
+BANPONCE  B AE1 N - P AA0 N S
+BANQUE  B AE1 NG K
+BANQUET  B AE1 NG - K W AH0 T
+BANQUETS  B AE1 NG - K W AH0 T S
+BANQUO'S  B AE1 NG - K W OW0 Z
+BANS  B AE1 N Z
+BANSAL  B AA0 N - S AE1 L
+BANSHEE  B AE0 N - SH IY1
+BANSHEE(2)  B AE1 N - SH IY0
+BANTA  B AE1 N - T AH0
+BANTAM  B AE1 N - T AH0 M
+BANTAM'S  B AE1 N - T AH0 M Z
+BANTAMS  B AE1 N - T AH0 M Z
+BANTER  B AE1 N - T ER0
+BANTERED  B AE1 N - T ER0 D
+BANTERING  B AE1 N - T ER0 - IH0 NG
+BANTLE  B AE1 N - T AH0 L
+BANTON  B AE1 N - T AH0 N
+BANTU  B AE1 N - T UW0
+BANTZ  B AE1 N T S
+BANUELOS  B AA0 N - W EH1 - L OW0 Z
+BANVILLE  B AA1 N - V IH0 L
+BANWART  B AE1 N - W AO2 R T
+BANXQUOTE  B AE0 NG K - S K W OW1 T
+BANYA  B AA1 - N Y AH0
+BANYA(2)  B AE1 - N Y AH0
+BANYALUCA  B AE1 - N Y AH0 - L UW2 - K AH0
+BANYAN  B AE1 - N Y AH0 N
+BANYAS  B AA1 - N Y AH0 Z
+BANYAS(2)  B AE1 - N Y AH0 Z
+BANYU  B AA1 - N UW0
+BANZHAF  B AE1 N Z - HH AH0 F
+BAO  B AW1
+BAOGUANG  B EY1 - OW0 - G W AE2 NG
+BAPLEY  B AE1 P - L IY0
+BAPNA  B AA1 P - N AH0
+BAPTISM  B AE1 P - T IH0 - Z AH0 M
+BAPTISMAL  B AE0 P - T IH1 Z - M AH0 L
+BAPTISMS  B AE1 P - T IH2 - Z AH0 M Z
+BAPTIST  B AE1 P - T AH0 S T
+BAPTIST(2)  B AE1 P - T IH0 S T
+BAPTISTA  B AE2 P - T IH1 - S T AH0
+BAPTISTE  B AH0 P - T IH1 S T
+BAPTISTERY  B AE1 P - T AH0 S - T R IY0
+BAPTISTS  B AE1 P - T AH0 S T S
+BAPTISTS(2)  B AE1 P - T AH0 S S
+BAPTIZE  B AE0 P - T AY1 Z
+BAPTIZED  B AE0 P - T AY1 Z D
+BAPTIZED(2)  B AE1 P - T AY2 Z D
+BAR  B AA1 R
+BAR'S  B AA1 R Z
+BAR-MITZVAH  B AA1 R - M IH1 T - S V AH0
+BARA  B AA1 - R AH0
+BARABAR  B EH1 - R AH0 - B AA0 R
+BARACH  B AH0 - R AA1 K
+BARACH(2)  B ER0 - AA1 K
+BARAFF  B AA0 - R AA1 F
+BARAHONA  B AE2 - R AH0 - HH OW1 - N AH0
+BARAJAS  B AA0 - R AA1 - Y AA0 Z
+BARAK  B AA1 - R AH0 K
+BARAKAT  B AA1 - R AH0 - K AA2 T
+BARAM  B EH1 - R AE0 M
+BARAN  B AA0 - R AA1 N
+BARANEK  B AE1 - R AH0 - N IH0 K
+BARANOSKI  B ER0 - AH0 - N AW1 S - K IY0
+BARANOWSKI  B ER0 - AH0 - N AO1 F S - K IY0
+BARANSKI  B ER0 - AE1 N S - K IY0
+BARANY  B ER0 - AO1 - N IY0
+BARASCH  B AE1 - R AH0 SH
+BARASH  B AE1 - R AH0 SH
+BARATH  B AE1 - R AH0 TH
+BARATTA  B AA0 - R AA1 - T AH0
+BARB  B AA1 R B
+BARB'S  B AA1 R B Z
+BARBA  B AA1 R - B AH0
+BARBADOS  B AA0 R - B EY1 - D OW0 S
+BARBAGALLO  B AA2 R - B AH0 - G AE1 - L OW0
+BARBAKOW  B AA1 R - B AH0 - K AW2
+BARBANEL  B AA1 R - B AH0 - N AH0 L
+BARBANO  B AA0 R - B AA1 - N OW0
+BARBARA  B AA1 R - B ER0 - AH0
+BARBARA'S  B AA1 R - B ER0 - AH0 Z
+BARBARA(2)  B AA1 R - B R AH0
+BARBAREE  B AA1 R - B ER0 - IY1
+BARBARIAN  B AA0 R - B EH1 - R IY0 - AH0 N
+BARBARIANS  B AA0 R - B EH1 - R IY0 - AH0 N Z
+BARBARIC  B AA0 R - B AE1 - R IH0 K
+BARBARIC(2)  B AA0 R - B EH1 - R IH0 K
+BARBARINO  B AA0 R - B AA0 - R IY1 - N OW0
+BARBARISM  B AA1 R - B ER0 - IH2 - Z AH0 M
+BARBARITY  B AA0 R - B AE1 - R AH0 - T IY0
+BARBARITY(2)  B AA0 R - B EH1 - R AH0 - T IY0
+BARBARO  B AA0 R - B AA1 - R OW0
+BARBAROUS  B AA1 R - B ER0 - AH0 S
+BARBARY  B AA1 R - B ER0 - IY0
+BARBASH  B AA1 R - B AE2 SH
+BARBATO  B AA0 R - B AA1 - T OW0
+BARBE  B AA1 R B
+BARBEAU  B AA0 R - B OW1
+BARBECUE  B AA1 R - B IH0 - K Y UW2
+BARBECUE'S  B AA1 R - B IH0 - K Y UW2 Z
+BARBECUED  B AA1 R - B IH0 - K Y UW2 D
+BARBECUEING  B AA1 R - B IH0 - K Y UW2 - IH0 NG
+BARBECUES  B AA1 R - B IH0 - K Y UW2 Z
+BARBED  B AA1 R B D
+BARBEE  B AA1 R - B IY1
+BARBELL  B AA1 R - B EH2 L
+BARBELLA  B AA2 R - B EH1 - L AH0
+BARBELLS  B AA1 R - B EH2 L Z
+BARBELS  B AA1 R - B AH0 L Z
+BARBEQUE  B AA1 R - B IH0 - K Y UW2
+BARBEQUED  B AA1 R - B IH0 - K Y UW2 D
+BARBEQUEING  B AA1 R - B IH0 - K Y UW2 - IH0 NG
+BARBEQUES  B AA1 R - B IH0 - K Y UW2 Z
+BARBER  B AA1 R - B ER0
+BARBER'S  B AA1 R - B ER0 Z
+BARBERA  B AA0 R - B EH1 - R AH0
+BARBERI  B AA0 R - B EH1 - R IY0
+BARBERIO  B AA2 R - B IY1 - R IY0 - OW0
+BARBERIS  B AA1 R - B ER0 - IH0 S
+BARBERO  B AA0 R - B EH1 - R OW0
+BARBERS  B AA1 R - B ER0 Z
+BARBERSHOP  B AA1 R - B ER0 - SH AA2 P
+BARBETTE  B AA0 R - B EH1 T
+BARBIAN  B AA1 R - B IY0 - AH0 N
+BARBIE  B AA1 R - B IY0
+BARBIE'S  B AA1 R - B IY0 Z
+BARBIER  B AA1 R - B IY0 - ER0
+BARBIERI  B AA0 R - B IH1 - R IY0
+BARBIERI'S  B AA0 R - B IH1 - R IY0 Z
+BARBIES  B AA1 R - B IY0 Z
+BARBIN  B AA1 R - B IH0 N
+BARBITURATE  B AA0 R - B IH1 - CH ER0 - AH0 T
+BARBITURATES  B AA0 R - B IH1 - CH ER0 - AH0 T S
+BARBO  B AA1 R - B OW0
+BARBONE  B AA1 R - B OW2 N
+BARBOSA  B AA0 R - B OW1 - S AH0
+BARBOUR  B AA1 R - B ER0
+BARBOUR'S  B AA1 R - B ER0 Z
+BARBOZA  B AA0 R - B OW1 - Z AH0
+BARBRA  B AA1 R - B R AH0
+BARBRE  B AA1 R - B ER0
+BARBS  B AA1 R B Z
+BARBY  B AA1 R - B IY0
+BARCA  B AA1 R - K AH0
+BARCELLA  B AA2 R - S EH1 - L AH0
+BARCELLOS  B AA0 R - S EH1 - L OW0 Z
+BARCELO  B AA0 R - CH EH1 - L OW0
+BARCELONA  B AA2 R - S IH0 - L OW1 - N AH0
+BARCENAS  B AA1 R - S IH0 - N AH0 Z
+BARCH  B AA1 R K
+BARCHEFSKY  B AA0 - CH EH1 F S - K IY0
+BARCIA  B AA1 R - CH AH0
+BARCLAY  B AA1 R K - L EY2
+BARCLAY'S  B AA1 R - K L IY0 Z
+BARCLAY'S(2)  B AA1 R K - L EY0 Z
+BARCLAY(2)  B AA1 R K - L IY2
+BARCLAYS  B AA1 R - K L IY0 Z
+BARCLAYS'  B AA1 R - K L IY0 Z
+BARCLAYS'(2)  B AA1 R K - L EY0 Z
+BARCLAYS'S  B AA1 R - K L IY2 - Z IH0 Z
+BARCLAYS'S(2)  B AA1 R - K L EY2 - Z IH0 Z
+BARCLAYS(2)  B AA1 R K - L EY0 Z
+BARCLIFT  B AA1 R K - L IH0 F T
+BARCO  B AA1 R - K OW0
+BARCO'S  B AA1 R - K OW0 Z
+BARCOMB  B AA1 R - K AH0 M
+BARCROFT  B AA1 R - K R AO2 F T
+BARCUS  B AA1 R - K AH0 S
+BARCZAK  B AA1 R - CH AE0 K
+BARD  B AA1 R D
+BARD'S  B AA1 R D Z
+BARDELL  B AA0 R - D EH1 L
+BARDEN  B AA1 R - D AH0 N
+BARDERA  B AA2 R - D EH1 - R AH0
+BARDIN  B AA1 R - D IH0 N
+BARDO  B AA1 R - D OW0
+BARDOLF  B AA1 R - D OW2 L F
+BARDOLPH  B AA1 R - D AA0 L F
+BARDON  B AA0 R - D AO1 N
+BARDRICK  B AA1 R - D R IH0 K
+BARDSLEY  B AA1 R D S - L IY0
+BARDULF  B AA1 R - D AH0 L F
+BARDULPH  B AA1 R - D AH0 L F
+BARDWELL  B AA1 R D - W EH2 L
+BARE  B EH1 R
+BARED  B EH1 R D
+BAREFIELD  B AE1 - R IH0 F - IY0 L D
+BAREFIELD(2)  B AE1 R - F IY0 L D
+BAREFOOT  B EH1 R - F UH2 T
+BAREIS  B AE1 - R AY0 Z
+BARELA  B AA0 - R EH1 - L AH0
+BARELY  B EH1 R - L IY0
+BARENBOIM  B EH1 - R AH0 N - B OY2 M
+BARENBOIM'S  B EH1 - R AH0 N - B OY2 M Z
+BARENS  B EH1 - R AH0 N Z
+BARENTINE  B AA0 - R EH0 N - T IY1 - N IY0
+BARENTINE(2)  B EH1 - R AH0 N - T IY1 N
+BARENTINE(3)  B EH1 - R AH0 N - T AY1 N
+BARENTS  B EH1 - R AH0 N T S
+BARES  B EH1 R Z
+BAREST  B EH1 - R AH0 S T
+BAREY  B EH1 - R IY0
+BARFIELD  B AA1 R - F IY2 L D
+BARFKNECHT  B AA1 R F - K AH0 - N EH0 K T
+BARFKNECHT(2)  B AA1 R F - N EH0 K T
+BARFOOT  B AA1 R - F UH2 T
+BARFUSS  B AA1 R - F AH2 S
+BARG  B AA1 R G
+BARGA  B AA1 R - G AH0
+BARGAIN  B AA1 R - G AH0 N
+BARGAIN(2)  B AA1 R - G IH0 N
+BARGAINED  B AA1 R - G AH0 N D
+BARGAINER  B AA1 R - G IH0 - N ER0
+BARGAINERS  B AA1 R - G IH0 - N ER0 Z
+BARGAINING  B AA1 R - G IH0 - N IH0 NG
+BARGAINS  B AA1 R - G AH0 N Z
+BARGAINS(2)  B AA1 R - G IH0 N Z
+BARGANIER  B AA1 R - G AH0 - N IY0 - ER0
+BARGAR  B AA0 R - G AA1 R
+BARGAS  B AA1 R - G AH0 Z
+BARGE  B AA1 R JH
+BARGED  B AA1 R JH D
+BARGER  B AA1 R - JH ER0
+BARGERON  B AA1 R - G ER0 - AH0 N
+BARGES  B AA1 R - JH AH0 Z
+BARGES(2)  B AA1 R - JH IH0 Z
+BARGMAN  B AA1 R G - M AH0 N
+BARGMANN  B AA1 R G - M AH0 N
+BARGO  B AA1 R - G OW2
+BARHORST  B AA1 R - HH AO0 R S T
+BARI  B AA1 - R IY0
+BARI'S  B AA1 - R IY0 Z
+BARIBEAU  B AE1 - R IH0 - B OW0
+BARICH  B AE1 - R IH0 K
+BARIL  B EH1 - R AH0 L
+BARILE  B AA1 - R AH0 L
+BARILLARI  B EH2 - R IH0 - L EH1 - R IY0
+BARILLARI'S  B EH2 - R IH0 - L EH1 - R IY0 Z
+BARILLO  B ER0 - IH1 - L OW0
+BARINCO  B ER0 - IH1 NG - K OW2
+BARING  B EH1 - R IH0 NG
+BARING'S  B EH1 - R IH0 NG Z
+BARINGER  B EH1 - R IH0 - NG ER0
+BARINGS  B EH1 - R IH0 NG Z
+BARINGS'  B EH1 - R IH0 NG Z
+BARIS  B AA1 - R IY0 Z
+BARISH  B EH1 - R IH0 SH
+BARITE  B EH1 - R AY0 T
+BARITES  B EH1 - R AY0 T S
+BARITONE  B EH1 - R AH0 - T OW2 N
+BARIUM  B EH1 - R IY0 - AH0 M
+BARK  B AA1 R K
+BARKAI  B AA0 R - K AY1
+BARKALOW  B AA1 R - K AH0 - L OW2
+BARKAN  B AA1 R - K AH0 N
+BARKDOLL  B AA1 R K - D AH0 L
+BARKDULL  B AA1 R K - D AH0 L
+BARKE  B AA1 R K
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+BARKER  B AA1 R - K ER0
+BARKERS  B AA1 R - K ER0 Z
+BARKES  B AA1 R K S
+BARKETT  B AA1 R - K IH0 T
+BARKEY  B AA1 R - K IY2
+BARKHURST  B AA1 R K - HH ER0 S T
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+BARKING  B AA1 R - K IH0 NG
+BARKLEY  B AA1 R K - L IY0
+BARKLOW  B AA1 R - K L OW2
+BARKMAN  B AA1 R K - M AH0 N
+BARKO  B AA1 R - K OW0
+BARKOCY  B AA1 R - K AH0 - S IY0
+BARKOW  B AA1 R - K OW0
+BARKS  B AA1 R K S
+BARKSDALE  B AA1 R K S - D EY2 L
+BARKSHIRE  B AA1 R K - SH AY2 R
+BARKUS  B AA1 R - K AH0 S
+BARLAGE  B AA1 R - L IH0 JH
+BARLETT  B AA1 R - L IH0 T
+BARLETTA  B AA0 R - L EH1 - T AH0
+BARLETTESVILLE  B AA1 R - L AH0 T S - V IH2 L
+BARLEY  B AA1 R - L IY0
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+BARLOW  B AA1 R - L OW2
+BARLOWE  B AA1 R - L OW2
+BARMAN  B AA1 R - M AH0 N
+BARMORE  B AA1 R - M AO0 R
+BARN  B AA1 R N
+BARNA  B AA1 R - N AH0
+BARNABAS  B AA1 R - N AH0 - B AH0 S
+BARNABY  B AA1 R - N AH0 - B IY0
+BARNABY'S  B AA1 R - N AH0 - B IY0 Z
+BARNACLE  B AA1 R - N AH0 - K AH0 L
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+BARNARD  B AA1 R - N ER0 R D
+BARNARD'S  B AA1 R - N ER0 D Z
+BARNARD(2)  B ER0 - N AA1 R D
+BARNARD(3)  B AA1 R - N AA0 R D
+BARNARDS  B AA1 R - N ER0 D Z
+BARNAS  B AA1 R - N AH0 Z
+BARNDT  B AA1 R N T
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+BARNER  B AA1 R - N ER0
+BARNES  B AA1 R N Z
+BARNET  B AA1 R - N IH0 T
+BARNETT  B AA0 R - N EH1 T
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+BARNETTE  B AA1 R - N EH1 T
+BARNEVIK  B AA0 R - N EH1 - V IH0 K
+BARNEY  B AA1 R - N IY0
+BARNEY'S  B AA1 R - N IY0 Z
+BARNEYS  B AA1 R - N IY0 Z
+BARNFIELD  B AA1 R N - F IY2 L D
+BARNHARD  B AA1 R N - HH AA2 R D
+BARNHARDT  B AA1 R N - HH AA2 R T
+BARNHART  B AA1 R N - HH AA2 R T
+BARNHILL  B AA1 R N - HH IH2 L
+BARNHOUSE  B AA1 R N - HH AW2 S
+BARNICK  B AA1 R - N IH0 K
+BARNICLE  B AA1 R - N IH0 - K AH0 L
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+BARNO  B AA1 R - N OW0
+BARNOWSKI  B AA0 R - N AW1 S - K IY0
+BARNS  B AA1 R N Z
+BARNSTORM  B AA1 R N - S T AO2 R M
+BARNSTORMING  B AA1 R N - S T AO2 R - M IH0 NG
+BARNUM  B AA1 R - N AH0 M
+BARNWELL  B AA1 R N - W EH2 L
+BARNY  B AA1 R - N IY0
+BARNYARD  B AA1 R N - Y AA2 R D
+BARO  B AA1 - R OW0
+BAROID  B ER0 - OY1 D
+BAROMETER  B ER0 - AA1 - M IH0 - T ER0
+BAROMETERS  B ER0 - AA1 - M IH0 - T ER0 Z
+BAROMETRIC  B AE2 - R AH0 - M EH1 - T R IH0 K
+BARON  B AE1 - R AH0 N
+BARON'S  B AE1 - R AH0 N Z
+BARON'S(2)  B EH1 - R AH0 N Z
+BARON(2)  B EH1 - R AH0 N
+BARONE  B ER0 - OW1 N
+BARONE'S  B ER0 - OW1 N Z
+BARONESS  B EH1 - R AH0 - N IH0 S
+BARONET  B EH1 - R AH0 - N AH0 T
+BARONET(2)  B EH2 - R AH0 - N EH1 T
+BARONETS  B EH1 - R AH0 - N AH0 T S
+BARONETS(2)  B EH2 - R AH0 - N EH1 T S
+BARONI  B AA0 - R OW1 - N IY0
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+BAROODY  B ER0 - UW1 - D IY0
+BAROQUE  B ER0 - OW1 K
+BAROS  B AA1 - R OW0 Z
+BAROVIC  B EH1 - R AH0 - V IH0 K
+BAROVSKY  B ER0 - AA1 V S - K IY0
+BARR  B AA1 R
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+BARRACKS(2)  B EH1 - R AH0 K S
+BARRACLOUGH  B AE1 - R AH0 K - L AW0
+BARRACO  B AA0 - R AA1 - K OW0
+BARRACUDA  B EH2 - R AH0 - K UW1 - D AH0
+BARRADINO  B EH2 - R AH0 - D IY1 - N OW0
+BARRAGAN  B EH1 - R AH0 - G AH0 N
+BARRAGE  B ER0 - AA1 ZH
+BARRAGED  B ER0 - AA1 ZH D
+BARRAGES  B ER0 - AA1 - ZH IH0 Z
+BARRANCO  B AA0 - R AA1 N - K OW0
+BARRAS  B AE1 - R AH0 Z
+BARRASSO  B AA2 - R AA1 - S OW0
+BARRATT  B AE1 - R AH0 T
+BARRAZA  B AA2 - R AA1 - Z AH0
+BARRE  B EH1 - R IY0
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+BARRED  B AA1 R D
+BARREDA  B AA0 - R EY1 - D AH0
+BARREIRO  B AA0 - R EH1 - R OW0
+BARREL  B AE1 - R AH0 L
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+BARRELED  B AE1 - R AH0 L D
+BARRELED(2)  B EH1 - R AH0 L D
+BARRELING  B AE1 - R AH0 L - IH0 NG
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+BARRELL  B AA0 - R EY1 L
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+BARREN  B AE1 - R AH0 N
+BARREN(2)  B EH1 - R AH0 N
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+BARRENTINE  B AE1 - R AH0 N - T AY2 N
+BARRERA  B AA2 - R EH1 - R AH0
+BARRERAS  B AA0 - R EH1 - R AA0 Z
+BARRES  B AA1 R Z
+BARRESI  B AA2 - R EH1 - S IY0
+BARRET  B AE1 - R IH0 T
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+CESSATION  S EH2 - S EY1 - SH AH0 N
+CESSNA  S EH1 S - N AH0
+CESSNA'S  S EH1 S - N AH0 Z
+CESSNA'S(2)  S EH1 Z - N AH0 Z
+CESSNA(2)  S EH1 Z - N AH0
+CESSPOOL  S EH1 S - P UW2 L
+CESTARO  CH EH0 - S T AA1 - R OW0
+CETA  S EH1 - T AH0
+CETA(2)  S IY1 - IY1 - T IY1 - EY1
+CETACEAN  S IH0 - T EY1 - SH AH0 N
+CETACEAN(2)  S IY0 - T EY1 - SH AH0 N
+CETEC  S IY1 - T EH2 K
+CETERA  S EH1 - T ER0 - AH0
+CETUS  S IY1 - T AH0 S
+CETUS'S  S IY1 - T AH0 - S IH0 Z
+CEVALLOS  S EY0 - V AA1 - L OW0 Z
+CEVAXS  S EH1 - V AE0 K - S IH0 Z
+CEYLON  S IH0 - L AA1 N
+CEYLON(2)  S IY0 - L AA1 N
+CEZANNE  S EH2 - Z AE1 N
+CEZANNE'S  S EH2 - Z AE1 N Z
+CHA  CH AA1
+CHA-CHAS  CH AA1 - CH AA2 Z
+CHABLIS  SH AH0 - B L IY1
+CHABON  CH EY1 - B AH0 N
+CHABOT  SH AH0 - B OW1
+CHACABUCO  CH AE2 - K AH0 - B Y UW1 - K OW0
+CHACE  CH EY1 S
+CHACHERE  SH AH0 - SH IH1 R
+CHACHI  CH AA1 - CH IY0
+CHACIN  SH EY1 - S IH0 N
+CHACKO  CH AE1 - K OW0
+CHACON  CH AE1 - K AH0 N
+CHAD  CH AE1 D
+CHAD'S  CH AE1 D Z
+CHADBOURNE  SH AH0 D - B UH1 R N
+CHADD  CH AE1 D
+CHADDERDON  CH AH0 - D ER1 - D AH0 N
+CHADDOCK  CH AE1 - D AH0 K
+CHADEL  CH AE1 - D AH0 L
+CHADIAN  CH EY1 - D IY0 - AH0 N
+CHADICK  CH AE1 - D IH0 K
+CHADLI  CH AE1 D - L IY0
+CHADRON  CH AE1 - D R AH0 N
+CHADWELL  CH AE1 D - W EH2 L
+CHADWICK  CH AE1 D - W IH0 K
+CHADWICK'S  CH AE1 D - W IH0 K S
+CHAE  CH AY1
+CHAEBOL  CH EY1 - B AH0 L
+CHAFE  CH EY1 F
+CHAFED  CH EY1 F T
+CHAFEE  CH AE1 - F IY0
+CHAFEE'S  CH EY1 - F IY0 Z
+CHAFEE'S(2)  CH AE1 - F IY0 Z
+CHAFEE(2)  CH EY1 - F IY0
+CHAFES  CH EY1 F S
+CHAFF  CH AE1 F
+CHAFFEE  CH AE1 - F IY0
+CHAFFIN  CH AE1 - F IH0 N
+CHAFFINS  CH AE1 - F IH0 N Z
+CHAFFY  CH AE1 - F IY0
+CHAFIN  CH AE1 - F IH0 N
+CHAFING  CH EY1 - F IH0 NG
+CHAGALL  CH AH0 - G AA1 L
+CHAGALL(2)  CH AH0 - G AE1 L
+CHAGNON  CH AE1 G - N AH0 N
+CHAGRIN  SH AH0 - G R IH1 N
+CHAGRINED  SH AH0 - G R IH1 N D
+CHAI  CH AY1
+CHAIDEZ  CH AA0 - IY1 - D EH0 Z
+CHAIKEN  CH EY1 - K AH0 N
+CHAIKIN  CH EY1 - K IH0 N
+CHAIM  HH AY1 - IH0 M
+CHAIN  CH EY1 N
+CHAIN'S  CH EY1 N Z
+CHAINED  CH EY1 N D
+CHAINING  CH EY1 - N IH0 NG
+CHAINS  CH EY1 N Z
+CHAINS'  CH EY1 N Z
+CHAINSAW  CH EY1 N - S AO2
+CHAINSAWS  CH EY1 N - S AO2 Z
+CHAIR  CH EH1 R
+CHAIRED  CH EH1 R D
+CHAIRES  SH EH1 R Z
+CHAIREZ  CH AA0 - IH1 - R EH0 Z
+CHAIRING  CH EH1 - R IH0 NG
+CHAIRMAN  CH EH1 R - M AH0 N
+CHAIRMAN'S  CH EH1 R - M AH0 N Z
+CHAIRMANSHIP  CH EH1 R - M AH0 N - SH IH2 P
+CHAIRMANSHIPS  CH EH1 R - M AH0 N - SH IH2 P S
+CHAIRMEN  CH EH1 R - M IH0 N
+CHAIRPEOPLE  CH EH1 R - P IY2 - P AH0 L
+CHAIRPERSON  CH EH1 R - P ER2 - S AH0 N
+CHAIRS  CH EH1 R Z
+CHAIRWOMAN  CH EH1 R - W UH2 - M AH0 N
+CHAIRWOMEN  CH EH1 R - W IH2 - M EH0 N
+CHAISE  SH EY1 Z
+CHAISSON  CH EY1 - S AH0 N
+CHAIT  CH EY1 T
+CHAJET  CH AE1 - JH AH0 T
+CHALABI  CH AH0 - L AA1 - B IY0
+CHALASANI  CH AE2 - L AH0 - S AE1 - N IY0
+CHALET  SH AE1 - L EY2
+CHALET(2)  SH AH0 - L EY1
+CHALETS  SH AH0 - L EY1 Z
+CHALETS(2)  SH AE1 - L EY2 Z
+CHALFANT  CH AE1 - F AH0 N T
+CHALFANT(2)  CH AE1 - F AA0 N T
+CHALFIN  CH AE1 L - F IH0 N
+CHALIFOUX  SH AE1 - L IH0 - F UW0
+CHALK  CH AA1 K
+CHALK(2)  CH AO1 K
+CHALKED  CH AO1 K T
+CHALKER  CH AO1 - K ER0
+CHALKING  CH AO1 - K IH0 NG
+CHALKLEY  CH AE1 L K - L IY0
+CHALKS  CH AO1 K S
+CHALLENDER  CH AH0 - L EH1 N - D ER0
+CHALLENGE  CH AE1 - L AH0 N JH
+CHALLENGED  CH AE1 - L AH0 JH D
+CHALLENGER  CH AE1 - L AH0 N - JH ER0
+CHALLENGER'S  CH AE1 - L AH0 N - JH ER0 Z
+CHALLENGER(2)  CH AE1 - L IH0 N - JH ER0
+CHALLENGERS  CH AE1 - L AH0 N - JH ER0 Z
+CHALLENGERY  CH AE1 - L AH0 N - JH ER0 - IY0
+CHALLENGES  CH AE1 - L AH0 N - JH IH0 Z
+CHALLENGING  CH AE1 - L AH0 N - JH IH0 NG
+CHALLIS  CH AE1 - L IH0 S
+CHALLIS(2)  SH AE1 - L IY0
+CHALMERS  CH AA1 - M ER0 Z
+CHALMETTE  SH AE0 L - M EH1 T
+CHALOUPKA  CH AH0 - L UW1 P - K AH0
+CHALOUX  SH AH0 - L UW1
+CHALSTY  CH AE1 L - S T IY0
+CHALUPA  CH AH0 - L UW1 - P AH0
+CHAM  CH AE1 M
+CHAMBER  CH EY1 M - B ER0
+CHAMBER'S  CH EY1 M - B ER0 Z
+CHAMBERED  CH EY1 M - B ER0 D
+CHAMBERLAIN  CH EY1 M - B ER0 - L AH0 N
+CHAMBERLAIN'S  CH EY1 M - B ER0 - L AH0 N Z
+CHAMBERLAIN(2)  CH EY1 M - B ER0 - L IH0 N
+CHAMBERLAND  CH AE1 M - B ER0 - L AH0 N D
+CHAMBERLAYNE  CH EY1 M - B ER0 - L EY2 N
+CHAMBERLIN  CH EY1 M - B ER0 - L IH0 N
+CHAMBERS  CH EY1 M - B ER0 Z
+CHAMBERS'  CH EY1 M - B ER0 Z
+CHAMBERS'S  CH EY1 M - B ER0 - Z IH0 Z
+CHAMBLEE  CH AE1 M - B L IY0
+CHAMBLESS  SH AH0 M - B L IY1 S
+CHAMBLIN  CH AE1 M - B L IH0 N
+CHAMBLISS  CH AE1 M - B L IH0 S
+CHAMBON  CH AE1 M - B AH0 N
+CHAMELEON  CH AH0 - M EH1 - L IY0 - AH0 N
+CHAMELEON(2)  K AH0 - M IY1 - L IY0 - AH0 N
+CHAMLEE  CH AE1 M - L IY0
+CHAMLONG  CH AE1 M - L AO2 NG
+CHAMNESS  CH AE1 M - N IH0 S
+CHAMORRO  CH AH0 - M AO1 - R OW0
+CHAMORRO'S  CH AH0 - M AO1 - R OW0 Z
+CHAMORRO(2)  CH OW0 - M AO1 - R OW0
+CHAMP  CH AE1 M P
+CHAMPA  K AA1 M - P AH0
+CHAMPAGNE  SH AE0 M - P EY1 N
+CHAMPAGNES  SH AE0 M - P EY1 N Z
+CHAMPAIGN  CH AE0 M - P EY1 N
+CHAMPEAU  SH AE0 M - P OW1
+CHAMPINE  CH AE1 M - P AY2 N
+CHAMPION  CH AE1 M - P IY0 - AH0 N
+CHAMPION'S  CH AE1 M - P IY0 - AH0 N Z
+CHAMPIONED  CH AE1 M - P IY0 - AH0 N D
+CHAMPIONING  CH AE1 M - P IY0 - AH0 - N IH0 NG
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+CHAMPIONSHIP  CH AE1 M - P IY0 - AH0 N - SH IH2 P
+CHAMPIONSHIPS  CH AE1 M - P IY0 - AH0 N - SH IH2 P S
+CHAMPLAIN  SH AE0 M - P L EY1 N
+CHAMPLIN  CH AE1 M - P L IH0 N
+CHAMPNEY  CH AE1 M P - N IY0
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+CHANA  CH AE1 - N AH0
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+CHANCELLOR  CH AE1 N - S AH0 - L ER0
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+CHANCELLOR(2)  CH AE1 N S - L ER0
+CHANCELLORS  CH AE1 N - S AH0 - L ER0 Z
+CHANCERY  CH AE1 N - S ER0 - IY0
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+CHANCEY  CH AE1 N - S IY0
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+CHAND  CH AE1 N D
+CHANDA  CH AE1 N - D AH0
+CHANDELIER  SH AE0 N - D AH0 - L IH1 R
+CHANDELIERS  SH AE2 N - D AH0 - L IH1 Z
+CHANDLER  CH AE1 N D - L ER0
+CHANDLER'S  CH AE1 N D - L ER0 Z
+CHANDLEY  CH AE1 N D - L IY0
+CHANDON  CH AE1 N - D IH0 N
+CHANDON(2)  SH AE2 N - D AA1 N
+CHANDRA  CH AE1 N - D R AH0
+CHANDROSS  CH AE0 N - D R AO1 S
+CHANEL  SH AH0 - N EH1 L
+CHANEY  CH EY1 - N IY0
+CHANG  CH AE1 NG
+CHANG-HSIN  CH AA1 NG - SH IH1 N
+CHANG-MING  CH AA1 NG - M IH1 NG
+CHANG-MING(2)  CH AE1 NG - M IH1 NG
+CHANGCHUN  CH AA1 NG - CH UH1 N
+CHANGE  CH EY1 N JH
+CHANGEABLE  CH EY1 N - JH AH0 - B AH0 L
+CHANGED  CH EY1 N JH D
+CHANGEOVER  CH EY1 N JH - OW2 - V ER0
+CHANGEOVERS  CH EY1 N JH - OW2 - V ER0 Z
+CHANGER  CH EY1 N - JH ER0
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+CHANGES  CH EY1 N - JH AH0 Z
+CHANGES(2)  CH EY1 N - JH IH0 Z
+CHANGING  CH EY1 N - JH IH0 NG
+CHANGSHO  CH AE1 NG - SH OW1
+CHANIN  CH AE1 - N IH0 N
+CHANLEY  CH AE1 N - L IY0
+CHANNEL  CH AE1 - N AH0 L
+CHANNEL'S  CH AE1 - N AH0 L Z
+CHANNELED  CH AE1 - N AH0 L D
+CHANNELING  CH AE1 - N AH0 L - IH0 NG
+CHANNELING(2)  CH AE1 N - L IH0 NG
+CHANNELL  CH AE1 - N AH0 L
+CHANNELL'S  SH AH0 - N EH1 L Z
+CHANNELS  CH AE1 - N AH0 L Z
+CHANNING  CH AE1 - N IH0 NG
+CHANNON  CH AE1 - N AH0 N
+CHANOS  CH AA1 - N OW0 S
+CHANT  CH AE1 N T
+CHANTAL  CH AE1 N - T AH0 L
+CHANTED  CH AE1 N - T IH0 D
+CHANTILLY  SH AE2 N - T IH1 - L IY0
+CHANTING  CH AE1 N - T IH0 NG
+CHANTS  CH AE1 N T S
+CHANUKAH  HH AA1 - N AH0 - K AH0
+CHANY  CH EY1 - N IY0
+CHAO  CH AW1
+CHAOS  K EY1 - AA0 S
+CHAOTIC  K EY0 - AA1 - T IH0 K
+CHAP  CH AE1 P
+CHAPA  CH AA1 - P AH0
+CHAPARRAL  SH AE2 - P ER0 - AE1 L
+CHAPARRO  K AA0 - P AA1 - R OW0
+CHAPAS  CH AA1 - P AH0 S
+CHAPDELAINE  SH AE1 P - D IH0 - L EY0 N
+CHAPEK  CH AE1 - P IH0 K
+CHAPEL  CH AE1 - P AH0 L
+CHAPEL'S  CH AE1 - P AH0 L Z
+CHAPELL  SH AH0 - P EH1 L
+CHAPELLE  SH AH0 - P EH1 L
+CHAPERONE  SH AE1 - P ER0 - OW2 N
+CHAPERONING  SH AE1 - P ER0 - OW2 - N IH0 NG
+CHAPIN  SH AH0 - P AE1 N
+CHAPLAIN  CH AE1 P - L AH0 N
+CHAPLAINS  CH AE1 P - L AH0 N Z
+CHAPLIN  CH AE1 P - L AH0 N
+CHAPLIN'S  CH AE1 P - L IH0 N Z
+CHAPLIN(2)  CH AE1 P - L IH0 N
+CHAPMAN  CH AE1 P - M AH0 N
+CHAPMAN'S  CH AE1 P - M AH0 N Z
+CHAPNICK  CH AE1 P - N IH0 K
+CHAPOTON  CH AE1 - P OW0 - AA2 N
+CHAPP  CH AE1 P
+CHAPPAQUIDDICK  CH AE2 - P AH0 - K W IH1 - D IH0 K
+CHAPPEL  CH AE1 - P AH0 L
+CHAPPELEAR  SH AE1 - P IH0 - L ER0
+CHAPPELL  CH AE1 - P AH0 L
+CHAPPELLE  SH AH0 - P EH1 L
+CHAPPIE  CH AE1 - P IY0
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+CHAPPUIS  SH AE1 - P UW0 - IH0 Z
+CHAPS  CH AE1 P S
+CHAPTER  CH AE1 P - T ER0
+CHAPTERS  CH AE1 P - T ER0 Z
+CHAPUT  CH AE1 - P AH0 T
+CHAR  CH AA1 R
+CHARACTER  K EH1 - R IH0 K - T ER0
+CHARACTER'S  K EH1 - R IH0 K - T ER0 Z
+CHARACTERISTIC  K EH2 - R AH0 K - T ER0 - IH1 - S T IH0 K
+CHARACTERISTICALLY  K EH2 - R AH0 K - T ER0 - IH1 - S T IH0 K - L IY0
+CHARACTERISTICS  K EH2 - R AH0 K - T ER0 - IH1 - S T IH0 K S
+CHARACTERIZATION  K EH2 - R AH0 K - T ER0 - IH0 - Z EY1 - SH AH0 N
+CHARACTERIZATIONS  K EH2 - R AH0 K - T ER0 - IH0 - Z EY1 - SH AH0 N Z
+CHARACTERIZE  K EH1 - R AH0 K - T ER0 - AY2 Z
+CHARACTERIZED  K EH1 - R AH0 K - T ER0 - AY2 Z D
+CHARACTERIZES  K EH1 - R AH0 K - T ER0 - AY2 - Z AH0 Z
+CHARACTERIZING  K EH1 - R IH0 K - T ER0 - AY2 - Z IH0 NG
+CHARACTERS  K AE1 - R IH0 K - T ER0 Z
+CHARACTERS'  CH EH1 - R AH0 K - T ER0 Z
+CHARACTERS(2)  K EH1 - R AH0 K - T ER0 Z
+CHARADE  SH ER0 - EY1 D
+CHARADES  SH ER0 - EY1 D Z
+CHARALAMBOS  CH AA2 - R AH0 - L AA1 M - B OW0 S
+CHARASSE  CH EH1 - R AE0 S
+CHARBONEAU  SH AA1 R - B AH0 - N OW0
+CHARBONNEAU  SH AA1 R - B AH0 - N OW2
+CHARBONNET  SH AA1 R - B AH0 - N IH0 T
+CHARBONNET(2)  SH AA1 R - B AH0 - N EY0
+CHARCOAL  CH AA1 R - K OW2 L
+CHARCOALS  CH AA1 R - K OW2 L Z
+CHARD  CH AA1 R D
+CHARDONNAY  CH AA0 R - D AA1 - N EY0
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+CHAREN  CH AA1 - R AH0 N
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+CHAREST  CH AA0 - R EY1 - IH0 S T
+CHARETTE  SH ER0 - EH1 T
+CHARGE  CH AA1 R JH
+CHARGEABLE  CH AA1 R - JH AH0 - B AH0 L
+CHARGED  CH AA1 R JH D
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+CHARGEURS  CH AA0 R - G Y UW1 R Z
+CHARGING  CH AA1 R - JH IH0 NG
+CHARGIT  CH AA1 R - JH IH0 T
+CHARGOIS  SH AA0 R - G W AA1
+CHARIOT  CH EH1 - R IY0 - AH0 T
+CHARIOTS  CH EH1 - R IY0 - AH0 T S
+CHARISMA  K ER0 - IH1 Z - M AH0
+CHARISMATIC  K EH0 - R IH0 Z - M AE1 - T IH0 K
+CHARISMATICS  K EH2 - R IH0 Z - M AE1 - T IH0 K S
+CHARISSA  K AA0 - R IY1 - S AH0
+CHARITA  K AA0 - R IY1 - T AH0
+CHARITABLE  CH AE1 - R AH0 - T AH0 - B AH0 L
+CHARITABLE(2)  CH EH1 - R AH0 - T AH0 - B AH0 L
+CHARITABLY  CH EH1 - R IH0 - T AH0 - B L IY0
+CHARITIES  CH EH1 - R AH0 - T IY0 Z
+CHARITIES'  CH EH1 - R IH0 - T IY0 Z
+CHARITIES(2)  CH EH1 - R IH0 - T IY0 Z
+CHARITY  CH EH1 - R IH0 - T IY0
+CHARITY'S  CH EH1 - R AH0 - T IY0 Z
+CHARLA  CH AA1 R - L AH0
+CHARLAND  CH AA1 R - L AH0 N D
+CHARLATAN  SH AA1 R - L AH0 - T AH0 N
+CHARLATANS  SH AA1 R - L AH0 - T AH0 N Z
+CHARLAYNE  SH AA0 R - L EY1 N
+CHARLE  CH AA1 R L
+CHARLEBOIS  SH AA1 R - L IH0 - B W AA0
+CHARLEEN  CH AA0 R - L IY1 N
+CHARLEMAGNE  SH AA1 R - L AH0 - M EY2 N
+CHARLENE  SH AA0 R - L IY1 N
+CHARLES  CH AA1 R L Z
+CHARLES'  CH AA1 R L Z
+CHARLES'(2)  CH AA1 - R AH0 L Z
+CHARLES'S  CH AA1 R L - Z IH0 Z
+CHARLES(2)  CH AA1 - R AH0 L Z
+CHARLESTON  CH AA1 R L - S T AH0 N
+CHARLESTON'S  CH AA1 R L - S T AH0 N Z
+CHARLESTON'S(2)  CH AA1 - R AH0 L - S T AH0 N Z
+CHARLESTON(2)  CH AA1 - R AH0 L - S T AH0 N
+CHARLESTOWN  CH AA1 R L - S T AW2 N
+CHARLESTOWN(2)  CH AA1 - R AH0 L - S T AW2 N
+CHARLESWORTH  CH AA1 - R AH0 L - S W ER0 TH
+CHARLESWORTH(2)  CH AA1 - R AH0 L Z - W ER0 TH
+CHARLET  CH AA1 R - L IH0 T
+CHARLEY  CH AA1 R - L IY0
+CHARLEY'S  CH AA1 R - L IY0 Z
+CHARLIE  CH AA1 R - L IY0
+CHARLIE'S  CH AA1 R - L IY0 Z
+CHARLIER  CH AA1 R - L IY0 - ER0
+CHARLINE  SH AA0 R - L IY1 N
+CHARLOT  CH AA1 R - L AH0 T
+CHARLOTTE  SH AA1 R - L AH0 T
+CHARLOTTE'S  SH AA1 R - L AH0 T S
+CHARLOTTESVILLE  SH AA1 R - L AH0 T S - V IH2 L
+CHARLOTTETOWN  SH AA1 R - L AH0 T - T AW2 N
+CHARLOTTEVILLE  SH AA1 R - L AH0 T - V IH2 L
+CHARLS  CH AA1 R L Z
+CHARLSON  CH AA1 R L - S AH0 N
+CHARLTON  CH AA1 R L - T AH0 N
+CHARM  CH AA1 R M
+CHARMAIN  SH AA0 R - M EY1 N
+CHARMAINE  SH AA0 R - M EY1 N
+CHARMED  CH AA1 R M D
+CHARMER  CH AA1 R - M ER0
+CHARMERS  CH AA1 R - M ER0 Z
+CHARMIAN  CH AA1 R - M IY0 - AH0 N
+CHARMING  CH AA1 R - M IH0 NG
+CHARMINGLY  CH AA1 R - M IH0 NG - L IY0
+CHARMION  CH AA1 R - M IY0 - AH0 N
+CHARMS  CH AA1 R M Z
+CHARNEY  CH AA1 R - N IY0
+CHARNLEY  CH AA1 R N - L IY0
+CHARNOCK  CH AA1 R - N AH0 K
+CHARON  K EH1 - R AH0 N
+CHARPENTIER  SH AA2 R - P AH0 N - T IH1 R
+CHARPIE  CH AA1 R - P IY0
+CHARRED  CH AA1 R D
+CHARREN  CH EH1 - R AH0 N
+CHARRIER  CH AE1 - R IY0 - ER0
+CHARRING  CH AA1 - R IH0 NG
+CHARRON  CH EH1 - R AH0 N
+CHARRON(2)  K EH1 - R AH0 N
+CHARRY  CH AE1 - R IY0
+CHART  CH AA1 R T
+CHARTED  CH AA1 R - T AH0 D
+CHARTED(2)  CH AA1 R - T IH0 D
+CHARTER  CH AA1 R - T ER0
+CHARTER'S  CH AA1 R - T ER0 Z
+CHARTERED  CH AA1 R - T ER0 D
+CHARTERHOUSE  CH AA1 R - T ER0 - HH AW2 S
+CHARTERING  CH AA1 R - T ER0 - IH0 NG
+CHARTERS  CH AA1 R - T ER0 Z
+CHARTIER  CH AA1 R - T IY0 - ER0
+CHARTING  CH AA1 R - T IH0 NG
+CHARTIST  CH AA1 R - T IH0 S T
+CHARTISTS  CH AA1 R - T IH0 S T S
+CHARTISTS(2)  CH AA1 R - T IH0 S S
+CHARTISTS(3)  CH AA1 R - T IH0 S
+CHARTRAND  CH AA1 R - T R AH0 N D
+CHARTRES  CH AA1 R - T R IY0 Z
+CHARTS  CH AA1 R T S
+CHARTWELL  CH AA1 R T - W EH2 L
+CHARVAT  CH AA1 R - V AH0 T
+CHARY  CH AA1 - R IY0
+CHARYL  CH AE1 - R AH0 L
+CHAS  CH AA1 R L Z
+CHAS(2)  CH AA1 Z
+CHASE  CH EY1 S
+CHASE'S  CH EY1 - S IH0 Z
+CHASED  CH EY1 S T
+CHASEN  CH EY1 - S AH0 N
+CHASER  CH EY1 - S ER0
+CHASERS  CH EY1 - S ER0 Z
+CHASES  CH EY1 - S AH0 Z
+CHASES(2)  CH EY1 - S IH0 Z
+CHASIN  CH AE1 - S IH0 N
+CHASING  CH EY1 - S IH0 NG
+CHASKA  CH AE1 S - K AH0
+CHASM  K AE1 - Z AH0 M
+CHASON  CH AE1 - S AH0 N
+CHASSE  CH AE1 S
+CHASSIS  CH AE1 - S IY0
+CHASTAIN  SH AH0 - S T EY1 N
+CHASTE  CH EY1 S T
+CHASTEEN  CH AH0 - S T IY1 N
+CHASTEN  CH EY1 - S AH0 N
+CHASTENED  CH EY1 - S AH0 N D
+CHASTISE  CH AE0 - S T AY1 Z
+CHASTISED  CH AE0 - S T AY1 Z D
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+COMMUNAL  K AH0 - M Y UW1 - N AH0 L
+COMMUNE  K AA1 - M Y UW0 N
+COMMUNE(2)  K AH0 - M Y UW1 N
+COMMUNES  K AA1 - M Y UW0 N Z
+COMMUNES(2)  K AH0 - M Y UW1 N Z
+COMMUNICABLE  K AH0 - M Y UW1 - N AH0 - K AH0 - B AH0 L
+COMMUNICATE  K AH0 - M Y UW1 - N AH0 - K EY2 T
+COMMUNICATED  K AH0 - M Y UW1 - N AH0 - K EY2 - T IH0 D
+COMMUNICATES  K AH0 - M Y UW1 - N IH0 - K EY2 T S
+COMMUNICATING  K AH0 - M Y UW1 - N AH0 - K EY2 - T IH0 NG
+COMMUNICATION  K AH0 - M Y UW2 - N AH0 - K EY1 - SH AH0 N
+COMMUNICATION'S  K AH0 - M Y UW2 - N IH0 - K EY1 - SH AH0 N Z
+COMMUNICATIONS  K AH0 - M Y UW2 - N AH0 - K EY1 - SH AH0 N Z
+COMMUNICATIONS'  K AH0 - M Y UW2 - N AH0 - K EY1 - SH AH0 N Z
+COMMUNICATIVE  K AH0 - M Y UW1 - N AH0 - K AH0 - T IH0 V
+COMMUNICATOR  K AH0 - M Y UW1 - N AH0 - K EY2 - T ER0
+COMMUNICATORS  K AH0 - M Y UW1 - N AH0 - K EY0 - T ER0 Z
+COMMUNION  K AH0 - M Y UW1 - N Y AH0 N
+COMMUNIQUE  K AH0 - M Y UW1 - N AH0 - K EY2
+COMMUNIQUE(2)  K AH0 - M Y UW2 - N AH0 - K EY1
+COMMUNIQUES  K AH0 - M Y UW2 - N IH0 - K EY1 Z
+COMMUNISM  K AA1 - M Y AH0 - N IH2 - Z AH0 M
+COMMUNISM'S  K AA1 - M Y AH0 - N IH2 - Z AH0 M Z
+COMMUNIST  K AA1 - M Y AH0 - N AH0 S T
+COMMUNIST'S  K AA1 - M Y AH0 - N AH0 S T S
+COMMUNISTS  K AA1 - M Y AH0 - N AH0 S T S
+COMMUNISTS'  K AA1 - M Y UW0 - N IH0 S T S
+COMMUNISTS(2)  K AA1 - M Y AH0 - N AH0 S S
+COMMUNISTS(3)  K AA1 - M Y AH0 - N AH0 S
+COMMUNITIES  K AH0 - M Y UW1 - N AH0 - T IY0 Z
+COMMUNITIES(2)  K AH0 - M Y UW1 - N IH0 - T IY0 Z
+COMMUNITY  K AH0 - M Y UW1 - N AH0 - T IY0
+COMMUNITY'S  K AH0 - M Y UW1 - N IH0 - T IY0 Z
+COMMUNITY(2)  K AH0 - M Y UW1 - N IH0 - T IY0
+COMMUNITYWIDE  K AH0 - M Y UW1 - N IH0 - T IY0 - W AY2 D
+COMMUNIZATION  K AA2 - M Y AH0 - N AH0 - Z EY1 - SH AH0 N
+COMMUNIZE  K AA1 - M Y AH0 - N AY2 Z
+COMMUTE  K AH0 - M Y UW1 T
+COMMUTED  K AH0 - M Y UW1 - T IH0 D
+COMMUTER  K AH0 - M Y UW1 - T ER0
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+COMMUTES  K AH0 - M Y UW1 T S
+COMMUTING  K AH0 - M Y UW1 - T IH0 NG
+COMO  K OW1 - M OW0
+COMP  K AA1 M P
+COMPACT  K AA1 M - P AE0 K T
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+COMPACTED  K AH0 M - P AE1 K - T IH0 D
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+COMPACTOR  K AH0 M - P AE1 K - T ER0
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+COMPAGNIE  K AH1 M - P AH0 - N IY0
+COMPANHIA  K AH0 M - P AE1 N - HH IY0 - AH0
+COMPANIA  K AH0 M - P EY1 - N IY0 - AH0
+COMPANIES  K AH1 M - P AH0 - N IY0 Z
+COMPANIES'  K AH1 M - P AH0 - N IY0 Z
+COMPANIES'S  K AH1 M - P AH0 - N IY0 - Z IH0 Z
+COMPANION  K AH0 M - P AE1 - N Y AH0 N
+COMPANIONS  K AH0 M - P AE1 - N Y AH0 N Z
+COMPANIONSHIP  K AH0 M - P AE1 - N Y AH0 N - SH IH2 P
+COMPANIONWAY  K AH0 M - P AE1 - N Y AH0 N - W EY2
+COMPANY  K AH1 M - P AH0 - N IY0
+COMPANY'S  K AH1 M - P AH0 - N IY0 Z
+COMPANYWIDE  K AH1 M - P AH0 - N IY0 - W AY2 D
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+COMPARABLE  K AA1 M - P ER0 - AH0 - B AH0 L
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+COMPARABLY(2)  K AA1 M - P R AH0 - B L IY0
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+COMPARATOR  K AH0 M - P ER1 - AH0 - T ER0
+COMPARE  K AH0 M - P EH1 R
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+COMPARTMENT  K AH0 M - P AA1 R T - M AH0 N T
+COMPARTMENTAL  K AH0 M - P AA2 R T - M EH1 N - T AH0 L
+COMPARTMENTALIZE  K AH0 M - P AA2 R T - M EH1 N - T AH0 - L AY2 Z
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+COMPASSIONATE  K AH0 M - P AE1 - SH AH0 - N AH0 T
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+COMPATIBILITY  K AH0 M - P AE2 - T AH0 - B IH1 - L AH0 - T IY0
+COMPATIBLE  K AH0 M - P AE1 - T AH0 - B AH0 L
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+COMPENSABLE  K AH0 M - P EH1 N - S AH0 - B AH0 L
+COMPENSATE  K AA1 M - P AH0 N - S EY2 T
+COMPENSATED  K AA1 M - P AH0 N - S EY2 - T AH0 D
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+COMPENSATION  K AA2 M - P AH0 N - S EY1 - SH AH0 N
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+COMPETENT  K AA1 M - P AH0 - T IH0 N T
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+COMPETITORS  K AH0 M - P EH1 - T AH0 - T ER0 Z
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+CONCATENATION  K AH0 N - K AE2 - T AH0 - N EY1 - SH AH0 N
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+CONFRONTED  K AH0 N - F R AH1 N - T AH0 D
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+CONFUSION  K AH0 N - F Y UW1 - ZH AH0 N
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+CONG  K AO1 NG
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+CONGENITAL  K AH0 N - JH EH1 - N AH0 - T AH0 L
+CONGER  K AO1 NG - ER0
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+CONGRATULATION  K AH0 N - G R AE2 - CH AH0 - L EY1 - SH AH0 N
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+CONGRATULATORY  K AH0 N - G R AE1 - CH AH0 - L AH0 - T AO2 - R IY0
+CONGREGATE  K AA1 NG - G R AH0 - G EY2 T
+CONGREGATED  K AA1 NG - G R IH0 - G EY2 - T IH0 D
+CONGREGATION  K AA2 NG - G R AH0 - G EY1 - SH AH0 N
+CONGREGATION'S  K AA2 NG - G R AH0 - G EY1 - SH AH0 N Z
+CONGREGATIONAL  K AA2 NG - G R AH0 - G EY1 - SH AH0 - N AH0 L
+CONGREGATIONS  K AA2 NG - G R AH0 - G EY1 - SH AH0 N Z
+CONGRESS  K AA1 NG - G R AH0 S
+CONGRESS'  K AA1 N - G R AH0 - S IH0 Z
+CONGRESS'(2)  K AA1 NG - G R AH0 S
+CONGRESS'S  K AA1 NG - G R AH0 - S IH0 Z
+CONGRESSES  K AA1 NG - G R AH0 - S IH0 Z
+CONGRESSIONAL  K AH0 N - G R EH1 - SH AH0 - N AH0 L
+CONGRESSIONALLY  K AH0 N - G R EH1 - SH AH0 N - AH0 - L IY0
+CONGRESSIONALLY(2)  K AH0 N - G R EH1 SH - N AH0 - L IY0
+CONGRESSMAN  K AA1 NG - G R AH0 S - M AH0 N
+CONGRESSMAN'S  K AA1 NG - G R AH0 S - M AH0 N Z
+CONGRESSMEN  K AA1 NG - G R AH0 S - M IH0 N
+CONGRESSPEOPLE  K AA1 NG - G R AH0 - S P IY2 - P AH0 L
+CONGRESSPERSON  K AA1 NG - G R AH0 - S P ER2 - S AH0 N
+CONGRESSPERSONS  K AA1 NG - G R AH0 - S P ER2 - S AH0 N Z
+CONGRESSWOMAN  K AA1 NG - G R AH0 S - W UH2 - M AH0 N
+CONGRESSWOMAN'S  K AA1 NG - G R AH0 S - W UH2 - M AH0 N Z
+CONGRESSWOMEN  K AA1 NG - G R AH0 S - W IH2 - M IH0 N
+CONGROVE  K AA1 NG - G R AH0 V
+CONGRUENCE  K AO1 N - G R UW0 - AH0 N S
+CONGRUITY  K AH0 N - G R UW1 - AH0 - T IY0
+CONIC  K AA1 - N IH0 K
+CONIC(2)  K OW1 - N IH0 K
+CONICAL  K AA1 - N IH0 - K AH0 L
+CONICAL(2)  K OW1 - N IH0 - K AH0 L
+CONICS  K AA1 - N IH0 K S
+CONICS(2)  K OW1 - N IH0 K S
+CONIFER  K AA1 - N AH0 - F ER0
+CONIFER'S  K AA1 - N AH0 - F ER0 Z
+CONIFEROUS  K AH0 - N IH1 - F ER0 - AH0 S
+CONIFERS  K AA1 - N AH0 - F ER0 Z
+CONIGLIARO  K AH0 - N IH2 G - L IY0 - AA1 - R OW0
+CONIGLIO  K AH0 - N IH1 G - L IY0 - OW0
+CONISTON  K AA1 - N AH0 - S T AH0 N
+CONJECTURE  K AH0 N - JH EH1 K - CH ER0
+CONJECTURE(2)  K AH0 N - JH EH1 K - SH ER0
+CONJECTURES  K AH0 N - JH EH1 K - CH ER0 Z
+CONJECTURES(2)  K AH0 N - JH EH1 K - SH ER0 Z
+CONJOIN  K AA2 N - JH OY1 N
+CONJOINED  K AA2 N - JH OY1 N D
+CONJUGAL  K AA1 N - JH AH0 - G AH0 L
+CONJUGATE  K AA2 N - JH AH0 - G EY1 T
+CONJUGATE(2)  K AA1 N - JH AH0 - G EY2 T
+CONJUGATED  K AA2 N - JH AH0 - G EY1 - T IH0 D
+CONJUGATED(2)  K AA1 N - JH AH0 - G EY2 - T IH0 D
+CONJUGATES  K AA2 N - JH AH0 - G EY1 T S
+CONJUGATES(2)  K AA1 N - JH AH0 - G EY2 T S
+CONJUGATION  K AA2 N - JH AH0 - G EY1 - SH AH0 N
+CONJUGATIONS  K AA2 N - JH AH0 - G EY1 - SH AH0 N Z
+CONJUL  K AA1 N - JH AH0 L
+CONJUNCTION  K AH0 N - JH AH1 NG K - SH AH0 N
+CONJUNCTIONS  K AH0 N - JH AH1 NG K - SH AH0 N Z
+CONJUNCTIVA  K AA2 N - JH AH0 NG K - T AY1 - V AH0
+CONJURE  K AA1 N - JH ER0
+CONJURED  K AA1 N - JH ER0 D
+CONJURES  K AA1 N - JH ER0 Z
+CONJURING  K AA1 N - JH ER0 - IH0 NG
+CONJUROR  K AA1 N - JH ER0 - ER0
+CONK  K AA1 NG K
+CONKEL  K AA1 NG - K AH0 L
+CONKEY  K AA1 N - K IY0
+CONKIN  K AA1 NG - K IH0 N
+CONKLE  K AA1 NG - K AH0 L
+CONKLIN  K AA1 NG - K L IH0 N
+CONKLING  K AA1 NG - K L IH0 NG
+CONKRIGHT  K AA1 NG K - R AY2 T
+CONLAN  K AA1 N - L AH0 N
+CONLEE  K AA1 N - L IY0
+CONLEY  K AA1 N - L IY0
+CONLIN  K AA1 N - L IH0 N
+CONLON  K AA1 N - L AH0 N
+CONLOW  K AA1 N - L OW0
+CONLY  K AA1 N - L IY0
+CONN  K AA1 N
+CONN.  K AA1 N
+CONN.(2)  K AH0 - N EH1 - T AH0 - K AH0 T
+CONNALLY  K AA1 - N AH0 - L IY0
+CONNALLY'S  K AA1 - N AH0 - L IY0 Z
+CONNAUGHT  K AA1 - N AO0 T
+CONNAUGHT'S  K AA1 - N AO0 T S
+CONNAUGHTON  K AA1 - N AO0 - T AA0 N
+CONNAWAY  K AA1 N - AH0 - W EY2
+CONNECT  K AH0 - N EH1 K T
+CONNECTED  K AH0 - N EH1 K - T AH0 D
+CONNECTED(2)  K AH0 - N EH1 K - T IH0 D
+CONNECTER  K AH0 - N EH1 K - T ER0
+CONNECTICUT  K AH0 - N EH1 - T AH0 - K AH0 T
+CONNECTICUT'S  K AH0 - N EH1 - T AH0 - K AH0 T S
+CONNECTING  K AH0 - N EH1 K - T IH0 NG
+CONNECTION  K AH0 - N EH1 K - SH AH0 N
+CONNECTIONS  K AH0 - N EH1 K - SH AH0 N Z
+CONNECTIVE  K AH0 - N EH1 K - T IH0 V
+CONNECTIVITY  K AH0 - N EH0 K - T IH1 - V IH0 - T IY0
+CONNECTOR  K AH0 - N EH1 K - T ER0
+CONNECTORS  K AH0 - N EH1 K - T ER0 Z
+CONNECTS  K AH0 - N EH1 K T S
+CONNED  K AA1 N D
+CONNEELY  K AH0 - N IY1 - L IY0
+CONNELL  K AA1 - N AH0 L
+CONNELLEY  K AA1 - N IH0 - L IY0
+CONNELLY  K AA1 - N AH0 - L IY0
+CONNELLY'S  K AA1 - N AH0 - L IY0 Z
+CONNELY  K AA1 N - L IY0
+CONNER  K AA1 - N ER0
+CONNER'S  K AA1 - N ER0 Z
+CONNERLY  K AA1 - N ER0 - L IY0
+CONNERS  K AA1 - N ER0 Z
+CONNERY  K AA1 - N ER0 - IY0
+CONNERY'S  K AA1 - N ER0 - IY0 Z
+CONNETT  K AA1 - N IH0 T
+CONNICK  K AA1 - N IH0 K
+CONNIE  K AO1 - N IY0
+CONNIE'S  K AO1 - N IY0 Z
+CONNIFF  K AA1 - N IH0 F
+CONNING  K AA1 - N IH0 NG
+CONNIPTION  K AH0 - N IH1 P - SH AH0 N
+CONNIVANCE  K AH0 - N AY1 - V AH0 N S
+CONNIVE  K AH0 - N AY1 V
+CONNIVING  K AH0 - N AY1 - V IH0 NG
+CONNOISSEUR  K AA2 - N AH0 - S ER1
+CONNOISSEURS  K AA2 - N AH0 - S ER1 Z
+CONNOLE  K AA1 - N AH0 L
+CONNOLLY  K AO1 - N AH0 - L IY0
+CONNON  K AA1 - N AH0 N
+CONNOR  K AA1 - N ER0
+CONNORS  K AA1 - N ER0 Z
+CONNOTATION  K AA2 - N AH0 - T EY1 - SH AH0 N
+CONNOTATIONAL  K AA2 - N AH0 - T EY1 - SH AH0 - N AH0 L
+CONNOTATIONS  K AA2 - N AH0 - T EY1 - SH AH0 N Z
+CONNOTE  K AH0 - N OW1 T
+CONNOTES  K AH0 - N OW1 T S
+CONNY  K AA1 - N IY0
+CONOCO  K AA1 - N AH0 - K OW0
+CONOCO'S  K AA1 - N AH0 - K OW0 Z
+CONOCO(2)  K AH0 - N AA1 - K OW0
+CONOLLY  K AA1 - N OW0 - L IY0
+CONOLY  K AA1 - N OW0 - L IY0
+CONOVER  K AA1 - N AH0 - V ER0
+CONQUER  K AA1 NG - K ER0
+CONQUERED  K AA1 NG - K ER0 D
+CONQUERING  K AA1 NG - K ER0 - IH0 NG
+CONQUEROR  K AA1 NG - K ER0 - ER0
+CONQUEROR'S  K AA1 NG - K ER0 - ER0 Z
+CONQUERORS  K AA1 NG - K ER0 - ER0 Z
+CONQUERS  K AA1 NG - K ER0 Z
+CONQUEST  K AA1 NG - K W EH0 S T
+CONQUEST'S  K AA1 NG - K W EH0 S T S
+CONQUESTS  K AA1 N - K W EH2 S T S
+CONQUESTS(2)  K AA1 N - K W EH2 S S
+CONQUESTS(3)  K AA1 N - K W EH2 S
+CONRAC  K AA1 N - R AE0 K
+CONRAD  K AA1 N - R AE0 D
+CONRAD'S  K AA1 N - R AE0 D Z
+CONRADES  K AH0 N - R EY1 D Z
+CONRADI  K AA0 N - R AA1 - D IY0
+CONRADINE  K AA1 N - R AH0 - D AY2 N
+CONRADS  K AA1 N - R AE0 D Z
+CONRADT  K AA1 N - R AE0 T
+CONRADY  K AH0 N - R AA1 - D IY0
+CONRAIL  K AA1 N - R EY2 L
+CONRAIL'S  K AA1 N - R EY2 L Z
+CONRAN  K AA1 N - R AH0 N
+CONRATH  K AA1 N - R AH0 TH
+CONREY  K AA1 N - R IY0
+CONROE  K AA1 N - R OW0
+CONROW  K AA1 N - R OW0
+CONROY  K AO1 N - R OY0
+CONRY  K AA1 N - R IY0
+CONS  K AA1 N Z
+CONSALVO  K AA0 N - S AA1 L - V OW0
+CONSCIENCE  K AA1 N - SH AH0 N S
+CONSCIENCES  K AA1 N - CH IH0 N - S IH0 Z
+CONSCIENTIOUS  K AA2 N - SH IY0 - EH1 N - SH AH0 S
+CONSCIENTIOUSLY  K AA2 N - CH IY0 - EH1 N - CH AH0 S - L IY0
+CONSCIOUS  K AA1 N - SH AH0 S
+CONSCIOUSLY  K AA1 N - SH AH0 S - L IY0
+CONSCIOUSNESS  K AA1 N - SH AH0 S - N AH0 S
+CONSCRIPT  K AA1 N - S K R IH2 P T
+CONSCRIPT(2)  K AH0 N - S K R IH1 P T
+CONSCRIPTED  K AH0 N - S K R IH1 P - T IH0 D
+CONSCRIPTION  K AH0 N - S K R IH1 P - SH AH0 N
+CONSCRIPTS  K AA1 N - S K R IH0 P T S
+CONSECO  K AA0 N - S EY1 - K OW0
+CONSECO'S  K AA0 N - S EY1 - K OW0 Z
+CONSECO'S(2)  K AH0 N - S EY1 - K OW0 Z
+CONSECO(2)  K AH0 N - S EY1 - K OW0
+CONSECRATE  K AA1 N - S AH0 - K R EY2 T
+CONSECRATED  K AA1 N - S AH0 - K R EY2 - T AH0 D
+CONSECRATED(2)  K AA1 N - S AH0 - K R EY2 - T IH0 D
+CONSECRATION  K AA2 N - S AH0 - K R EY1 - SH AH0 N
+CONSECRATIONS  K AA2 N - S AH0 - K R EY1 - SH AH0 N Z
+CONSECUTIVE  K AH0 N - S EH1 - K Y AH0 - T IH0 V
+CONSECUTIVELY  K AH0 N - S EH1 - K Y AH0 - T IH0 V - L IY0
+CONSENSUAL  K AH0 N - S EH1 N - S UW0 - AH0 L
+CONSENSUAL(2)  K AH0 N - S EH1 N - SH UW0 - AH0 L
+CONSENSUS  K AH0 N - S EH1 N - S AH0 S
+CONSENT  K AH0 N - S EH1 N T
+CONSENTED  K AH0 N - S EH1 N - T IH0 D
+CONSENTED(2)  K AH0 N - S EH1 - N IH0 D
+CONSENTING  K AH0 N - S EH1 N - T IH0 NG
+CONSENTING(2)  K AH0 N - S EH1 - N IH0 NG
+CONSENTINO  K AA0 N - S EH0 N - T IY1 - N OW0
+CONSENTS  K AH0 N - S EH1 N T S
+CONSEQUENCE  K AA1 N - S AH0 - K W AH0 N S
+CONSEQUENCES  K AA1 N - S AH0 - K W EH2 N - S AH0 Z
+CONSEQUENT  K AA1 N - S AH0 - K W AH0 N T
+CONSEQUENTIAL  K AA2 N - S AH0 - K W EH1 N - CH AH0 L
+CONSEQUENTLY  K AA1 N - S AH0 - K W AH0 N T - L IY0
+CONSEQUENTLY(2)  K AA1 N - S AH0 - K W EH2 N T - L IY0
+CONSER  K AA1 N - S ER0
+CONSERVANCY  K AH0 N - S ER1 - V AH0 N - S IY0
+CONSERVANCY'S  K AH0 N - S ER1 - V AH0 N - S IY0 Z
+CONSERVATION  K AA2 N - S ER0 - V EY1 - SH AH0 N
+CONSERVATIONIST  K AA2 N - S ER0 - V EY1 - SH AH0 N - AH0 S T
+CONSERVATIONISTS  K AA2 N - S ER0 - V EY1 - SH AH0 N - AH0 S T S
+CONSERVATIONISTS(2)  K AA2 N - S ER0 - V EY1 - SH AH0 N - AH0 S S
+CONSERVATIONISTS(3)  K AA2 N - S ER0 - V EY1 - SH AH0 N - AH0 S
+CONSERVATISM  K AH0 N - S ER1 - V AH0 - T IH2 - Z AH0 M
+CONSERVATIVE  K AH0 N - S ER1 - V AH0 - T IH0 V
+CONSERVATIVELY  K AH0 N - S ER1 - V AH0 - T IH0 V - L IY0
+CONSERVATIVES  K AH0 N - S ER1 - V AH0 - T IH0 V Z
+CONSERVATIVES'  K AH0 N - S ER1 - V AH0 - T IH0 V Z
+CONSERVATIVISM  K AH0 N - S ER1 - V AH0 - T IH0 - V IH2 - Z AH0 M
+CONSERVATOR  K AH0 N - S ER1 - V AH0 - T ER0
+CONSERVATORIES  K AH0 N - S ER1 - V AH0 - T AO2 - R IY0 Z
+CONSERVATORS  K AH0 N - S ER1 - V AH0 - T ER0 Z
+CONSERVATORSHIP  K AH0 N - S ER1 - V AH0 - T ER0 - SH IH2 P
+CONSERVATORY  K AH0 N - S ER1 - V AH0 - T AO0 - R IY0
+CONSERVE  K AH0 N - S ER1 V
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+CONSERVING  K AH0 N - S ER1 - V IH0 NG
+CONSHOHOCKEN  K AA2 N - SH AH0 - HH AA1 - K AH0 N
+CONSIDER  K AH0 N - S IH1 - D ER0
+CONSIDERABLE  K AH0 N - S IH1 - D ER0 - AH0 - B AH0 L
+CONSIDERABLY  K AH0 N - S IH1 - D ER0 - AH0 - B L IY0
+CONSIDERATE  K AH0 N - S IH1 - D ER0 - AH0 T
+CONSIDERATION  K AH0 N - S IH2 - D ER0 - EY1 - SH AH0 N
+CONSIDERATIONS  K AH0 N - S IH2 - D ER0 - EY1 - SH AH0 N Z
+CONSIDERED  K AH0 N - S IH1 - D ER0 D
+CONSIDERING  K AH0 N - S IH1 - D ER0 - IH0 NG
+CONSIDERS  K AH0 N - S IH1 - D ER0 Z
+CONSIDINE  K AA1 N - S IH0 - D AY2 N
+CONSIGLIO  K AA0 N - S IY1 - G L IY0 - OW0
+CONSIGN  K AH0 N - S AY1 N
+CONSIGNED  K AH0 N - S AY1 N D
+CONSIGNMENT  K AH0 N - S AY1 N - M AH0 N T
+CONSIST  K AH0 N - S IH1 S T
+CONSISTED  K AH0 N - S IH1 - S T AH0 D
+CONSISTED(2)  K AH0 N - S IH1 - S T IH0 D
+CONSISTENCE  K AH0 N - S IH1 - S T AH0 N S
+CONSISTENCY  K AH0 N - S IH1 - S T AH0 N - S IY0
+CONSISTENT  K AH0 N - S IH1 - S T AH0 N T
+CONSISTENTLY  K AH0 N - S IH1 - S T AH0 N T - L IY0
+CONSISTING  K AH0 N - S IH1 - S T IH0 NG
+CONSISTS  K AH0 N - S IH1 S T S
+CONSISTS(2)  K AH0 N - S IH1 S S
+CONSISTS(3)  K AH0 N - S IH1 S
+CONSOB  K AA1 N - S AA0 B
+CONSOL  K AA1 N - S AA0 L
+CONSOL'S  K AA1 N - S AA0 L Z
+CONSOLATA  K AA0 N - S OW0 - L AA1 - T AH0
+CONSOLATION  K AA2 N - S AH0 - L EY1 - SH AH0 N
+CONSOLATIONS  K AA2 N - S AH0 - L EY1 - SH AH0 N Z
+CONSOLE  K AA1 N - S OW0 L
+CONSOLE(2)  K AH0 N - S OW1 L
+CONSOLED  K AH0 N - S OW1 L D
+CONSOLES  K AH0 N - S OW1 L Z
+CONSOLI  K AA0 N - S OW1 - L IY0
+CONSOLIDATE  K AH0 N - S AA1 - L IH0 - D EY2 T
+CONSOLIDATED  K AH0 N - S AA1 - L AH0 - D EY2 - T AH0 D
+CONSOLIDATED'S  K AH0 N - S AA1 - L IH0 - D EY2 - T IH0 D Z
+CONSOLIDATES  K AH0 N - S AA1 - L IH0 - D EY2 T S
+CONSOLIDATING  K AH0 N - S AA1 - L AH0 - D EY2 - T IH0 NG
+CONSOLIDATION  K AH0 N - S AA2 - L AH0 - D EY1 - SH AH0 N
+CONSOLIDATIONS  K AH0 N - S AA2 - L IH0 - D EY1 - SH AH0 N Z
+CONSOLIDATOR  K AH0 N - S AA1 - L IH0 - D EY2 - T ER0
+CONSOLIDATORS  K AH0 N - S AA1 - L IH0 - D EY2 - T ER0 Z
+CONSOLING  K AH0 N - S OW1 - L IH0 NG
+CONSOLO  K AA0 N - S OW1 - L OW0
+CONSONANT  K AA1 N - S AH0 - N AH0 N T
+CONSONANTAL  K AA2 N - S AH0 - N AA1 N - T AH0 L
+CONSONANTAL(2)  K AA2 N - S AH0 - N AA1 - N AH0 L
+CONSONANTS  K AA1 N - S AH0 - N AH0 N T S
+CONSORT  K AH0 N - S AO1 R T
+CONSORTIA  K AH0 N - S AO1 R - SH AH0
+CONSORTING  K AH0 N - S AO1 R - T IH0 NG
+CONSORTIUM  K AH0 N - S AO1 R - SH IY0 - AH0 M
+CONSORTIUM'S  K AH0 N - S AO1 R - SH IY0 - AH0 M Z
+CONSORTIUM'S(2)  K AH0 N - S AO1 R - SH Y AH0 M Z
+CONSORTIUM(2)  K AH0 N - S AO1 R - SH Y AH0 M
+CONSORTIUMS  K AH0 N - S AO1 R - SH AH0 M Z
+CONSORTIUMS(2)  K AH0 N - S AO1 R - SH Y AH0 M Z
+CONSPICUOUS  K AH0 N - S P IH1 - K Y UW0 - AH0 S
+CONSPICUOUSLY  K AH0 N - S P IH1 - K Y UW0 - AH0 S - L IY0
+CONSPIRACIES  K AH0 N - S P IH1 - R AH0 - S IY0 Z
+CONSPIRACY  K AH0 N - S P IH1 - R AH0 - S IY0
+CONSPIRATOR  K AH0 N - S P IH1 - R AH0 - T ER0
+CONSPIRATORIAL  K AH0 N - S P IH2 - R AH0 - T AO1 - R IY0 - AH0 L
+CONSPIRATORIALLY  K AH0 N - S P IH2 - R AH0 - T AO1 - R IY0 - AH0 - L IY0
+CONSPIRATORIALLY(2)  K AH0 N - S P IH2 - R AH0 - T AO1 - R Y AH0 - L IY0
+CONSPIRATORS  K AH0 N - S P IH1 - R AH0 - T ER0 Z
+CONSPIRE  K AH0 N - S P AY1 - ER0
+CONSPIRED  K AH0 N - S P AY1 - ER0 D
+CONSPIRING  K AH0 N - S P AY1 - R IH0 NG
+CONSTABLE  K AA1 N - S T AH0 - B AH0 L
+CONSTABLES  K AA1 N - S T AH0 - B AH0 L Z
+CONSTABULARY  K AH0 N - S T AE1 - B Y AH0 - L EH2 - R IY0
+CONSTANCE  K AA1 N - S T AH0 N S
+CONSTANCY  K AA1 N - S T AH0 N - S IY0
+CONSTANT  K AA1 N - S T AH0 N T
+CONSTANT'S  K AA1 N - S T AH0 N T S
+CONSTANTA  K AA0 N - S T AA1 N - T AH0
+CONSTANTIN  K AH0 N - S T AE1 N - T IH0 N
+CONSTANTINA  K AA0 N - S T AA0 N - T IY1 - N AH0
+CONSTANTINE  K AA1 N - S T AH0 N - T IY2 N
+CONSTANTINE(2)  K AA1 N - S T AH0 N - T AY2 N
+CONSTANTINO  K AA2 N - S T AH0 N - T IY1 - N OW0
+CONSTANTINOPLE  K AA2 N - S T AE0 N - T AH0 - N OW1 - P AH0 L
+CONSTANTINOS  K AA2 N - S T AH0 N - T IY1 - N OW0 S
+CONSTANTLY  K AA1 N - S T AH0 N T - L IY0
+CONSTANTS  K AA1 N - S T AH0 N T S
+CONSTAR  K AA1 N - S T AA2 R
+CONSTELLATION  K AA2 N - S T AH0 - L EY1 - SH AH0 N
+CONSTELLATION'S  K AA2 N - S T AH0 - L EY1 - SH AH0 N Z
+CONSTELLATIONS  K AA2 N - S T AH0 - L EY1 - SH AH0 N Z
+CONSTERNATION  K AA2 N - S T ER0 - N EY1 - SH AH0 N
+CONSTIPATE  K AA1 N - S T AH0 - P EY2 T
+CONSTIPATED  K AA1 N - S T AH0 - P EY2 - T AH0 D
+CONSTIPATION  K AA2 N - S T AH0 - P EY1 - SH AH0 N
+CONSTITUENCIES  K AH0 N - S T IH1 - CH UW0 - AH0 N - S IY0 Z
+CONSTITUENCY  K AH0 N - S T IH1 - CH UW0 - AH0 N - S IY0
+CONSTITUENT  K AH0 N - S T IH1 - CH UW0 - AH0 N T
+CONSTITUENTS  K AH0 N - S T IH1 - CH UW0 - AH0 N T S
+CONSTITUENTS'  K AH0 N - S T IH1 - CH UW0 - AH0 N T S
+CONSTITUTE  K AA1 N - S T AH0 - T UW2 T
+CONSTITUTED  K AA1 N - S T AH0 - T UW2 - T AH0 D
+CONSTITUTES  K AA1 N - S T AH0 - T UW2 T S
+CONSTITUTING  K AA1 N - S T AH0 - T UW2 - T IH0 NG
+CONSTITUTION  K AA2 N - S T AH0 - T UW1 - SH AH0 N
+CONSTITUTION'S  K AA2 N - S T IH0 - T UW1 - SH AH0 N Z
+CONSTITUTIONAL  K AA2 N - S T AH0 - T UW1 - SH AH0 - N AH0 L
+CONSTITUTIONALITY  K AA2 N - S T IH0 - T UW2 - SH AH0 - N AE1 - L IH0 - T IY0
+CONSTITUTIONALLY  K AA2 N - S T AH0 - T UW1 - SH AH0 N - AH0 L - IY0
+CONSTITUTIONIST  K AA2 N - S T AH0 - T UW1 - SH AH0 - N IH0 S T
+CONSTITUTIONISTS  K AA2 N - S T AH0 - T UW1 - SH AH0 - N IH0 S T S
+CONSTITUTIONISTS(2)  K AA2 N - S T AH0 - T UW1 - SH AH0 N - IH0 S S
+CONSTITUTIONISTS(3)  K AA2 N - S T AH0 - T UW1 - SH AH0 N - IH0 S
+CONSTITUTIONS  K AA2 N - S T IH0 - T UW1 - SH AH0 N Z
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+CONSTRAINS  K AH0 N - S T R EY1 N Z
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+CONSTRAINTS  K AH0 N - S T R EY1 N T S
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+CONSTRICTION  K AH0 N - S T R IH1 K - SH AH0 N
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+CONSTRUCT  K AH0 N - S T R AH1 K T
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+CONSTRUCTION  K AH0 N - S T R AH1 K - SH AH0 N
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+CONSTRUCTOR  K AH0 N - S T R AH1 K - T ER0
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+CONSTRUE  K AH0 N - S T R UW1
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+CONSULTANTS  K AH0 N - S AH1 L - T AH0 N T S
+CONSULTANTS'  K AH0 N - S AH1 L - T AH2 N T S
+CONSULTATION  K AA2 N - S AH0 L - T EY1 - SH AH0 N
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+CONSUMERIST  K AH0 N - S UW1 - M ER0 - IH0 S T
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+CONSUMERS  K AH0 N - S UW1 - M ER0 Z
+CONSUMERS'  K AH0 N - S UW1 - M ER0 Z
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+CONTAINING  K AH0 N - T EY1 - N IH0 NG
+CONTAINMENT  K AH0 N - T EY1 N - M AH0 N T
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+CONTAMINATING  K AH0 N - T AE1 - M AH0 - N EY2 - T IH0 NG
+CONTAMINATION  K AH0 N - T AE2 - M AH0 - N EY1 - SH AH0 N
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+CONTEMPLATING  K AA1 N - T AH0 M - P L EY2 - T IH0 NG
+CONTEMPLATION  K AA2 N - T AH0 M - P L EY1 - SH AH0 N
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+CONTEMPT  K AH0 N - T EH1 M P T
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+CONTEND  K AH0 N - T EH1 N D
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+CONTENTO  K AH0 N - T EH1 N - T OW0
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+CONTINENTAL  K AA2 N - T AH0 - N EH1 N - T AH0 L
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+CONTINENTALLY  K AA2 N - T AH0 - N EH1 N - T AH0 - L IY0
+CONTINENTALLY(2)  K AA2 N - T AH0 - N EH1 - N AH0 - L IY0
+CONTINENTALS  K AA2 N - T AH0 - N EH1 N - T AH0 L Z
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+CONTINGENCY  K AH0 N - T IH1 N - JH AH0 N - S IY0
+CONTINGENT  K AH0 N - T IH1 N - JH AH0 N T
+CONTINGENTS  K AH0 N - T IH1 N - JH AH0 N T S
+CONTINI  K AH0 N - T IY1 - N IY0
+CONTINO  K AA0 N - T IY1 - N OW0
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+CONTINUANCE  K AH0 N - T IH1 - N Y UW0 - AH0 N S
+CONTINUANCES  K AH0 N - T IH1 - N Y UW0 - AH0 N - S IH0 Z
+CONTINUATION  K AH0 N - T IH2 - N Y UW0 - EY1 - SH AH0 N
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+CONTINUOUS  K AH0 N - T IH1 - N Y UW0 - AH0 S
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+CONTINUUM  K AH0 N - T IH1 - N Y UW0 - AH0 M
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+CONTRACEPTIVES  K AA2 N - T R AH0 - S EH1 P - T IH0 V Z
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+CONTRADICTORY  K AA2 N - T R AH0 - D IH1 K - T ER0 - IY0
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+DEBONA  D IH0 - B OW1 - N AH0
+DEBONAIR  D EH2 - B AH0 - N EH1 R
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+DEBORAH  D EH1 - B ER0 - AH0
+DEBORAH'S  D EH1 - B ER0 - AH0 Z
+DEBORAH'S(2)  D EH1 - B R AH0 Z
+DEBORAH(2)  D EH1 - B R AH0
+DEBORD  D IH0 - B AO1 R D
+DEBORDE  D IH0 - B AO1 R D
+DEBOSE  D EH1 - B AH0 S
+DEBOW  D EH1 - B OW0
+DEBOY  D IH0 - B OY1
+DEBRA  D EH1 - B R AH0
+DEBRAUDWICK  D IH0 - B R AA1 D - W IH0 K
+DEBRIEF  D IH0 - B R IY1 F
+DEBRIEFED  D IH0 - B R IY1 F T
+DEBRIEFING  D IH0 - B R IY1 - F IH0 NG
+DEBRIS  D AH0 - B R IY1
+DEBROSSE  D EH1 - B R AH0 S
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+DEBRUIN  D EH1 - B R UW0 - IH0 N
+DEBRULER  D EH1 - B R UW0 - L ER0
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+DEBRUYNE  D EH1 - B R AY0 N
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+DEBTHOLDER  D EH1 T - HH OW2 L - D ER0
+DEBTHOLDERS  D EH1 T - HH OW2 L - D ER0 Z
+DEBTOR  D EH1 - T ER0
+DEBTOR'S  D EH1 - T ER0 Z
+DEBTORS  D EH1 - T ER0 Z
+DEBTORS'  D EH1 - T ER0 Z
+DEBTS  D EH1 T S
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+DEBUGGING  D IY0 - B AH1 - G IH0 NG
+DEBUHR  D EH1 - B UH0 R
+DEBUNK  D IH0 - B AH1 NG K
+DEBUNKED  D IH0 - B AH1 NG K T
+DEBUNKING  D IH0 - B AH1 NG - K IH0 NG
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+DEBUSK  D EH1 - B AH0 S K
+DEBUSSY  D IH0 - B AH1 - S IY0
+DEBUSSY'S  D IH0 - B AH1 - S IY0 Z
+DEBUSSY'S(2)  D IH0 - B Y UW1 - S IY0 Z
+DEBUSSY(2)  D IH0 - B Y UW1 - S IY0
+DEBUT  D EY0 - B Y UW1
+DEBUT(2)  D EY1 - B Y UW0
+DEBUTANTE  D EH1 - B Y AH0 - T AA1 N T
+DEBUTANTES  D EH1 - B Y AH0 - T AA1 N T S
+DEBUTED  D EY0 - B Y UW1 D
+DEBUTED(2)  D EY1 - B Y UW0 D
+DEBUTING  D EY0 - B Y UW1 - IH0 NG
+DEBUTS  D EY1 - B Y UW0 Z
+DEC  D EH1 K
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+DECADENCE  D EH1 - K AH0 - D AH0 N S
+DECADENT  D EH1 - K AH0 - D AH0 N T
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+DECAF  D IY1 - K AE0 F
+DECAFFEINATE  D IY0 - K AE1 - F AH0 - N EY2 T
+DECAFFEINATED  D IY0 - K AE1 - F AH0 - N EY2 - T IH0 D
+DECAFFEINATING  D IY0 - K AE1 - F AH0 - N EY2 - T IH0 NG
+DECAFFEINATION  D IY0 - K AE2 - F AH0 - N EY1 - SH AH0 N
+DECAIRE  D IY1 - K EH0 R
+DECALS  D IY1 - K AE2 L Z
+DECAMILLO  D EH2 - K AH0 - M IH1 - L OW0
+DECAMILLO'S  D EH2 - K AH0 - M IH1 - L OW0 Z
+DECAMP  D AH0 - K AE1 M P
+DECAMPED  D IY0 - K AE1 M P T
+DECANDIA  D IH0 - K AA1 N - D IY0 - AH0
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+DECANTING  D AH0 - K AE1 N - T IH0 NG
+DECAPITATE  D IY0 - K AE1 - P AH0 - T EY2 T
+DECAPITATED  D IY0 - K AE1 - P AH0 - T EY2 - T IH0 D
+DECAPITATION  D IH0 - K AE2 - P IH0 - T EY1 - SH AH0 N
+DECAPITATIONS  D IH0 - K AE2 - P IH0 - T EY1 - SH AH0 N Z
+DECAPRIO  D IH0 - K AA1 - P R IY0 - OW0
+DECAPUA  D IH0 - K AA0 - P UW1 - AH0
+DECARAVA  D IH0 - K AE1 - AH0 - V AH0
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+DECARLI  D IH0 - K AA1 R - L IY0
+DECARLO  D IH0 - K AA1 R - L OW0
+DECARO  D IH0 - K AA1 - R OW0
+DECAROLIS  D EH1 - K ER0 - AH0 - L IH0 S
+DECAROLIS(2)  D IH0 - K ER1 - AH0 - L IH0 S
+DECASTRO  D IH0 - K AE1 - S T R OW0
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+DECAYED  D AH0 - K EY1 D
+DECAYING  D AH0 - K EY1 - IH0 NG
+DECAYING(2)  D IH0 - K EY1 - IH0 NG
+DECAYS  D AH0 - K EY1 Z
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+DECEASED  D IH0 - S IY1 S T
+DECECCO  D IH0 - CH EH1 - K OW0
+DECEDENT  D EH0 - S IY1 - D AH0 N T
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+DECEDENTS  D EH0 - S IY1 - D AH0 N T S
+DECEIT  D AH0 - S IY1 T
+DECEIT(2)  D IH0 - S IY1 T
+DECEITFUL  D AH0 - S IY1 T - F AH0 L
+DECEITFUL(2)  D IH0 - S IY1 T - F AH0 L
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+DECEIVED  D IH0 - S IY1 V D
+DECEIVING  D IH0 - S IY1 - V IH0 NG
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+DECELERATED  D IH0 - S EH1 - L ER0 - EY2 - T IH0 D
+DECELERATING  D IH0 - S EH1 - L ER0 - EY2 - T IH0 NG
+DECELERATION  D IH0 - S EH2 - L ER0 - EY1 - SH AH0 N
+DECELLE  D IH0 - S EH1 L
+DECELLES  D EH1 - S AH0 L Z
+DECEMBER  D IH0 - S EH1 M - B ER0
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+DECENCY  D IY1 - S AH0 N - S IY0
+DECENNIAL  D AH0 - S EH1 - N IY0 - AH0 L
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+DECENTLY  D IY1 - S AH0 N T - L IY0
+DECENTRALIZATION  D IH0 - S EH2 N - T R AH0 - L IH0 - Z EY1 - SH AH0 N
+DECENTRALIZE  D IH0 - S EH1 N - T R AH0 - L AY2 Z
+DECENTRALIZED  D IH0 - S EH1 N - T R AH0 - L AY2 Z D
+DECENTRALIZING  D IH0 - S EH1 N - T R AH0 - L AY2 - Z IH0 NG
+DECEPTION  D IH0 - S EH1 P - SH AH0 N
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+DECERTIFICATION  D IY0 - S ER2 - T AH0 - F AH0 - K EY1 - SH AH0 N
+DECERTIFIED  D IY0 - S ER1 - T AH0 - F AY2 D
+DECERTIFY  D IY0 - S ER1 - T AH0 - F AY2
+DECESARE  D IH0 - CH EH0 - S AA1 - R IY0
+DECESARIS  D IH0 - S EH1 - S ER0 - IH0 S
+DECH  D EH1 K
+DECHANT  D EY1 - CH AH0 N T
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+DECHENE  D EH1 - K IY0 N
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+DECHINE  D EH1 - CH IH2 N
+DECHINE(2)  D AH0 - CH IH1 N
+DECHRISTOPHER  D EH1 - K R IH0 - S T AA0 - F ER0
+DECIBEL  D EH1 - S AH0 - B EH2 L
+DECIBELS  D EH1 - S AH0 - B AH0 L Z
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+DECIMA  D IH0 - CH IY1 - M AH0
+DECIMAL  D EH1 - S AH0 - M AH0 L
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+DECIMATE  D EH1 - S AH0 - M EY2 T
+DECIMATED  D EH1 - S AH0 - M EY2 - T IH0 D
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+DECIMATION  D EH1 - S AH0 - M EY2 - SH AH0 N
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+DECIPHERED  D IH0 - S AY1 - F ER0 D
+DECIPHERING  D AH0 - S AY1 - F ER0 - IH0 NG
+DECISION  D IH0 - S IH1 - ZH AH0 N
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+DECISIONS  D IH0 - S IH1 - ZH AH0 N Z
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+DECKED  D EH1 K T
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+DECLAIMED  D IH0 - K L EY1 M D
+DECLARANT  D IH0 - K L EH1 - R AH0 N T
+DECLARATION  D EH2 - K L ER0 - EY1 - SH AH0 N
+DECLARATIONS  D EH2 - K L ER0 - EY1 - SH AH0 N Z
+DECLARATORY  D IH0 - K L EH1 - R AH0 - T AO2 - R IY0
+DECLARE  D IH0 - K L EH1 R
+DECLARED  D IH0 - K L EH1 R D
+DECLARES  D IH0 - K L EH1 R Z
+DECLARING  D IH0 - K L EH1 - R IH0 NG
+DECLASSIFIED  D IH0 - K L AE1 - S AH0 - F AY2 D
+DECLASSIFY  D IH0 - K L AE1 - S AH0 - F AY2
+DECLERCK  D AH0 K - L ER1 K
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+DECLERCQ  D AH0 K - L ER1 K
+DECLERCQ'S  D AH0 K - L ER1 K S
+DECLERK  D AH0 K - L ER1 K
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+DECLERQUE  D AH0 K - L ER1 K
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+DECLINE  D IH0 - K L AY1 N
+DECLINED  D IH0 - K L AY1 N D
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+DECLINES  D IH0 - K L AY1 N Z
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+DECLUE  D EH1 - K L UW0
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+DECOMMISSIONED  D IY0 - K AH0 - M IH1 - SH AH0 N D
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+DECOMPOSE  D IY2 - K AH0 M - P OW1 Z
+DECOMPOSED  D IY2 - K AH0 M - P OW1 Z D
+DECOMPOSES  D IY2 - K AH0 M - P OW1 - Z IH0 Z
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+DECREASED  D IH0 - K R IY1 S T
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+DEFAMATORY  D IH0 - F AE1 - M AH0 - T AO2 - R IY0
+DEFAME  D IH0 - F EY1 M
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+DEFAULT  D IH0 - F AO1 L T
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+DEFAULTING  D IH0 - F AO1 L - T IH0 NG
+DEFAULTS  D IH0 - F AO1 L T S
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+DEFAZIO(2)  D IH0 - F EY1 - Z IY0 - OW0
+DEFEASANCE  D IH0 - F IY1 - Z AH0 N S
+DEFEAT  D IH0 - F IY1 T
+DEFEATED  D IH0 - F IY1 - T AH0 D
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+DEFECT(2)  D IH0 - F EH1 K T
+DEFECTED  D IH0 - F EH1 K - T IH0 D
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+DEFECTOR  D IH0 - F EH1 K - T ER0
+DEFECTORS  D IH0 - F EH1 K - T ER0 Z
+DEFECTS  D IY1 - F EH0 K T S
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+DEFEE  D EH1 - F IY0
+DEFELICE  D IH0 - F EH1 - L IH0 S
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+DEFENDANTS  D IH0 - F EH1 N - D AH0 N T S
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+DEFER  D IH0 - F ER1
+DEFERENCE  D EH1 - F ER0 - AH0 N S
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+DEFERMENT  D IH0 - F ER1 - M AH0 N T
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+DEFERRAL  D IH0 - F ER1 - AH0 L
+DEFERRALS  D IH0 - F ER1 - AH0 L Z
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+DEFIBAUGH  D EH1 - F IH0 - B AO2
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+DEFICIENT  D IH0 - F IH1 - SH AH0 N T
+DEFICIT  D EH1 - F AH0 - S AH0 T
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+DEFICITS  D EH1 - F IH0 - S IH0 T S
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+DEFILIPPO  D IH0 - F IY0 - L IY1 - P OW0
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+DEFINING  D IH0 - F AY1 - N IH0 NG
+DEFINITE  D EH1 - F AH0 - N AH0 T
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+DEFINITION  D EH2 - F AH0 - N IH1 - SH AH0 N
+DEFINITIONS  D EH2 - F AH0 - N IH1 - SH AH0 N Z
+DEFINITIVE  D IH0 - F IH1 - N IH0 - T IH0 V
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+DEMING  D EH1 - M IH0 NG
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+DEMIRAG  D EY2 - M IH0 - R AA1 JH
+DEMIREL  D AH0 - M IH1 - R AH0 L
+DEMIRJIAN  D IH0 - M ER1 - JH IY0 - AH0 N
+DEMISCH  D AH0 - M IH1 SH
+DEMISE  D IH0 - M AY1 Z
+DEMISH  D EH1 - M IH0 SH
+DEMJANJUK  D EH0 - M Y AA1 - N Y UW0 K
+DEMJANJUK'S  D EH0 - M Y AA1 - N Y UW0 K S
+DEMJANJUK'S(2)  D EH0 - M Y AE1 - N Y UW0 K S
+DEMJANJUK(2)  D EH0 - M Y AE1 - N Y UW0 K
+DEMKO  D EH1 M - K OW0
+DEMLER  D EH1 M - L ER0
+DEMMA  D IY1 - M AH0
+DEMME  D EH1 M
+DEMMER  D EH1 - M ER0
+DEMMING  D EH1 - M IH0 NG
+DEMMON  D EH1 - M AH0 N
+DEMMONS  D EH1 - M AH0 N Z
+DEMO  D EH1 - M OW0
+DEMOBILIZATION  D IY0 - M OW2 - B AH0 - L AY0 - Z EY1 - SH AH0 N
+DEMOBILIZATION(2)  D IY0 - M OW2 - B AH0 - L AH0 - Z EY1 - SH AH0 N
+DEMOBILIZE  D IH0 - M OW1 - B AH0 - L AY2 Z
+DEMOBILIZED  D IH0 - M OW1 - B AH0 - L AY2 Z D
+DEMOCRACIES  D IH0 - M AA1 - K R AH0 - S IY0 Z
+DEMOCRACY  D IH0 - M AA1 - K R AH0 - S IY0
+DEMOCRACY'S  D IH0 - M AA1 - K R AH0 - S IY0 Z
+DEMOCRAT  D EH1 - M AH0 - K R AE2 T
+DEMOCRAT'S  D EH1 - M AH0 - K R AE2 T S
+DEMOCRATIC  D EH2 - M AH0 - K R AE1 - T IH0 K
+DEMOCRATIC'S  D EH2 - M AH0 - K R AE1 - T IH0 K S
+DEMOCRATICA  D EH2 - M AH0 - K R AE1 - T IH0 - K AH0
+DEMOCRATICALLY  D EH2 - M AH0 - K R AE1 - T IH0 K - L IY0
+DEMOCRATICS  D EH2 - M AH0 - K R AE1 - T IH0 K S
+DEMOCRATIZATION  D IH0 - M AA2 - K R AH0 - T AH0 - Z EY1 - SH AH0 N
+DEMOCRATIZE  D IH0 - M AA1 - K R AH0 - T AY2 Z
+DEMOCRATIZED  D IH0 - M AA1 - K R AH0 - T AY2 Z D
+DEMOCRATIZING  D IH0 - M AA1 - K R AH0 - T AY2 - Z IH0 NG
+DEMOCRATS  D EH1 - M AH0 - K R AE2 T S
+DEMOCRATS'  D EH1 - M AH0 - K R AE2 T S
+DEMODULATE  D IY2 - M AA2 - JH AH0 - L EY1 T
+DEMODULATION  D IY2 - M AA2 - JH AH0 - L EY1 - SH AH0 N
+DEMOGRAPHER  D IH0 - M AA1 - G R AH0 - F ER0
+DEMOGRAPHERS  D IH0 - M AA1 - G R AH0 - F ER0 Z
+DEMOGRAPHIC  D EH2 - M AH0 - G R AE1 - F IH0 K
+DEMOGRAPHICALLY  D EH2 - M AH0 - G R AE1 - F IH0 K - L IY0
+DEMOGRAPHICS  D EH2 - M AH0 - G R AE1 - F IH0 K S
+DEMOGRAPHY  D IH0 - M AA1 - G R AH0 - F IY0
+DEMOLISH  D IH0 - M AA1 - L IH0 SH
+DEMOLISHED  D IH0 - M AA1 - L IH0 SH T
+DEMOLISHING  D IH0 - M AA1 - L IH0 - SH IH0 NG
+DEMOLITION  D EH2 - M AH0 - L IH1 - SH AH0 N
+DEMON  D IY1 - M AH0 N
+DEMOND  D AH0 - M AA1 N D
+DEMONIC  D IH0 - M AA1 - N IH0 K
+DEMONIZATION  D IY2 - M AH0 - N AH0 - Z EY1 - SH AH0 N
+DEMONIZE  D IY1 - M AH0 - N AY2 Z
+DEMONIZED  D IY1 - M AH0 - N AY2 Z D
+DEMONIZER  D IY1 - M AH0 - N AY2 - Z ER0
+DEMONIZEZ  D IY1 - M AH0 - N AY2 - Z IH0 Z
+DEMONIZING  D IY1 - M AH0 - N AY2 - Z IH0 NG
+DEMONS  D IY1 - M AH0 N Z
+DEMONSTRABLE  D EH1 - M AH0 N - S T R AH0 - B AH0 L
+DEMONSTRABLY  D IH0 - M AA1 N - S T R AH0 - B L IY0
+DEMONSTRATE  D EH1 - M AH0 N - S T R EY2 T
+DEMONSTRATED  D EH1 - M AH0 N - S T R EY2 - T AH0 D
+DEMONSTRATED(2)  D EH1 - M AH0 N - S T R EY2 - T IH0 D
+DEMONSTRATES  D EH1 - M AH0 N - S T R EY2 T S
+DEMONSTRATING  D EH1 - M AH0 N - S T R EY2 - T IH0 NG
+DEMONSTRATION  D EH2 - M AH0 N - S T R EY1 - SH AH0 N
+DEMONSTRATIONS  D EH2 - M AH0 N - S T R EY1 - SH AH0 N Z
+DEMONSTRATIVE  D IH0 - M AA1 N - S T R AH0 - T IH0 V
+DEMONSTRATOR  D EH1 - M AH0 N - S T R EY2 - T ER0
+DEMONSTRATORS  D EH1 - M AH0 N - S T R EY2 - T ER0 Z
+DEMONT  D EH1 - M AH0 N T
+DEMONTE  D AH0 - M AA1 N - T IY0
+DEMOPOULOS  D AH0 - M AA1 - P AH0 - L IH0 S
+DEMORALIZATION  D IH0 - M AO2 - R AH0 - L IH0 - Z EY1 - SH AH0 N
+DEMORALIZE  D IH0 - M AO1 - R AH0 - L AY2 Z
+DEMORALIZED  D IH0 - M AO1 - R AH0 - L AY2 Z D
+DEMORALIZING  D IH0 - M AO1 - R AH0 - L AY2 - Z IH0 NG
+DEMORE  D EH1 - M AO0 R
+DEMOREST  D EY0 - M AO1 - R IH0 S T
+DEMORY  D IH0 - M ER1 - IY0
+DEMORY'S  D IH0 - M ER1 - IY0 Z
+DEMOS  D EH1 - M OW2 Z
+DEMOSS  D AH0 - M AA1 S
+DEMOTE  D IH0 - M OW1 T
+DEMOTED  D IH0 - M OW1 - T IH0 D
+DEMOTION  D IH0 - M OW1 - SH AH0 N
+DEMOTIONS  D IH0 - M OW1 - SH AH0 N Z
+DEMOTT  D AH0 - M AA1 T
+DEMOULIN  D EH1 - M UW0 - L AE0 N
+DEMOV  D EH1 - M AA0 V
+DEMPEWOLF  D EH1 M - P Y UW0 - UH0 L F
+DEMPS  D EH1 M P S
+DEMPSEY  D EH1 M P - S IY0
+DEMPSTER  D EH1 M P - S T ER0
+DEMSKI  D EH1 M S - K IY0
+DEMSKY  D EH1 M S - K IY0
+DEMUR  D IH0 - M ER1
+DEMURE  D IH0 - M Y UH1 R
+DEMURELY  D IH0 - M Y UH1 R - L IY0
+DEMURO  D IH0 - M UH1 - R OW0
+DEMURRED  D IH0 - M ER1 D
+DEMURRING  D IH0 - M ER1 - IH0 NG
+DEMURS  D IH0 - M ER1 Z
+DEMUS  D IY1 - M AH0 S
+DEMUTH  D IY1 - M AH0 TH
+DEMYAN  D EH1 - M Y AH0 N
+DEMYSTIFY  D IY0 - M IH1 - S T AH0 - F AY2
+DEN  D EH1 N
+DENA  D IY1 - N AH0
+DENAPOLI  D IH0 - N AA1 - P AH0 - L IY0
+DENARD  D IH0 - N AA1 R D
+DENARDO  D IH0 - N AA1 R - D OW0
+DENARII  D IH0 - N AE1 - R IY0
+DENARIUS  D IH0 - N AE1 - R IY0 - AH0 S
+DENARO  D IH0 - N AA1 - R OW0
+DENATALE  D IH0 - N AA0 - T AA1 - L IY0
+DENATIONALIZATION  D IY2 - N AE2 - SH AH0 N - AH0 - L IH0 - Z EY1 - SH AH0 N
+DENATIONALIZATIONS  D IY0 - N AE2 - SH AH0 N - AH0 - L IH0 - Z EY1 - SH AH0 N Z
+DENATIONALIZE  D IH0 - N AE1 - SH AH0 N - AH0 - L AY2 Z
+DENATIONALIZED  D IH0 - N AE1 - SH AH0 N - AH0 - L AY2 Z D
+DENATIONALIZING  D IH0 - N AE1 - SH AH0 N - AH0 - L AY2 - Z IH0 NG
+DENATURE  D IH0 - N EY1 - CH ER0
+DENATURED  D IH0 - N EY1 - CH ER0 D
+DENAULT  D IH0 - N OW1
+DENBO  D IY1 N - B OW0
+DENBOER  D EH1 N - B OW0 - ER0
+DENBOW  D EH1 N - B OW0
+DENBY  D EH1 N - B IY0
+DENDEN  D EH1 N - D AH0 N
+DENDINGER  D IY1 N - D IH0 - NG ER0
+DENDRITIC  D EH0 N - D R IH1 - T IH0 K
+DENDROCHRONOLOGY  D EH2 N - D R OW2 - K R AH0 - N AA1 - L AH0 - JH IY0
+DENDY  D EH1 N - D IY0
+DENEAU  D IH0 - N OW1
+DENEAULT  D IH0 - N OW1
+DENEEN  D IH0 - N IY1 N
+DENEKE  D EH1 - N IH0 K
+DENENBERG  D EH1 - N AH0 N - B ER0 G
+DENES  D IY1 N Z
+DENEUVE  D IH0 - N AH1 V
+DENEUVE(2)  D IY0 - N AH1 V
+DENEVE  D EH1 - N IH0 V
+DENG  D EH1 NG
+DENG'S  D EH1 NG Z
+DENGEL  D EH1 NG - G AH0 L
+DENGLER  D IH1 - NG AH0 - L ER0
+DENGLER(2)  D IH1 NG - L ER0
+DENGUE  D EH1 N G
+DENHAM  D EH1 - N AH0 M
+DENHART  D EH1 N - HH AA2 R T
+DENHARTOG  D EH1 N - HH AA0 R - T AH0 G
+DENHERDER  D EH1 N - HH ER2 - D ER0
+DENHOLM  D EH1 N - HH OW2 L M
+DENIABILITY  D IH0 - N AY2 - AH0 - B IH1 - L IH0 - T IY0
+DENIAL  D IH0 - N AY1 - AH0 L
+DENIALS  D IH0 - N AY1 - AH0 L Z
+DENICE  D IH0 - N IY1 S
+DENICOLA  D IH0 - N IY0 - K OW1 - L AH0
+DENIED  D IH0 - N AY1 D
+DENIES  D IH0 - N AY1 Z
+DENIGRATE  D EH1 - N AH0 - G R EY2 T
+DENIGRATED  D EH1 - N IH0 - G R EY2 - T IH0 D
+DENIGRATING  D EH1 - N IH0 - G R EY2 - T IH0 NG
+DENIGRIS  D EH1 - N IH0 - G R IH0 S
+DENIKE  D EH1 - N IH0 K
+DENIM  D EH1 - N AH0 M
+DENIO  D IY1 - N IY0 - OW0
+DENIRO  D IH0 - N IH1 - R OW0
+DENIRO'S  D IH0 - N IH1 - R OW0 Z
+DENIS  D EH1 - N IH0 S
+DENISE  D IH0 - N IY1 S
+DENISON  D EH1 - N IH0 - S AH0 N
+DENISON'S  D EH1 - N IH0 - S AH0 N Z
+DENISTON  D EH1 - N IH0 - S T AA0 N
+DENIZ  D EY1 - N IY0 Z
+DENIZEN  D EH1 - N AH0 - Z AH0 N
+DENIZENS  D EH1 - N AH0 - Z AH0 N Z
+DENK  D EH1 NG K
+DENKER  D EH1 NG - K ER0
+DENKI  D EH1 NG - K IY0
+DENKINS  D EH1 NG - K IH0 N Z
+DENKO  D EH1 NG - K OW0
+DENKTAS  D EH1 NG K - T AH0 S
+DENLEA  D EH1 N - L IY2
+DENLEY  D EH1 N - L IY0
+DENLINGER  D EH1 - N AH0 L - IH0 - NG ER0
+DENLINGER(2)  D EH1 N - L IH0 - NG ER0
+DENLINGER(3)  D EH1 N - L IH0 N - JH ER0
+DENMAN  D EH1 N - M AH0 N
+DENMARK  D EH1 N - M AA2 R K
+DENMARK'S  D EH1 N - M AA2 R K S
+DENMON  D EH1 N - M AH0 N
+DENN  D EH1 N
+DENNARD  D IH0 - N AA1 R D
+DENNE  D EH1 N
+DENNEHY  D EH1 - N IH0 - HH IY0
+DENNEN  D EH1 - N AH0 N
+DENNER  D EH1 - N ER0
+DENNETT  D EH1 - N IH0 T
+DENNEY  D EH1 - N IY0
+DENNIE  D EH1 - N IY0
+DENNIN  D EH1 - N IH0 N
+DENNING  D EH1 - N IH0 NG
+DENNINGER  D EH1 - N IH0 - NG ER0
+DENNINGTON  D EH1 - N IH0 NG - T AH0 N
+DENNIS  D EH1 - N IH0 S
+DENNISON  D EH1 - N IH0 - S AH0 N
+DENNISTON  D EH1 - N IH0 - S T AA0 N
+DENNO  D EH1 - N OW0
+DENNY  D EH1 - N IY0
+DENNY'S  D EH1 - N IY0 Z
+DENO  D IY1 - N OW0
+DENOBLE  D EH1 - N OW0 - B AH0 L
+DENOMINATE  D IH0 - N AA1 - M AH0 - N EY2 T
+DENOMINATED  D IH0 - N AA1 - M AH0 - N EY2 - T IH0 D
+DENOMINATION  D IH0 - N AO2 - M AH0 - N EY1 - SH AH0 N
+DENOMINATION'S  D IH0 - N AO2 - M AH0 - N EY1 - SH AH0 N Z
+DENOMINATIONAL  D IH0 - N AO2 - M AH0 - N EY1 - SH AH0 - N AH0 L
+DENOMINATIONS  D IH0 - N AO2 - M AH0 - N EY1 - SH AH0 N Z
+DENOMINATOR  D IH0 - N AA1 - M AH0 - N EY2 - T ER0
+DENOMME  D EH1 - N AH0 M
+DENOSSE  D IH0 - N OW1 S
+DENOSSE(2)  D IH0 - N AO1 - S IY0
+DENOTE  D IH0 - N OW1 T
+DENOTED  D IH0 - N OW1 - T AH0 D
+DENOTES  D IH0 - N OW1 T S
+DENOUEMENT  D EY2 - N UW2 - M AA1 N
+DENOUNCE  D IH0 - N AW1 N S
+DENOUNCED  D IH0 - N AW1 N S T
+DENOUNCES  D IH0 - N AW1 N - S IH0 Z
+DENOUNCING  D IH0 - N AW1 N - S IH0 NG
+DENOYER  D EH1 - N OY0 - ER0
+DENS  D EH1 N Z
+DENSE  D EH1 N S
+DENSELY  D EH1 N S - L IY0
+DENSER  D EH1 N - S ER0
+DENSEST  D EH1 N - S AH0 S T
+DENSHIN  D EH1 N - SH IH0 N
+DENSITIES  D EH1 N - S AH0 - T IY0 Z
+DENSITOMETER  D EH2 N - S AH0 - T AA1 - M AH0 - T ER0
+DENSITY  D EH1 N - S AH0 - T IY0
+DENSITY(2)  D EH1 N - S IH0 - T IY0
+DENSLEY  D EH1 N S - L IY0
+DENSLOW  D EH1 N - S L OW2
+DENSMORE  D IY1 N S - M AO0 R
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+DENSON  D EH1 N - S AH0 N
+DENT  D EH1 N T
+DENTAL  D EH1 N - T AH0 L
+DENTAL(2)  D EH1 - N AH0 L
+DENTALS  D EH1 N - T AH0 L Z
+DENTALS(2)  D EH1 - N AH0 L Z
+DENTE  D EH1 N T
+DENTED  D EH1 N - T IH0 D
+DENTIN  D EH1 N - T AH0 N
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+DENTING  D EH1 N - T IH0 NG
+DENTINO  D IH0 N - T IY1 - N OW0
+DENTIST  D EH1 N - T AH0 S T
+DENTIST'S  D EH1 N - T IH0 S T S
+DENTIST'S(2)  D EH1 - N IH0 S S
+DENTIST'S(3)  D EH1 - N IH0 S
+DENTIST(2)  D EH1 N - T IH0 S T
+DENTIST(3)  D EH1 - N IH0 S T
+DENTISTRY  D EH1 N - T AH0 S - T R IY0
+DENTISTRY(2)  D EH1 N - T IH0 S - T R IY0
+DENTISTRY(3)  D EH1 - N IH0 - S T R IY0
+DENTISTS  D EH1 N - T AH0 S T S
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+DENTISTS'(2)  D EH1 - N IH0 S T S
+DENTISTS(2)  D EH1 N - T IH0 S T S
+DENTISTS(3)  D EH1 - N IH0 S S
+DENTISTS(4)  D EH1 - N IH0 S
+DENTITION  D EH0 N - T IH1 - SH AH0 N
+DENTLER  D EH1 N T - L ER0
+DENTON  D EH1 N - T AH0 N
+DENTREMONT  D EY0 N - T R EY1 - M AA0 N T
+DENTS  D EH1 N T S
+DENTSU  D EH1 N T - S UW0
+DENTTON  D EH1 N - T AH0 N
+DENTURE  D EH1 N - CH ER0
+DENTURES  D EH1 N - CH ER0 Z
+DENTZER  D EH1 N T - Z ER0
+DENUCCI  D IH0 - N UW1 - CH IY0
+DENUCLEARIZATION  D IY0 - N UW2 - K L IY0 - ER0 - AH0 - Z EY1 - SH AH0 N
+DENUCLEARIZED  D IH0 - N UW1 - K L IY0 - ER0 - AY2 Z D
+DENUCLEARIZED(2)  D IY0 - N UW1 - K L IY0 - ER0 - AY2 Z D
+DENUDE  D IH0 - N UW1 D
+DENUDED  D IH0 - N UW1 - D IH0 D
+DENUDING  D IH0 - N UW1 - D IH0 NG
+DENUNCIATION  D IH0 - N AH2 N - S IY0 - EY1 - SH AH0 N
+DENUNCIATIONS  D IH0 - N AH2 N - S IY0 - EY1 - SH AH0 N Z
+DENUNZIO  D AH0 - N AH1 N - Z IY0 - OW0
+DENVER  D EH1 N - V ER0
+DENVER'S  D EH1 N - V ER0 Z
+DENWA  D EH1 N - W AA2
+DENY  D IH0 - N AY1
+DENYING  D IH0 - N AY1 - IH0 NG
+DENYS  D EH1 - N IH0 S
+DENYSE  D EH1 - N AY0 S
+DENZ  D EH1 N Z
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+DENZER  D EH1 N - Z ER0
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+DENZLER  D EH1 N Z - L ER0
+DEO  D IY1 - OW0
+DEODORANT  D IY0 - OW1 - D ER0 - AH0 N T
+DEODORANTS  D IY0 - OW1 - D ER0 - AH0 N T S
+DEOLIVEIRA  D IY2 - AA2 - L IH0 - V EY1 - R AH0
+DEON  D IY1 - AA0 N
+DEOXYRIBONUCLEIC  D IY0 - AA2 K - S IY0 - R AY2 - B OW0 - N UW0 - K L EY1 - IH0 K
+DEP  D IH0 - P AA1 R T - M AH0 N T
+DEP(2)  D EH1 P
+DEPACE  D IH0 - P AA1 - CH IY0
+DEPALMA  D IH0 - P AA1 L - M AH0
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+DEPALO  D IH0 - P AA1 - L OW0
+DEPAOLA  D IH0 - P AW1 - L AH0
+DEPAOLI  D IH0 - P AW1 - L IY0
+DEPAOLIS  D EH0 - P AW1 - L IH0 S
+DEPAOLO  D IH0 - P AW1 - L OW0
+DEPARDIEU  D IY2 - P AA0 R - D Y AH1
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+DEPART  D IH0 - P AA1 R T
+DEPARTED  D IH0 - P AA1 R - T AH0 D
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+DEPARTMENTAL  D IH0 - P AA2 R T - M EH1 - N AH0 L
+DEPARTMENTAL(2)  D IH0 - P AA2 R T - M EH1 N - T AH0 L
+DEPARTMENTALIZE  D IH0 - P AA2 R T - M EH1 N - T AH0 - L AY2 Z
+DEPARTMENTALIZE(2)  D IH0 - P AA2 R T - M EH1 - N AH0 - L AY2 Z
+DEPARTMENTALIZED  D IH0 - P AA2 R T - M EH1 N - T AH0 - L AY2 Z D
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+DEPARTMENTS  D IH0 - P AA1 R T - M AH0 N T S
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+DEPARTURE  D IH0 - P AA1 R - CH ER0
+DEPARTURES  D IH0 - P AA1 R - CH ER0 Z
+DEPASCALE  D IH0 - P AA0 - S K AA1 - L IY0
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+DEPENDABLE  D IH0 - P EH1 N - D AH0 - B AH0 L
+DEPENDED  D IH0 - P EH1 N - D AH0 D
+DEPENDED(2)  D IH0 - P EH1 N - D IH0 D
+DEPENDENCE  D IH0 - P EH1 N - D AH0 N S
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+DEPENDENCY  D IH0 - P EH1 N - D AH0 N - S IY0
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+DEPETRO  D IH0 - P EH1 - T R OW0
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+DESIRABILITY  D IH0 - Z AY2 - R AH0 - B IH1 - L IH0 - T IY0
+DESIRABLE  D IH0 - Z AY1 - R AH0 - B AH0 L
+DESIRABLE(2)  D IH0 - Z AY1 - ER0 - AH0 - B AH0 L
+DESIRE  D IH0 - Z AY1 - ER0
+DESIRED  D IH0 - Z AY1 - ER0 D
+DESIREE  D EH1 - S AY0 - R IY0
+DESIRES  D IH0 - Z AY1 - ER0 Z
+DESIRING  D IH0 - Z AY1 - ER0 - IH0 NG
+DESIROUS  D IH0 - Z AY1 - R AH0 S
+DESIST  D IH0 - S IH1 S T
+DESIST(2)  D IH0 - Z IH1 S T
+DESISTO  D IH0 - S IY1 - S T OW0
+DESJARDIN  D EH1 S - ZH AA0 R - D AE0 N
+DESJARDINS  D EH1 S - ZH AA0 R - D IH0 N Z
+DESJARLAIS  D EH1 S - ZH AA0 R - L EY0
+DESK  D EH1 S K
+DESKIN  D EH1 - S K IH0 N
+DESKINS  D EH1 - S K IH0 N Z
+DESKJET  D EH1 - S K JH EH2 T
+DESKPRO  D EH1 S K - P R OW2
+DESKS  D EH1 S K S
+DESKTOP  D EH1 S K - T AA2 P
+DESKTOPS  D EH1 S K - T AA2 P S
+DESLATTE  D IH0 S - L AE1 T
+DESLAURIERS  D EH1 S - L AO0 - R IY0 - ER0 Z
+DESMA  D IY1 S - M AH0
+DESMA(2)  D EH1 Z - M AH0
+DESMAN  D EH1 Z - M AH0 N
+DESMARAIS  D EH1 Z - M ER0 - EY0
+DESMET  D EH1 S - M IH0 T
+DESMIDS  D EH1 S - M AH0 D Z
+DESMITH  D EH1 - S M IH0 TH
+DESMONA  D IH0 S - M OW1 - N AH0
+DESMOND  D EH1 Z - M AH0 N D
+DESNOYERS  D EH1 S - N OY0 - ER0 Z
+DESOLATE  D EH1 - S AH0 - L AH0 T
+DESOLATE(2)  D EH1 - Z AH0 - L AH0 T
+DESOLATE(3)  D EH1 - S AH0 - L EY2 T
+DESOLATION  D EH2 - S AH0 - L EY1 - SH AH0 N
+DESORMEAUX  D EH1 - S ER0 - M OW0
+DESOTO  D IH0 S - OW1 - T OW0
+DESOUSA  D IH0 - S AW1 - S AH0
+DESOUSA(2)  D IH0 - S UW1 - S AH0
+DESOUZA  D EY0 - S UW1 - Z AH0
+DESPAIN  D IH0 - S P EY1 N
+DESPAIR  D IH0 - S P EH1 R
+DESPAIRED  D IH0 - S P EH1 R D
+DESPAIRING  D IH0 - S P EH1 - R IH0 NG
+DESPAIRS  D IH0 - S P EH1 R Z
+DESPER  D EH1 - S P ER0
+DESPERADO  D EH2 - S P ER0 - AA1 - D OW0
+DESPERADOES  D EH2 - S P ER0 - AA1 - D OW0 Z
+DESPERATE  D EH1 - S P R IH0 T
+DESPERATE(2)  D EH1 - S P ER0 - IH0 T
+DESPERATELY  D EH1 - S P ER0 - AH0 T - L IY0
+DESPERATELY(2)  D EH1 - S P R AH0 T - L IY0
+DESPERATION  D EH2 - S P ER0 - EY1 - SH AH0 N
+DESPERATION(2)  D EH2 - S P ER0 - EY1 - SH IH0 N
+DESPICABLE  D IH0 - S P IH1 - K AH0 - B AH0 L
+DESPINA'S  D EH1 - S P IY0 - N AH0 Z
+DESPISE  D IH0 - S P AY1 Z
+DESPISED  D IH0 - S P AY1 Z D
+DESPISES  D IH0 - S P AY1 - Z IH0 Z
+DESPITE  D IH0 - S P AY1 T
+DESPONDENCY  D IH0 - S P AA1 N - D AH0 N - S IY0
+DESPONDENT  D IH0 - S P AA1 N - D AH0 N T
+DESPOSITO  D IH0 - S P OW0 - S IY1 - T OW0
+DESPOT  D EH1 - S P AH0 T
+DESPOTIC  D IH0 - S P AA1 - T IH0 K
+DESPOTISM  D EH1 - S P AH0 - T IH2 - Z AH0 M
+DESPRES  D EH1 - S P ER0 Z
+DESROCHERS  D EY0 - R OW1 - SH ER0 Z
+DESROCHES  D EY0 - R OW1 - SH IH0 Z
+DESROSIER  D EY0 - R OW1 - SH IY0 - ER0
+DESROSIERS  D EY0 - R OW1 - SH IY0 - ER0 Z
+DESROSIERS(2)  D EY2 - R OW0 - ZH IH1 R Z
+DESSAUER  D IH0 - S AW1 R
+DESSAUER(2)  D EH1 - S AW2 R
+DESSELLE  D IH0 - S EH1 L
+DESSENT  D IH0 - S EH1 N T
+DESSERT  D IH0 - Z ER1 T
+DESSERTS  D IH0 - Z ER1 T S
+DEST  D EH1 S T
+DESTABILIZATION  D IY0 - S T EY2 - B AH0 - L AH0 - Z EY1 - SH AH0 N
+DESTABILIZE  D IH0 - S T EY1 - B AH0 - L AY2 Z
+DESTABILIZED  D IH0 - S T EY1 - B AH0 - L AY2 Z D
+DESTABILIZING  D IH0 - S T EY1 - B AH0 - L AY2 - Z IH0 NG
+DESTEC  D EH1 - S T EH2 K
+DESTEFANIS  D IH0 - S T IH0 - F AA1 - N IH0 S
+DESTEFANO  D IH0 - S T EH0 - F AA1 - N OW0
+DESTIN  D EH1 - S T IH0 N
+DESTINATION  D EH2 - S T AH0 - N EY1 - SH AH0 N
+DESTINATION(2)  D EH2 - S T IH0 - N EY1 - SH AH0 N
+DESTINATIONS  D EH2 - S T AH0 - N EY1 - SH AH0 N Z
+DESTINED  D EH1 - S T IH0 N D
+DESTINIES  D EH1 - S T AH0 - N IY0 Z
+DESTINY  D EH1 - S T AH0 - N IY0
+DESTITUTE  D EH1 - S T AH0 - T UW2 T
+DESTITUTION  D EH1 - S T AH0 - T UW2 - SH AH0 N
+DESTROY  D IH0 - S T R OY1
+DESTROYED  D IH0 - S T R OY1 D
+DESTROYER  D IH0 - S T R OY1 - ER0
+DESTROYERS  D IH0 - S T R OY1 - ER0 Z
+DESTROYING  D IH0 - S T R OY1 - IH0 NG
+DESTROYS  D IH0 - S T R OY1 Z
+DESTRUCT  D IH0 - S T R AH1 K T
+DESTRUCTED  D IH0 - S T R AH1 K - T IH0 D
+DESTRUCTING  D IH0 - S T R AH1 K - T IH0 NG
+DESTRUCTION  D IH0 S - T R AH1 K - SH AH0 N
+DESTRUCTIVE  D IH0 - S T R AH1 K - T IH0 V
+DESTRUCTIVENESS  D IH0 - S T R AH1 K - T IH0 V - N IH0 S
+DESTRUCTS  D IH0 - S T R AH1 K T S
+DESULTORY  D EH1 - S AH0 L - T AO2 - R IY0
+DETACH  D IH0 - T AE1 CH
+DETACH(2)  D IY0 - T AE1 CH
+DETACHABLE  D IH0 - T AE1 - CH AH0 - B AH0 L
+DETACHABLE(2)  D IY0 - T AE1 - CH AH0 - B AH0 L
+DETACHED  D IH0 - T AE1 CH T
+DETACHED(2)  D IY0 - T AE1 CH T
+DETACHES  D IH0 - T AE1 - CH IH0 Z
+DETACHES(2)  D IY0 - T AE1 - CH AH0 Z
+DETACHMENT  D IH0 - T AE1 CH - M AH0 N T
+DETACHMENT(2)  D IY0 - T AE1 CH - M AH0 N T
+DETAIL  D IH0 - T EY1 L
+DETAIL(2)  D IY1 - T EY0 L
+DETAILED  D IH0 - T EY1 L D
+DETAILEE  D IH0 - T EY2 - L IY1
+DETAILER  D IY1 - T EY0 - L ER0
+DETAILING  D IH0 - T EY1 - L IH0 NG
+DETAILS  D IH0 - T EY1 L Z
+DETAILS(2)  D IY1 - T EY0 L Z
+DETAIN  D IH0 - T EY1 N
+DETAINED  D IH0 - T EY1 N D
+DETAINEE  D IY2 - T EY0 - N IY1
+DETAINEES  D IH0 - T EY2 - N IY1 Z
+DETAINING  D IH0 - T EY1 - N IH0 NG
+DETAMORE  D IH0 - T AA1 - M AO0 R
+DETAR  D IH0 - T AA1 R
+DETAR(2)  D IY0 - T AA0 R
+DETECT  D IH0 - T EH1 K T
+DETECTABLE  D IH0 - T EH1 K - T AH0 - B AH0 L
+DETECTED  D IH0 - T EH1 K - T AH0 D
+DETECTED(2)  D IH0 - T EH1 K - T IH0 D
+DETECTING  D IH0 - T EH1 K - T IH0 NG
+DETECTION  D IH0 - T EH1 K - SH AH0 N
+DETECTIVE  D IH0 - T EH1 K - T IH0 V
+DETECTIVE'S  D IH0 - T EH1 K - T IH0 V Z
+DETECTIVES  D IH0 - T EH1 K - T IH0 V Z
+DETECTOR  D IH0 - T EH1 K - T ER0
+DETECTORS  D IH0 - T EH1 K - T ER0 Z
+DETECTS  D IH0 - T EH1 K T S
+DETEMPLE  D EH1 - T IH0 M - P AH0 L
+DETENTE  D EY0 - T AA1 N T
+DETENTION  D IH0 - T EH1 N - SH AH0 N
+DETENTIONS  D IH0 - T EH1 N - SH AH0 N Z
+DETER  D IH0 - T ER1
+DETERDING  D EH1 - T ER0 - D IH0 NG
+DETERGENT  D IH0 - T ER1 - JH AH0 N T
+DETERGENTS  D IH0 - T ER1 - JH AH0 N T S
+DETERIORATE  D IH0 - T IH1 - R IY0 - ER0 - EY2 T
+DETERIORATED  D IH0 - T IH1 - R IY0 - ER0 - EY2 - T IH0 D
+DETERIORATES  D IH0 - T IH1 - R IY0 - ER0 - EY2 T S
+DETERIORATING  D IH0 - T IH1 - R IY0 - ER0 - EY2 - T IH0 NG
+DETERIORATION  D IH0 - T IH1 - R IY0 - ER0 - EY2 - SH AH0 N
+DETERMAN  D IY1 - T ER0 - M AH0 N
+DETERMENT  D IH0 - T ER1 - M AH0 N T
+DETERMINABLE  D IH0 - T ER1 - M AH0 - N AH0 - B AH0 L
+DETERMINANT  D IH0 - T ER1 - M AH0 - N AH0 N T
+DETERMINANTS  D IH0 - T ER1 - M AH0 - N AH0 N T S
+DETERMINATE  D IH0 - T ER1 - M AH0 - N EY2 T
+DETERMINATION  D IH0 - T ER2 - M AH0 - N EY1 - SH AH0 N
+DETERMINATION'S  D IH0 - T ER2 - M IH0 - N EY1 - SH AH0 N Z
+DETERMINATIONS  D IH0 - T ER2 - M IH0 - N EY1 - SH AH0 N Z
+DETERMINATIVE  D IH0 - T ER1 - M IH0 - N AH0 - T IH2 V
+DETERMINE  D AH0 - T ER1 - M AH0 N
+DETERMINE(2)  D IH0 - T ER1 - M AH0 N
+DETERMINED  D IH0 - T ER1 - M AH0 N D
+DETERMINEDLY  D AH0 - T ER1 - M AH0 - N AH0 D - L IY0
+DETERMINEDLY(2)  D AH0 - T ER1 - M AH0 N D - L IY0
+DETERMINES  D AH0 - T ER1 - M AH0 N Z
+DETERMINES(2)  D IH0 - T ER1 - M AH0 N Z
+DETERMINING  D IH0 - T ER1 - M AH0 - N IH0 NG
+DETERMINISM  D IH0 - T ER1 - M AH0 - N IH2 - Z AH0 M
+DETERMINIST  D IH0 - T ER1 - M AH0 - N AH0 S T
+DETERMINISTIC  D IH0 - T ER2 - M AH0 - N IH1 - S T IH0 K
+DETERRED  D IH0 - T ER1 D
+DETERRENCE  D IH0 - T ER1 - AH0 N S
+DETERRENT  D IH0 - T ER1 - R AH0 N T
+DETERRENTS  D IH0 - T ER1 - AH0 N T S
+DETERRING  D IH0 - T ER1 - IH0 NG
+DETERS  D IH0 - T ER1 Z
+DETERT  D EH1 - T ER0 T
+DETEST  D IH0 - T EH1 S T
+DETEST(2)  D IY0 - T EH1 S T
+DETESTED  D IH0 - T EH1 - S T IH0 D
+DETESTED(2)  D IY0 - T EH1 - S T IH0 D
+DETHERAGE  D EH1 - DH ER0 - IH0 JH
+DETHLEFS  D EH1 TH - L IH0 F S
+DETHLEFSEN  D EH1 TH - L IH0 F - S AH0 N
+DETHLOFF  D EH1 TH - L AO0 F
+DETHOMAS  D IH0 - TH OW1 - M AH0 Z
+DETHOMAS(2)  D IH0 - T AA1 - M AH0 S
+DETHRONE  D IH0 - TH R OW1 N
+DETHRONE(2)  D IY0 - TH R OW1 N
+DETHRONED  D IH0 - TH R OW1 N D
+DETHRONED(2)  D IY0 - TH R OW1 N D
+DETIENNE  D EH1 - T IY0 - EH0 N
+DETJEN  D EH1 T - JH AH0 N
+DETLEFSEN  D EH1 T - L IH0 F - S AH0 N
+DETLOFF  D EH1 T - L AO0 F
+DETMER  D EH1 T - M ER0
+DETONATE  D EH1 - T AH0 - N EY2 T
+DETONATED  D EH1 - T AH0 - N EY2 - T AH0 D
+DETONATING  D EH1 - T AH0 - N EY2 - T IH0 NG
+DETONATION  D EH2 - T AH0 - N EY1 - SH AH0 N
+DETONATIONS  D EH2 - T AH0 - N EY1 - SH AH0 N Z
+DETONATOR  D EH1 - T AH0 - N EY2 - T ER0
+DETONATORS  D EH1 - T AH0 - N EY2 - T ER0 Z
+DETORE  D IH0 - T AO1 - R IY0
+DETOUR  D IH0 - T UH1 R
+DETOUR(2)  D IY1 - T UH0 R
+DETOURED  D IY1 - T UH0 R D
+DETOURS  D IH0 - T UH1 R Z
+DETOURS(2)  D IY1 - T UH0 R Z
+DETOX  D IY1 - T AA2 K S
+DETOXICATION  D IH0 - T AA2 K - S AH0 - K EY1 - SH AH0 N
+DETOXIFICATION  D IH0 - T AA2 K - S IH0 - F IH0 - K EY1 - SH AH0 N
+DETOXIFY  D IH0 - T AA1 K - S AH0 - F AY2
+DETRACT  D IH0 - T R AE1 K T
+DETRACTED  D IH0 - T R AE1 K - T IH0 D
+DETRACTING  D IH0 - T R AE1 K - T IH0 NG
+DETRACTOR(2)  D IY0 - T R AE1 K - T ER0
+DETRACTORS  D IH0 - T R AE1 K - T ER0 Z
+DETRACTORS(2)  D IY0 - T R AE1 K - T ER0 Z
+DETRACTS  D IH0 - T R AE1 K T S
+DETRICH  D EH1 - T R IH0 K
+DETRICK  D EH1 - T R IH0 K
+DETRIMENT  D EH1 - T R AH0 - M AH0 N T
+DETRIMENTAL  D EH2 - T R AH0 - M EH1 N - T AH0 L
+DETRIMENTAL(2)  D EH2 - T R AH0 - M EH1 - N AH0 L
+DETRITUS  D IH0 - T R AY1 - T AH0 S
+DETRITUS(2)  D EH1 - T R AH0 - T AH0 S
+DETRO  D IY1 - T R OW0
+DETROIT  D IH0 - T R OY1 T
+DETROIT'S  D AH0 - T R OY1 T S
+DETROIT'S(2)  D IH0 - T R OY1 T S
+DETROIT(2)  D IY1 - T R OY2 T
+DETROITERS  D AH0 - T R OY1 - T ER0 Z
+DETROITERS(2)  D IY1 - T R OY2 - T ER0 Z
+DETTER  D EH1 - T ER0
+DETTINGER  D EH1 - T IH0 N - JH ER0
+DETTLING  D EH1 T - L IH0 NG
+DETTLOFF  D EH1 T - L AO0 F
+DETTMAN  D EH1 T - M AH0 N
+DETTMANN  D EH1 T - M AH0 N
+DETTMER  D EH1 T - M ER0
+DETTORE  D IH0 - T AO1 - R IY0
+DETTY  D EH1 - T IY0
+DETURK  D EH1 - T ER0 K
+DETWEILER  D EH1 T - W AY2 - L ER0
+DETWILER  D EH1 T - W AY2 - L ER0
+DETZEL  D EH1 T - Z AH0 L
+DEUBEL  D OY1 - B AH0 L
+DEUBLER  D OY1 - B AH0 L - ER0
+DEUBLER(2)  D OY1 - B L ER0
+DEUCE  D UW1 S
+DEUEL  D UW1 - EH0 L
+DEUKMEJIAN  D UW0 K - M EY1 - JH IY0 - AH0 N
+DEUKMEJIAN'S  D UW0 K - M EY1 - JH IY0 - AH0 N Z
+DEUPREE  D UW0 - P R IY1
+DEUSCHLE  D OY1 - SH AH0 L
+DEUSER  D OY1 - S ER0
+DEUSS  D UW1 S
+DEUTCH  D OY1 CH
+DEUTERIUM  D UW0 - T IY1 - R IY0 - AH0 M
+DEUTERONS  D UW1 - T ER0 - AA2 N Z
+DEUTSCH  D OY1 CH
+DEUTSCHE  D OY1 CH
+DEUTSCHEMARK  D OY1 CH - M AA2 R K
+DEUTSCHEMARK'S  D OY1 CH - M AA2 R K S
+DEUTSCHEMARKS  D OY1 CH - M AA2 R K S
+DEUTSCHER  D OY1 - CH ER0
+DEUTSCHLAND  D OY1 CH - L AE0 N D
+DEUTSCHMAN  D OY1 CH - M AH0 N
+DEUTZ  D OY1 T S
+DEUX  D UW1
+DEV  D EH1 V
+DEVA  D EY1 - V AH0
+DEVAL  D IH0 - V AA1 L
+DEVAL'S  D IH0 - V AA1 L Z
+DEVALL  D EY0 - V AA1 L
+DEVALLE  D IH0 - V AE1 L
+DEVALLE(2)  D IH0 - V AE1 - L IY0
+DEVALUATE  D IH0 - V AE1 L - Y UW0 - EY2 T
+DEVALUATED  D IH0 - V AE1 L - Y UW0 - EY2 - T IH0 D
+DEVALUATION  D IH0 - V AE2 L - Y UW0 - EY1 - SH AH0 N
+DEVALUATION(2)  D IY2 - V AE0 L - Y UW0 - EY1 - SH AH0 N
+DEVALUATIONS  D IY2 - V AE0 L - Y UW0 - EY1 - SH AH0 N Z
+DEVALUE  D IH0 - V AE1 L - Y UW2
+DEVALUED  D IH0 - V AE1 L - Y UW2 D
+DEVALUED(2)  D IY0 - V AE1 L - Y UW2 D
+DEVALUING  D IH0 - V AE1 L - Y UW0 - IH0 NG
+DEVAN  D EH1 - V AH0 N
+DEVANE  D IH0 - V EY1 N
+DEVANEY  D EH1 - V AH0 - N IY0
+DEVANY  D EH1 - V AH0 - N IY0
+DEVASTATE  D EH1 - V AH0 - S T EY2 T
+DEVASTATED  D EH1 - V AH0 - S T EY2 - T AH0 D
+DEVASTATED(2)  D EH1 - V AH0 - S T EY2 - T IH0 D
+DEVASTATING  D EH1 - V AH0 - S T EY2 - T IH0 NG
+DEVASTATINGLY  D EH1 - V AH0 - S T EY2 - T IH0 NG - L IY0
+DEVASTATION  D EH2 - V AH0 - S T EY1 - SH AH0 N
+DEVAUGHN  D EH1 - V AO0 N
+DEVAUL  D IH0 - V OW1 L
+DEVAULT  D IH0 - V OW1
+DEVAUX  D IH0 - V OW1
+DEVEAU  D IH0 - V OW1
+DEVEAUX  D IH0 - V OW1
+DEVELCON  D AH0 - V EH1 L - K AH0 N
+DEVELLE  D AH0 - V EH1 L
+DEVELOP  D IH0 - V EH1 - L AH0 P
+DEVELOPABLE  D IH0 - V EH1 - L AH0 - P AH0 - B AH0 L
+DEVELOPED  D IH0 - V EH1 - L AH0 P T
+DEVELOPER  D IH0 - V EH1 - L AH0 - P ER0
+DEVELOPER'S  D IH0 - V EH1 - L AH0 - P ER0 Z
+DEVELOPERS  D IH0 - V EH1 - L AH0 - P ER0 Z
+DEVELOPERS'  D IH0 - V EH1 - L AH0 - P ER0 Z
+DEVELOPING  D IH0 - V EH1 - L AH0 - P IH0 NG
+DEVELOPMENT  D IH0 - V EH1 - L AH0 P - M AH0 N T
+DEVELOPMENT'S  D IH0 - V EH1 - L AH0 P - M AH0 N T S
+DEVELOPMENTAL  D IH0 - V EH2 - L AH0 P - M EH1 N - T AH0 L
+DEVELOPMENTAL(2)  D IH0 - V EH2 - L AH0 P - M EH1 - N AH0 L
+DEVELOPMENTALLY  D IH0 - V EH2 - L AH0 P - M EH1 N - AH0 - L IY0
+DEVELOPMENTALLY(2)  D IH0 - V EH2 - L AH0 P - M EH1 N - T AH0 - L IY0
+DEVELOPMENTS  D IH0 - V EH1 - L AH0 P - M AH0 N T S
+DEVELOPS  D IH0 - V EH1 - L AH0 P S
+DEVENDORF  D EH1 - V IH0 N - D AO0 R F
+DEVENEY  D EH1 - V IH0 - N IY0
+DEVENNEY  D EH1 - V IH0 - N IY0
+DEVENNY  D EH1 - V IH0 - N IY0
+DEVENPORT  D IH0 - V EH1 N - P AO0 R T
+DEVENS  D IY1 - V AH0 N Z
+DEVENY  D IH0 - V IY1 - N IY0
+DEVER  D IY1 - V ER0
+DEVERA  D EY0 - V EH1 - R AH0
+DEVERAUX  D EH1 - V ER0 - OW0
+DEVERE  D EY0 - V EH1 - R EY0
+DEVEREAUX  D EH1 - V ER0 - OW0
+DEVERELL  D EY0 - V EH0 - R EY1 L
+DEVEREUX  D EH1 - V ER0 - UW2
+DEVEROY  D EH1 - V ER0 - OY2
+DEVERS  D EH1 - V ER0 Z
+DEVEY  D IH0 - V EY1
+DEVIANCE  D IY1 - V IY0 - AH0 N S
+DEVIANCY  D IY1 - V IY0 - EH2 N - S IY0
+DEVIANCY(2)  D IY1 - V Y EH0 N - S IY0
+DEVIANT  D IY1 - V IY0 - AH0 N T
+DEVIANTS  D IY1 - V IY0 - AH0 N T S
+DEVIATE  D IY1 - V IY0 - EY2 T
+DEVIATED  D IY1 - V IY0 - EY2 - T IH0 D
+DEVIATES  D IY1 - V IY0 - EY2 T S
+DEVIATION  D IY2 - V IY0 - EY1 - SH AH0 N
+DEVIATIONS  D IY0 - V IY0 - EY1 - SH AH0 N Z
+DEVICE  D IH0 - V AY1 S
+DEVICE'S  D IH0 - V AY1 - S IH0 Z
+DEVICES  D IH0 - V AY1 - S AH0 Z
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+DEVIL  D EH1 - V AH0 L
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+DEVILBISS  D EH1 - V IH0 L - B IH0 S
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+DINAN  D IH1 - N AH0 N
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+DINAR  D IH0 - N AA1 R
+DINARDO  D IH0 - N AA1 R - D OW0
+DINARS  D AY1 - N ER0 Z
+DINARS(2)  D IH2 - N AA1 R Z
+DINATALE  D IH0 - N AA0 - T AA1 - L IY0
+DINATALE(2)  D IY0 - N AA0 - T AA1 - L IY0
+DINDA  D IH1 N - D AH0
+DINE  D AY1 N
+DINED  D AY1 N D
+DINEEN  D IH0 - N IY1 N
+DINEHART  D AY1 N - HH AA2 R T
+DINER  D AY1 - N ER0
+DINERS  D AY1 - N ER0 Z
+DINES  D AY1 N Z
+DINESH  D IH1 - N EH0 SH
+DING  D IH1 NG
+DINGEE  D IH1 NG - G IY0
+DINGEL  D IH1 NG - G AH0 L
+DINGELL  D IH1 NG - G AH0 L
+DINGELL'S  D IH1 NG - G AH0 L Z
+DINGER  D IH1 - NG ER0
+DINGES  D IH1 N - JH IH0 Z
+DINGESS  D IH1 NG - G IH0 S
+DINGHAM  D IH1 - NG AH0 M
+DINGHY  D IH1 NG - IY0
+DINGLE  D IH1 NG - G AH0 L
+DINGLEDINE  D IH1 NG - G AH0 L - D AY0 N
+DINGLER  D IH1 NG - G AH0 - L ER0
+DINGLER(2)  D IH1 NG - G L ER0
+DINGLEY  D IH1 NG - G L IY0
+DINGMAN  D IH1 NG - M AH0 N
+DINGO  D IH1 NG - G OW0
+DINGS  D IH1 NG Z
+DINGUS  D IH1 NG - G IH0 S
+DINGWALL  D IH1 NG - G W AH0 L
+DINGY  D IH1 N - JH IY0
+DINH  D IH1 N
+DINI  D IY1 - N IY0
+DINICOLA  D IH0 - N IY0 - K OW1 - L AH0
+DINING  D AY1 - N IH0 NG
+DININO  D IH0 - N IY1 - N OW0
+DINIUS  D AY1 - N IY0 - IH0 S
+DINK  D IH1 NG K
+DINKEL  D IH1 NG - K AH0 L
+DINKINS  D IH1 NG - K IH0 N Z
+DINKINS'  D IH1 NG - K IH0 N Z
+DINKY  D IH1 NG - K IY0
+DINMUKHAMED  D IH2 N - M UW0 - K AA1 - M EH0 D
+DINNEEN  D IH0 - N IY1 N
+DINNER  D IH1 - N ER0
+DINNERS  D IH1 - N ER0 Z
+DINNERTIME  D IH1 - N ER0 - T AY2 M
+DINNERWARE  D IH1 - N ER0 - W EH2 R
+DINNING  D IH1 - N IH0 NG
+DINO  D IY1 - N OW0
+DINOSAUR  D AY1 - N AH0 - S AO2 R
+DINOSAURS  D AY1 - N AH0 - S AO2 R Z
+DINOSEB  D AY1 - N OW0 - S EH2 B
+DINOTO  D IH0 - N OW1 - T OW0
+DINOTOPIA  D AY2 - N AH0 - T OW1 - P IY0 - AH0
+DINOVO  D IH0 - N OW1 - V OW0
+DINSA  D IH1 N - S AH0
+DINSDALE  D IH1 N Z - D EY2 L
+DINSE  D IH1 N S
+DINSMORE  D IY1 N S - M AO0 R
+DINT  D IH1 N T
+DINUNZIO  D IH0 - N AH1 N - Z IY0 - OW0
+DINWIDDIE  D IH1 N - W IH0 - D IY0
+DIOCESAN  D AY0 - AA1 - S AH0 - S AH0 N
+DIOCESE  D AY1 - AH0 - S IY2 Z
+DIOCESE(2)  D AY1 - AH0 - S AH0 S
+DIOCESES  D AY1 - AH0 - S IY2 Z
+DIOCESES(2)  D AY1 - AH0 - S IY2 - Z AH0 Z
+DIODATI  D IY0 - OW0 - D AA1 - T IY0
+DIODATO  D IY0 - OW0 - D AA1 - T OW0
+DIODE  D AY1 - OW2 D
+DIODES  D AY1 - OW2 D Z
+DIOGUARDI  D IY0 - OW0 - G AA1 R - D IY0
+DION  D AY1 - AH0 N
+DION(2)  D IY1 - AO2 N
+DIONA  D IY0 - OW1 - N AH0
+DIONE  D IY1 - AA0 N
+DIONISIO  D AY2 - AH0 - N IH1 - S IY0 - OW0
+DIONNE  D IY1 - AA0 N
+DIONYSIUS  D AY2 - AH0 - N IH1 - S IY0 - AH0 S
+DIOR  D IY2 - AO1 R
+DIORIO  D IY0 - AO1 - R IY0 - OW0
+DIORITE  D AY1 - ER0 - AY2 T
+DIOS  D IY1 - OW0 S
+DIOS'  D IY1 - OW0 S
+DIOS'S  D IY1 - OW0 - S IH0 Z
+DIOXIDE  D AY0 - AA1 K - S AY2 D
+DIOXIDES  D AY0 - AA1 K - S AY0 D Z
+DIOXIN  D AY2 - AA1 K - S IH0 N
+DIOXINS  D AY2 - AA1 K - S IH0 N Z
+DIP  D IH1 P
+DIPALMA  D IH0 - P AA1 L - M AH0
+DIPAOLA  D IH0 - P AA0 - OW1 - L AH0
+DIPAOLO  D IH0 - P AA0 - OW1 - L OW0
+DIPASQUALE  D IH0 - P AA0 S - K W AA1 - L IY0
+DIPASQUALE(2)  D IY0 - P AA0 S - K W AA1 - L IY0
+DIPERNA  D IH0 - P EH1 R - N AH0
+DIPHTHERIA  D IH0 F - TH IH1 - R IY0 - AH0
+DIPIAZZA  D IH0 - P IY0 - AA1 T - S AH0
+DIPIAZZA(2)  D IY0 - P IY0 - AA1 T - S AH0
+DIPIERO  D IH0 - P IH1 - R OW0
+DIPIERRO  D IH0 - P IH1 - R OW0
+DIPIETRO  D IH0 - P IY1 - T R OW0
+DIPINTO  D IH0 - P IH1 N - T OW0
+DIPIRRO  D IH0 - P IH1 - R OW0
+DIPLOMA  D IH0 - P L OW1 - M AH0
+DIPLOMACY  D IH0 - P L OW1 - M AH0 - S IY0
+DIPLOMAS  D IH0 - P L OW1 - M AH0 Z
+DIPLOMAT  D IH1 - P L AH0 - M AE2 T
+DIPLOMAT'S  D IH1 - P L AH0 - M AE2 T S
+DIPLOMATIC  D IH2 - P L AH0 - M AE1 - T IH0 K
+DIPLOMATICALLY  D IH2 - P L AH0 - M AE1 - T IH0 K - L IY0
+DIPLOMATS  D IH1 - P L AH0 - M AE2 T S
+DIPLOMATS'  D IH1 - P L AH0 - M AE2 T S
+DIPOLE  D AY1 - P OW2 L
+DIPPED  D IH1 P T
+DIPPEL  D IH1 - P AH0 L
+DIPPER  D IH1 - P ER0
+DIPPERS  D IH1 - P ER0 Z
+DIPPING  D IH1 - P IH0 NG
+DIPPLE  D IH1 - P AH0 L
+DIPPOLD  D IH1 - P OW2 L D
+DIPPOLITO  D IH0 - P OW0 - L IY1 - T OW0
+DIPPY  D IH1 - P IY0
+DIPRIMA  D IH0 - P R IY1 - M AH0
+DIPS  D IH1 P S
+DIPSTICK  D IH1 P - S T IH2 K
+DIRCKS  D ER1 K S
+DIRE  D AY1 R
+DIRE(2)  D AY1 - ER0
+DIRECT  D ER0 - EH1 K T
+DIRECT(2)  D AY0 - R EH1 K T
+DIRECT(3)  D IY0 - R EH1 K T
+DIRECT(4)  D IH0 - R EH1 K T
+DIRECTED  D ER0 - EH1 K - T AH0 D
+DIRECTED(2)  D ER0 - EH1 K - T IH0 D
+DIRECTED(3)  D AY0 - R EH1 K - T IH0 D
+DIRECTED(4)  D IY0 - R EH1 K - T IH0 D
+DIRECTED(5)  D IH0 - R EH1 K - T IH0 D
+DIRECTING  D ER0 - EH1 K - T IH0 NG
+DIRECTING(2)  D IY0 - R EH1 K - T IH0 NG
+DIRECTING(3)  D AY0 - R EH1 K - T IH0 NG
+DIRECTING(4)  D IH0 - R EH1 K - T IH0 NG
+DIRECTION  D ER0 - EH1 K - SH AH0 N
+DIRECTION(2)  D IY0 - R EH1 K - SH IH0 N
+DIRECTION(3)  D AY0 - R EH1 K - SH IH0 N
+DIRECTION(4)  D IH0 - R EH1 K - SH IH0 N
+DIRECTIONAL  D ER0 - EH1 K - SH AH0 - N AH0 L
+DIRECTIONAL(2)  D IY0 - R EH1 K - SH IH0 - N AH0 L
+DIRECTIONAL(3)  D AY0 - R EH1 K - SH IH0 - N AH0 L
+DIRECTIONAL(4)  D IH0 - R EH1 K - SH IH0 - N AH0 L
+DIRECTIONLESS  D ER0 - EH1 K - SH AH0 N - L AH0 S
+DIRECTIONLESS(2)  D IY0 - R EH1 K - SH IH0 N - L AH0 S
+DIRECTIONLESS(3)  D AY0 - R EH1 K - SH IH0 N - L AH0 S
+DIRECTIONLESS(4)  D IH0 - R EH1 K - SH IH0 N - L AH0 S
+DIRECTIONS  D ER0 - EH1 K - SH AH0 N Z
+DIRECTIONS(2)  D IY0 - R EH1 K - SH IH0 N Z
+DIRECTIONS(3)  D AY0 - R EH1 K - SH IH0 N Z
+DIRECTIONS(4)  D IH0 - R EH1 K - SH IH0 N Z
+DIRECTIVE  D ER0 - EH1 K - T IH0 V
+DIRECTIVE(2)  D IY0 - R EH1 K - T IH0 V
+DIRECTIVE(3)  D AY0 - R EH1 K - T IH0 V
+DIRECTIVE(4)  D IH0 - R EH1 K - T IH0 V
+DIRECTIVES  D AY0 - R EH1 K - T IH0 V Z
+DIRECTIVES(2)  D IY0 - R EH1 K - T IH0 V Z
+DIRECTIVES(3)  D ER0 - EH1 K - T IH0 V Z
+DIRECTIVES(4)  D IH0 - R EH1 K - T IH0 V Z
+DIRECTLY  D ER0 - EH1 K T - L IY0
+DIRECTLY(2)  D IY0 - R EH1 K - L IY0
+DIRECTLY(3)  D AY0 - R EH1 K - L IY0
+DIRECTLY(4)  D IH0 - R EH1 K - L IY0
+DIRECTNESS  D ER0 - EH1 K T - N AH0 S
+DIRECTNESS(2)  D IY0 - R EH1 K - N AH0 S
+DIRECTNESS(3)  D AY0 - R EH1 K - N AH0 S
+DIRECTNESS(4)  D IH0 - R EH1 K - N AH0 S
+DIRECTOR  D ER0 - EH1 K - T ER0
+DIRECTOR'S  D AY0 - R EH1 K - T ER0 Z
+DIRECTOR'S(2)  D ER0 - EH1 K - T ER0 Z
+DIRECTOR'S(3)  D IY0 - R EH1 K - T ER0 Z
+DIRECTOR'S(4)  D IH0 - R EH1 K - T ER0 Z
+DIRECTOR(2)  D AY0 - R EH1 K - T ER0
+DIRECTOR(3)  D IY0 - R EH1 K - T ER0
+DIRECTOR(4)  D IH0 - R EH1 K - T ER0
+DIRECTORAL  D ER0 - EH1 K - T ER0 - AH0 L
+DIRECTORATE  D ER0 - EH1 K - T ER0 - AH0 T
+DIRECTORATE(2)  D AY0 - R EH1 K - T ER0 - AH0 T
+DIRECTORATE(3)  D IY0 - R EH1 K - T ER0 - AH0 T
+DIRECTORATE(4)  D IH0 - R EH1 K - T ER0 - AH0 T
+DIRECTORIAL  D ER0 - EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIAL(2)  D AY0 - R EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIAL(3)  D IY0 - R EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIAL(4)  D IH0 - R EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIES  D AY0 - R EH1 K - T ER0 - IY0 Z
+DIRECTORIES(2)  D ER0 - EH1 K - T ER0 - IY0 Z
+DIRECTORIES(3)  D IY0 - R EH1 K - T ER0 - IY0 Z
+DIRECTORIES(4)  D IH0 - R EH1 K - T ER0 - IY0 Z
+DIRECTORS  D ER0 - EH1 K - T ER0 Z
+DIRECTORS'  D IH0 - R EH1 K - T ER0 Z
+DIRECTORS'(2)  D ER0 - EH1 K - T ER0 Z
+DIRECTORS'(3)  D IY0 - R EH1 K - T ER0 Z
+DIRECTORS(2)  D AY0 - R EH1 K - T ER0 Z
+DIRECTORS(3)  D IY0 - R EH1 K - T ER0 Z
+DIRECTORS(4)  D IH0 - R EH1 K - T ER0 Z
+DIRECTORSHIP  D ER0 - EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIP(2)  D AY0 - R EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIP(3)  D IY0 - R EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIP(4)  D IH0 - R EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIPS  D ER0 - EH1 K - T ER0 - SH IH2 P S
+DIRECTORSHIPS(2)  D AY0 - R EH1 K - T ER0 - SH IH2 P S
+DIRECTORSHIPS(3)  D IY0 - R EH1 K - T ER0 - SH IH2 P S
+DIRECTORSHIPS(4)  D IH0 - R EH1 K - T ER0 - SH IH2 P S
+DIRECTORY  D ER0 - EH1 K - T ER0 - IY0
+DIRECTORY(2)  D AY0 - R EH1 K - T ER0 - IY0
+DIRECTORY(3)  D IY0 - R EH1 K - T ER0 - IY0
+DIRECTORY(4)  D IH0 - R EH1 K - T ER0 - IY0
+DIRECTS  D ER0 - EH1 K T S
+DIRECTS(2)  D AY0 - R EH1 K T S
+DIRECTS(3)  D IY0 - R EH1 K T S
+DIRECTS(4)  D IH0 - R EH1 K T S
+DIRECTV  D ER0 - EH1 K - T IY1 - V IY1
+DIRECTV(2)  D AY0 - R EH1 K - T IY1 - V IY1
+DIRECTV(3)  D IY0 - R EH1 K - T IY1 - V IY1
+DIRECTV(4)  D IH0 - R EH1 K - T IY1 - V IY1
+DIRENZO  D IH0 - R EH1 N - Z OW0
+DIREST  D AY1 - R AH0 S T
+DIRGE  D ER1 JH
+DIRHAMS  D ER1 - AH0 M Z
+DIRICKSON  D AO1 - R IH0 K - S AH0 N
+DIRIENZO  D IH0 - R IY1 N - Z OW0
+DIRK  D ER1 K
+DIRKES  D ER1 K S
+DIRKS  D ER1 K S
+DIRKSE  D ER1 K S
+DIRKSEN  D ER1 K - S AH0 N
+DIRLAM  D ER0 - L AE1 M
+DIROCCO  D IH0 - R AA1 - K OW0
+DIROSA  D IH0 - R OW1 - S AH0
+DIRR  D ER1
+DIRT  D ER1 T
+DIRT'S  D ER1 T S
+DIRTIER  D ER1 - T IY0 - ER0
+DIRTIEST  D ER1 - T IY0 - AH0 S T
+DIRTY  D ER1 - T IY0
+DIRUSSO  D IH0 - R UW1 - S OW0
+DIS  D IH1 S
+DISA  D IH1 - S AH0
+DISABATINO  D IH0 S - AA0 - B AA0 - T IY1 - N OW0
+DISABATO  D IH0 S - AA0 - B AA1 - T OW0
+DISABILITIES  D IH0 S - AH0 - B IH1 - L AH0 - T IY0 Z
+DISABILITIES(2)  D IH0 S - AH0 - B IH1 - L IH0 - T IY0 Z
+DISABILITY  D IH2 S - AH0 - B IH1 - L IH0 - T IY0
+DISABILITY(2)  D IH0 S - AH0 - B IH1 - L IH0 - T IY0 Z
+DISABLE  D IH0 S - EY1 - B AH0 L
+DISABLED  D IH0 S - EY1 - B AH0 L D
+DISABLES  D IH0 S - EY1 - B AH0 L Z
+DISABLING  D IH0 S - EY1 - B AH0 L - IH0 NG
+DISABLING(2)  D IH2 S - EY1 - B L IH0 NG
+DISABUSE  D IH0 S - AH0 - B Y UW1 S
+DISABUSE(2)  D IH0 S - AH0 - B Y UW1 Z
+DISABUSED  D IH0 S - AH0 - B Y UW1 Z D
+DISABUSES  D IH0 S - AH0 - B Y UW1 - S IH0 Z
+DISADVANTAGE  D IH2 S - AH0 D - V AE1 N - T IH0 JH
+DISADVANTAGE(2)  D IH2 S - AH0 D - V AE1 - N IH0 JH
+DISADVANTAGED  D IH0 S - AH0 D - V AE1 N - T IH0 JH D
+DISADVANTAGED(2)  D IH2 S - AH0 D - V AE1 - N IH0 JH D
+DISADVANTAGEOUS  D IH2 S - AE2 D - V AE2 N - T EY1 - JH AH0 S
+DISADVANTAGES  D IH2 S - AH0 D - V AE1 N - T IH0 - JH IH0 Z
+DISADVANTAGES(2)  D IH2 S - AH0 D - V AE1 - N IH0 JH Z
+DISAFFECT  D IH2 S - AH0 - F EH1 K T
+DISAFFECTED  D IH2 S - AH0 - F EH1 K - T IH0 D
+DISAFFECTION  D IH2 S - AH0 - F EH1 K - SH AH0 N
+DISAGREE  D IH0 S - AH0 - G R IY1
+DISAGREEABLE  D IH2 S - AH0 - G R IY1 - AH0 - B AH0 L
+DISAGREED  D IH0 S - AH0 - G R IY1 D
+DISAGREEING  D IH0 S - AH0 - G R IY1 - IH0 NG
+DISAGREEMENT  D IH0 S - AH0 - G R IY1 - M AH0 N T
+DISAGREEMENTS  D IH2 S - AH0 - G R IY1 - M AH0 N T S
+DISAGREES  D IH0 S - AH0 - G R IY1 Z
+DISALLOW  D IH2 S - AH0 - L AW1
+DISALLOWANCE  D IH2 S - AH0 - L AW1 - AH0 N S
+DISALLOWANCES  D IH2 S - AH0 - L AW1 - AH0 N - S IH0 Z
+DISALLOWED  D IH2 S - AH0 - L AW1 D
+DISALLOWING  D IH0 S - AH0 - L AW1 - IH0 NG
+DISALVO  D IH0 - S AA1 L - V OW0
+DISANO  D IH0 S - AA1 - N OW0
+DISANTI  D IH0 - S AE1 N - T IY0
+DISANTIS  D IH0 - S AA1 N - T IH0 S
+DISANTO  D IH0 - S AE1 N - T OW0
+DISAPPEAR  D IH2 S - AH0 - P IH1 R
+DISAPPEAR(2)  D IH2 - S AH0 - P IY1 R
+DISAPPEARANCE  D IH2 S - AH0 - P IH1 - R AH0 N S
+DISAPPEARANCE(2)  D IH2 S - AH0 - P IY1 - R AH0 N S
+DISAPPEARANCES  D IH2 S - AH0 - P IH1 - R AH0 N - S IH0 Z
+DISAPPEARANCES(2)  D IH2 S - AH0 - P IY1 - R AH0 N - S IH0 Z
+DISAPPEARED  D IH2 S - AH0 - P IH1 R D
+DISAPPEARED(2)  D IH2 S - AH0 - P IY1 R D
+DISAPPEARING  D IH2 S - AH0 - P IH1 - R IH0 NG
+DISAPPEARING(2)  D IH2 S - AH0 - P IY1 - R IH0 NG
+DISAPPEARS  D IH2 S - AH0 - P IH1 R Z
+DISAPPEARS(2)  D IH2 S - AH0 - P IY1 R Z
+DISAPPOINT  D IH2 S - AH0 - P OY1 N T
+DISAPPOINTED  D IH2 S - AH0 - P OY1 N - T IH0 D
+DISAPPOINTED(2)  D IH2 S - AH0 - P OY1 - N IH0 D
+DISAPPOINTING  D IH2 S - AH0 - P OY1 N - T IH0 NG
+DISAPPOINTING(2)  D IH2 S - AH0 - P OY1 - N IH0 NG
+DISAPPOINTINGLY  D IH0 S - AH0 - P OY1 N - T IH0 NG - L IY0
+DISAPPOINTINGLY(2)  D IH0 S - AH0 - P OY1 - N IH0 NG - L IY0
+DISAPPOINTMENT  D IH2 S - AH0 - P OY1 N T - M AH0 N T
+DISAPPOINTMENTS  D IH0 S - AH0 - P OY1 N T - M AH0 N T S
+DISAPPOINTS  D IH2 S - AH0 - P OY1 N T S
+DISAPPROVAL  D IH0 S - AH0 - P R UW1 - V AH0 L
+DISAPPROVE  D IH2 S - AH0 - P R UW1 V
+DISAPPROVED  D IH2 S - AH0 - P R UW1 V D
+DISAPPROVES  D IH0 S - AH0 - P R UW1 V Z
+DISAPPROVING  D IH0 S - AH0 - P R UW1 - V IH0 NG
+DISARM  D IH0 S - AA1 R M
+DISARMAMENT  D IH0 S - AA1 R - M AH0 - M AH0 N T
+DISARMED  D IH0 S - AA1 R M D
+DISARMING  D IH0 S - AA1 R - M IH0 NG
+DISARMINGLY  D IH0 S - AA1 R - M IH0 NG - L IY0
+DISARRAY  D IH2 S - ER0 - EY1
+DISARRAY(2)  D IH2 S - AH0 - R EY1
+DISASSEMBLE  D IH2 S - AH0 - S EH1 M - B AH0 L
+DISASSEMBLED  D IH2 S - AH0 - S EH1 M - B AH0 L D
+DISASSOCIATE  D IH2 S - AH0 - S OW1 - SH IY0 - EY0 T
+DISASSOCIATE(2)  D IH2 S - AH0 - S OW1 - S IY0 - EY0 T
+DISASSOCIATED  D IH2 S - AH0 - S OW1 - SH IY0 - EY0 - T AH0 D
+DISASSOCIATED(2)  D IH2 S - AH0 - S OW1 - S IY0 - EY0 - T AH0 D
+DISASTER  D IH0 - Z AE1 - S T ER0
+DISASTERS  D IH0 - Z AE1 - S T ER0 Z
+DISASTROUS  D IH0 - Z AE1 - S T R AH0 S
+DISASTROUSLY  D IH0 - Z AE1 - S T R AH0 S - L IY0
+DISAVOW  D IH2 S - AH0 - V AW1
+DISAVOWED  D IH2 S - AH0 - V AW1 D
+DISAVOWING  D IH0 S - AH0 - V AW1 - IH0 NG
+DISBAND  D IH0 S - B AE1 N D
+DISBANDED  D IH0 S - B AE1 N - D IH0 D
+DISBANDING  D IH0 S - B AE1 N - D IH0 NG
+DISBAR  D IH2 S - B AA1 R
+DISBARMENT  D IH2 S - B AA1 R - M AH0 N T
+DISBARRED  D IH0 S - B AA1 R D
+DISBELIEF  D IH2 S - B IH0 - L IY1 F
+DISBELIEVE  D IH0 S - B AH0 - L IY1 V
+DISBELIEVING  D IH0 S - B AH0 - L IY1 - V IH0 NG
+DISBRO  D IH1 S - B R OW0
+DISBROW  D IH1 S - B R AW0
+DISBURSE  D IH0 S - B ER1 S
+DISBURSED  D IH0 S - B ER1 S T
+DISBURSEMENT  D IH0 S - B ER1 S - M AH0 N T
+DISBURSEMENTS  D IH0 S - B ER1 S - M AH0 N T S
+DISBURSING  D IH0 S - B ER1 - S IH0 NG
+DISC  D IH1 S K
+DISCARD  D IH0 S - K AA1 R D
+DISCARDED  D IH0 S - K AA1 R - D IH0 D
+DISCARDING  D IH0 S - K AA1 R - D IH0 NG
+DISCARDS  D IH0 S - K AA1 R D Z
+DISCENZA  D IH0 S - CH EH1 N - Z AH0
+DISCERN  D IH0 - S ER1 N
+DISCERNED  D IH0 - S ER1 N D
+DISCERNIBLE  D IH0 - S ER1 - N AH0 - B AH0 L
+DISCERNING  D IH0 - S ER1 - N IH0 NG
+DISCERNMENT  D IH0 - S ER1 N - M AH0 N T
+DISCH  D IH1 SH
+DISCHARGE  D IH0 S - CH AA1 R JH
+DISCHARGE(2)  D IH1 S - CH AA2 R JH
+DISCHARGED  D IH0 S - CH AA1 R JH D
+DISCHARGED(2)  D IH1 S - CH AA2 R JH D
+DISCHARGES  D IH0 S - CH AA1 R - JH AH0 Z
+DISCHARGES(2)  D IH1 S - CH AA2 R - JH AH0 Z
+DISCHARGING  D IH0 S - CH AA1 R - JH IH0 NG
+DISCHARGING(2)  D IH1 S - CH AA2 R - JH IH0 NG
+DISCHER  D IH1 - SH ER0
+DISCHINGER  D IH1 - SH IH0 N - JH ER0
+DISCIPLE  D IH0 - S AY1 - P AH0 L
+DISCIPLES  D IH0 - S AY1 - P AH0 L Z
+DISCIPLINARIAN  D IH2 - S IH0 - P L IH0 - N EH1 - R IY0 - AH0 N
+DISCIPLINARIANS  D IH2 - S IH0 - P L IH0 - N EH1 - R IY0 - AH0 N Z
+DISCIPLINARY  D IH1 - S AH0 - P L AH0 - N EH2 - R IY0
+DISCIPLINE  D IH1 - S AH0 - P L AH0 N
+DISCIPLINED  D IH1 - S AH0 - P L AH0 N D
+DISCIPLINES  D IH1 - S AH0 - P L AH0 N Z
+DISCIPLINING  D IH1 - S AH0 - P L AH0 - N IH0 NG
+DISCLAIM  D IH0 S - K L EY1 M
+DISCLAIMED  D IH0 S - K L EY1 M D
+DISCLAIMER  D IH0 S - K L EY1 - M ER0
+DISCLAIMERS  D IH0 S - K L EY1 - M ER0 Z
+DISCLAIMING  D IH0 S - K L EY1 - M IH0 NG
+DISCLAIMS  D IH0 S - K L EY1 M Z
+DISCLAND  D IH1 S K - L AE2 N D
+DISCLOSE  D IH0 S - K L OW1 Z
+DISCLOSED  D IH0 S - K L OW1 Z D
+DISCLOSES  D IH0 S - K L OW1 - Z IH0 Z
+DISCLOSING  D IH0 S - K L OW1 - Z IH0 NG
+DISCLOSURE  D IH0 S - K L OW1 - ZH ER0
+DISCLOSURES  D IH0 S - K L OW1 - ZH ER0 Z
+DISCO  D IH1 - S K OW0
+DISCOGRAPHY  D IH0 - S K AO1 - G R AH0 - F IY0
+DISCOLOR  D IH0 - S K AH1 - L ER0
+DISCOLORATION  D IH0 - S K AH2 - L ER0 - EY1 - SH AH0 N
+DISCOLORATIONS  D IH0 - S K AH2 - L ER0 - EY1 - SH AH0 N Z
+DISCOLORED  D IH0 S - K AH1 - L ER0 D
+DISCOLORS  D IH0 - S K AH1 - L ER0 Z
+DISCOMFORT  D IH0 S - K AH1 M - F ER0 T
+DISCONCERT  D IH2 S - K AH0 N - S ER1 T
+DISCONCERTING  D IH2 S - K AH0 N - S ER1 - T IH0 NG
+DISCONNECT  D IH0 S - K AH0 - N EH1 K T
+DISCONNECTED  D IH2 S - K AH0 - N EH1 K - T IH0 D
+DISCONNECTING  D IH2 S - K AH0 - N EH1 K - T IH0 NG
+DISCONNECTION  D IH2 S - K AH0 - N EH1 K - SH AH0 N
+DISCONTENT  D IH0 S - K AH0 N - T EH1 N T
+DISCONTENTED  D IH2 S - K AH0 N - T EH1 N - T IH0 D
+DISCONTENTS  D IH2 S - K AH0 N - T EH1 N T S
+DISCONTINUANCE  D IH2 S - K AH0 N - T IH1 - N Y UW0 - AH0 N S
+DISCONTINUATION  D IH2 S - K AH0 N - T IH2 - N Y UW0 - EY1 - SH AH0 N
+DISCONTINUE  D IH0 S - K AH0 N - T IH1 - N Y UW0
+DISCONTINUED  D IH0 S - K AH0 N - T IH1 - N Y UW0 D
+DISCONTINUING  D IH0 S - K AH0 N - T IH1 - N Y UW0 - IH0 NG
+DISCONTINUITY  D IH0 S - K AA2 N - T IH0 - N UW1 - IH0 - T IY0
+DISCORD  D IH1 - S K AO0 R D
+DISCORDANT  D IH0 - S K AO1 R - D AH0 N T
+DISCOS  D IH1 - S K OW0 Z
+DISCOTHEQUE  D IH1 - S K OW0 - T EH2 K
+DISCOUNT  D IH0 S - K AW1 N T
+DISCOUNT(2)  D IH1 S - K AW0 N T
+DISCOUNTABLE  D IH1 S - K AW2 N - T AH0 - B AH0 L
+DISCOUNTED  D IH1 S - K AW2 N - T IH0 D
+DISCOUNTED(2)  D IH1 S - K AW2 - N IH0 D
+DISCOUNTER  D IH1 S - K AW2 N - T ER0
+DISCOUNTERS  D IH0 S - K AW1 N - T ER0 Z
+DISCOUNTERS(2)  D IH1 S - K AW2 - N ER0 R Z
+DISCOUNTING  D IH1 S - K AW2 N - T IH0 NG
+DISCOUNTING(2)  D IH1 S - K AW2 - N IH0 NG
+DISCOUNTS  D IH0 S - K AW1 N T S
+DISCOUNTS(2)  D IH1 S - K AW2 N T S
+DISCOURAGE  D IH0 - S K ER1 - IH0 JH
+DISCOURAGED  D IH0 - S K ER1 - AH0 JH D
+DISCOURAGED(2)  D IH0 - S K ER1 - IH0 JH D
+DISCOURAGEMENT  D IH0 - S K ER1 - IH0 JH - M AH0 N T
+DISCOURAGES  D IH0 - S K ER1 - IH0 - JH IH0 Z
+DISCOURAGING  D IH0 - S K ER1 - AH0 - JH IH0 NG
+DISCOURAGING(2)  D IH0 - S K ER1 - IH0 - JH IH0 NG
+DISCOURSE  D IH1 S - K AO0 R S
+DISCOURSES  D IH0 S - K AO1 R - S IH0 Z
+DISCOURSES(2)  D IH1 S - K AO0 R - S IH0 Z
+DISCOVER  D IH0 - S K AH1 - V ER0
+DISCOVERABLE  D IH0 - S K AH1 - V ER0 - AH0 - B AH0 L
+DISCOVERABLE(2)  D IH0 - S K AH1 - V R AH0 - B AH0 L
+DISCOVERED  D IH0 - S K AH1 - V ER0 D
+DISCOVERIES  D IH0 - S K AH1 - V ER0 - IY0 Z
+DISCOVERING  D IH0 - S K AH1 - V ER0 - IH0 NG
+DISCOVERS  D IH0 - S K AH1 - V ER0 Z
+DISCOVERY  D IH0 - S K AH1 - V ER0 - IY0
+DISCOVERY'S  D IH0 - S K AH1 - V ER0 - IY0 Z
+DISCOVERY'S(2)  D IH0 - S K AH1 - V R IY0 Z
+DISCOVERY(2)  D IH0 - S K AH1 - V R IY0
+DISCOVERY(2)  D IH2 - S K AH1 - V R IY0
+DISCREDIT  D IH0 S - K R EH1 - D AH0 T
+DISCREDITED  D IH0 S - K R EH1 - D IH0 - T IH0 D
+DISCREDITING  D IH0 S - K R EH1 - D AH0 - T IH0 NG
+DISCREDITS  D IH0 S - K R EH1 - D AH0 T S
+DISCREET  D IH0 - S K R IY1 T
+DISCREETLY  D IH0 - S K R IY1 T - L IY0
+DISCREPANCIES  D IH0 - S K R EH1 - P AH0 N - S IY0 Z
+DISCREPANCY  D IH0 - S K R EH1 - P AH0 N - S IY0
+DISCRETE  D IH0 - S K R IY1 T
+DISCRETION  D IH0 - S K R EH1 - SH AH0 N
+DISCRETIONARY  D IH0 - S K R EH1 - SH AH0 N - EH2 - R IY0
+DISCRIMINATE  D IH0 - S K R IH1 - M AH0 - N EY2 T
+DISCRIMINATED  D IH0 - S K R IH1 - M AH0 - N EY2 - T AH0 D
+DISCRIMINATED(2)  D IH0 - S K R IH1 - M AH0 - N EY2 - T IH0 D
+DISCRIMINATES  D IH0 - S K R IH1 - M AH0 - N EY2 T S
+DISCRIMINATING  D IH0 - S K R IH1 - M AH0 - N EY2 - T IH0 NG
+DISCRIMINATION  D IH0 - S K R IH2 - M AH0 - N EY1 - SH AH0 N
+DISCRIMINATORY  D IH0 - S K R IH1 - M AH0 - N AH0 - T AO2 - R IY0
+DISCS  D IH1 S K S
+DISCURSIVE  D IH0 - S K ER1 - S IH0 V
+DISCUS  D IH1 - S K AH0 S
+DISCUSS  D IH0 - S K AH1 S
+DISCUSSED  D IH0 - S K AH1 S T
+DISCUSSES  D IH0 - S K AH1 - S AH0 Z
+DISCUSSES(2)  D IH0 - S K AH1 - S IH0 Z
+DISCUSSING  D IH0 - S K AH1 - S IH0 NG
+DISCUSSION  D IH0 - S K AH1 - SH AH0 N
+DISCUSSIONS  D IH0 - S K AH1 - SH AH0 N Z
+DISDAIN  D IH0 S - D EY1 N
+DISDAINED  D IH0 S - D EY1 N D
+DISDAINFUL  D IH0 S - D EY1 N - F AH0 L
+DISDAINING  D IH0 S - D EY1 - N IH0 NG
+DISDAINS  D IH0 S - D EY1 N Z
+DISE  D AY1 S
+DISE(2)  D AY1 Z
+DISEASE  D IH0 - Z IY1 Z
+DISEASE'S  D IH0 - Z IY1 - Z IH0 Z
+DISEASED  D IH0 - Z IY1 Z D
+DISEASES  D IH0 - Z IY1 - Z AH0 Z
+DISEASES(2)  D IH0 - Z IY1 - Z IH0 Z
+DISEMBARK  D IH0 S - EH0 M - B AA1 R K
+DISEMBARKATION  D IH0 S - EH2 M - B AA0 R - K EY1 - SH AH0 N
+DISEMBODIED  D IH0 S - IH0 - B AA1 - D IY0 D
+DISEMBODY  D IH0 S - IH0 - B AA1 - D IY0
+DISENCHANTED  D IH0 S - IH0 N - CH AE1 N - T IH0 D
+DISENCHANTED(2)  D IH0 S - IH0 N - CH AE1 - N IH0 D
+DISENCHANTMENT  D IH0 S - IH0 N - CH AE1 N T - M AH0 N T
+DISENFRANCHISE  D IH0 S - IH0 N - F R AE1 N - CH AY2 Z
+DISENFRANCHISED  D IH0 S - IH0 N - F R AE1 N - CH AY2 Z D
+DISENFRANCHISEMENT  D IH0 S - IH0 N - F R AE1 N - CH AY2 Z - M AH0 N T
+DISENGAGE  D IH0 S - IH0 N - G EY1 JH
+DISENGAGED  D IH0 S - IH0 N - G EY1 JH D
+DISENGAGEMENT  D IH0 S - IH0 N - G EY1 JH - M AH0 N T
+DISENGAGING  D IH0 S - IH0 N - G EY1 - JH IH0 NG
+DISENTANGLE  D IH2 S - AH0 N - T AE1 NG - G AH0 L
+DISEQUILIBRIUM  D IH0 - S IY2 - K W AH0 - L IH1 - B R IY0 - AH0 M
+DISESTABLISHMENT  D IH0 - S IH0 - S T AE1 - B L IH0 SH - M AH0 N T
+DISFAVOR  D IH0 S - F EY1 - V ER0
+DISFAVORING  D IH0 S - F EY1 - V ER0 - IH0 NG
+DISFIGURE  D IH0 S - F IH1 - G Y ER0
+DISFIGURED  D IH0 S - F IH1 - G Y ER0 D
+DISFIGUREMENT  D IH0 S - F IH1 - G Y ER0 - M EH0 N T
+DISFIGURING  D IH0 S - F IH1 - G Y ER0 - IH0 NG
+DISGORGE  D IH0 S - G AO1 R JH
+DISGORGED  D IH0 S - G AO1 R JH D
+DISGORGEMENT  D IH0 S - G AO1 R JH - M AH0 N T
+DISGRACE  D IH0 S - G R EY1 S
+DISGRACED  D IH0 S - G R EY1 S T
+DISGRACEFUL  D IH0 S - G R EY1 S - F AH0 L
+DISGRUNTLED  D IH0 S - G R AH1 N - T AH0 L D
+DISGRUNTLED(2)  D IH0 S - G R AH1 - N AH0 L D
+DISGRUNTLEMENT  D IH0 S - G R AH1 N - T AH0 L - M AH0 N T
+DISGRUNTLING  D IH0 S - G R AH1 N - T AH0 L - IH0 NG
+DISGRUNTLING(2)  D IH0 S - G R AH1 N T - L IH0 NG
+DISGUISE  D IH0 S - G AY1 Z
+DISGUISED  D IH0 S - G AY1 Z D
+DISGUISES  D IH0 S - G AY1 - Z IH0 Z
+DISGUISING  D IH0 S - G AY1 - Z IH0 NG
+DISGUST  D IH0 S - G AH1 S T
+DISGUSTED  D IH0 S - G AH1 - S T AH0 D
+DISGUSTED(2)  D IH0 S - G AH1 - S T IH0 D
+DISGUSTING  D IH0 S - G AH1 - S T IH0 NG
+DISGUSTS  D IH0 S - G AH1 S T S
+DISGUSTS(2)  D IH0 S - G AH1 S S
+DISGUSTS(3)  D IH0 S - G AH1 S
+DISH  D IH1 SH
+DISHARMONY  D IH0 S - HH AA1 R - M AH0 - N IY0
+DISHAROON  D IH0 - SH ER0 - UW1 N
+DISHAW  D IH1 - SH AO2
+DISHEARTENED  D IH0 S - HH AA1 R - T AH0 N D
+DISHEARTENING  D IH0 S - HH AA1 R - T AH0 N - IH0 NG
+DISHEARTENING(2)  D IH2 S - HH AA1 R T - N IH0 NG
+DISHED  D IH1 SH T
+DISHER  D IH1 - SH ER0
+DISHES  D IH1 - SH AH0 Z
+DISHES(2)  D IH1 - SH IH0 Z
+DISHEVEL  D IH0 - SH EH1 - V AH0 L
+DISHEVELED  D IH0 - SH EH1 - V AH0 L D
+DISHING  D IH1 - SH IH0 NG
+DISHMAN  D IH1 SH - M AH0 N
+DISHNER  D IH1 SH - N ER0
+DISHON  D IH1 - S AH0 N
+DISHONEST  D IH0 S - AA1 - N AH0 S T
+DISHONESTLY  D IH0 S - AO1 - N AH0 S T - L IY0
+DISHONESTY  D IH0 S - AA1 - N AH0 - S T IY0
+DISHONG  D IH1 - S AO0 NG
+DISHONOR  D IH0 S - AA1 - N ER0
+DISHONORABLE  D IH0 S - AA1 - N ER0 - AH0 - B AH0 L
+DISHONORED  D IH0 S - AA1 - N ER0 D
+DISHWASHER  D IH1 SH - W AA2 - SH ER0
+DISHWASHERS  D IH1 SH - W AA2 - SH ER0 Z
+DISHWASHING  D IH1 SH - W AA2 - SH IH0 NG
+DISILLUSION  D IH2 S - IH0 - L UW1 - ZH AH0 N
+DISILLUSIONED  D IH2 S - IH0 - L UW1 - ZH AH0 N D
+DISILLUSIONING  D IH2 S - IH0 - L UW1 - ZH AH0 N - IH0 NG
+DISILLUSIONMENT  D IH2 S - IH0 - L UW1 - ZH AH0 N - M AH0 N T
+DISILVESTRO  D IH0 - S IY0 L - V EH1 - S T R OW0
+DISIMONE  D IH0 - S IY0 - M OW1 - N IY0
+DISINCENTIVE  D IH2 S - IH0 N - S EH1 N - T IH0 V
+DISINCENTIVES  D IH2 S - IH0 N - S EH1 N - T IH0 V Z
+DISINCLINATION  D IH0 S - IH0 N - K L AH0 - N EY1 - SH AH0 N
+DISINCLINE  D IH2 S - IH0 N - K L AY1 N
+DISINCLINED  D IH2 S - IH0 N - K L AY1 N D
+DISINFECT  D IH0 S - IH0 N - F EH1 K T
+DISINFECTANT  D IH0 S - IH0 N - F EH1 K - T AH0 N T
+DISINFECTANTS  D IH0 S - IH0 N - F EH1 K - T AH0 N T S
+DISINFECTION  D IH0 S - IH0 N - F EH1 K - SH AH0 N
+DISINFLATE  D IH2 S - IH0 N - F L EY1 T
+DISINFLATION  D IH2 S - IH0 N - F L EY1 - SH AH0 N
+DISINFLATIONARY  D IH2 S - IH0 N - F L EY1 - SH AH0 N - EH2 - R IY0
+DISINFORM  D IH0 S - IH0 N - F AO1 R M
+DISINFORMATION  D IH0 - Z IH2 N - F ER0 - M EY1 - SH AH0 N
+DISINGENUOUS  D IH0 S - IH0 N - JH EH1 - N Y UW0 - AH0 S
+DISINGENUOUSNESS  D IH2 S - IH0 N - JH EH1 - N Y UW0 - AH0 S - N AH0 S
+DISINTEGRATE  D IH0 S - IH1 N - T AH0 - G R EY2 T
+DISINTEGRATED  D IH0 S - IH1 N - T AH0 - G R EY2 - T IH0 D
+DISINTEGRATES  D IH0 S - IH1 N - T AH0 - G R EY2 T S
+DISINTEGRATING  D IH0 S - IH1 N - T AH0 - G R EY2 - T IH0 NG
+DISINTEGRATION  D IH0 S - IH2 N - T AH0 - G R EY1 - SH AH0 N
+DISINTEGRATION(2)  D IH0 S - IH2 - N AH0 - G R EY1 - SH AH0 N
+DISINTEREST  D IH0 S - IH1 N - T ER0 - AH0 S T
+DISINTERESTED  D IH0 S - IH1 N - T R IH0 - S T IH0 D
+DISINTERESTED(2)  D IH0 S - IH1 N - R IH0 - S T IH0 D
+DISINVESTMENT  D IH0 S - IH0 N - V EH1 S T - M AH0 N T
+DISINVESTMENT(2)  D IH0 S - IH0 N - V EH1 S - M AH0 N T
+DISINVITE  D IH0 - S IH0 N - V AY1 T
+DISINVITED  D IH2 - S IH2 N - V AY1 - T IH0 D
+DISJOINT  D IH0 S - JH OY1 N T
+DISJOINTED  D IH0 S - JH OY1 N - T IH0 D
+DISK  D IH1 S K
+DISKETTE  D IH0 - S K EH1 T
+DISKETTES  D IH0 - S K EH1 T S
+DISKIN  D IH1 - S K IH0 N
+DISKLESS  D IH1 S K - L AH0 S
+DISKS  D IH1 S K S
+DISLIKE  D IH0 S - L AY1 K
+DISLIKED  D IH0 S - L AY1 K T
+DISLIKES  D IH0 S - L AY1 K S
+DISLIKING  D IH0 S - L AY1 - K IH0 NG
+DISLOCATE  D IH1 S - L OW0 - K EY0 T
+DISLOCATED  D IH1 S - L OW0 - K EY0 - T IH0 D
+DISLOCATION  D IH0 S - L OW1 - K EY1 - SH AH0 N
+DISLOCATIONS  D IH0 S - L OW1 - K EY1 - SH AH0 N Z
+DISLODGE  D IH0 S - L AA1 JH
+DISLODGED  D IH0 S - L AA1 JH D
+DISLODGING  D IH0 S - L AA1 - JH IH0 NG
+DISLOYAL  D IH0 S - L OY1 - AH0 L
+DISLOYALTY  D IH0 S - L OY1 - AH0 L - T IY0
+DISMAL  D IH1 Z - M AH0 L
+DISMALLY  D IH1 Z - M AH0 - L IY0
+DISMANTLE  D IH0 S - M AE1 N - T AH0 L
+DISMANTLE(2)  D IH0 S - M AE1 - N AH0 L
+DISMANTLED  D IH0 S - M AE1 N - T AH0 L D
+DISMANTLED(2)  D IH0 S - M AE1 - N AH0 L D
+DISMANTLEMENT  D IH0 S - M AE1 N - T AH0 L - M AH0 N T
+DISMANTLES  D IH0 S - M AE1 N - T AH0 L Z
+DISMANTLING  D IH0 S - M AE1 N - T AH0 L - IH0 NG
+DISMANTLING(2)  D IH0 S - M AE1 - N AH0 L - IH0 NG
+DISMANTLING(3)  D IH2 S - M AE1 N T - L IH0 NG
+DISMAY  D IH0 S - M EY1
+DISMAYED  D IH0 S - M EY1 D
+DISMAYING  D IH0 S - M EY1 - IH0 NG
+DISMAYS  D IH0 S - M EY1 Z
+DISMEMBER  D IH0 S - M EH1 M - B ER0
+DISMEMBERED  D IH0 S - M EH1 M - B ER0 D
+DISMEMBERING  D IH0 S - M EH1 M - B ER0 - IH0 NG
+DISMEMBERMENT  D IH0 S - M EH1 M - B ER0 - M AH0 N T
+DISMISS  D IH0 S - M IH1 S
+DISMISSAL  D IH0 S - M IH1 - S AH0 L
+DISMISSALS  D IH0 S - M IH1 - S AH0 L Z
+DISMISSED  D IH0 S - M IH1 S T
+DISMISSES  D IH0 S - M IH1 - S IH0 Z
+DISMISSING  D IH0 S - M IH1 - S IH0 NG
+DISMISSIVE  D IH0 S - M IH1 - S IH0 V
+DISMORE  D IH1 S - M AO0 R
+DISMUKE  D IH1 S - M Y UW0 K
+DISMUKES  D IH1 S - M Y UW0 K S
+DISMUTASE  D IH2 S - M Y UW0 - T EY1 Z
+DISNEY  D IH1 Z - N IY0
+DISNEY'S  D IH1 Z - N IY0 Z
+DISNEYLAND  D IH1 Z - N IY0 - L AE2 N D
+DISNEYWORLD  D IH1 Z - N IY0 - W ER0 L D
+DISOBEDIENCE  D IH2 S - AH0 - B IY1 - D IY0 - AH0 N S
+DISOBEDIENCE(2)  D IH2 S - OW0 - B IY1 - D IY0 - AH0 N S
+DISOBEDIENT  D IH2 S - AH0 - B IY1 - D IY0 - AH0 N T
+DISOBEDIENT(2)  D IH2 S - OW0 - B IY1 - D IY0 - AH0 N T
+DISOBEY  D IH2 S - AH0 - B EY1
+DISOBEYED  D IH2 S - OW0 - B EY1 D
+DISOBEYING  D IH2 S - OW0 - B EY1 - IH0 NG
+DISON  D IH1 - S AH0 N
+DISORDER  D IH0 S - AO1 R - D ER0
+DISORDERED  D IH0 S - AO1 R - D ER0 D
+DISORDERLY  D IH0 S - AO1 R - D ER0 - L IY0
+DISORDERS  D IH0 S - AO1 R - D ER0 Z
+DISORGANIZATION  D IH0 S - AO2 R - G AH0 - N AH0 - Z EY1 - SH AH0 N
+DISORGANIZE  D IH0 S - AO1 R - G AH0 - N AY2 Z
+DISORGANIZED  D IH0 S - AO1 R - G AH0 - N AY2 Z D
+DISORIENT  D IH0 S - AO1 - R IY0 - EH2 N T
+DISORIENTATION  D IH0 - S AO2 - R IY0 - AH0 N - T EY1 - SH AH0 N
+DISORIENTED  D IH0 S - AO1 - R IY0 - EH2 N - T IH0 D
+DISORIENTING  D IH0 S - AO1 - R IY0 - EH2 N - T IH0 NG
+DISOWN  D IH0 S - OW1 N
+DISOWNED  D IH0 S - OW1 N D
+DISPAIR  D IH0 - S P EH1 R
+DISPARAGE  D IH0 - S P EH1 - R IH0 JH
+DISPARAGED  D IH0 - S P EH1 - R IH0 JH D
+DISPARAGES  D IH0 - S P EH1 - R IH0 - JH IH0 Z
+DISPARAGING  D IH0 - S P EH1 - R IH0 - JH IH0 NG
+DISPARAGINGLY  D IH0 - S P EH1 - R IH0 - JH IH0 NG - L IY0
+DISPARATE  D IH1 - S P ER0 - IH0 T
+DISPARATE(2)  D IH0 - S P EH1 - R IH0 T
+DISPARITIES  D IH0 - S P EH1 - R AH0 - T IY0 Z
+DISPARITY  D IH0 - S P EH1 - R AH0 - T IY0
+DISPASSIONATE  D IH0 - S P AE1 - SH AH0 N - AH0 T
+DISPASSIONATELY  D IH0 - S P AE1 - SH AH0 N - AH0 T - L IY0
+DISPATCH  D IH0 - S P AE1 CH
+DISPATCHED  D IH0 - S P AE1 CH T
+DISPATCHER  D IH0 - S P AE1 - CH ER0
+DISPATCHER'S  D IH0 - S P AE1 - CH ER0 Z
+DISPATCHER'S(2)  D IH1 - S P AE2 - CH ER0 Z
+DISPATCHER(2)  D IH1 - S P AE2 - CH ER0
+DISPATCHERS  D IH0 - S P AE1 - CH ER0 Z
+DISPATCHES  D IH0 - S P AE1 - CH IH0 Z
+DISPATCHING  D IH0 - S P AE1 - CH IH0 NG
+DISPEL  D IH0 - S P EH1 L
+DISPELL  D IH0 - S P EH1 L
+DISPELLED  D IH0 - S P EH1 L D
+DISPELLING  D IH0 - S P EH1 - L IH0 NG
+DISPELS  D IH0 - S P EH1 L Z
+DISPENSABLE  D IH0 - S P EH1 N - S AH0 - B AH0 L
+DISPENSARY  D IH0 - S P EH1 N - S ER0 - IY0
+DISPENSARY(2)  D IH1 - S P EH0 N - S EH2 - R IY0
+DISPENSATION  D IH2 - S P AH0 N - S EY1 - SH AH0 N
+DISPENSE  D IH0 - S P EH1 N S
+DISPENSED  D IH0 - S P EH1 N S T
+DISPENSER  D IH0 - S P EH1 N - S ER0
+DISPENSERS  D IH0 - S P EH1 N - S ER0 Z
+DISPENSES  D IH0 - S P EH1 N - S IH0 Z
+DISPENSING  D IH0 - S P EH1 N - S IH0 NG
+DISPENZA  D IH0 - S P EH1 N - Z AH0
+DISPERSAL  D IH0 - S P ER1 - S AH0 L
+DISPERSANT  D IH2 - S P ER1 - S AH0 N T
+DISPERSANTS  D IH2 - S P ER1 - S AH0 N T S
+DISPERSE  D IH0 - S P ER1 S
+DISPERSED  D IH0 - S P ER1 S T
+DISPERSING  D IH0 - S P ER1 - S IH0 NG
+DISPERSION  D IH0 - S P ER1 - ZH AH0 N
+DISPERSIVE  D IH0 - S P ER1 - S IH0 V
+DISPIRITED  D IH0 - S P IH1 - R AH0 - T IH0 D
+DISPIRITING  D IH0 - S P IH1 - R IH0 - T IH0 NG
+DISPLACE  D IH0 S - P L EY1 S
+DISPLACED  D IH0 S - P L EY1 S T
+DISPLACEMENT  D IH0 S - P L EY1 S - M AH0 N T
+DISPLACEMENTS  D IH0 S - P L EY1 S - M AH0 N T S
+DISPLACES  D IH0 S - P L EY1 - S IH0 Z
+DISPLACING  D IH0 S - P L EY1 - S IH0 NG
+DISPLAY  D IH0 - S P L EY1
+DISPLAYED  D IH0 - S P L EY1 D
+DISPLAYING  D IH0 - S P L EY1 - IH0 NG
+DISPLAYS  D IH0 - S P L EY1 Z
+DISPLAYWRITE  D IH0 - S P L EY1 - R AY2 T
+DISPLEASE  D IH0 S - P L IY1 Z
+DISPLEASED  D IH0 S - P L IY1 Z D
+DISPLEASURE  D IH0 S - P L EH1 - ZH ER0
+DISPOSABLE  D IH0 - S P OW1 - Z AH0 - B AH0 L
+DISPOSABLES  D IH0 - S P OW1 - Z AH0 - B AH0 L Z
+DISPOSAL  D IH0 - S P OW1 - Z AH0 L
+DISPOSALS  D IH0 - S P OW1 - Z AH0 L Z
+DISPOSE  D IH0 - S P OW1 Z
+DISPOSED  D IH0 - S P OW1 Z D
+DISPOSER  D IH0 - S P OW1 - Z ER0
+DISPOSES  D IH0 - S P OW1 - Z IH0 Z
+DISPOSING  D IH0 - S P OW1 - Z IH0 NG
+DISPOSITION  D IH2 - S P AH0 - Z IH1 - SH AH0 N
+DISPOSITIONS  D IH2 - S P AH0 - Z IH1 - SH AH0 N Z
+DISPOSITIVE  D IH2 - S P AA1 - Z AH0 - T IH0 V
+DISPOSSESS  D IH2 S - P AH0 - Z EH1 S
+DISPOSSESSED  D IH2 S - P AH0 - Z EH1 S T
+DISPROPORTIONATE  D IH2 - S P R AH0 - P AO1 R - SH AH0 N - IH0 T
+DISPROPORTIONATELY  D IH2 - S P R AH0 - P AO1 R - SH AH0 N - AH0 T - L IY0
+DISPROVE  D IH0 S - P R UW1 V
+DISPROVED  D IH0 S - P R UW1 V D
+DISPROVEN  D IH0 S - P R UW1 - V IH0 N
+DISPROVES  D IH0 S - P R UW1 V Z
+DISPUTATION  D IH0 - S P Y UW1 - T EY1 - SH AH0 N
+DISPUTE  D IH0 - S P Y UW1 T
+DISPUTED  D IH0 - S P Y UW1 - T AH0 D
+DISPUTED(2)  D IH0 - S P Y UW1 - T IH0 D
+DISPUTES  D IH0 - S P Y UW1 T S
+DISPUTING  D IH0 - S P Y UW1 - T IH0 NG
+DISQUALIFICATION  D IH0 S - K W AA2 - L AH0 - F AH0 - K EY1 - SH AH0 N
+DISQUALIFIED  D IH0 S - K W AA1 - L AH0 - F AY2 D
+DISQUALIFIES  D IH0 S - K W AA1 - L AH0 - F AY2 Z
+DISQUALIFY  D IH0 S - K W AA1 - L AH0 - F AY2
+DISQUALIFYING  D IH0 S - K W AA1 - L AH0 - F AY2 - IH0 NG
+DISQUE  D IH1 S K
+DISQUIET  D IH0 S - K W AY1 - AH0 T
+DISQUIETING  D IH0 S - K W AY1 - AH0 - T IH0 NG
+DISRAELI  D IH0 Z - R EY1 - L IY0
+DISREGARD  D IH2 S - R IH0 - G AA1 R D
+DISREGARDED  D IH2 S - R IH0 - G AA1 R - D IH0 D
+DISREGARDING  D IH2 S - R IH0 - G AA1 R - D IH0 NG
+DISREGARDS  D IH2 S - R IH0 - G AA1 R D Z
+DISREPAIR  D IH2 S - R IH0 - P EH1 R
+DISREPUTABLE  D IH0 S - R EH1 - P Y AH0 - T AH0 - B AH0 L
+DISREPUTE  D IH2 S - R IH0 - P Y UW1 T
+DISRESPECT  D IH2 S - R IH0 - S P EH1 K T
+DISRESPECTED  D IH2 S - R IH0 - S P EH1 K - T IH0 D
+DISRESPECTFUL  D IH2 S - R IH0 - S P EH1 K T - F AH0 L
+DISRESPECTING  D IH2 S - R IH0 - S P EH1 K - T IH0 NG
+DISRESPECTS  D IH2 S - R IH0 - S P EH1 K T S
+DISRUPT  D IH0 S - R AH1 P T
+DISRUPTED  D IH0 S - R AH1 P - T IH0 D
+DISRUPTING  D IH0 S - R AH1 P - T IH0 NG
+DISRUPTION  D IH0 S - R AH1 P - SH AH0 N
+DISRUPTIONS  D IH0 S - R AH1 P - SH AH0 N Z
+DISRUPTIVE  D IH0 S - R AH1 P - T IH0 V
+DISRUPTS  D IH0 S - R AH1 P T S
+DISS  D IH1 S
+DISSATISFACTION  D IH2 S - AE0 - T IH0 S - F AE1 K - SH AH0 N
+DISSATISFIED  D IH0 S - AE1 - T AH0 S - F AY2 D
+DISSATISFY  D IH0 S - AE1 - T AH0 S - F AY2
+DISSECT  D AY0 - S EH1 K T
+DISSECTED  D AY0 - S EH1 K - T AH0 D
+DISSECTING  D AY0 - S EH1 K - T IH0 NG
+DISSECTION  D AY0 - S EH1 K - SH AH0 N
+DISSECTION(2)  D AY1 - S EH0 K - SH AH0 N
+DISSECTIONS  D AY0 - S EH1 K - SH AH0 N Z
+DISSECTIONS(2)  D AY1 - S EH0 K - SH AH0 N Z
+DISSECTS  D AY0 - S EH1 K T S
+DISSECTS(2)  D AY0 - S EH1 K S
+DISSEMBLE  D IH0 - S EH1 M - B AH0 L
+DISSEMBLING  D IH0 - S EH1 M - B L IH0 NG
+DISSEMINATE  D IH0 - S EH1 - M AH0 - N EY2 T
+DISSEMINATED  D IH0 - S EH1 - M AH0 - N EY2 - T AH0 D
+DISSEMINATING  D IH0 - S EH1 - M AH0 - N EY2 - T IH0 NG
+DISSEMINATION  D IH0 - S EH2 - M AH0 - N EY1 - SH AH0 N
+DISSENSION  D IH0 - S EH1 N - SH AH0 N
+DISSENT  D IH0 - S EH1 N T
+DISSENTED  D IH0 - S EH1 N - T IH0 D
+DISSENTED(2)  D IH0 - S EH1 - N IH0 D
+DISSENTER  D IH0 - S EH1 N - T ER0
+DISSENTERS  D IH0 - S EH1 N - T ER0 Z
+DISSENTERS(2)  D IH0 - S EH1 - N ER0 Z
+DISSENTING  D IH0 - S EH1 N - T IH0 NG
+DISSENTING(2)  D IH0 - S EH1 - N IH0 NG
+DISSENTS  D IH0 - S EH1 N T S
+DISSERTATION  D IH2 - S ER0 - T EY1 - SH AH0 N
+DISSERVICE  D IH0 S - S ER1 - V AH0 S
+DISSERVICE(2)  D IH0 - S ER1 - V AH0 S
+DISSIDENCE  D IH1 - S AH0 - D IH0 N S
+DISSIDENT  D IH1 - S AH0 - D IH0 N T
+DISSIDENTS  D IH1 - S AH0 - D AH0 N T S
+DISSIDENTS'  D IH1 - S AH0 - D AH0 N T S
+DISSIMILAR  D IH0 - S S IH1 - M AH0 - L ER0
+DISSIMILAR(2)  D IH0 - S IH1 - M AH0 - L ER0
+DISSIMILARITY  D IH0 - S S IH2 - M AH0 - L AE1 - R AH0 - T IY0
+DISSIMILARITY(2)  D IH2 - S IH2 - M AH0 - L AE1 - R AH0 - T IY0
+DISSINGER  D IH1 S - IH0 N - JH ER0
+DISSIPATE  D IH1 - S AH0 - P EY2 T
+DISSIPATED  D IH1 - S AH0 - P EY2 - T IH0 D
+DISSIPATES  D IH1 - S AH0 - P EY2 T S
+DISSIPATING  D IH1 - S AH0 - P EY2 - T IH0 NG
+DISSIPATION  D IH2 - S IH0 - P EY1 - SH AH0 N
+DISSIPATIVE  D IH1 - S AH0 - P EY2 - T IH0 V
+DISSOCIATE  D IH0 - S OW1 - S IY0 - EY0 T
+DISSOCIATION  D IH0 - S OW2 - S IY0 - EY1 - SH AH0 N
+DISSOLUTION  D IH2 - S AH0 - L UW1 - SH AH0 N
+DISSOLVE  D IH0 - Z AA1 L V
+DISSOLVED  D IH0 - Z AA1 L V D
+DISSOLVER  D IH0 - Z AA1 L - V ER0
+DISSOLVERS  D IH0 - Z AA1 L - V ER0 Z
+DISSOLVES  D IH0 - Z AA1 L V Z
+DISSOLVING  D IH0 - Z AO1 L - V IH0 NG
+DISSONANCE  D IH1 - S AH0 - N AH0 N S
+DISSONANT  D IH1 - S AH0 - N AH0 N T
+DISSUADE  D IH0 - S W EY1 D
+DISSUADED  D IH0 - S W EY1 - D IH0 D
+DISSYMMETRIC  D IH2 - S IH0 - M EH1 - T R IH0 K
+DISSYMMETRY  D IH0 - S IH1 - M AH0 - T R IY0
+DISTAD  D IH1 - S T AH0 D
+DISTAL  D IH1 - S T AH0 L
+DISTANCE  D IH1 - S T AH0 N S
+DISTANCED  D IH1 - S T AH0 N S T
+DISTANCES  D IH1 - S T AH0 N - S AH0 Z
+DISTANCES(2)  D IH1 - S T AH0 N - S IH0 Z
+DISTANCING  D IH1 - S T AH0 N - S IH0 NG
+DISTANT  D IH1 - S T AH0 N T
+DISTASI  D IH0 - S T AA1 - S IY0
+DISTASIO  D IH0 - S T AA1 - S IY0 - OW0
+DISTASTE  D IH0 S - T EY1 S T
+DISTASTEFUL  D IH0 S - T EY1 S T - F AH0 L
+DISTEFANO  D IH0 - S T EH0 - F AA1 - N OW0
+DISTEL  D IH1 - S T AH0 L
+DISTEMPER  D IH0 - S T EH1 M - P ER0
+DISTEND  D IH0 - S T EH1 N D
+DISTENDED  D IH0 - S T EH1 N - D IH0 D
+DISTIL  D IH0 - S T IH1 L
+DISTILL  D IH0 - S T IH1 L
+DISTILLATE  D IH1 - S T AH0 - L EY2 T
+DISTILLATE(2)  D IH1 - S T AH0 - L AH0 T
+DISTILLATES  D IH1 - S T AH0 - L EY2 T S
+DISTILLATION  D IH2 - S T AH0 - L EY1 - SH AH0 N
+DISTILLED  D IH0 - S T IH1 L D
+DISTILLER  D IH0 - S T IH1 - L ER0
+DISTILLER'S  D IH0 - S T IH1 - L ER0 Z
+DISTILLERIES  D IH0 - S T IH1 - L ER0 - IY0 Z
+DISTILLERS  D IH0 - S T IH1 - L ER0 Z
+DISTILLERS'  D IH0 - S T IH1 - L ER0 Z
+DISTILLERS'S  D IH0 - S T IH1 - L ER0 - Z IH0 Z
+DISTILLERY  D IH0 - S T IH1 - L ER0 - IY0
+DISTILLING  D IH0 - S T IH1 - L IH0 NG
+DISTILLS  D IH0 - S T IH1 L Z
+DISTINCT  D IH0 - S T IH1 NG K T
+DISTINCTION  D IH0 - S T IH1 NG K - SH AH0 N
+DISTINCTIONS  D IH0 - S T IH1 NG K - SH AH0 N Z
+DISTINCTIVE  D IH0 - S T IH1 NG K - T IH0 V
+DISTINCTIVELY  D IH0 - S T IH1 NG K - T IH0 V - L IY0
+DISTINCTIVENESS  D IH0 - S T IH1 NG K - T IH0 V - N AH0 S
+DISTINCTLY  D IH0 - S T IH1 NG K T - L IY0
+DISTINGUISH  D IH0 - S T IH1 NG - G W IH0 SH
+DISTINGUISHABLE  D IH0 - S T IH1 NG - G W IH0 - SH AH0 - B AH0 L
+DISTINGUISHED  D IH0 - S T IH1 NG - G W IH0 SH T
+DISTINGUISHES  D IH0 - S T IH1 NG - G W IH0 - SH IH0 Z
+DISTINGUISHING  D IH0 - S T IH1 NG - G W IH0 - SH IH0 NG
+DISTLER  D IH1 S T - L ER0
+DISTORT  D IH0 - S T AO1 R T
+DISTORTED  D IH0 - S T AO1 R - T AH0 D
+DISTORTED(2)  D IH0 - S T AO1 R - T IH0 D
+DISTORTING  D IH0 - S T AO1 R - T IH0 NG
+DISTORTION  D IH0 - S T AO1 R - SH AH0 N
+DISTORTIONS  D IH0 - S T AO1 R - SH AH0 N Z
+DISTORTS  D IH0 - S T AO1 R T S
+DISTRACT  D IH0 - S T R AE1 K T
+DISTRACTED  D IH0 - S T R AE1 K - T AH0 D
+DISTRACTED(2)  D IH0 - S T R AE1 K - T IH0 D
+DISTRACTING  D IH0 - S T R AE1 K - T IH0 NG
+DISTRACTION  D IH0 S - T R AE1 K - SH AH0 N
+DISTRACTIONS  D IH0 S - T R AE1 K - SH AH0 N Z
+DISTRACTS  D IH0 - S T R AE1 K T S
+DISTRAUGHT  D IH0 - S T R AO1 T
+DISTRESS  D IH0 - S T R EH1 S
+DISTRESSED  D IH0 - S T R EH1 S T
+DISTRESSES  D IH0 - S T R EH1 - S IH0 Z
+DISTRESSING  D IH0 - S T R EH1 - S IH0 NG
+DISTRESSINGLY  D IH0 - S T R EH1 - S IH0 NG - L IY0
+DISTRIBUTE  D IH0 - S T R IH1 - B Y UW0 T
+DISTRIBUTED  D IH0 S - T R IH1 - B Y AH0 - T AH0 D
+DISTRIBUTES  D IH0 - S T R IH1 - B Y UW0 T S
+DISTRIBUTING  D IH0 - S T R IH1 - B Y UW0 - T IH0 NG
+DISTRIBUTION  D IH2 S - T R AH0 - B Y UW1 - SH AH0 N
+DISTRIBUTIONS  D IH2 S - T R AH0 - B Y UW1 - SH AH0 N Z
+DISTRIBUTIVE  D IH0 - S T R IH1 - B Y UW0 - T IH0 V
+DISTRIBUTOR  D IH0 S - T R IH1 - B Y AH0 - T ER0
+DISTRIBUTOR'S  D IH0 - S T R IH1 - B Y UW0 - T ER0 Z
+DISTRIBUTORS  D IH0 S - T R IH1 - B Y AH0 - T ER0 Z
+DISTRIBUTORS'  D IH0 S - T R IH1 - B Y AH0 - T ER0 Z
+DISTRIBUTORSHIP  D IH0 - S T R IH1 - B Y UW0 - T ER0 - SH IH2 P
+DISTRIBUTORSHIPS  D IH0 - S T R IH1 - B Y UW0 - T ER0 - SH IH2 P S
+DISTRICT  D IH1 - S T R IH0 K T
+DISTRICT'S  D IH1 - S T R IH0 K T S
+DISTRICTING  D IH1 - S T R IH0 K - T IH0 N G
+DISTRICTS  D IH1 - S T R IH0 K T S
+DISTRIGAS  D IH0 S - T R IY1 - G AH0 S
+DISTRON  D IH1 - S T R AA2 N
+DISTRUST  D IH0 S - T R AH1 S T
+DISTRUSTED  D IH0 S - T R AH1 - S T AH0 D
+DISTRUSTED(2)  D IH0 S - T R AH1 - S T IH0 D
+DISTRUSTFUL  D IH0 S - T R AH1 S T - F AH0 L
+DISTRUSTS  D IH0 S - T R AH1 S T S
+DISTRUSTS(2)  D IH0 - S T R AH1 S S
+DISTRUSTS(3)  D IH0 - S T R AH1 S
+DISTURB  D IH0 - S T ER1 B
+DISTURBANCE  D IH0 - S T ER1 - B AH0 N S
+DISTURBANCES  D IH0 - S T ER1 - B AH0 N - S AH0 Z
+DISTURBANCES(2)  D IH0 - S T ER1 - B AH0 N - S IH0 Z
+DISTURBED  D IH0 - S T ER1 B D
+DISTURBING  D IH0 - S T ER1 - B IH0 NG
+DISTURBINGLY  D IH0 - S T ER1 - B IH0 NG - L IY0
+DISTURBS  D IH0 - S T ER1 B Z
+DISUNION  D IH0 S - Y UW1 - N Y AH0 N
+DISUNITY  D IH0 S - Y UW1 - N AH0 - T IY0
+DISUSE  D IH0 S - Y UW1 S
+DITCH  D IH1 CH
+DITCHED  D IH1 CH T
+DITCHES  D IH1 - CH AH0 Z
+DITCHES(2)  D IH1 - CH IH0 Z
+DITCHING  D IH1 - CH IH0 NG
+DITH  D IH1 TH
+DITHER  D IH1 - DH ER0
+DITHERING  D IH1 - DH ER0 - IH0 NG
+DITHERS  D IH1 - DH ER0 Z
+DITHYRAMB  D IH1 - TH ER0 - AE2 M
+DITKA  D IH1 T - K AH0
+DITKA'S  D IH1 T - K AH0 Z
+DITLOW  D IH1 T - L OW0
+DITMARS  D IH1 T - M ER0 Z
+DITMER  D IH1 T - M ER0
+DITMORE  D IH1 T - M AO0 R
+DITOMASSO  D IH0 - T OW0 - M AA1 - S OW0
+DITOMMASO  D IH0 - T OW0 - M AA1 - S OW0
+DITSY  D IH1 T - S IY0
+DITTBERNER  D IH1 T - B ER0 - N ER0
+DITTEMORE  D IH0 - T EH1 - M AO0 R
+DITTER  D IH1 - T ER0
+DITTIES  D IH1 - T IY0 Z
+DITTMAN  D IH1 T - M AH0 N
+DITTMANN  D IH1 T - M AH0 N
+DITTMAR  D IH1 T - M ER0
+DITTMER  D IH1 T - M ER0
+DITTO  D IH1 - T OW0
+DITTON  D IH1 - T AH0 N
+DITTRICH  D IH1 - T R IH0 K
+DITTUS  D IH1 - T AH0 S
+DITTY  D IH1 - T IY0
+DITULLIO  D IH0 - T AH1 - L IY0 - OW0
+DITZEL  D IH1 T - Z AH0 L
+DITZLER  D IH1 T - S L ER0
+DIURETIC  D AY2 - UW0 - R EH1 - T IH0 K
+DIURETICS  D AY2 - UW0 - R EH1 - T IH0 K S
+DIURNAL  D AY0 - ER1 - N AH0 L
+DIURNALLY  D AY0 - ER1 - N AH0 - L IY0
+DIVA  D IY1 - V AH0
+DIVAD  D IH1 - V AE0 D
+DIVALENT  D AY0 - V EY1 - L AH0 N T
+DIVALL  D IH1 - V AH0 L
+DIVAN  D IH0 - V AE1 N
+DIVAS  D IY1 - V AH0 Z
+DIVE  D AY1 V
+DIVED  D AY1 V D
+DIVELBISS  D IH0 - V EH1 L - B IH0 S
+DIVELEY  D IH1 - V IH0 - L IY0
+DIVELY  D AY1 V - L IY0
+DIVEN  D AY1 - V AH0 N
+DIVENS  D AY1 - V AH0 N Z
+DIVER  D AY1 - V ER0
+DIVER'S  D AY1 - V ER0 Z
+DIVERGE  D IH0 - V ER1 JH
+DIVERGED  D AY0 - V ER1 JH D
+DIVERGENCE  D AY0 - V ER1 - JH AH0 N S
+DIVERGENCE(2)  D IH0 - V ER1 - JH AH0 N S
+DIVERGENCES  D AY0 - V ER1 - JH AH0 N - S IH0 Z
+DIVERGENT  D AY0 - V ER1 - JH AH0 N T
+DIVERGENT(2)  D IH0 - V ER1 - JH AH0 N T
+DIVERGES  D AY0 - V ER1 - JH IH0 Z
+DIVERGING  D AY0 - V ER1 - JH IH0 NG
+DIVERS  D AY1 - V ER0 Z
+DIVERSE  D AY0 - V ER1 S
+DIVERSE(2)  D IH0 - V ER1 S
+DIVERSICARE  D IH1 - V ER0 - S IH0 - K EH2 R
+DIVERSIFICATION  D AY0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N
+DIVERSIFICATION(2)  D IH0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N
+DIVERSIFICATIONS  D AY0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N Z
+DIVERSIFICATIONS(2)  D IH0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N Z
+DIVERSIFIED  D AY0 - V ER1 - S AH0 - F AY2 D
+DIVERSIFIED(2)  D IH0 - V ER1 - S AH0 - F AY2 D
+DIVERSIFY  D AY0 - V ER1 - S AH0 - F AY2
+DIVERSIFY(2)  D IH0 - V ER1 - S AH0 - F AY2
+DIVERSIFYING  D AY0 - V ER1 - S AH0 - F AY2 - IH0 NG
+DIVERSIFYING(2)  D IH0 - V ER1 - S AH0 - F AY2 - IH0 NG
+DIVERSION  D AY0 - V ER1 - ZH AH0 N
+DIVERSION(2)  D IH0 - V ER1 - ZH AH0 N
+DIVERSIONARY  D AY0 - V ER1 - ZH AH0 N - EH2 - R IY0
+DIVERSIONARY(2)  D IH0 - V ER1 - ZH AH0 N - EH2 - R IY0
+DIVERSIONS  D IH0 - V ER1 - ZH AH0 N Z
+DIVERSIONS(2)  D AY0 - V ER1 - ZH AH0 N Z
+DIVERSITY  D IH0 - V ER1 - S AH0 - T IY0
+DIVERSITY(2)  D IH0 - V ER1 - S IH0 - T IY0
+DIVERSITY(3)  D AY0 - V ER1 - S AH0 - T IY0
+DIVERSITY(4)  D AY0 - V ER1 - S IH0 - T IY0
+DIVERT  D AY0 - V ER1 T
+DIVERT(2)  D IH0 - V ER1 T
+DIVERTED  D AY0 - V ER1 - T IH0 D
+DIVERTED(2)  D IH0 - V ER1 - T IH0 D
+DIVERTICULA  D AY2 - V ER0 - T IH1 - K Y AH0 - L AH0
+DIVERTICULUM  D AY2 - V ER0 - T IH1 - K Y AH0 - L AH0 M
+DIVERTIMENTO  D IH0 - V ER2 - T AH0 - M EH1 N - T OW2
+DIVERTING  D AY0 - V ER1 - T IH0 NG
+DIVERTING(2)  D IH0 - V ER1 - T IH0 NG
+DIVERTS  D AY0 - V ER1 T S
+DIVERTS(2)  D IH0 - V ER1 T S
+DIVES  D AY1 V Z
+DIVEST  D AY0 - V EH1 S T
+DIVEST(2)  D IH0 - V EH1 S T
+DIVESTED  D AY0 - V EH1 - S T IH0 D
+DIVESTING  D AY0 - V EH1 - S T IH0 NG
+DIVESTITURE  D IH0 - V EH1 - S T IH0 - CH ER0
+DIVESTITURE(2)  D AY0 - V EH1 - S T IH0 - CH ER0
+DIVESTITURES  D IH0 - V EH1 - S T IH0 - CH ER0 Z
+DIVESTITURES(2)  D AY0 - V EH1 - S T IH0 - CH ER0 Z
+DIVESTMENT  D AY0 - V EH1 S T - M AH0 N T
+DIVESTMENT(2)  D AY0 - V EH1 S - M AH0 N T
+DIVESTMENT(3)  D IH0 - V EH1 S - M AH0 N T
+DIVESTMENTS  D AY0 - V EH1 S T - M AH0 N T S
+DIVESTMENTS(2)  D AY0 - V EH1 S - M AH0 N T S
+DIVESTMENTS(3)  D IH0 - V EH1 S - M AH0 N T S
+DIVESTS  D AY0 - V EH1 S T S
+DIVESTS(2)  D AY0 - V EH1 S S
+DIVESTS(3)  D AY0 - V EH1 S
+DIVI  D IY1 - V IY0
+DIVIDE  D IH0 - V AY1 D
+DIVIDED  D IH0 - V AY1 - D AH0 D
+DIVIDEND  D IH1 - V IH0 - D EH2 N D
+DIVIDEND'S  D IH1 - V AH0 - D EH2 N D Z
+DIVIDENDS  D IH1 - V AH0 - D EH2 N D Z
+DIVIDER  D IH0 - V AY1 - D ER0
+DIVIDES  D IH0 - V AY1 D Z
+DIVIDING  D IH0 - V AY1 - D IH0 NG
+DIVINATION  D IH2 - V AH0 - N EY1 - SH AH0 N
+DIVINCENZO  D IH0 - V IY0 N - CH EH1 N - Z OW0
+DIVINE  D IH0 - V AY1 N
+DIVINELY  D IH0 - V AY1 N - L IY0
+DIVINEY  D IH1 - V IH0 - N IY0
+DIVING  D AY1 - V IH0 NG
+DIVINING  D AH0 - V AY1 - N IH0 NG
+DIVINITAS  D IH2 - V IH0 - N IY1 - T AH0 S
+DIVINITIES  D IH0 - V IH1 - N AH0 - T IY0 Z
+DIVINITY  D IH0 - V IH1 - N AH0 - T IY0
+DIVIRGILIO  D IH0 - V IH0 R - JH IY1 - L IY0 - OW0
+DIVIS  D IY1 - V IH0 S
+DIVISIBLE  D IH0 - V IH1 - Z AH0 - B AH0 L
+DIVISION  D IH0 - V IH1 - ZH AH0 N
+DIVISION'S  D IH0 - V IH1 - ZH AH0 N Z
+DIVISIONAL  D IH0 - V IH1 - ZH AH0 - N AH0 L
+DIVISIONS  D IH0 - V IH1 - ZH AH0 N Z
+DIVISIONS'  D IH0 - V IH1 - ZH AH0 N Z
+DIVISIVE  D IH0 - V AY1 - S IH0 V
+DIVISIVENESS  D IH0 - V AY1 - S IH0 V - N AH0 S
+DIVISON  D IH0 - V IH1 - ZH AH0 N
+DIVISON(2)  D IH0 - V IH1 - S AH0 N
+DIVISOR  D IH0 - V AY1 - Z ER0
+DIVITA  D IH0 - V IY1 - T AH0
+DIVITO  D IH0 - V IY1 - T OW0
+DIVORCE  D IH0 - V AO1 R S
+DIVORCED  D IH0 - V AO1 R S T
+DIVORCEE  D AH0 - V AO1 R - S IY2
+DIVORCEE(2)  D AH0 - V AO1 R - S EY2
+DIVORCES  D IH0 - V AO1 R - S IH0 Z
+DIVORCING  D IH0 - V AO1 R - S IH0 NG
+DIVULGE  D IH0 - V AH1 L JH
+DIVULGE(2)  D AY0 - V AH1 L JH
+DIVULGED  D IH0 - V AH1 L JH D
+DIVULGED(2)  D AY0 - V AH1 L JH D
+DIVULGING  D IH0 - V AH1 L - JH IH0 NG
+DIVULGING(2)  D AY0 - V AH1 L - JH IH0 NG
+DIVVIED  D IH1 - V IY0 D
+DIVVY  D IH1 - V IY0
+DIWA  D IY1 - W AH0
+DIX  D IH1 K S
+DIXIE  D IH1 K - S IY0
+DIXIELAND  D IH1 K - S IY0 - L AE2 N D
+DIXON  D IH1 K - S AH0 N
+DIXON'S  D IH1 K - S AH0 N Z
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+DIXSON  D IH1 K - S AH0 N
+DIXVILLE  D IH1 K S - V IH0 L
+DIXY  D IH1 K - S IY0
+DIZON  D IH1 - Z AH0 N
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+DIZZY  D IH1 - Z IY0
+DIZZYING  D IH1 - Z IY0 - IH0 NG
+DJAKARTA  JH AH0 - K AA1 R - T AH0
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+DJIBOUTI  JH IH0 - B UW1 - T IY2
+DJURDJEVIC  JH ER1 - JH AH0 - V IH0 K
+DK  D IY1 - K EY1
+DLOUHY  D AH0 - L AW1 - IY0
+DLUGOS  D AH0 - L UW1 - G OW0 S
+DLUGOSZ  D AH0 - L UW1 - G OW0 S
+DLUGOSZ(2)  D AH0 - L UW1 - G OW0 SH
+DMITRI  D AH0 - M IY1 - T R IY0
+DMITRI(2)  D M IY1 - T R IY0
+DNASE  D IY1 - N EY2 S
+DNASE(2)  D IY1 - N EY2 Z
+DNIESTER  D AH0 - N IY1 - S T ER0
+DO  D UW1
+DO'S  D UW1 Z
+DOABLE  D UW1 - AH0 - B AH0 L
+DOAK  D OW1 K
+DOAN  D OW1 N
+DOANE  D OW1 N
+DOANH  D OW1 N
+DOANNA  D OW1 - N AH0
+DOB  D AA1 B
+DOBB  D AA1 B
+DOBBERSTEIN  D AA1 - B ER0 - S T IY2 N
+DOBBERSTEIN(2)  D AA1 - B ER0 - S T AY2 N
+DOBBIE  D AA1 - B IY0
+DOBBIN  D AA1 - B IH0 N
+DOBBINS  D AA1 - B IH0 N Z
+DOBBS  D AA1 B Z
+DOBEK  D OW1 - B IH0 K
+DOBER  D OW1 - B ER0
+DOBERMAN  D OW1 - B ER0 - M AH0 N
+DOBERSTEIN  D OW1 - B ER0 - S T AY0 N
+DOBERSTEIN(2)  D OW1 - B ER0 - S T IY0 N
+DOBESH  D AA1 - B IH0 SH
+DOBEY  D AA1 - B IY0
+DOBIAS  D OW0 - B IY1 - AH0 Z
+DOBIE  D AA1 - B IY0
+DOBIES  D OW1 - B IY0 Z
+DOBIS  D OW1 - B IH0 S
+DOBKIN  D AA1 B - K IH0 N
+DOBKINS  D AA1 B - K IH0 N Z
+DOBLE  D OW1 - B AH0 L
+DOBLER  D OW1 - B AH0 L - ER0
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+DOBMEIER  D AA1 B - M AY0 - ER0
+DOBOS  D OW1 - B OW0 Z
+DOBOSZ  D AA1 - B AH0 SH
+DOBRANSKY  D AH0 - B R AE1 N S - K IY0
+DOBRATZ  D AA1 - B R AH0 T S
+DOBRIMIR  D AH0 - B R IY1 - M IH0 R
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+DOBRINJA  D AH0 - B R IY1 N - JH AH0
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+DOBROWOLSKI  D AH0 - B R AW0 - OW1 L - S K IY0
+DOBROWSKI  D AH0 - B R AO1 F S - K IY0
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+DOBRZYNSKI  D OW2 - B R AH0 - ZH IH1 N - S K IY0
+DOBSON  D AA1 B - S AH0 N
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+DOBY  D OW1 - B IY0
+DOBYNS  D OW1 - B IH0 N Z
+DOC  D AA1 K
+DOCENT  D OW1 - S AH0 N T
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+DOCHERTY  D AA1 - CH ER0 - T IY0
+DOCHOW  D OW1 - CH AW0
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+DOCIE  D AA1 - K IY0
+DOCILA  D AA1 - S IH0 - L AH0
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+DOCILITY  D AA0 - S IH1 - L AH0 - T IY0
+DOCK  D AA1 K
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+DOCKED  D AA1 K T
+DOCKEN  D AA1 - K AH0 N
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+DOCKERY  D AA1 - K ER0 - IY0
+DOCKET  D AA1 - K AH0 T
+DOCKETS  D AA1 - K AH0 T S
+DOCKHAM  D AA1 K - HH AH0 M
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+DOCKING  D AA1 - K IH0 NG
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+DOCKLAND  D AA1 K - L AH0 N D
+DOCKLANDS  D AA1 K - L AH0 N D Z
+DOCKS  D AA1 K S
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+DOCKTER  D AA1 K - T ER0
+DOCKWORKER  D AA1 K - W ER2 - K ER0
+DOCKWORKERS  D AA1 K - W ER2 - K ER0 Z
+DOCKYARD  D AA1 K - Y AA2 R D
+DOCTOR  D AA1 K - T ER0
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+DOCTOR(2)  D AO1 K - T ER0
+DOCTORAL  D AA1 K - T ER0 - AH0 L
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+DOCTRINES  D AA1 K - T R AH0 N Z
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+DOCUMENT  D AA1 - K Y AH0 - M EH0 N T
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+DOCUMENTARY  D AA2 - K Y AH0 - M EH1 N - T ER0 - IY0
+DOCUMENTARY(2)  D AA2 - K Y AH0 - M EH1 - N ER0 - IY0
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+DOCUMENTATION  D AA2 - K Y AH0 - M EH0 N - T EY1 - SH AH0 N
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+DOCUMENTED  D AA1 - K Y AH0 - M EH2 N - T AH0 D
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+DOCUMENTING  D AA1 - K Y AH0 - M AH0 N - T IH0 NG
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+DOCUMENTING(3)  D AA1 - K Y UW0 - M AH0 N - T IH0 NG
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+DODARO  D OW0 - D AA1 - R OW0
+DODD  D AA1 D
+DODD'S  D AA1 D Z
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+DODDRIDGE  D AA1 - D R IH0 JH
+DODDS  D AA1 D Z
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+DODGED  D AA1 JH D
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+DODGERS'  D AA1 - JH ER0 Z
+DODGES  D AA1 - JH IH0 Z
+DODGING  D AA1 - JH IH0 NG
+DODO  D OW1 - D OW0
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+DODRILL  D AA1 - D R AH0 L
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+DOE'S  D OW1 Z
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+DOEDEN  D OW1 - D AH0 N
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+DOERFLINGER  D AO1 R - F AH0 L - IH0 - NG ER0
+DOERFLINGER(2)  D AO1 R - F L IH0 - NG ER0
+DOERING  D UW1 - ER0 - IH0 NG
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+DOERR  D AO1 R
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+DOFFS  D AO1 F S
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+DOHENY  D AA1 - HH IH0 - N IY0
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+DOSSIERS  D AO2 - S Y EY1 Z
+DOSSIERS(2)  D AO2 - S IY0 - EY1 Z
+DOST  D AA1 S T
+DOSTAL  D AA1 - S T AH0 L
+DOSTER  D AA1 - S T ER0
+DOSTIE  D AA1 - S T IY0
+DOSTOEVSKI  D AO2 - S T OW1 V - S K IY0
+DOSTOEVSKY  D AO2 - S T OW1 V - S K IY0
+DOSTOEVSKY(2)  D AO0 - S T OY0 - EH1 V S - K IY0
+DOSTOEVSKY(3)  D AO0 - S T OY0 - EH1 F S - K IY0
+DOSWELL  D AA1 S - W EH0 L
+DOT  D AA1 T
+DOTAN  D OW1 - T AH0 N
+DOTE  D OW1 T
+DOTEN  D OW1 - T AH0 N
+DOTH  D AO1 TH
+DOTHAN  D AA1 - TH AH0 N
+DOTING  D OW1 - T IH0 NG
+DOTO  D OW1 - T OW0
+DOTS  D AA1 T S
+DOTSON  D AA1 T - S AH0 N
+DOTT  D AA1 T
+DOTTAVIO  D OW0 - T AA1 - V IY0 - OW0
+DOTTED  D AA1 - T AH0 D
+DOTTED(2)  D AA1 - T IH0 D
+DOTTER  D AA1 - T ER0
+DOTTERER  D AA1 - T ER0 - ER0
+DOTTIE  D AA1 - T IY0
+DOTTING  D AA1 - T IH0 NG
+DOTTS  D AA1 T S
+DOTTY  D AA1 - T IY0
+DOTY  D OW1 - T IY0
+DOTZLER  D AA1 T S - L ER0
+DOUB  D AW1 B
+DOUBEK  D AW1 - B IH0 K
+DOUBET  D AW1 - B IH0 T
+DOUBLE  D AH1 - B AH0 L
+DOUBLE-ENTENDRE  D AH1 - B AH0 L - AA0 N - T AA1 N - D R AH0
+DOUBLED  D AH1 - B AH0 L D
+DOUBLEDAY  D AH1 - B AH0 L - D EY2
+DOUBLES  D AH1 - B AH0 L Z
+DOUBLESPEAK  D AH1 - B AH0 L - S P IY2 K
+DOUBLETHINK  D AH1 - B AH0 L - TH IH2 NG K
+DOUBLETREE  D AH1 - B AH0 L - T R IY2
+DOUBLING  D AH1 - B AH0 L - IH0 NG
+DOUBLING(2)  D AH1 - B L IH0 NG
+DOUBLY  D AH1 - B L IY0
+DOUBRAVA  D AW1 - B R AH0 - V AH0
+DOUBT  D AW1 T
+DOUBTED  D AW1 - T IH0 D
+DOUBTER  D AW1 - T ER0
+DOUBTERS  D AW1 - T ER0 Z
+DOUBTFIRE  D AW1 T - F AY1 - ER0
+DOUBTFIRE'S  D AW1 T - F AY1 - ER0 Z
+DOUBTFIRES  D AW1 T - F AY1 - ER0 Z
+DOUBTFUL  D AW1 T - F AH0 L
+DOUBTING  D AW1 - T IH0 NG
+DOUBTLESS  D AW1 T - L AH0 S
+DOUBTS  D AW1 T S
+DOUCET  D UW0 - S EH1 T
+DOUCETTE  D UW1 - S EH1 T
+DOUD  D AW1 D
+DOUDNA  D AW1 D - N AH0
+DOUDS  D AW1 D Z
+DOUG  D AH1 G
+DOUG'S  D AH1 G Z
+DOUGAL  D UW1 - G AH0 L
+DOUGALL  D AW1 - G AH0 L
+DOUGALL(2)  D UW1 - G AH0 L
+DOUGAN  D AW1 - G AH0 N
+DOUGAN(2)  D UW1 - G AH0 N
+DOUGANS  D AW1 - G AH0 N
+DOUGANS(2)  D UW1 - G AH0 N
+DOUGENS  D AW1 - G AH0 N Z
+DOUGENS(2)  D UW1 - G AH0 N Z
+DOUGH  D OW1
+DOUGHBOY  D OW1 - B OY2
+DOUGHER  D OW1 - ER0
+DOUGHERTY  D OW1 - ER0 - T IY0
+DOUGHERTY(2)  D AA1 - G ER0 - T IY0
+DOUGHMAN  D OW1 - M AH0 N
+DOUGHNUT  D OW1 - N AH2 T
+DOUGHNUTS  D OW1 - N AH2 T S
+DOUGHTEN  D AO1 - T AH0 N
+DOUGHTIE  D AO1 - T IY0
+DOUGHTY  D AO1 - T IY0
+DOUGHY  D OW1 - IY0
+DOUGIE  D UW1 - G IY0
+DOUGIE'S  D UW1 - G IY0 Z
+DOUGL  D UW1 - G AH0 L
+DOUGLAS  D AH1 G - L AH0 S
+DOUGLAS'  D AH1 G - L AH0 S
+DOUGLAS'S  D AH1 G - L AH0 - S IH0 Z
+DOUGLASS  D AH1 G - L AH0 - S IH0 Z
+DOUKAS  D AW1 - K AH0 Z
+DOULTON  D OW1 L - T AH0 N
+DOUMA  D OW1 - M AH0
+DOUPE  D UW1 P
+DOUR  D AW1 - ER0
+DOUR(2)  D AW1 R
+DOUSE  D AW1 S
+DOUSED  D AW1 S T
+DOUSING  D AW1 - S IH0 NG
+DOUTHAT  D AW1 - TH AH0 T
+DOUTHETT  D AW1 - TH IH0 T
+DOUTHIT  D UW0 - TH IH1 T
+DOUTHITT  D UW0 - TH IH1 T
+DOUTT  D AW1 T
+DOUTY  D AW1 - T IY0
+DOUVILLE  D UW0 - V IH1 L
+DOUWE  D UW1
+DOV  D AA1 V
+DOVE  D AH1 V
+DOVE(2)  D OW1 V
+DOVEL  D OW0 - V EH1 L
+DOVER  D OW1 - V ER0
+DOVER'S  D OW1 - V ER0 Z
+DOVERSPIKE  D AH0 - V ER1 - S P IH0 K
+DOVES  D AH1 V Z
+DOVETAIL  D AH1 V - T EY2 L
+DOVETAILED  D AH1 V - T EY2 L D
+DOVETAILS  D AH1 V - T EY2 L Z
+DOVEY  D AH0 - V EY1
+DOVIDIO  D OW0 - V IY1 - D IY0 - OW0
+DOVISH  D OW1 - V IH0 SH
+DOVISH(2)  D AH1 - V IH0 SH
+DOW  D AW1
+DOW'S  D AW1 Z
+DOWAGER  D AW1 - AH0 - JH ER0
+DOWD  D AW1 D
+DOWDA  D AW1 - D AH0
+DOWDALL  D AW1 - D AH0 L
+DOWDELL  D AW1 - D AH0 L
+DOWDEN  D AW1 - D AH0 N
+DOWDING  D AW1 - D IH0 NG
+DOWDLE  D AW1 - D AH0 L
+DOWDS  D AW1 D Z
+DOWDY  D AW1 - D IY0
+DOWE  D AW1
+DOWEL  D AW1 - AH0 L
+DOWELL  D AA1 - W EH0 L
+DOWELS  D AW1 - AH0 L Z
+DOWEN  D AW1 - AH0 N
+DOWER  D AW1 R
+DOWERS  D AW1 R Z
+DOWIE  D AW1 - IY0
+DOWIS  D AW1 - IH0 S
+DOWLAND  D AW1 - L AH0 N D
+DOWLEN  D AW1 - L AH0 N
+DOWLER  D AW1 - L ER0
+DOWLESS  D AW1 - L AH0 S
+DOWLING  D AW1 - L IH0 NG
+DOWN  D AW1 N
+DOWN'S  D AW1 N Z
+DOWNARD  D AW1 - N ER0 D
+DOWNBEAT  D AW0 N - B IY1 T
+DOWNCAST  D AW1 N - K AE2 S T
+DOWNDRAFT  D AW1 N - D R AE2 F T
+DOWNE  D AW1 N
+DOWNED  D AW1 N D
+DOWNEN  D AW1 - N AH0 N
+DOWNER  D AW1 - N ER0
+DOWNERS  D AW1 - N ER0 Z
+DOWNES  D AW1 N Z
+DOWNES'S  D AW1 N - Z IH0 Z
+DOWNEY  D AW1 - N IY0
+DOWNEY'S  D AW1 - N IY0 Z
+DOWNFALL  D AW1 N - F AO2 L
+DOWNGRADE  D AW1 N - G R EY1 D
+DOWNGRADED  D AW1 N - G R EY1 - D AH0 D
+DOWNGRADES  D AW1 N - G R EY1 D Z
+DOWNGRADING  D AW1 N - G R EY1 - D IH0 NG
+DOWNGRADINGS  D AW1 N - G R EY2 - D IH0 NG Z
+DOWNHAM  D AW1 N - HH AH0 M
+DOWNHILL  D AW1 N - HH IH1 L
+DOWNIE  D AW1 - N IY0
+DOWNING  D AW1 - N IH0 NG
+DOWNINGTOWN  D AW1 - N IH0 NG - T AW2 N
+DOWNLOAD  D AW1 N - L OW2 D
+DOWNLOADED  D AW1 N - L OW2 - D IH0 D
+DOWNLOADING  D AW1 N - L OW2 - D IH0 NG
+DOWNPAYMENT  D AW2 N - P EY1 - M AH0 N T
+DOWNPLAY  D AW1 N - P L EY2
+DOWNPLAYED  D AW1 N - P L EY2 D
+DOWNPLAYING  D AW1 N - P L EY2 - IH0 NG
+DOWNPLAYS  D AW1 N - P L EY2 Z
+DOWNPOUR  D AW1 N - P AO0 R
+DOWNPOURS  D AW1 N - P AO2 R Z
+DOWNRANGE  D AW1 N - R EY1 N JH
+DOWNRIGHT  D AW1 N - R AY2 T
+DOWNRIVER  D AW2 N - R IH1 - V ER0
+DOWNS  D AW1 N Z
+DOWNSCALE  D AW1 N - S K EY2 L
+DOWNSIDE  D AW1 N - S AY2 D
+DOWNSIDES  D AW1 N - S AY2 D Z
+DOWNSIZE  D AW1 N - S AY2 Z
+DOWNSIZED  D AW1 N - S AY2 Z D
+DOWNSIZING  D AW1 N - S AY2 - Z IH0 NG
+DOWNSIZINGS  D AW1 N - S AY2 - Z IH0 NG Z
+DOWNSTAGE  D AW1 N - S T EY2 JH
+DOWNSTAIRS  D AW1 N - S T EH1 R Z
+DOWNSTATE  D AW1 N - S T EY2 T
+DOWNSTREAM  D AW1 N - S T R IY1 M
+DOWNSWING  D AW1 N - S W IH2 NG
+DOWNTIME  D AW1 N - T AY2 M
+DOWNTOWN  D AW1 N - T AW1 N
+DOWNTOWNS  D AW1 N - T AW1 N Z
+DOWNTREND  D AW1 N - T R EH2 N D
+DOWNTRODDEN  D AW2 N - T R AA1 - D AH0 N
+DOWNTURN  D AW1 N - T ER2 N
+DOWNTURNS  D AW1 N - T ER2 N Z
+DOWNUM  D AW1 - N AH0 M
+DOWNWARD  D AW1 N - W ER0 D
+DOWNWARDLY  D AW1 N - W ER0 D - L IY0
+DOWNWARDS  D AW1 N - W ER0 D Z
+DOWNWIND  D AW0 N - W IH1 N D
+DOWNY  D AW1 - N IY0
+DOWRY  D AW1 - R IY0
+DOWSE  D AW1 S
+DOWSING  D AW1 - S IH0 NG
+DOWSMAN  D AW1 Z - M AH0 N
+DOWTY  D AW1 - T IY0
+DOXEY  D AA1 K - S IY0
+DOXIE  D AA1 K - S IY0
+DOXOLOGIES  D AA0 K - S AA1 - L AH0 - JH IY0 Z
+DOXOLOGY  D AA0 K - S AA1 - L AH0 - JH IY0
+DOXTATER  D AA1 K - S T EY0 - T ER0
+DOXY  D AA1 K - S IY0
+DOYAL  D OY0 - AA1 L
+DOYEL  D OY1 - AH0 L
+DOYEN  D OY1 - IH0 N
+DOYLE  D OY1 L
+DOYLE'S  D OY1 L Z
+DOYON  D OY1 - AH0 N
+DOZE  D OW1 Z
+DOZED  D OW1 Z D
+DOZEN  D AH1 - Z AH0 N
+DOZENS  D AH1 - Z AH0 N Z
+DOZES  D OW1 - Z IH0 Z
+DOZIER  D OW1 - Z IY0 - ER0
+DOZING  D OW1 - Z IH0 NG
+DQALPHA  D IY1 - K Y UW1 - AE1 L - F AH0
+DR  D R AY1 V
+DR(2)  D AA1 K - T ER0
+DR.  D R AY1 V
+DR.(2)  D AA1 K - T ER0
+DRAB  D R AE1 B
+DRABBED  D R AE1 B D
+DRABBLE  D R AE1 - B AH0 L
+DRABEK  D R AE1 - B IH0 K
+DRABENSTOTT  D R AH0 - B EH1 N - S T AH0 T
+DRABIK  D R AA1 - B IH0 K
+DRABINSKY  D R AH0 - B IH1 N - S K IY0
+DRABKIN  D R AE1 B - K IH2 N
+DRABS  D R AE1 B Z
+DRACE  D R EY1 S
+DRACH  D R AE1 CH
+DRACHENBERG  D R AE1 - K AH0 N - B ER0 G
+DRACHMA  D R AE1 K - M AH0
+DRACHMAS  D R AA1 K - M AH0 Z
+DRACKETT  D R AE1 - K IH0 T
+DRACO  D R EY1 - K OW0
+DRACO(2)  D R AE1 - K OW0
+DRACONIAN  D R EY0 - K OW1 - N IY0 - AH0 N
+DRACONIAN(2)  D R AH0 - K OW1 - N IY0 - AH0 N
+DRACULA  D R AE1 - K Y UW0 - L AH0
+DRAEGER  D R EH1 - G ER0
+DRAFFEN  D R AE1 - F AH0 N
+DRAFT  D R AE1 F T
+DRAFT'S  D R AE1 F T S
+DRAFTED  D R AE1 F - T IH0 D
+DRAFTEE  D R AE1 F - T IY1
+DRAFTEES  D R AE1 F - T IY1 Z
+DRAFTER  D R AE1 F - T ER0
+DRAFTERS  D R AE1 F - T ER0 Z
+DRAFTING  D R AE1 F - T IH0 NG
+DRAFTS  D R AE1 F T S
+DRAFTSMAN  D R AE1 F T S - M AH0 N
+DRAFTSMANSHIP  D R AE1 F T S - M AH0 N - SH IH2 P
+DRAFTSMEN  D R AE1 F T S - M AH0 N
+DRAFTY  D R AE1 F - T IY0
+DRAG  D R AE1 G
+DRAGAN  D R AA1 - G AH0 N
+DRAGE  D R EY1 JH
+DRAGER  D R EY1 - G ER0
+DRAGGED  D R AE1 G D
+DRAGGING  D R AE1 - G IH0 NG
+DRAGGY  D R AE1 - G IY0
+DRAGLINE  D R AE1 - G L AY2 N
+DRAGNET  D R AE1 G - N EH2 T
+DRAGNETS  D R AE1 G - N EH2 T S
+DRAGO  D R AA1 - G OW0
+DRAGON  D R AE1 - G AH0 N
+DRAGON'S  D R AE1 - G AH0 N Z
+DRAGONAIR  D R AE1 - G AH0 - N EH2 R
+DRAGONE  D R AH0 - G OW1 N
+DRAGONHEAD  D R AE1 - G AH0 N - HH EH2 D
+DRAGONHEART  D R AE1 - G AH0 N - HH AA2 R T
+DRAGONS  D R AE1 - G AH0 N Z
+DRAGOO  D R AA1 - G UW0
+DRAGOVICH  D R AE1 - G AH0 - V IH0 CH
+DRAGS  D R AE1 G Z
+DRAHEIM  D R AE1 - HH AY0 M
+DRAHOS  D R EY1 - HH OW0 Z
+DRAHUSCHAK  D R AE1 - HH AH0 - SH AE0 K
+DRAHUSCHAK(2)  D R AH0 - HH UW1 - SH AH0 K
+DRAIN  D R EY1 N
+DRAINAGE  D R EY1 - N AH0 JH
+DRAINAGE(2)  D R EY1 - N IH0 JH
+DRAINE  D R EY1 N
+DRAINED  D R EY1 N D
+DRAINER  D R EY1 - N ER0
+DRAINERS  D R EY1 - N ER0 Z
+DRAINING  D R EY1 - N IH0 NG
+DRAINS  D R EY1 N Z
+DRAKE  D R EY1 K
+DRAKEFORD  D R AE1 K - F AO0 R D
+DRAKES  D R EY1 K S
+DRAKOS  D R EY1 - K OW0 Z
+DRALLE  D R EY1 L
+DRAM  D R AE1 M
+DRAM(2)  D IY1 - R AE2 M
+DRAMA  D R AA1 - M AH0
+DRAMAMINE  D R AE1 - M AH0 - M IY2 N
+DRAMAS  D R AA1 - M AH0 Z
+DRAMATIC  D R AH0 - M AE1 - T IH0 K
+DRAMATICALLY  D R AH0 - M AE1 - T IH0 K - L IY0
+DRAMATICALLY(2)  D R AH0 - M AE1 - T IH0 - K AH0 - L IY0
+DRAMATIST  D R AA1 - M AH0 - T IH0 S T
+DRAMATIZATION  D R AE2 - M AH0 - T AH0 - Z EY1 - SH AH0 N
+DRAMATIZATIONS  D R AE2 - M AH0 - T AH0 - Z EY1 - SH AH0 N Z
+DRAMATIZE  D R AA1 - M AH0 - T AY2 Z
+DRAMATIZE(2)  D R AE1 - M AH0 - T AY2 Z
+DRAMATIZED  D R AE1 - M AH0 - T AY2 Z D
+DRAMATIZES  D R AE1 - M AH0 - T AY2 - Z IH0 Z
+DRAMATIZING  D R AE1 - M AH0 - T AY2 - Z IH0 NG
+DRAMS  D R AE1 M Z
+DRAMS(2)  D IY1 - R AE2 M Z
+DRANE  D R EY1 N
+DRANEY  D R EY1 - N IY0
+DRANG  D R AE1 NG
+DRANK  D R AE1 NG K
+DRANSFIELD  D R AE1 N S - F IY2 L D
+DRAPE  D R EY1 P
+DRAPEAU  D R AH0 - P OW1
+DRAPED  D R EY1 P T
+DRAPER  D R EY1 - P ER0
+DRAPERIES  D R EY1 - P ER0 - IY0 Z
+DRAPERY  D R EY1 - P ER0 - IY0
+DRAPES  D R EY1 P S
+DRAPING  D R EY1 - P IH0 NG
+DRAPKIN  D R AE1 P - K IH0 N
+DRASNER  D R AE1 S - N ER0
+DRASTIC  D R AE1 - S T IH0 K
+DRASTICALLY  D R AE1 - S T IH0 K - L IY0
+DRAUGHN  D R AO1 N
+DRAUGHON  D R AO1 - AH0 N
+DRAUGHT  D R AE1 F T
+DRAUGHTS  D R AE1 F T S
+DRAUS  D R AO1 Z
+DRAVECKY  D R AH0 - V EH1 - K IY0
+DRAVES  D R EY1 V Z
+DRAVIS  D R AE1 - V IH0 S
+DRAVO  D R AE1 - V OW0
+DRAVO'S  D R AE1 - V OW0 Z
+DRAW  D R AO1
+DRAWBACK  D R AO1 - B AE2 K
+DRAWBACKS  D R AO1 - B AE2 K S
+DRAWBAUGH  D R AO1 - B AO2
+DRAWBRIDGE  D R AO1 - B R IH2 JH
+DRAWDOWN  D R AO1 - D AW2 N
+DRAWDOWNS  D R AO1 - D AW2 N Z
+DRAWDY  D R AO1 - D IY0
+DRAWER  D R AO1 R
+DRAWERS  D R AO1 R Z
+DRAWING  D R AO1 - IH0 NG
+DRAWINGS  D R AO1 - IH0 NG Z
+DRAWL  D R AO1 L
+DRAWLED  D R AO1 L D
+DRAWLS  D R AO1 L Z
+DRAWN  D R AO1 N
+DRAWS  D R AO1 Z
+DRAY  D R EY1
+DRAYER  D R EY1 - ER0
+DRAYTON  D R EY1 - T AH0 N
+DRAYTON'S  D R EY1 - T AH0 N Z
+DREAD  D R EH1 D
+DREADED  D R EH1 - D IH0 D
+DREADFUL  D R EH1 D - F AH0 L
+DREADFULLY  D R EH1 D - F AH0 - L IY0
+DREADING  D R EH1 - D IH0 NG
+DREADNOUGHT  D R EH1 D - N AO2 T
+DREADS  D R EH1 D Z
+DREAM  D R IY1 M
+DREAMED  D R IY1 M D
+DREAMER  D R IY1 - M ER0
+DREAMERS  D R IY1 - M ER0 Z
+DREAMING  D R IY1 - M IH0 NG
+DREAMLAND  D R IY1 M - L AE2 N D
+DREAMLIKE  D R IY1 M - L AY2 K
+DREAMS  D R IY1 M Z
+DREAMT  D R EH1 M T
+DREAMWORKS  D R IY1 M - W ER2 K S
+DREAMWORKS'  D R IY1 M - W ER2 K S
+DREAMWORLD  D R IY1 M - W ER2 L D
+DREAMY  D R IY1 - M IY0
+DREARINESS  D R IY1 - R IY0 - N AH0 S
+DREARY  D R IH1 - R IY0
+DREBSKY  D R EH1 B S - K IY0
+DRECHSEL  D R EH1 K - S AH0 L
+DRECHSLER  D R EH1 K - S AH0 - L ER0
+DRECHSLER(2)  D R EH1 K S - L ER0
+DRED  D R EH1 D
+DREDD  D R EH1 D
+DREDGE  D R EH1 JH
+DREDGED  D R EH1 JH D
+DREDGES  D R EH1 - JH AH0 Z
+DREDGES(2)  D R EH1 - JH IH0 Z
+DREDGING  D R EH1 - JH IH0 NG
+DREES  D R IY1 Z
+DREESE  D R IY1 Z
+DREESSEN  D R IY1 - S AH0 N
+DREGER  D R EH1 - G ER0
+DREGS  D R EH1 G Z
+DREHER  D R EH1 R
+DREIBELBIS  D R AY1 - B IH0 L - B IH0 S
+DREIER  D R AY1 - ER0
+DREIGHTON  D R AY1 - T IH0 N
+DREILING  D R AY1 - L IH0 NG
+DREIS  D R IY1 Z
+DREISBACH  D R AY1 S - B AA2 K
+DREMAN  D R IY1 - M AH0 N
+DRENCH  D R EH1 N CH
+DRENCHED  D R EH1 N CH T
+DRENCHING  D R EH1 N - CH IH0 NG
+DRENNAN  D R EH1 - N AH0 N
+DRENNEN  D R EH1 - N AH0 N
+DRENNING  D R EH1 - N IH0 NG
+DRENNON  D R EH1 - N AH0 N
+DRENTH  D R EH1 N TH
+DREPUNG  D R EY1 - P AH2 NG
+DREPUNG(2)  D R EY2 - P AO1 NG
+DRESBACH  D R EH1 S - B AA2 K
+DRESCH  D R EH1 SH
+DRESCHER  D R EH1 - SH ER0
+DRESDEN  D R EH1 Z - D IH0 N
+DRESDNER  D R EH1 Z D - N ER0
+DRESDNER'S  D R EH1 Z D - N ER0 Z
+DRESEN  D R IY1 - Z AH0 N
+DRESHER  D R EH1 - SH ER0
+DRESNER  D R EH1 Z - N ER0
+DRESS  D R EH1 S
+DRESSAGE  D R EH0 - S AA1 ZH
+DRESSED  D R EH1 S T
+DRESSEL  D R EH1 - S AH0 L
+DRESSEN  D R EH1 - S AH0 N
+DRESSER  D R EH1 - S ER0
+DRESSER'S  D R EH1 - S ER0 Z
+DRESSERS  D R EH1 - S ER0 Z
+DRESSES  D R EH1 - S AH0 Z
+DRESSES(2)  D R EH1 - S IH0 Z
+DRESSIER  D R EH1 - S IY0 - ER0
+DRESSING  D R EH1 - S IH0 NG
+DRESSINGS  D R EH1 - S IH0 NG Z
+DRESSLER  D R EH1 S - L ER0
+DRESSMAKER  D R EH1 S - M EY2 - K ER0
+DRESSMAKERS  D R EH1 S - M EY2 - K ER0 Z
+DRESSMAKING  D R EH1 S - M EY2 - K IH0 NG
+DRESSY  D R EH1 - S IY0
+DREW  D R UW1
+DREWERY  D R UW1 - ER0 - IY0
+DREWES  D R UW1 Z
+DREWETT  D R UW1 - IH0 T
+DREWRY  D R UW1 - R IY0
+DREWS  D R UW1 Z
+DREXEL  D R EH1 K - S AH0 L
+DREXEL'S  D R EH1 K - S AH0 L Z
+DREXLER  D R EH1 K S - L ER0
+DREY  D R EY1
+DREYER  D R EY1 - ER0
+DREYFUS  D R AY1 - F AH0 S
+DREYFUS'S  D R EY1 - F AH0 - S IH0 Z
+DREYFUS(2)  D R EY1 - F AH0 S
+DREYFUSS  D R EY1 - F AH0 S
+DRIBBED  D R IH1 B D
+DRIBBLE  D R IH1 - B AH0 L
+DRIBBLED  D R IH1 - B AH0 L D
+DRIBBLING  D R IH1 - B AH0 L - IH0 NG
+DRIBBLING(2)  D R IH1 - B L IH0 NG
+DRIBS  D R IH1 B Z
+DRIED  D R AY1 D
+DRIEHAUS  D R IY1 - HH AW2 S
+DRIER  D R AY1 - ER0
+DRIES  D R AY1 Z
+DRIESSEN  D R IY1 - S AH0 N
+DRIEST  D R AY1 - AH0 S T
+DRIEVER  D R IY1 - V ER0
+DRIFT  D R IH1 F T
+DRIFTED  D R IH1 F - T AH0 D
+DRIFTED(2)  D R IH1 F - T IH0 D
+DRIFTER  D R IH1 F - T ER0
+DRIFTERS  D R IH1 F - T ER0 Z
+DRIFTING  D R IH1 F - T IH0 NG
+DRIFTNET  D R IH1 F T - N EH2 T
+DRIFTS  D R IH1 F T S
+DRIFTWOOD  D R IH1 F T - W UH2 D
+DRIGGERS  D R IH1 - G ER0 Z
+DRIGGS  D R IH1 G Z
+DRILL  D R IH1 L
+DRILLBIT  D R IH1 L - B IH2 T
+DRILLED  D R IH1 L D
+DRILLER  D R IH1 - L ER0
+DRILLERS  D R IH1 - L ER0 Z
+DRILLING  D R IH1 - L IH0 NG
+DRILLING'S  D R IH1 - L IH0 NG Z
+DRILLS  D R IH1 L Z
+DRINA  D IY1 - N AH0
+DRINA'S  D IY1 - N AH0 Z
+DRINAS  D IY1 - N AH0 Z
+DRING  D R IH1 NG
+DRINK  D R IH1 NG K
+DRINKABLE  D R IH1 N - K AH0 - B AH0 L
+DRINKARD  D R IH1 NG - K ER0 D
+DRINKER  D R IH1 NG - K ER0
+DRINKER'S  D R IH1 NG - K ER0 Z
+DRINKERS  D R IH1 NG - K ER0 Z
+DRINKING  D R IH1 NG - K IH0 NG
+DRINKS  D R IH1 NG K S
+DRINKWATER  D R IH1 NG - K W AO2 - T ER0
+DRINKWINE  D R IH1 NG - K W AY2 N
+DRINNON  D R IH1 - N AH0 N
+DRIP  D R IH1 P
+DRIPPED  D R IH1 P T
+DRIPPING  D R IH1 - P IH0 NG
+DRIPPS  D R IH1 P S
+DRIPS  D R IH1 P S
+DRISCOLL  D R IH1 S - K AH0 L
+DRISKELL  D R IH1 S - K AH0 L
+DRISKILL  D R IH1 - S K IH0 L
+DRIVABLE  D R AY1 - V AH0 - B AH0 L
+DRIVE  D R AY1 V
+DRIVE'S  D R AY1 V Z
+DRIVEL  D R IH1 - V AH0 L
+DRIVEN  D R IH1 - V AH0 N
+DRIVER  D R AY1 - V ER0
+DRIVER'S  D R AY1 - V ER0 Z
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+DRIVERS  D R AY1 - V ER0 Z
+DRIVERS'  D R AY1 - V ER0 Z
+DRIVES  D R AY1 V Z
+DRIVEWAY  D R AY1 V - W EY2
+DRIVEWAYS  D R AY1 V - W EY2 Z
+DRIVING  D R AY1 - V IH0 NG
+DRIZZLE  D R IH1 - Z AH0 L
+DRIZZLING  D R IH1 - Z AH0 L - IH0 NG
+DRIZZLING(2)  D R IH1 Z - L IH0 NG
+DRIZZLY  D R IH1 Z - L IY0
+DROBKOV  D R AO1 B - K AO0 V
+DROBKOV(2)  D R AO1 B - K AO0 F
+DROBNY  D R AA1 B - N IY0
+DRODDY  D R AA1 - D IY0
+DROEGE  D R OW1 JH
+DROESSLER  D R OW1 - S AH0 - L ER0
+DROESSLER(2)  D R OW1 S - L ER0
+DROGE  D R OW1 JH
+DROGOUL  D R OW0 - G UW1 L
+DROGOUL'S  D R OW0 - G UW1 L Z
+DROGUE  D R OW1 G
+DROHAN  D R OW1 - AH0 N
+DROKE  D R OW1 K
+DROLET  D R OW1 - L IH0 T
+DROLL  D R OW1 L
+DROLLINGER  D R OW1 - L IH0 - NG ER0
+DROMEDARY  D R AA1 - M AH0 - D EH2 - R IY0
+DROMER  D R OW1 - M ER0
+DROMEY  D R OW1 - M IY0
+DROMGOOLE  D R AA1 M - G UW2 L
+DROMGOOLES  D R AA0 M - G UW1 L Z
+DROMI  D R AA1 - M IY0
+DROMOMANIA  D R OW2 - M OW0 - M EY1 - N IY0 - AH0
+DROMOMANIA(2)  D R OW2 - M OW0 - M EY1 - N Y AH0
+DROMON  D R OW1 - M AH0 N
+DRONE  D R OW1 N
+DRONED  D R OW1 N D
+DRONES  D R OW1 N Z
+DRONET  D R OW1 - N IH0 T
+DRONEY  D R OW1 - N IY0
+DRONING  D R OW1 - N IH0 NG
+DROOL  D R UW1 L
+DROOLING  D R UW1 - L IH0 NG
+DROOP  D R UW1 P
+DROOPED  D R UW1 P T
+DROOPING  D R UW1 - P IH0 NG
+DROOPY  D R UW1 - P IY0
+DROP  D R AA1 P
+DROP(2)  D R AO1 P
+DROPKIN  D R AA1 P - K IH0 N
+DROPLET  D R AA1 - P L AH0 T
+DROPLETS  D R AA1 - P L AH0 T S
+DROPOFF  D R AA1 - P AO2 F
+DROPOUT  D R AA1 P - AW2 T
+DROPOUTS  D R AA1 P - AW2 T S
+DROPPED  D R AA1 P T
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+DROPPING  D R AA1 - P IH0 NG
+DROPPINGS  D R AA1 - P IH0 NG Z
+DROPS  D R AA1 P S
+DROPSY  D R AA1 P - S IY0
+DROSER  D R OW1 - Z ER0
+DROSS  D R AO1 S
+DROST  D R AA1 S T
+DROSTE  D R OW1 S T
+DROTAR  D R OW1 - T ER0
+DROUGHT  D R AW1 T
+DROUGHT'S  D R AW1 T S
+DROUGHTS  D R AW1 T S
+DROUILLARD  D R W IY0 - L AA1 R D
+DROUIN  D R W IY1 N
+DROVE  D R OW1 V
+DROVER  D R OW1 - V ER0
+DROVES  D R OW1 V Z
+DROWN  D R AW1 N
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+DROWNING  D R AW1 - N IH0 NG
+DROWNINGS  D R AW1 - N IH0 NG Z
+DROWNS  D R AW1 N Z
+DROWSINESS  D R AW1 - Z IY0 - N AH0 S
+DROWSY  D R AW1 - Z IY0
+DROZ  D R AA1 Z
+DROZD  D R AA1 Z D
+DROZDA  D R AA1 Z - D AH0
+DROZDOWSKI  D R AH0 Z - D AO1 F S - K IY0
+DRU  D R UW1
+DRUB  D R AH1 B
+DRUBBED  D R AH1 B D
+DRUBBING  D R AH1 - B IH0 NG
+DRUCE  D R UW1 S
+DRUCIE  D R AH1 - K IY0
+DRUCK  D R AH1 K
+DRUCKENMILLER  D R AH1 - K IH0 N - M IH2 - L ER0
+DRUCKER  D R AH1 - K ER0
+DRUCKMAN  D R AH1 K - M AH0 N
+DRUDGE  D R AH1 JH
+DRUDGERY  D R AH1 - JH ER0 - IY0
+DRUELLA  D R UW2 - EH1 - L AH0
+DRUG  D R AH1 G
+DRUG'S  D R AH1 G Z
+DRUGAN  D R UW1 - G AH0 N
+DRUGGED  D R AH1 G D
+DRUGGING  D R AH1 - G IH0 NG
+DRUGGIST  D R AH1 - G IH0 S T
+DRUGGIST'S  D R AH1 - G AH0 S T S
+DRUGGIST'S(2)  D R AH1 - G IH0 S T S
+DRUGGISTS  D R AH1 - G AH0 S T S
+DRUGGISTS(2)  D R AH1 - G IH0 S T S
+DRUGGISTS(3)  D R AH1 - G IH0 S S
+DRUGGISTS(4)  D R AH1 - G IH0 S
+DRUGMAKER  D R AH1 G - M EY2 - K ER0
+DRUGMAKERS  D R AH1 G - M EY2 - K ER0 Z
+DRUGS  D R AH1 G Z
+DRUGS'  D R AH1 G Z
+DRUGSTORE  D R AH1 G - S T AO2 R
+DRUGSTORES  D R AH1 G - S T AO2 R Z
+DRUID  D R UW1 - IH0 D
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+DRUM  D R AH1 M
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+DRUMHEAD  D R AH1 M - HH EH2 D
+DRUMHELLER  D R AH1 M - HH EH2 - L ER0
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+DRUMM  D R AH1 M
+DRUMMED  D R AH1 M D
+DRUMMER  D R AH1 - M ER0
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+DRUMMING  D R AH1 - M IH0 NG
+DRUMMOND  D R AH1 - M AH0 N D
+DRUMMONDS  D R AH1 - M AH0 N D Z
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+DRUMS  D R AH1 M Z
+DRUMSTICK  D R AH1 M - S T IH0 K
+DRUMWRIGHT  D R AH1 M - R AY2 T
+DRUNK  D R AH1 NG K
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+DRUNKARDS  D R AH1 NG - K ER0 D Z
+DRUNKEN  D R AH1 NG - K AH0 N
+DRUNKENNESS  D R AH1 NG - K AH0 N - N AH0 S
+DRUNKS  D R AH1 NG K S
+DRUPE  D R UW1 P
+DRUPES  D R UW1 P S
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+DRUSE  D R UW1 Z
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+DRUZE  D R UW1 Z
+DRY  D R AY1
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+DRYDEN  D R AY1 - D AH0 N
+DRYE  D R AY1
+DRYER  D R AY1 - ER0
+DRYERS  D R AY1 - ER0 Z
+DRYING  D R AY1 - IH0 NG
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+DRYSDALE  D R AY1 Z - D EY2 L
+DRYSER  D R AY1 - S ER0
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+DRZEWIECKI  JH UW0 - IY1 T S - K IY0
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+DU  D UW1
+DU(2)  D AH0
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+DUAL(2)  D UW1 L
+DUALISM  D UW1 - AH0 - L IH2 - Z AH0 M
+DUALISMS  D UW1 - AH0 - L IH2 - Z AH0 M Z
+DUALIST  D UW1 - AH0 - L IH0 S T
+DUALISTIC  D UW2 - AH0 - L IH1 - S T IH0 K
+DUALITY  D UW0 - AE1 - L AH0 - T IY0
+DUALS  D UW1 - AH0 L Z
+DUAN  D W AE1 N
+DUAN(2)  D W EY1 N
+DUANA  D UW0 - AE1 - N AH0
+DUANE  D W EY1 N
+DUARTE  D W AA1 R - T EY2
+DUARTE'S  D W AA1 R - T EY2 Z
+DUB  D AH1 B
+DUBA  D UW1 - B AH0
+DUBACH  D AH1 - B AA0 K
+DUBAI  D UW0 - B AY1
+DUBARRY  D UW1 - B EH2 - R IY0
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+DUBAY  D AH1 - B EY0
+DUBBED  D AH1 B D
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+DUBBING  D AH1 - B IH0 NG
+DUBBS  D AH1 B Z
+DUBCEK  D AH1 B - CH EH2 K
+DUBCEK(2)  D UW1 B - CH EH2 K
+DUBE  D UW1 B
+DUBEAU  D AH0 - B OW1
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+DUBERSTEIN'S  D UW1 - B ER0 - S T AY2 N Z
+DUBERSTEIN'S(2)  D UW1 - B ER0 - S T IY2 N Z
+DUBERSTEIN(2)  D UW1 - B ER0 - S T IY2 N
+DUBEY  D AH1 - B IY0
+DUBHI  D UW1 - B IY0
+DUBICKI  D AH0 - B IH1 T S - K IY0
+DUBIE  D AH1 - B IY0
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+DUBILIER  D UW2 - B AH0 - L IH1 R
+DUBILIER(2)  D UW2 - B AH0 - L AY1 - ER0
+DUBIN  D UW1 - B IH0 N
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+DUBINSKY  D AH0 - B IH1 N - S K IY0
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+DUBIS  D UW1 - B IH0 S
+DUBLIN  D AH1 - B L IH0 N
+DUBLIN'S  D AH1 - B L IH0 N Z
+DUBOFF  D AH1 - B AO2 F
+DUBOIS  D UW0 - B OY1 S
+DUBOIS(2)  D UW0 - B W AA1
+DUBOISE  D UW0 - B OY1 S
+DUBOISE(2)  D UW0 - B W AA1
+DUBORD  D AH0 - B AO1 R D
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+DUBRAWSKI  D UW0 - B R AW1 S - K IY0
+DUBRAY  D AH1 - B R EY2
+DUBREE  D AH0 - B R IY1
+DUBREUIL  D AH1 - B R UW0 L
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+DUBROVNIKS  D UW0 - B R AA1 V - N IH0 K Z
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+DUBS  D AH1 B Z
+DUBUC  D UW1 - B AH0 K
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+DUBUQUE'S  D AH0 - B Y UW1 K S
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+DUCHAMP'S  D UW0 - SH AA1 M P S
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+DUCHESNEAU  D AH1 - SH IH0 S - N OW0
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+DUCK'S  D AH1 K S
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+DUCKS'  D AH1 K S
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+DUCKWALL  D AH1 K - W AO2 L
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+DUDES  D Y UW1 D Z
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+DUE(2)  D Y UW1
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+DUEKER  D UW1 - K ER0
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+DUENA(2)  D W EY1 - N Y AH0
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+EMBAYMENT  EH0 M - B EY1 - M AH0 N T
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+EMBELLISHED  EH0 M - B EH1 - L IH0 SH T
+EMBELLISHING  EH0 M - B EH1 - L IH0 - SH IH0 NG
+EMBELLISHMENT  EH0 M - B EH1 - L IH0 SH - M AH0 N T
+EMBER  EH1 M - B ER0
+EMBERS  EH1 M - B ER0 Z
+EMBERSON  EH1 M - B ER0 - S AH0 N
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+EMBEZZLE  IH0 M - B EH1 - Z AH0 L
+EMBEZZLED  IH0 M - B EH1 - Z AH0 L D
+EMBEZZLEMENT  EH0 M - B EH1 - Z AH0 L - M AH0 N T
+EMBEZZLER  IH0 M - B EH1 - Z AH0 L - ER0
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+EMBEZZLERS  IH0 M - B EH1 - Z AH0 L - ER0 Z
+EMBEZZLERS(2)  EH0 M - B EH1 Z - L ER0 Z
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+EMBEZZLING  IH0 M - B EH1 - Z AH0 L - IH0 NG
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+EMBEZZLING(3)  IH0 M - B EH1 Z - L IH0 NG
+EMBEZZLING(4)  EH0 M - B EH1 - Z AH0 L - IH0 NG
+EMBITTER  EH0 M - B IH1 - T ER0
+EMBITTERED  EH0 M - B IH1 - T ER0 D
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+EMBLAZONED  EH0 M - B L EY1 - Z AH0 N D
+EMBLEM  EH1 M - B L AH0 M
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+EMBODIES  EH0 M - B AA1 - D IY0 Z
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+EMBODY  IH0 M - B AA1 - D IY0
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+EMBOLDEN  EH0 M - B OW1 L - D AH0 N
+EMBOLDENED  EH0 M - B OW1 L - D AH0 N D
+EMBOLISM  EH1 M - B OW0 - L IH2 - Z AH0 M
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+EMBOSS  IH0 M - B AO1 S
+EMBOSSED  IH0 M - B AO1 S T
+EMBRACE  EH0 M - B R EY1 S
+EMBRACE(2)  IH0 M - B R EY1 S
+EMBRACEABLE  IH0 M - B R EY1 - S AH0 - B AH0 L
+EMBRACED  EH0 M - B R EY1 S T
+EMBRACES  EH0 M - B R EY1 - S IH0 Z
+EMBRACING  EH0 M - B R EY1 - S IH0 NG
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+EMBREY  EH1 M - B R IY0
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+EMBROIDERED  EH0 M - B R OY1 - D ER0 D
+EMBROIDERER  EH0 M - B R OY1 - D ER0 - ER0
+EMBROIDERERS  EH0 M - B R OY1 - D ER0 - ER0 Z
+EMBROIDERIES  IH0 M - B R OY1 - D ER0 - IY0 Z
+EMBROIDERING  EH0 M - B R OY1 - D ER0 - IH0 NG
+EMBROIDERY  EH0 M - B R OY1 - D ER0 - IY0
+EMBROIL  EH0 M - B R OY1 L
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+EMBROSE  EH1 M - B R OW0 Z
+EMBRY  EH1 M - B R IY0
+EMBRYO  EH1 M - B R IY0 - OW2
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+EMBRYONIC  EH2 M - B R IY0 - AA1 - N IH0 K
+EMBRYOS  EH1 M - B R IY0 - OW2 Z
+EMBURY  EH1 M - B EH2 - R IY0
+EMCEE  EH1 M - S IY1
+EMCH  EH1 M CH
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+EMEL  EH1 - M AH0 L
+EMELDA  EH0 - M EH1 L - D AH0
+EMELIE  EH1 - M AH0 - L IY0
+EMELINA  EH2 - M EH0 - L IY1 - N AH0
+EMELINE  EH1 - M IH0 - L AY2 N
+EMELITA  EH0 - M EH0 - L IY1 - T AH0
+EMELYNE  EH1 - M IH0 - L AY0 N
+EMENS  EH1 - M EH0 N Z
+EMERA  EH0 - M EH1 - R AH0
+EMERALD  EH1 M - R AH0 L D
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+EMERALDS  EH1 M - R AH0 L D Z
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+EMERANT  EY0 - M EH1 - R AH0 N T
+EMERAUDE  EH1 - M ER0 - AW2 D
+EMERGE  IH0 - M ER1 JH
+EMERGE(2)  IY1 - M ER0 JH
+EMERGED  IH0 - M ER1 JH D
+EMERGED(2)  IY1 - M ER0 JH D
+EMERGENCE  IH0 - M ER1 - JH AH0 N S
+EMERGENCE(2)  IY1 - M ER0 - JH AH0 N S
+EMERGENCIES  IH0 - M ER1 - JH AH0 N - S IY0 Z
+EMERGENCIES(2)  IY1 - M ER0 - JH AH0 N - S IY0 Z
+EMERGENCY  IH0 - M ER1 - JH AH0 N - S IY0
+EMERGENCY(2)  IY1 - M ER0 - JH AH0 N - S IY0
+EMERGENT  IH0 - M ER1 - JH AH0 N T
+EMERGENT(2)  IY1 - M ER0 - JH AH0 N T
+EMERGES  IH0 - M ER1 - JH IH0 Z
+EMERGES(2)  IY1 - M ER0 - JH IH0 Z
+EMERGING  IH0 - M ER1 - JH IH0 NG
+EMERGING(2)  IY1 - M ER0 - JH IH0 NG
+EMERICH  EH1 - M ER0 - IH0 K
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+EMERINE  EH1 - M ER0 - IY2 N
+EMERITUS  IH0 - M EH1 - R AH0 - T AH0 S
+EMERSE  IH0 - M ER1 S
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+EMERSON  EH1 - M ER0 - S AH0 N
+EMERSON'S  EH1 - M ER0 - S AH0 N Z
+EMERT  EH1 - M ER0 T
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+EMERY  EH1 - M ER0 - IY0
+EMERY'S  EH1 - M ER0 - IY0 Z
+EMERYVILLE  EH1 - M ER0 - IY0 - V IH2 L
+EMETIC  IH0 - M EH1 - T IH0 K
+EMFINGER  EH1 M - F IH0 - NG ER0
+EMGE  EH1 M JH
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+EMICK  EH1 - M IH0 K
+EMIG  EH1 - M IH0 G
+EMIGH  EH1 - M AY0
+EMIGRANT  EH1 - M AH0 - G R AH0 N T
+EMIGRANTS  EH1 - M AH0 - G R AH0 N T S
+EMIGRATE  EH1 - M AH0 - G R EY2 T
+EMIGRATED  EH1 - M AH0 - G R EY2 - T IH0 D
+EMIGRATES  EH1 - M AH0 - G R EY2 T S
+EMIGRATING  EH1 - M AH0 - G R EY2 - T IH0 NG
+EMIGRATION  EH2 - M AH0 - G R EY1 - SH AH0 N
+EMIGRATIONS  EH2 - M AH0 - G R EY1 - SH AH0 N Z
+EMIGRE  EH1 - M AH0 - G R EY2
+EMIGRES  EH1 - M AH0 - G R EY2 Z
+EMIL  EH0 - M IY1 L
+EMILE  EY0 - M IY1 L
+EMILIA  AH0 - M IY1 - L IY0 - AH0
+EMILIE  EH1 - M AH0 - L IY0
+EMILIO  AH0 - M IY1 - L IY0 - OW0
+EMILO  EH0 - M IY1 - L OW0
+EMILO'S  EH0 - M IY1 - L OW0 Z
+EMILY  EH1 - M IH0 - L IY0
+EMILY'S  EH1 - M IH0 - L IY0 Z
+EMINA  EH0 - M IY1 - N AH0
+EMINASE  EH2 - M IH0 - N AA1 - S IY0
+EMINENCE  EH1 - M AH0 - N AH0 N S
+EMINENCES  EH1 - M AH0 - N AH0 N - S IH0 Z
+EMINENT  EH1 - M AH0 - N AH0 N T
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+EMIRATE  EH1 - M ER0 - AH0 T
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+EMIRATES  EH1 - M ER0 - AH0 T S
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+EMIRATES'(2)  EH1 - M ER0 - EY2 T S
+EMIRATES(2)  EH1 - M ER0 - EY2 T S
+EMISON  EH1 - M IH0 - S AH0 N
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+EMISSARY  EH1 - M AH0 - S EH2 - R IY0
+EMISSION  IH0 - M IH1 - SH AH0 N
+EMISSIONS  IH0 - M IH1 - SH AH0 N Z
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+EMITTING  IH0 - M IH1 - T IH0 NG
+EMLER  EH1 M - L ER0
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+EMLING  EH1 M - L IH0 NG
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+EMLYNNE  IH0 M - L AY1 N
+EMMA  EH1 - M AH0
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+EMMANUEL  IH0 - M AE1 - N Y UW0 - AH0 L
+EMMAUS  EH1 - M AW0 S
+EMME  EH1 M
+EMMEL  EH1 - M AH0 L
+EMMELINE  EH1 - M AH0 - L AY2 N
+EMMENDORFER  EH1 - M IH0 N - D AO0 R - F ER0
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+EMMERICH  EH1 - M ER0 - IH0 K
+EMMERLING  EH1 - M ER0 - L IH0 NG
+EMMERSON  EH1 - M ER0 - S AH0 N
+EMMERT  EH1 - M ER0 T
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+EMMET  EH1 - M IH0 T
+EMMETT  EH1 - M IH0 T
+EMMI  EH1 - M IY0
+EMMICK  EH1 - M IH0 K
+EMMIE  EH1 - M IY0
+EMMINGER  EH1 - M IH0 - NG ER0
+EMMIS  EH1 - M IH0 S
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+EMMITT  EH1 - M IH0 T
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+EMOGENE  EH1 - M AH0 - G IY0 N
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+EMORY'S  EH1 - M ER0 - IY0 Z
+EMOTION  IH0 - M OW1 - SH AH0 N
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+EMOTIONAL  IH0 - M OW1 - SH AH0 - N AH0 L
+EMOTIONAL(2)  IY1 - M OW0 - SH AH0 - N AH0 L
+EMOTIONALISM  IH0 - M OW1 - SH AH0 N - AH0 - L IH2 - Z AH0 M
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+EMOTIONALLY(2)  IY1 - M OW0 SH - N AH0 - L IY0
+EMOTIONS  IH0 - M OW1 - SH AH0 N Z
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+EMPANEL  IH0 M - P AE1 - N AH0 L
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+EMPANELING  EH0 M - P AE1 - N AH0 - L IH0 NG
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+EMPATH  EH1 M - P AE2 TH
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+EMPATHIZE  EH1 M - P AH0 - TH AY2 Z
+EMPATHY  EH1 M - P AH0 - TH IY0
+EMPEROR  EH1 M - P ER0 - ER0
+EMPEROR'S  EH1 M - P ER0 - ER0 Z
+EMPERORS  EH1 M - P ER0 - ER0 Z
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+EMPLACEMENTS  IH0 M - P L EY1 S - M AH0 N T S
+EMPLOY  EH0 M - P L OY1
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+EMPLOYABLE  EH0 M - P L OY1 - AH0 - B AH0 L
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+EMPLOYEE  EH0 M - P L OY1 - IY0
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+EMPLOYEES  EH0 M - P L OY1 - IY0 Z
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+EMPLOYER  EH0 M - P L OY1 - ER0
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+EMPLOYERS  EH0 M - P L OY1 - ER0 Z
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+EMPLOYERS(2)  IH0 M - P L OY1 - ER0 Z
+EMPLOYING  EH0 M - P L OY1 - IH0 NG
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+EMPLOYMENT  EH0 M - P L OY1 - M AH0 N T
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+EMPLOYS  EH0 M - P L OY1 Z
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+EMPORIUM  EH2 M - P AO1 - R IY0 - AH0 M
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+EMPRESS  EH1 M - P R EH0 S
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+EMPT  EH1 M P T
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+EMPTED  EH1 M P - T IH0 D
+EMPTED(2)  EH1 M - T IH0 D
+EMPTIED  EH1 M P - T IY0 D
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+EMPTIER  EH1 M P - T IY0 - ER0
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+EMPTIES(2)  EH1 M - T IY0 Z
+EMPTINESS  EH1 M P - T IY0 - N AH0 S
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+EMPTING  EH1 M P - T IH0 NG
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+EMPTION  EH1 M P - SH AH0 N
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+EMPTOR  EH1 M P - T ER0
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+EMPTYING  EH1 M P - T IY0 - IH0 NG
+EMPTYING(2)  EH1 M - T IY0 - IH0 NG
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+EMSWILER  EH1 M Z - W AY2 - L ER0
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+EMUIL'S  EH1 - M Y UW0 - IH2 L Z
+EMULATE  EH1 - M Y AH0 - L EY2 T
+EMULATED  EH1 - M Y AH0 - L EY2 - T IH0 D
+EMULATING  EH1 - M Y AH0 - L EY2 - T IH0 NG
+EMULATION  EH2 - M Y AH0 - L EY1 - SH AH0 N
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+EMULSION  IH0 - M AH1 L - SH AH0 N
+EN  EH1 N
+ENA  EH1 - N AH0
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+ENABLED  EH0 - N EY1 - B AH0 L D
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+ENABLER  EH0 - N EY1 - B AH0 L - ER0
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+ENABLER(3)  IH0 N - EY1 - B L ER0
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+ENABLES  EH0 - N EY1 - B AH0 L Z
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+ENABLING  EH0 - N EY1 - B AH0 L - IH0 NG
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+ENACTMENTS(2)  EH2 - N AE1 K - M AH0 N T S
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+ENAMELED  IH0 - N AE1 - M AH0 L D
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+ENCATA(2)  EH0 N - K AA1 - T AH0
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+ENCHANT  EH0 N - CH AE1 N T
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+ENCHANTERS(4)  IH0 N - CH AE1 - N ER0 Z
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+ENCHANTMENT  EH0 N - CH AE1 N T - M AH0 N T
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+ENTERGY  EH1 N - T ER0 - JH IY0
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+ENTOMOLOGY  EH2 N - T AH0 - M AA1 - L AH0 - JH IY0
+ENTOURAGE  AA2 N - T UH0 - R AA1 ZH
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+ENTRAIL  EH1 N - T R AH0 L
+ENTRAILS  EH1 N - T R AH0 L Z
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+ENTRANCED  IH0 N - T R AE1 N S T
+ENTRANCES  EH1 N - T R AH0 N - S AH0 Z
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+ENTRAP  IH0 N - T R AE1 P
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+ENTRAPPED  IH0 N - T R AE1 P T
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+ENTREATY  EH0 N - T R IY1 - T IY0
+ENTREE  AA1 N - T R EY2
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+ENTREKIN  EH1 N - T R IH0 - K IH0 N
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+ENTRENCHMENT  EH0 N - T R EH1 N CH - M AH0 N T
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+ENTREPRENEURIAL  AA2 N - T R AH0 - P R AH0 - N ER1 - IY0 - AH0 L
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+ENTREPRENEURS  AA2 N - T R AH0 - P R AH0 - N ER1 Z
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+ENTWINE  EH0 N - T W AY1 N
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+ENVELOP  IH0 N - V EH1 - L AH0 P
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+ENVIRONMENTALISTS(2)  EH0 N - V AY1 - R AH0 N - M EH2 N - T AH0 - L IH0 S S
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+ENVIRONMENTALISTS(4)  EH0 N - V AY1 - R AH0 N - M EH2 - N AH0 - L IH0 S S
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+EPIDERMIS  EH2 - P AH0 - D ER1 - M AH0 S
+EPIDURAL  EH2 - P AH0 - D ER1 - AH0 L
+EPIGENETIC  EH2 - P AH0 - JH AH0 - N EH1 - T IH0 K
+EPIGRAM  EH1 - P AH0 - G R AE2 M
+EPIGRAPHIC  EH2 - P AH0 - G R AE1 - F IH0 K
+EPILEPSIES  EH1 - P AH0 - L EH2 P - S IY0 Z
+EPILEPSY  EH1 - P AH0 - L EH2 P - S IY0
+EPILEPTIC  EH2 - P AH0 - L EH1 P - T IH0 K
+EPILOGUE  EH1 - P AH0 - L AO2 G
+EPIPHANY  IH0 - P IH1 - F AH0 - N IY0
+EPISCOPAL  IH0 - P IH1 S - K AH0 - P AH0 L
+EPISCOPALIAN  IH0 - P IH2 S - K AH0 - P EY1 - L IY0 - AH0 N
+EPISCOPALIAN(2)  IH0 - P IH2 S - K AH0 - P EY1 - L Y AH0 N
+EPISCOPALIANS  IH0 - P IH2 S - K AH0 - P EY1 - L IY0 - AH0 N Z
+EPISCOPALIANS(2)  IH0 - P IH2 S - K AH0 - P EY1 - L Y AH0 N Z
+EPISCOPO  IH0 - P IH0 S - K OW1 - P OW0
+EPISODE  EH1 - P AH0 - S OW2 D
+EPISODE(2)  EH1 - P IH0 - S OW2 D
+EPISODES  EH1 - P AH0 - S OW2 D Z
+EPISODES(2)  EH1 - P IH0 - S OW2 D Z
+EPISODIC  EH2 - P AH0 - S AA1 - D IH0 K
+EPISTEME  EH1 - P IH0 - S T IY2 M
+EPISTEMOLOGY  IH0 - P IH2 - S T AH0 - M AA1 - L AH0 - JH IY0
+EPISTLE  IH0 - P IH1 - S AH0 L
+EPISTOLARY  IH0 - P IH1 - S T AH0 - L EH2 - R IY0
+EPITAPH  EH1 - P AH0 - T AE2 F
+EPITAPHS  EH1 - P AH0 - T AE2 F S
+EPITHELIAL  EH0 - P IH0 - TH EH1 - L Y AH0 L
+EPITHET  EH1 - P AH0 - TH EH2 T
+EPITHETS  EH1 - P AH0 - TH EH2 T S
+EPITOME  IH0 - P IH1 - T AH0 - M IY0
+EPITOMIZE  IH0 - P IH1 - T AH0 - M AY2 Z
+EPITOMIZED  IH0 - P IH1 - T AH0 - M AY2 Z D
+EPITOMIZES  IH0 - P IH1 - T AH0 - M AY2 - Z IH0 Z
+EPITOPE  EH1 - P IH0 - T OW2 P
+EPLER  EH1 P - L ER0
+EPLEY  EH1 P - L IY0
+EPLIN  EH1 P - L IH0 N
+EPLING  EH1 - P L IH0 NG
+EPOCH  EH1 - P AH0 K
+EPOCH(2)  IY1 - P AH0 K
+EPOCHAL  EH1 - P AH0 - K AH0 L
+EPOCHS  EH1 - P AH0 K S
+EPOCHS(2)  IY1 - P AH0 K S
+EPOGEN  EH1 - P AH0 - JH EH0 N
+EPOXY  IH0 - P AA1 K - S IY0
+EPP  EH1 P
+EPPARD  EH1 - P ER0 D
+EPPEL  EH1 - P AH0 L
+EPPERLY  EH1 - P ER0 - L IY0
+EPPERS  EH1 - P ER0 Z
+EPPERSON  EH1 - P ER0 - S AH0 N
+EPPES  EH1 P S
+EPPICH  EH1 - P IH0 CH
+EPPING  EH1 - P IH0 NG
+EPPINGER  EH1 - P IH0 - NG ER0
+EPPLE  EH1 - P AH0 L
+EPPLER  EH1 P - L ER0
+EPPLEY  EH1 P - L IY0
+EPPNER  EH1 P - N ER0
+EPPOLITO  EH0 - P OW0 - L IY1 - T OW0
+EPPS  EH1 P S
+EPROM  EH1 - P R AH0 M
+EPROMS  EH1 - P R AH0 M Z
+EPSCO  EH1 P - S K OW0
+EPSILON  EH1 P - S AH0 - L AA2 N
+EPSOM  EH1 P - S AH0 M
+EPSOMITE  EH1 P - S AH0 - M AY2 T
+EPSON  EH1 P - S AH0 N
+EPSTEIN  EH1 P - S T IY2 N
+EPSTEIN(2)  EH1 P - S T AY2 N
+EPTING  EH1 P - T IH0 NG
+EQUABLE  EH1 - K W AH0 - B AH0 L
+EQUAL  IY1 - K W AH0 L
+EQUALED  IY1 - K W AH0 L D
+EQUALING  IY1 - K W AH0 L - IH0 NG
+EQUALITY  IH0 - K W AA1 - L AH0 - T IY0
+EQUALIZATION  IY2 - K W AH0 - L IH0 - Z EY1 - SH AH0 N
+EQUALIZE  IY1 - K W AH0 - L AY2 Z
+EQUALIZED  IY1 - K W AH0 - L AY2 Z D
+EQUALIZER  IY1 - K W AH0 - L AY2 - Z ER0
+EQUALIZING  IY1 - K W AH0 - L AY2 - Z IH0 NG
+EQUALLY  IY1 - K W AH0 - L IY0
+EQUALS  IY1 - K W AH0 L Z
+EQUANIMITY  IY2 - K W AH0 - N IH1 - M IH0 - T IY0
+EQUATE  IH0 - K W EY1 T
+EQUATED  IH0 - K W EY1 - T IH0 D
+EQUATES  IH0 - K W EY1 T S
+EQUATING  IH0 - K W EY1 - T IH0 NG
+EQUATION  IH0 - K W EY1 - ZH AH0 N
+EQUATIONS  IH0 - K W EY1 - ZH AH0 N Z
+EQUATOR  IH0 - K W EY1 - T ER0
+EQUATORIAL  IY2 - K W AH0 - T AO1 - R IY0 - AH0 L
+EQUESTRIAN  IH0 - K W EH1 S - T R IY0 - AH0 N
+EQUIANGULAR  IY2 - K W AH0 - AE1 NG - G Y AH0 - L ER0
+EQUIBANK  EH1 - K W AH0 - B AE2 NG K
+EQUICOR  EH1 - K W IH2 - K AO2 R
+EQUIFAX  EH1 - K W IH0 - F AE2 K S
+EQUILIBRIA  IY2 - K W AH0 - L IH1 - B R IY0 - AH0
+EQUILIBRIUM  IY2 - K W AH0 - L IH1 - B R IY0 - AH0 M
+EQUILINK  EH1 - K W AH0 L - IH2 NG K
+EQUIMARK  EH1 - K W IH0 - M AA2 R K
+EQUINE  IY1 - K W AY2 N
+EQUINOX  IY1 - K W AH0 - N AA2 K S
+EQUION  EH1 - K W IY0 - AA0 N
+EQUIP  IH0 - K W IH1 P
+EQUIPMENT  IH0 - K W IH1 P - M AH0 N T
+EQUIPMENT'S  IH0 - K W IH1 P - M AH0 N T S
+EQUIPMENTS  IH0 - K W IH1 P - M AH0 N T S
+EQUIPPED  IH0 - K W IH1 P T
+EQUIPPING  IH0 - K W IH1 - P IH0 NG
+EQUIPS  IH0 - K W IH1 P S
+EQUITABLE  EH1 - K W AH0 - T AH0 - B AH0 L
+EQUITABLE'S  EH1 - K W AH0 - T AH0 - B AH0 L Z
+EQUITABLE(2)  EH1 - K W IH0 - T AH0 - B AH0 L
+EQUITABLY  EH1 - K W IH0 - T AH0 - B L IY0
+EQUITAS  EH1 - K W AH0 - T AH0 S
+EQUITATION  EH2 - K W AH0 - T EY1 - SH AH0 N
+EQUITEC  EH1 - K W AH0 - T EH2 K
+EQUITEX  EH1 - K W AH0 - T EH2 K S
+EQUITICORP  EH1 - K W IH0 - T IY0 - K AO2 R P
+EQUITIES  EH1 - K W AH0 - T IY0 Z
+EQUITIES'  EH1 - K W AH0 - T IY0 Z
+EQUITY  EH1 - K W AH0 - T IY0
+EQUITY'S  EH1 - K W AH0 - T IY0 Z
+EQUIVALENCE  IH0 - K W IH1 - V AH0 - L AH0 N S
+EQUIVALENCY  IH0 - K W IH1 - V AH0 - L AH0 N - S IY0
+EQUIVALENT  IH0 - K W IH1 - V AH0 - L AH0 N T
+EQUIVALENTS  IH0 - K W IH1 - V AH0 - L AH0 N T S
+EQUIVOCAL  IH0 - K W IH1 - V AH0 - K AH0 L
+EQUIVOCATE  IH0 - K W IH1 - V AH0 - K EY2 T
+EQUIVOCATING  IH0 - K W IH1 - V AH0 - K EY2 - T IH0 NG
+EQUIVOCATION  IH0 - K W IH0 - V AH0 - K EY1 - SH AH0 N
+ER  ER0
+ERA  EH1 - R AH0
+ERA'S  EH1 - R AH0 Z
+ERA'S(2)  IH1 - R AH0 Z
+ERA(2)  IH1 - R AH0
+ERADICATE  IH0 - R AE1 - D AH0 - K EY2 T
+ERADICATED  IH0 - R AE1 - D AH0 - K EY2 - T IH0 D
+ERADICATING  IH0 - R AE1 - D AH0 - K EY2 - T IH0 NG
+ERADICATION  IH0 - R AE2 - D AH0 - K EY1 - SH AH0 N
+ERAKAT  EH0 - R AA1 - K AH0 T
+ERALP  EH1 - R AO0 L P
+ERAMO  EH0 - R AA1 - M OW0
+ERANTHE  EH1 - R AH0 N TH
+ERANY  AH0 - R EY1 - N IY0
+ERAS  IH1 - R AH0 Z
+ERASABLE  IH0 - R EY1 - S AH0 - B AH0 L
+ERASABLE(2)  IY1 - R EY0 - S AH0 - B AH0 L
+ERASE  IH0 - R EY1 S
+ERASE(2)  IY0 - R EY1 S
+ERASED  IH0 - R EY1 S T
+ERASED(2)  IY0 - R EY1 S T
+ERASER  IH0 - R EY1 - S ER0
+ERASER(2)  IY0 - R EY1 - S ER0
+ERASERS  IH0 - R EY1 - S ER0 Z
+ERASERS(2)  IY0 - R EY1 - S ER0 Z
+ERASES  IH0 - R EY1 - S IH0 Z
+ERASES(2)  IY0 - R EY1 - S IH0 Z
+ERASING  IH0 - R EY1 - S IH0 NG
+ERASING(2)  IY0 - R EY1 - S IH0 NG
+ERASMUS  IH0 - R AE1 Z - M AH0 S
+ERASTUS  IH0 - R AE1 - S T AH0 S
+ERAWAN  EH1 - R AH0 W - AA2 N
+ERAZO  EH0 - R AA1 - Z OW0
+ERB  ER1 B
+ERBACHER  ER1 - B AA0 - K ER0
+ERBAMONT  ER1 - B AH0 - M AA2 N T
+ERBAMONT'S  ER1 - B AH0 - M AA2 N T S
+ERBE  ER1 B
+ERBER  ER1 - B ER0
+ERBES  ER1 B Z
+ERBURU  ER0 - B UH1 - R UW0
+ERBY  ER1 - B IY0
+ERCEG  ER1 - S IH0 G
+ERCK  ER1 K
+ERCOLE  ER0 - K OW1 - L IY0
+ERCROS  ER1 - K R OW0 Z
+ERDA  EH1 R - D AH0
+ERDAHL  ER1 - D AA0 L
+ERDMAN  ER1 D - M AH0 N
+ERDMANN  ER1 D - M AH0 N
+ERDOS  ER1 - D OW0 Z
+ERDRICH  ER1 - D R IH0 K
+ERECT  IH0 - R EH1 K T
+ERECTED  IH0 - R EH1 K - T AH0 D
+ERECTED(2)  IH0 - R EH1 K - T IH0 D
+ERECTING  IH0 - R EH1 K - T IH0 NG
+ERECTION  IH0 - R EH1 K - SH AH0 N
+ERECTIONS  IH0 - R EH1 K - SH AH0 N Z
+ERECTOR  IH0 - R EH1 K - T ER0
+ERECTS  IH0 - R EH1 K T S
+ERENA  ER0 - EH1 - N AH0
+ERENSEL  EH1 - R AH0 N - S EH0 L
+ERGLE  ER1 - G AH0 L
+ERGO  ER1 - G OW0
+ERGONOMIC  ER2 - G AH0 - N AA1 - M IH0 K
+ERGONOMICS  ER2 - G AH0 - N AA1 - M IH0 K S
+ERGOT  ER1 - G AH0 T
+ERGOT'S  ER1 - G AH0 T S
+ERGOTISM  ER1 - G AH0 - T IH2 - Z AH0 M
+ERGOTS  ER1 - G AH0 T S
+ERHARD  EH1 R - HH AA0 R D
+ERHARDT  ER1 - HH AA0 R T
+ERHART  ER1 - HH AA0 R T
+ERIC  EH1 - R IH0 K
+ERIC'S  EH1 - R IH0 K S
+ERICA  EH1 - R IH0 - K AH0
+ERICH  EH1 - R IH0 K
+ERICHSEN  EH1 - R IH0 K - S AH0 N
+ERICK  EH1 - R IH0 K
+ERICKA  EH1 - R IH0 - K AH0
+ERICKSEN  EH1 - R IH0 K - S AH0 N
+ERICKSON  EH1 - R IH0 K - S AH0 N
+ERICSON  EH1 - R IH0 K - S AH0 N
+ERICSSON  EH1 - R IH0 K - S AH0 N
+ERICSSON'S  EH1 - R IH0 K - S AH0 N Z
+ERIDANIA  EH2 - R IH0 - D AE1 - N IY0 - AH0
+ERIE  IH1 - R IY0
+ERIK  EH1 - R IH0 K
+ERIK'S  EH1 - R IH0 K S
+ERIKA  EH1 - R IH0 - K AH0
+ERIKSEN  EH1 - R IH0 K - S AH0 N
+ERIKSON  EH1 - R IH0 K - S AH0 N
+ERIKSON'S  EH1 - R IH0 K - S AH0 N Z
+ERIKSSON  EH1 - R IH0 K - S AH0 N
+ERIN  EH1 - R IH0 N
+ERINA  ER0 - IY1 - N AH0
+ERION  EH0 - R IY0 - AO1 N
+ERISA  EH0 - R IY1 - S AH0
+ERISMAN  EH1 - R IH0 S - M AH0 N
+ERITREA  EH2 - R IH0 - T R IY1 - AH0
+ERITREA(2)  EH2 - R IH0 - T R EY1 - AH0
+ERITREAN  EH2 - R IH0 - T R IY1 - AH0 N
+ERITREAN(2)  EH2 - R IH0 - T R EY1 - AH0 N
+ERITREANS  EH1 - R IH0 - T R IY2 N Z
+ERITREANS(2)  EH2 - R IH0 - T R EY1 - AH0 N Z
+ERK  ER1 K
+ERKER  ER1 - K ER0
+ERKKILA  ER1 - K IH0 - L AH0
+ERL  ER1 L
+ERLACH  ER1 - L AA2 K
+ERLAND  ER1 - L AH0 N D
+ERLANDSON  ER1 - L AH0 N D - S AH0 N
+ERLANGEN  ER0 - L AE1 NG - G AH0 N
+ERLANGER  EH1 R - L AE0 - NG ER0
+ERLANGER(2)  EH1 R - L AE0 NG - G ER0
+ERLE  AO1 - R AH0 L
+ERLENE  ER1 - L IY0 N
+ERLER  ER1 - L ER0
+ERLICH  ER1 - L IH0 K
+ERLICHMAN  ER1 - L IH0 K - M AH0 N
+ERLICHMAN(2)  EH1 R - L IH0 K - M AH0 N
+ERLICK  ER1 - L IH0 K
+ERLICK(2)  EH1 R - L IH0 K
+ERLINE  ER1 - L AY0 N
+ERLING  ER1 - L IH0 NG
+ERLY  ER1 - L IY0
+ERMA  ER1 - M AH0
+ERMA'S  ER1 - M AH0 Z
+ERMAN  ER1 - M AH0 N
+ERMER  ER1 - M ER0
+ERMIN  ER1 - M IH0 N
+ERMINA  ER0 - M IY1 - N AH0
+ERMINE  ER1 - M AH0 N
+ERMINIA  ER0 - M IY1 - N IY0 - AH0
+ERMINIE  ER1 - M IH0 - N IY0
+ERMIS  ER1 - M IH0 S
+ERNA  ER1 - N AH0
+ERNALINE  ER1 - N AH0 - L AY0 N
+ERNE  ER1 N
+ERNEST  ER1 - N AH0 S T
+ERNEST'S  ER1 - N AH0 S T S
+ERNEST(2)  ER1 - N IH0 S T
+ERNESTINE  ER1 - N IH0 - S T IY2 N
+ERNESTO  ER0 - N EH1 - S T OW0
+ERNESTS  ER1 - N AH0 S T S
+ERNESTS(2)  ER1 - N AH0 S S
+ERNESTS(3)  ER1 - N AH0 S
+ERNEY  ER1 - N IY0
+ERNIE  ER1 - N IY0
+ERNO  EH1 R - N OW0
+ERNSBERGER  ER1 N Z - B ER0 - G ER0
+ERNST  ER1 N S T
+ERNSTER  ER1 N - S T ER0
+ERNY  ER1 - N IY0
+ERO  IH1 - R OW0
+ERODE  IH0 - R OW1 D
+ERODE(2)  IY1 - R OW0 D
+ERODED  IH0 - R OW1 - D AH0 D
+ERODED(2)  IH0 - R OW1 - D IH0 D
+ERODED(3)  IH0 - R OW1 - D IH0 D
+ERODED(4)  IH0 - R OW1 - D AH0 D
+ERODES  IH0 - R OW1 D Z
+ERODES(2)  IH0 - R OW1 D Z
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+ERODIBLE(2)  IH0 - R OW1 - D AH0 - B AH0 L
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+EROH  EH1 - R OW0
+EROICA  ER0 - OY1 - K AH0
+EROL  EH1 - R AH0 L
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+ERON  IH1 - R AA0 N
+EROS  IH1 - R AA0 S
+EROSION  IH0 - R OW1 - ZH AH0 N
+EROSIONAL  IH0 - R OW1 - ZH AH0 - N AH0 L
+EROSIVE  IH0 - R OW1 - S IH0 V
+EROTIC  IH0 - R AA1 - T IH0 K
+EROTICA  IH0 - R AA1 - T IH0 - K AH0
+EROTICISM  ER0 - AA1 - T IH0 - S IH2 - Z AH0 M
+ERPELDING  ER1 - P IH0 L - D IH0 NG
+ERPS  ER1 P S
+ERR  EH1 R
+ERR(2)  ER1
+ERRA  EH1 - R AH0
+ERRAND  EH1 - R AH0 N D
+ERRANDS  EH1 - R AH0 N D Z
+ERRANT  EH1 - R AH0 N T
+ERRATIC  IH0 - R AE1 - T IH0 K
+ERRATICALLY  EH0 - R AE1 - T IH0 K - L IY0
+ERRED  EH1 R D
+ERRETT  EH1 - R IH0 T
+ERRIA  EH1 - R IY0 - AH0
+ERRICKSON  EH1 - R IH0 K - S AH0 N
+ERRICO  ER0 - IY1 - K OW0
+ERRINGTON  EH1 - R IH0 NG - T AH0 N
+ERROL  EH1 - R AH0 L
+ERRONEOUS  EH0 - R OW1 - N IY0 - AH0 S
+ERRONEOUS(2)  ER0 - OW1 - N IY0 - AH0 S
+ERRONEOUSLY  EH0 - R OW1 - N IY0 - AH0 S - L IY0
+ERROR  EH1 - R ER0
+ERRORS  EH1 - R ER0 Z
+ERRS  EH1 R Z
+ERS  ER1 Z
+ERS(2)  IY1 - AA1 - R EH1 S
+ERSATZ  EH1 R - S AA2 T S
+ERSATZ(2)  EH1 R - Z AA2 T S
+ERSHAD  ER1 - SH AE2 D
+ERSHAD(2)  ER1 - SH AA2 D
+ERSKIN  ER1 - S K IH0 N
+ERSKINE  ER1 - S K AY2 N
+ERSTWHILE  ER1 S T - W AY2 L
+ERTE  ER1 T
+ERTE(2)  ER1 - T EY0
+ERTEL  ER1 - T AH0 L
+ERTHA  ER1 - DH AH0
+ERTL  ER1 - T AH0 L
+ERTLE  ER1 - T AH0 L
+ERTMAN  ER1 T - M AH0 N
+ERTZ  ER1 T S
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+ERUDITION  EH2 - R AH0 - D IH1 - SH AH0 N
+ERUPT  IH0 - R AH1 P T
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+ERUPTED  IH0 - R AH1 P - T AH0 D
+ERUPTED(2)  IY0 - R AH1 P - T IH0 D
+ERUPTED(3)  IH0 - R AH1 P - T IH0 D
+ERUPTED(4)  IY0 - R AH1 P - T AH0 D
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+ERUPTING(2)  IY0 - R AH1 P - T IH0 NG
+ERUPTION  IH0 - R AH1 P - SH AH0 N
+ERUPTION(2)  IY0 - R AH1 P - SH AH0 N
+ERUPTIONS  IH0 - R AH1 P - SH AH0 N Z
+ERUPTIONS(2)  IY0 - R AH1 P - SH AH0 N Z
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+ERUPTS(3)  IH0 - R AH1 P S
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+ERVEN  ER1 - V AH0 N
+ERVIN  ER1 - V IH0 N
+ERVING  ER1 - V IH0 NG
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+ERWIN  ER1 - W IH0 N
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+ERXLEBEN  ER0 K - S L EH1 - B AH0 N
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+ESAU  IY1 - S AO2
+ESBENSHADE  EH1 S - B IH0 N - SH AH0 D
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+ESCALATE  EH1 - S K AH0 - L EY2 T
+ESCALATED  EH1 - S K AH0 - L EY2 - T IH0 D
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+ESCALATION  EH2 - S K AH0 - L EY1 - SH AH0 N
+ESCALATOR  EH1 - S K AH0 - L EY2 - T ER0
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+ESCALERA  EH0 - S K AA0 - L EH1 - R AH0
+ESCALONA  EH0 - S K AA0 - L OW1 - N AH0
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+ESCAMEZ  EH0 - S K AA1 - M EH0 Z
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+ESCAPISM  IH0 - S K EY1 - P IH2 - Z AH0 M
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+ESCARPMENTS  EH0 - S K AA1 R P - M AH0 N T S
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+ESCHE  EH1 SH
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+ESCHENBACH  EH1 - SH IH0 N - B AA0 K
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+ESCHER  EH1 - SH ER0
+ESCHETE  EH1 - SH IY0 T
+ESCHEW  EH0 S - CH UW1
+ESCHEWED  EH2 SH - UW1 D
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+ESHELMAN  EH1 - SH AH0 L - M AH0 N
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+EXAR  EH1 K - S ER0
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+EXPEDITION  EH2 K - S P AH0 - D IH1 - SH AH0 N
+EXPEDITIONARY  EH2 K - S P AH0 - D IH1 - SH AH0 N - EH2 - R IY0
+EXPEDITIONS  EH2 K - S P AH0 - D IH1 - SH AH0 N Z
+EXPEDITIOUS  EH2 K - S P AH0 - D IH1 - SH AH0 S
+EXPEDITIOUSLY  EH2 K - S P AH0 - D IH1 - SH AH0 S - L IY0
+EXPEL  IH0 K - S P EH1 L
+EXPELLED  IH0 K - S P EH1 L D
+EXPELLING  IH0 K - S P EH1 - L IH0 NG
+EXPEND  IH0 K - S P EH1 N D
+EXPENDABLE  IH0 K - S P EH1 N - D AH0 - B AH0 L
+EXPENDED  IH0 K - S P EH1 N - D IH0 D
+EXPENDING  EH2 K - S P EH1 N - D IH0 NG
+EXPENDITURE  IH0 K - S P EH1 N - D AH0 - CH ER0
+EXPENDITURE(2)  IH0 K - S P EH1 N - D IH0 - CH ER0
+EXPENDITURES  IH0 K - S P EH1 N - D AH0 - CH ER0 Z
+EXPENDITURES(2)  IH0 K - S P EH1 N - D IH0 - CH ER0 Z
+EXPENSE  IH0 K - S P EH1 N S
+EXPENSES  IH0 K - S P EH1 N - S AH0 Z
+EXPENSES(2)  IH0 K - S P EH1 N - S IH0 Z
+EXPENSING  IH0 K - S P EH1 N - S IH0 NG
+EXPENSIVE  IH0 K - S P EH1 N - S IH0 V
+EXPENSIVELY  EH2 K - S P EH1 N - S IH0 V - L IY0
+EXPERIENCE  IH0 K - S P IH1 - R IY0 - AH0 N S
+EXPERIENCED  IH0 K - S P IH1 - R IY0 - AH0 N S T
+EXPERIENCES  IH0 K - S P IH1 - R IY0 - AH0 N - S IH0 Z
+EXPERIENCING  IH0 K - S P IH1 - R IY0 - AH0 N - S IH0 NG
+EXPERIENTIAL  EH0 K - S P EH2 - R IY0 - EH1 N - SH AH0 L
+EXPERIMENT  IH0 K - S P EH1 - R AH0 - M AH0 N T
+EXPERIMENT'S  IH0 K - S P EH1 - R AH0 - M AH0 N T S
+EXPERIMENTAL  IH0 K - S P EH2 - R AH0 - M EH1 N - T AH0 L
+EXPERIMENTAL(2)  IH0 K - S P EH2 - R IH0 - M EH1 N - T AH0 L
+EXPERIMENTAL(3)  IH0 K - S P ER0 - M EH1 N - T AH0 L
+EXPERIMENTAL(4)  IH0 K - S P EH2 - R AH0 - M EH1 - N AH0 L
+EXPERIMENTAL(5)  IH0 K - S P EH2 - R IH0 - M EH1 - N AH0 L
+EXPERIMENTAL(6)  IH0 K - S P ER0 - M EH1 - N AH0 L
+EXPERIMENTALIST  IH0 K - S P EH2 - R AH0 - M EH1 N - T AH0 - L IH0 S T
+EXPERIMENTALIST(2)  IH0 K - S P EH2 - R AH0 - M EH1 - N AH0 - L IH0 S T
+EXPERIMENTALLY  IH0 K - S P EH0 - R AH0 - M EH1 N - T AH0 - L IY0
+EXPERIMENTALLY(2)  IH0 K - S P EH0 - R AH0 - M EH1 - N AH0 - L IY0
+EXPERIMENTATION  IH0 K - S P EH2 - R AH0 - M AH0 N - T EY1 - SH AH0 N
+EXPERIMENTED  IH0 K - S P EH1 - R AH0 - M AH0 N - T AH0 D
+EXPERIMENTER  IH0 K - S P EH1 - R AH0 - M EH2 N - T ER0
+EXPERIMENTERS  IH0 K - S P EH1 - R AH0 - M EH2 N - T ER0 Z
+EXPERIMENTING  EH0 K - S P EH1 - R AH0 - M EH2 N - T IH0 NG
+EXPERIMENTING(2)  EH0 K - S P EH1 - R AH0 - M EH0 - N IH0 NG
+EXPERIMENTS  IH0 K - S P EH1 - R AH0 - M AH0 N T S
+EXPERT  EH1 K - S P ER0 T
+EXPERT'S  EH1 K - S P ER0 T S
+EXPERTISE  EH2 K - S P ER0 - T IY1 Z
+EXPERTLY  EH1 K - S P ER0 T - L IY0
+EXPERTS  EH1 K - S P ER0 T S
+EXPERTS'  EH1 K - S P ER0 T S
+EXPIATE  EH1 K - S P IY0 - EY2 T
+EXPIRATION  EH2 K - S P ER0 - EY1 - SH AH0 N
+EXPIRATIONS  EH2 K - S P ER0 - EY1 - SH AH0 N Z
+EXPIRATORY  IH0 K - S P AY1 - R AH0 - T AO2 - R IY0
+EXPIRE  IH0 K - S P AY1 R
+EXPIRED  IH0 K - S P AY1 R D
+EXPIRES  IH0 K - S P AY1 - ER0 Z
+EXPIRING  IH0 K - S P AY1 - R IH0 NG
+EXPIRY  EH2 K - S P AY1 - R IY0
+EXPLAIN  IH0 K - S P L EY1 N
+EXPLAINABLE  IH0 K - S P L EY1 - N AH0 - B AH0 L
+EXPLAINED  IH0 K - S P L EY1 N D
+EXPLAINING  IH0 K - S P L EY1 - N IH0 NG
+EXPLAINS  IH0 K - S P L EY1 N Z
+EXPLANATION  EH2 K - S P L AH0 - N EY1 - SH AH0 N
+EXPLANATIONS  EH2 K - S P L AH0 - N EY1 - SH AH0 N Z
+EXPLANATORY  IH0 K - S P L AE1 - N AH0 - T AO2 - R IY0
+EXPLETIVE  EH1 K - S P L AH0 - T IH0 V
+EXPLETIVES  EH1 K - S P L AH0 - T IH0 V Z
+EXPLICATION  EH2 K - S P L AH0 - K EY1 - SH AH0 N
+EXPLICIT  IH0 K - S P L IH1 - S AH0 T
+EXPLICITLY  IH0 K - S P L IH1 - S AH0 T - L IY0
+EXPLODE  IH0 K - S P L OW1 D
+EXPLODED  IH0 K - S P L OW1 - D AH0 D
+EXPLODED(2)  IH0 K - S P L OW1 - D IH0 D
+EXPLODES  IH0 K - S P L OW1 D Z
+EXPLODING  IH0 K - S P L OW1 - D IH0 NG
+EXPLOIT  EH1 K - S P L OY2 T
+EXPLOIT(2)  EH2 K - S P L OY1 T
+EXPLOITATION  EH2 K - S P L OY2 - T EY1 - SH AH0 N
+EXPLOITATIVE  EH2 K - S P L OY1 - T AH0 - T IH0 V
+EXPLOITED  EH1 K - S P L OY2 - T AH0 D
+EXPLOITED(2)  IH0 K - S P L OY1 - T AH0 D
+EXPLOITING  EH1 K - S P L OY2 - T IH0 NG
+EXPLOITING(2)  IH0 K - S P L OY1 - T IH0 NG
+EXPLOITIVE  IH0 K - S P L OY1 - T IH0 V
+EXPLOITIVE(2)  EH0 K - S P L OY1 - T IH0 V
+EXPLOITS  EH1 K - S P L OY2 T S
+EXPLORATION  EH2 K - S P L ER0 - EY1 - SH AH0 N
+EXPLORATION'S  EH2 K - S P L ER0 - EY1 - SH AH0 N Z
+EXPLORATION'S(2)  EH1 K - S P L AO0 - R EY1 - SH AH0 N Z
+EXPLORATION(2)  EH2 K - S P L AO0 - R EY1 - SH AH0 N
+EXPLORATIONS  EH2 K - S P L ER0 - EY1 - SH AH0 N Z
+EXPLORATIONS(2)  EH1 K - S P L AO0 - R EY1 - SH AH0 N Z
+EXPLORATORY  IH0 K - S P L AO1 - R AH0 - T AO2 - R IY0
+EXPLORE  IH0 K - S P L AO1 R
+EXPLORED  IH0 K - S P L AO1 R D
+EXPLORER  IH0 K - S P L AO1 - R ER0
+EXPLORERS  IH0 K - S P L AO1 - R ER0 Z
+EXPLORES  IH0 K - S P L AO1 R Z
+EXPLORING  IH0 K - S P L AO1 - R IH0 NG
+EXPLOSION  IH0 K - S P L OW1 - ZH AH0 N
+EXPLOSIONS  IH0 K - S P L OW1 - ZH AH0 N Z
+EXPLOSIVE  IH0 K - S P L OW1 - S IH0 V
+EXPLOSIVELY  EH2 K - S P L OW1 - S IH0 V - L IY0
+EXPLOSIVES  IH0 K - S P L OW1 - S IH0 V Z
+EXPLOSIVOS  EH2 K - S P L AH0 - S IY1 - V OW0 S
+EXPO  EH1 K - S P OW0
+EXPONENT  EH1 K - S P OW2 - N AH0 N T
+EXPONENTIAL  EH2 K - S P OW0 - N EH1 N - CH AH0 L
+EXPONENTIAL(2)  EH2 K - S P OW0 - N EH1 N - SH AH0 L
+EXPONENTIALLY  EH2 K - S P OW0 - N EH1 N - SH AH0 - L IY0
+EXPONENTIALLY(2)  EH2 K - S P OW0 - N EH1 N - CH AH0 - L IY0
+EXPONENTS  IH0 K - S P OW1 - N AH0 N T S
+EXPORT  EH1 K - S P AO0 R T
+EXPORTABLE  EH0 K - S P AO1 R - T AH0 - B AH0 L
+EXPORTED  IH0 K - S P AO1 R - T AH0 D
+EXPORTER  IH0 K - S P AO1 R - T ER0
+EXPORTERS  IH0 K - S P AO1 R - T ER0 Z
+EXPORTERS'  EH2 K - S P AO1 R - T ER0 Z
+EXPORTING  IH0 K - S P AO1 R - T IH0 NG
+EXPORTS  EH1 K - S P AO0 R T S
+EXPOS  EH1 K - S P OW0 Z
+EXPOSE  IH0 K - S P OW1 Z
+EXPOSED  IH0 K - S P OW1 Z D
+EXPOSES  IH0 K - S P OW1 - Z IH0 Z
+EXPOSING  IH0 K - S P OW1 - Z IH0 NG
+EXPOSITION  EH2 K - S P AH0 - Z IH1 - SH AH0 N
+EXPOSITIONS  EH2 K - S P AH0 - Z IH1 - SH AH0 N Z
+EXPOSITO  EH0 K - S P AH0 - S AY1 - T OW0
+EXPOSITO(2)  EH0 K - S P AH0 - Z IY1 - T OW0
+EXPOSURE  IH0 K - S P OW1 - ZH ER0
+EXPOSURES  IH0 K - S P OW1 - ZH ER0 Z
+EXPOUND  IH0 K - S P AW1 N D
+EXPOUNDED  IH0 K - S P AW1 N - D AH0 D
+EXPOUNDING  IH0 K - S P AW1 N - D IH0 NG
+EXPOUNDS  IH0 K - S P AW1 N D Z
+EXPRESS  IH0 K - S P R EH1 S
+EXPRESS'  IH0 K - S P R EH1 S
+EXPRESS'S  IH0 K - S P R EH1 - S IH0 Z
+EXPRESSED  IH0 K - S P R EH1 S T
+EXPRESSES  IH0 K - S P R EH1 - S AH0 Z
+EXPRESSES(2)  IH0 K - S P R EH1 - S IH0 Z
+EXPRESSING  IH0 K - S P R EH1 - S IH0 NG
+EXPRESSION  IH0 K - S P R EH1 - SH AH0 N
+EXPRESSIONISM  IH0 K - S P R EH1 - SH AH0 N - IH2 - Z AH0 M
+EXPRESSIONIST  IH0 K - S P R EH1 - SH AH0 N - AH0 S T
+EXPRESSIONISTIC  IH0 K - S P R EH2 - SH AH0 - N IH1 - S T IH0 K
+EXPRESSIONLESS  IH0 K - S P R EH2 - SH AH0 N - L IH0 S
+EXPRESSIONS  IH0 K - S P R EH1 - SH AH0 N Z
+EXPRESSIVE  IH0 K - S P R EH1 - S IH0 V
+EXPRESSIVITY  EH2 K - S P R AH0 - S IH1 - V IH0 - T IY0
+EXPRESSLY  EH0 K - S P R EH1 S - L IY0
+EXPRESSO  IH0 K - S P EH1 - S OW0
+EXPRESSO(2)  EH0 K - S P EH1 - S OW0
+EXPRESSWAY  IH0 K - S P R EH1 S - W EY2
+EXPROPRIATE  EH0 K S - P R OW1 - P R IY0 - EY2 T
+EXPROPRIATED  EH0 K S - P R OW1 - P R IY0 - EY2 - T IH0 D
+EXPROPRIATION  EH2 K S - P R OW2 - P R IY0 - EY1 - SH AH0 N
+EXPROPRIATIONS  EH2 K S - P R OW2 - P R IY0 - EY1 - SH AH0 N Z
+EXPULSION  IH0 K - S P AH1 L - SH AH0 N
+EXPULSIONS  IH0 K - S P AH1 L - SH AH0 N Z
+EXPUNGE  IH0 K - S P AH1 N JH
+EXPUNGED  IH0 K - S P AH1 N JH D
+EXQUISITE  EH1 K - S K W AH0 - Z AH0 T
+EXQUISITELY  EH2 K - S K W IH1 - Z IH0 T - L IY0
+EXTANT  EH1 K - S T AH0 N T
+EXTEL  EH1 K - S T EH2 L
+EXTEND  IH0 K - S T EH1 N D
+EXTENDABLE  EH2 K - S T EH1 N - D AH0 - B AH0 L
+EXTENDED  IH0 K - S T EH1 N - D AH0 D
+EXTENDED(2)  IH0 K - S T EH1 N - D IH0 D
+EXTENDER  EH1 K - S T EH2 N - D ER0
+EXTENDERS  EH1 K - S T EH2 N - D ER0 Z
+EXTENDIBLE  EH2 K - S T EH1 N - D IH0 - B AH0 L
+EXTENDING  IH0 K - S T EH1 N - D IH0 NG
+EXTENDS  IH0 K - S T EH1 N D Z
+EXTENSION  IH0 K - S T EH1 N - SH AH0 N
+EXTENSIONS  IH0 K - S T EH1 N - SH AH0 N Z
+EXTENSIVE  IH0 K - S T EH1 N - S IH0 V
+EXTENSIVELY  IH0 K - S T EH1 N - S IH0 V - L IY0
+EXTENT  IH0 K - S T EH1 N T
+EXTENUATE  IH0 K - S T EH1 - N Y UW0 - EY2 T
+EXTENUATING  IH0 K - S T EH1 - N Y UW0 - EY2 - T IH0 NG
+EXTERIOR  IH0 K - S T IH1 - R IY0 - ER0
+EXTERIORS  EH0 K - S T IH1 - R IY0 - ER0 Z
+EXTERMINATE  IH0 K - S T ER1 - M AH0 - N EY2 T
+EXTERMINATED  IH0 K - S T ER1 - M AH0 - N EY2 - T IH0 D
+EXTERMINATES  IH0 K - S T ER1 - M AH0 - N EY2 T S
+EXTERMINATING  IH0 K - S T ER1 - M AH0 - N EY2 - T IH0 NG
+EXTERMINATION  IH0 K - S T ER2 - M AH0 - N EY1 - SH AH0 N
+EXTERMINATOR  IH0 K - S T ER1 - M AH0 - N EY2 - T ER0
+EXTERMINATORS  IH0 K - S T ER1 - M AH0 - N EY2 - T ER0 Z
+EXTERNAL  IH0 K - S T ER1 - N AH0 L
+EXTERNALLY  IH0 K - S T ER1 - N AH0 - L IY0
+EXTINCT  IH0 K - S T IH1 NG K T
+EXTINCTION  IH0 K - S T IH1 NG K - SH AH0 N
+EXTINCTION(2)  IH0 K - S T IH1 NG - SH AH0 N
+EXTINGUISH  IH0 K - S T IH1 NG - G W IH0 SH
+EXTINGUISHED  IH0 K - S T IH1 NG - G W IH0 SH T
+EXTINGUISHER  IH0 K - S T IH1 NG - G W IH0 - SH ER0
+EXTINGUISHERS  IH0 K - S T IH1 NG - G W IH0 - SH ER0 Z
+EXTINGUISHING  IH0 K - S T IH1 NG - G W IH0 - SH IH0 NG
+EXTINGUISHMENT  IH0 K - S T IH1 NG - G W IH0 SH - M AH0 N T
+EXTIRPATE  EH1 K - S T ER0 - P EY2 T
+EXTOL  IH0 K - S T OW1 L
+EXTOLLED  IH0 K - S T OW1 L D
+EXTOLLING  IH0 K - S T OW1 - L IH0 NG
+EXTOLS  IH0 K - S T OW1 L Z
+EXTON  EH1 K - S T AH0 N
+EXTORT  IH0 K - S T AO1 R T
+EXTORTED  IH0 K - S T AO1 R - T IH0 D
+EXTORTING  IH0 K - S T AO1 R - T IH0 NG
+EXTORTION  IH0 K - S T AO1 R - SH AH0 N
+EXTORTIONATE  IH0 K - S T AO1 R - SH AH0 N - AH0 T
+EXTORTIONATE(2)  IH0 K - S T AO1 R - SH AH0 N - EY2 T
+EXTRA  EH1 K - S T R AH0
+EXTRACELLULARLY  EH1 K - S T R AH0 - S EH2 - L Y AH0 - L ER0 - L IY0
+EXTRACT  IH0 K - S T R AE1 K T
+EXTRACT(2)  EH1 K - S T R AE2 K T
+EXTRACTED  IH0 K - S T R AE1 K - T AH0 D
+EXTRACTED(2)  IH0 K - S T R AE1 K - T IH0 D
+EXTRACTING  IH0 K - S T R AE1 K - T IH0 NG
+EXTRACTION  IH0 K - S T R AE1 K - SH AH0 N
+EXTRACTIONS  IH0 K - S T R AE1 K - SH AH0 N Z
+EXTRACTS  IH0 K - S T R AE1 K T S
+EXTRACTS(2)  EH1 K - S T R AE2 K T S
+EXTRACURRICULAR  EH2 K - S T R AH0 - K ER0 - IH1 - K Y AH0 - L ER0
+EXTRADITE  EH1 K - S T R AH0 - D AY2 T
+EXTRADITED  EH1 K - S T R AH0 - D AY2 - T IH0 D
+EXTRADITING  EH1 K - S T R AH0 - D AY2 - T IH0 NG
+EXTRADITION  EH2 K - S T R AH0 - D IH1 - SH AH0 N
+EXTRAGALACTIC  EH2 K - S T R AH0 - G AH0 - L AE1 K - T IH0 K
+EXTRALEGAL  EH2 K - S T R AH0 - L IY1 - G AH0 L
+EXTRAMARITAL  EH2 K - S T R AH0 - M EH1 - R AH0 - T AH0 L
+EXTRANEOUS  EH0 K - S T R EY1 - N IY0 - AH0 S
+EXTRAORDINAIRE  EH2 K - S T R AH0 - AO1 R - D IH0 - N EH2 R
+EXTRAORDINARILY  IH0 K - S T R AO2 R - D AH0 - N EH1 - R AH0 - L IY0
+EXTRAORDINARY  IH0 K - S T R AO1 R - D AH0 N - EH2 - R IY0
+EXTRAORDINARY(2)  EH2 K - S T R AH0 - AO1 R - D AH0 N - EH2 - R IY0
+EXTRAPOLATE  IH0 K - S T R AE1 - P AH0 - L EY2 T
+EXTRAPOLATED  IH0 K - S T R AE1 - P AH0 - L EY2 - T IH0 D
+EXTRAPOLATING  IH0 K - S T R AE1 - P AH0 - L EY2 - T IH0 NG
+EXTRAPOLATION  IH0 K - S T R AE2 - P AH0 - L EY1 - SH AH0 N
+EXTRAS  EH1 K - S T R AH0 Z
+EXTRASENSORY  EH2 K - S T R AH0 - S EH1 N - S ER0 - IY0
+EXTRATERRESTRIAL  EH2 K - S T R AH0 - T ER0 - EH1 S - T R IY0 - AH0 L
+EXTRATERRESTRIALS  EH2 K - S T R AH0 - T ER2 - EH1 S - T R IY0 - AH0 L Z
+EXTRATERRITORIAL  EH2 K - S T R AH0 - T EH2 - R IH0 - T AO1 - R IY0 - AH0 L
+EXTRATERRITORIALITY  EH2 K - S T R AH0 - T EH2 - R AH0 - T AO2 - R IY0 - AE1 - L AH0 - T IY0
+EXTRAVAGANCE  IH0 K - S T R AE1 - V AH0 - G AH0 N S
+EXTRAVAGANT  IH0 K - S T R AE1 - V AH0 - G AH0 N T
+EXTRAVAGANTLY  EH2 K - S T R AE1 - V AH0 - G AH0 N T - L IY0
+EXTRAVAGANZA  IH0 K - S T R AE2 - V AH0 - G AE1 N - Z AH0
+EXTRAVAGANZAS  IH0 K - S T R AE2 - V AH0 - G AE1 N - Z AH0 Z
+EXTREME  IH0 K - S T R IY1 M
+EXTREMELY  IH0 K - S T R IY1 M - L IY0
+EXTREMES  IH0 K - S T R IY1 M Z
+EXTREMISM  EH2 K - S T R EH1 - M IH0 - Z AH0 M
+EXTREMISM(2)  EH2 K - S T R IY1 - M IH2 - Z AH0 M
+EXTREMIST  IH0 K - S T R IY1 - M IH0 S T
+EXTREMISTS  IH0 K - S T R IY1 - M AH0 S T S
+EXTREMISTS(2)  IH0 K - S T R IY1 - M IH0 S T S
+EXTREMISTS(3)  IH0 K - S T R IY1 - M IH0 S S
+EXTREMISTS(4)  IH0 K - S T R IY1 - M IH0 S
+EXTREMITIES  IH0 K S - T R EH1 - M AH0 - T IY0 Z
+EXTREMITY  IH0 K S - T R EH1 - M AH0 - T IY0
+EXTRICATE  EH1 K - S T R AH0 - K EY2 T
+EXTRICATED  EH1 K - S T R IH0 - K EY2 - T IH0 D
+EXTRINSIC  EH0 K - S T R IH1 N - S IH0 K
+EXTROVERT  EH1 K - S T R AH0 - V ER2 T
+EXTROVERTED  EH1 K - S T R AH0 - V ER2 - T IH0 D
+EXTRUDE  IH0 K - S T R UW1 D
+EXTRUDED  IH0 K - S T R UW1 - D AH0 D
+EXTRUDING  IH0 K - S T R UW1 - D IH0 NG
+EXTRUSION  IH0 K S - T R UW1 - ZH AH0 N
+EXUBERANCE  IH0 G - Z UW1 - B ER0 - AH0 N S
+EXUBERANT  IH0 G - Z UW1 - B ER0 - AH0 N T
+EXUDE  IH0 G - Z UW1 D
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+EXULT  IH0 G - Z AH1 L T
+EXULTANT  IH0 G - Z AH1 L - T AH0 N T
+EXULTANTLY  IH0 G - Z AH1 L - T AH0 N T - L IY0
+EXULTED  IH0 G - Z AH1 L - T IH0 D
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+EXUM  IH0 G - Z AH1 M
+EXXON  EH1 K - S AA0 N
+EXXON'S  EH1 K - S AA0 N Z
+EYDE  EY1 D
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+EYE  AY1
+EYE'S  AY1 Z
+EYEBALL  AY1 - B AO2 L
+EYEBALLS  AY1 - B AO2 L Z
+EYEBROW  AY1 - B R AW2
+EYEBROWS  AY1 - B R AW2 Z
+EYECARE  AY1 - K EH2 R
+EYED  AY1 D
+EYEDROP  AY1 - D R AA2 P
+EYEDROPPER  AY1 - D R AA2 - P ER0
+EYEDROPS  AY1 - D R AA2 P S
+EYEGLASS  AY1 - G L AE2 S
+EYEGLASSES  AY1 - G L AE2 - S AH0 Z
+EYEGLASSES(2)  AY1 - G L AE2 - S IH0 Z
+EYEING  AY1 - IH0 NG
+EYELAB  AY1 - L AE2 B
+EYELASH  AY1 - L AE2 SH
+EYELASHES  AY1 - L AE2 - SH IH0 Z
+EYELESS  AY1 - L AH0 S
+EYELET  AY1 - L AH0 T
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+EYELID  AY1 - L IH2 D
+EYELIDS  AY1 - L IH2 D Z
+EYELIKE  AY1 - L AY2 K
+EYELINER  AY1 - L AY2 - N ER0
+EYEPIECE  AY1 - P IY2 S
+EYER  AY1 - ER0
+EYERLY  IY1 - ER0 - L IY0
+EYERMAN  IY1 - ER0 - M AH0 N
+EYES  AY1 Z
+EYES'  AY1 Z
+EYESHADE  AY1 - SH EY2 D
+EYESIGHT  AY1 - S AY2 T
+EYESORE  AY1 - S AO2 R
+EYESPOT  AY1 - S P AA2 T
+EYESTONE  AY1 - S T OW2 N
+EYESTRAIN  AY1 - S T R EY2 N
+EYETECH  AY1 - T EH2 K
+EYETECH'S  AY1 - T EH2 K S
+EYEWEAR  AY1 - W EH2 R
+EYEWITNESS  AY1 - W IH1 T - N AH0 S
+EYEWITNESSES  AY1 - W IH2 T - N AH0 - S IH0 Z
+EYLER  EY1 - L ER0
+EYMAN  EY1 - M AH0 N
+EYNON  EY1 - N AH0 N
+EYRE  EH1 R
+EYRICH  EH1 - R IH0 CH
+EYRIE  EH1 - R IY0
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+FIGLEY  F IH1 G - L IY0
+FIGMENT  F IH1 G - M IH0 N T
+FIGMENTS  F IH1 G - M IH0 N T S
+FIGONE  F IH1 - G AH0 N
+FIGS  F IH1 G Z
+FIGUEIRA  F IY0 - G EH1 - R AH0
+FIGUEIREDO  F IY0 - G ER0 - EY1 - D OW0
+FIGUERAS  F IY0 - G EH1 - R AA0 Z
+FIGUEREDO  F IY0 - G EH0 - R EY1 - D OW0
+FIGUEROA  F IY0 - G EH0 - R OW1 - AH0
+FIGURA  F IY0 - G UH1 - R AH0
+FIGURATION  F IH2 - G Y ER0 - EY1 - SH AH0 N
+FIGURATIVE  F IH1 - G Y ER0 - AH0 - T IH0 V
+FIGURATIVELY  F IH0 - G Y UH1 - R AH0 - T IH0 V - L IY0
+FIGURE  F IH1 - G Y ER0
+FIGURE'S  F IH1 - G Y ER0 Z
+FIGURED  F IH1 - G Y ER0 D
+FIGUREHEAD  F IH1 - G Y ER0 - HH EH2 D
+FIGURES  F IH1 - G Y ER0 Z
+FIGURINE  F IH2 - G Y ER0 - IY1 N
+FIGURINES  F IH2 - G Y ER0 - IY1 N Z
+FIGURING  F IH1 - G Y ER0 - IH0 NG
+FIGURSKI  F IH0 - G ER1 S - K IY0
+FIJI  F IY1 - JH IY0
+FIJI'S  F IY1 - JH IY0 Z
+FIJIAN  F IY1 - JH IY0 - AH0 N
+FIJIANS  F IH1 - JH IY0 - AH0 N Z
+FIKE  F AY1 K
+FIKES  F AY1 K S
+FIL  F IH1 L
+FILA  F AY1 - L AH0
+FILAMENT  F IH1 - L AH0 - M AH0 N T
+FILAMENTS  F IH1 - L AH0 - M AH0 N T S
+FILBERT  F IH1 L - B ER0 T
+FILBERT'S  F IH1 L - B ER0 T S
+FILBERTS  F IH1 L - B ER0 T S
+FILBY  F IH1 L - B IY0
+FILCH  F IH1 L CH
+FILCHED  F IH1 L CH T
+FILDES  F IH1 L D Z
+FILE  F AY1 L
+FILED  F AY1 L D
+FILEGATE  F AY1 L - G EY2 T
+FILENE  F IH0 - L IY1 N
+FILENE'S  F IH0 - L IY1 N Z
+FILENET  F AY1 L - N EH2 T
+FILER  F AY1 - L ER0
+FILERS  F AY1 - L ER0 Z
+FILES  F AY1 L Z
+FILET  F IH0 - L EY1
+FILETS  F IH0 - L EY1 Z
+FILIAL  F IH1 - L IY0 - AH0 L
+FILIBUSTER  F IH1 - L AH0 - B AH2 - S T ER0
+FILIBUSTERED  F IH1 - L AH0 - B AH2 - S T ER0 D
+FILIBUSTERING  F IH1 - L AH0 - B AH2 - S T ER0 - IH0 NG
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+FILICE  F IY1 - L IH0 S
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+FILIGREES  F IH1 - L AH0 - G R IY2 Z
+FILING  F AY1 - L IH0 NG
+FILINGS  F AY1 - L IH0 NG Z
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+FILION  F IH1 - L Y AH0 N
+FILIP  F IH1 - L IH0 P
+FILIPA  F IY0 - L IY1 - P AH0
+FILIPEK  F IH1 - L IH0 - P IH0 K
+FILIPIAK  F IH0 - L IH1 - P IY0 - AE0 K
+FILIPINO  F IH2 - L AH0 - P IY1 - N OW0
+FILIPINOS  F IH2 - L AH0 - P IY1 - N OW0 Z
+FILIPKOWSKI  F IH0 - L IH0 P - K AO1 F S - K IY0
+FILIPOVIC  F IH0 - L IH0 - P OW1 - V IH0 K
+FILIPOWICZ  F IH0 - L IH1 - P AH0 - V IH0 CH
+FILIPPELLI  F IY0 - L IY0 - P EH1 - L IY0
+FILIPPELLO  F IY2 - L IH0 - P EH1 - L OW0
+FILIPPI  F IH0 - L IH1 - P IY0
+FILIPPINI  F IY0 - L IY0 - P IY1 - N IY0
+FILIPPO  F IH0 - L IY1 - P OW0
+FILIPPONE  F IH1 - L IH0 - P OW2 N
+FILIPSKI  F IH0 - L IH1 P - S K IY0
+FILKINS  F IH1 L - K IH0 N Z
+FILL  F IH1 L
+FILL-A-BUSTER  F IH1 - L AH0 - B AH2 - S T ER0
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+FILLED  F IH1 L D
+FILLER  F IH1 - L ER0
+FILLERS  F IH1 - L ER0 Z
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+FILLETED(2)  F AH0 - L EY1 D
+FILLETS  F IH1 - L IH0 T S
+FILLEY  F IH1 - L IY0
+FILLIES  F IH1 - L IY0 Z
+FILLING  F IH1 - L IH0 NG
+FILLINGAME  F IY0 - L IH0 NG - G AA1 - M IY0
+FILLINGER  F IH1 - L IH0 - NG ER0
+FILLINGHAM  F IH1 - L IH0 NG - HH AE2 M
+FILLINGIM  F IH1 - L IH0 NG - G IH0 M
+FILLINGS  F IH1 - L IH0 NG Z
+FILLION  F IH1 - L Y AH0 N
+FILLIP  F IH1 - L AH0 P
+FILLMAN  F IH1 L - M AH0 N
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+FILLS  F IH1 L Z
+FILLY  F IH1 - L IY0
+FILM  F IH1 L M
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+FILMA  F IH1 L - M AH0
+FILMED  F IH1 L M D
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+FILMING  F IH1 L - M IH0 NG
+FILMLESS  F IH1 L M - L AH0 S
+FILMMAKER  F IH1 L - M EY2 - K ER0
+FILMMAKER'S  F IH1 L - M EY2 - K ER0 Z
+FILMMAKERS  F IH1 L - M EY2 - K ER0 Z
+FILMMAKING  F IH1 L - M EY2 - K IH0 NG
+FILMORE  F IH1 L - M AO2 R
+FILMS  F IH1 L M Z
+FILO  F IY1 - L OW0
+FILOFAX  F IH1 - L OW0 - F AE2 K S
+FILOFAX(2)  F AY1 - L OW0 - F AE2 K S
+FILOMENA  F IH2 - L AH0 - M IY1 - N AH0
+FILOSA  F IY0 - L OW1 - S AH0
+FILSINGER  F IH1 L - S IH0 - NG ER0
+FILSON  F IH1 L - S AH0 N
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+FILTERED  F IH1 L - T ER0 D
+FILTERING  F IH1 L - T ER0 - IH0 NG
+FILTERS  F IH1 L - T ER0 Z
+FILTERTEK  F IH1 L - T ER0 - T EH2 K
+FILTH  F IH1 L TH
+FILTHY  F IH1 L - TH IY0
+FILTRATION  F IH0 L - T R EY1 - SH AH0 N
+FILTROL  F IH1 L - T R AA0 L
+FILYAW  F IH0 - L IY1 - AA0 V
+FILYAW(2)  F IH1 - L Y AW2
+FIMA  F IY1 - M AH0
+FIMBRES  F IH1 M - B ER0 Z
+FIN  F IH1 N
+FINA  F AY1 - N AH0
+FINAGLE  F IH1 - N AH0 - G AH0 L
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+FINAL  F AY1 - N AH0 L
+FINALCO  F IH0 - N AE1 L - K OW0
+FINALE  F AH0 - N AE1 - L IY0
+FINALIST  F AY1 - N AH0 - L IH0 S T
+FINALISTS  F AY1 - N AH0 - L AH0 S T S
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+FINALISTS(3)  F AY1 - N AH0 - L AH0 S
+FINALITY  F AY0 - N AE1 - L AH0 - T IY0
+FINALIZE  F AY1 - N AH0 - L AY2 Z
+FINALIZED  F AY1 - N AH0 - L AY2 Z D
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+FINALLY  F AY1 - N AH0 - L IY0
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+FINAN  F IH1 - N AH0 N
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+FINANCED  F IH0 - N AE1 N S T
+FINANCED(2)  F AY1 - N AE2 N S T
+FINANCEMENT  F IH0 - N AE1 N S - M AH0 N T
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+FINANCER  F AY1 - N AE2 N - S ER0
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+FINANCES  F IH0 - N AE1 N - S IH0 Z
+FINANCES(2)  F AY1 - N AE2 N - S IH0 Z
+FINANCIAL  F AH0 - N AE1 N - SH AH0 L
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+FINANCIAL(3)  F AY2 - N AE1 N - SH AH0 L
+FINANCIALLY  F AH0 - N AE1 N - SH AH0 - L IY0
+FINANCIALLY(2)  F IH0 - N AE1 N - SH AH0 - L IY0
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+FINANCIALS  F AY2 - N AE1 N - SH AH0 L Z
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+FINANCIER  F IH2 - N AH0 N - S IH1 R
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+FINANCIERA  F IH0 - N AE2 N - S IY0 - EH1 - R AH0
+FINANCIERE  F IH2 - N AE0 N - S IH1 R
+FINANCIERO  F IH0 - N AE2 N - S IY0 - EH1 - R OW0
+FINANCIERS  F IH2 - N AH0 N - S IH1 R Z
+FINANCING  F AH0 - N AE1 N - S IH0 NG
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+FINANCINGS  F AH0 - N AE1 N - S IH0 NG Z
+FINANCINGS(2)  F IH0 - N AE1 N - S IH0 NG Z
+FINANCINGS(3)  F AY1 - N AE2 N - S IH0 NG Z
+FINANCO  F IH0 - N AE1 NG - K OW0
+FINANZ  F IH1 - N AE0 N Z
+FINANZA  F IH0 - N AE1 N - Z AH0
+FINANZIARIA  F IH0 - N AE2 N - Z IY0 - EH1 - R IY0 - AH0
+FINANZIARIO  F IH0 - N AE2 N - Z IY0 - EH1 - R IY0 - OW0
+FINARTE  F IH1 - N AA0 R T
+FINAZZO  F IY0 - N AA1 - Z OW0
+FINBERG  F IH1 N - B ER0 G
+FINCANNON  F IH1 N - K AH0 - N AA0 N
+FINCANNON(2)  F IH0 N - K AE1 - N AH0 N
+FINCH  F IH1 N CH
+FINCHAM  F IH1 N - CH AH0 M
+FINCHER  F IH1 N - CH ER0
+FINCHES  F IH1 N - CH IH0 Z
+FINCHUM  F IH1 N - K AH0 M
+FINCK  F IH1 NG K
+FINCKE  F IH1 NG K
+FINCKLE  F IH1 NG - K AH0 L
+FIND  F AY1 N D
+FINDER  F AY1 N - D ER0
+FINDER'S  F AY1 N - D ER0 Z
+FINDERS  F AY1 N - D ER0 Z
+FINDING  F AY1 N - D IH0 NG
+FINDINGS  F AY1 N - D IH0 NG Z
+FINDLAY  F IH1 N D - L IY0
+FINDLER  F IH1 N D - L ER0
+FINDLEY  F IH1 N D - L IY0
+FINDLING  F IH1 N - D AH0 L - IH0 NG
+FINDLING(2)  F IH1 N D - L IH0 NG
+FINDS  F AY1 N D Z
+FINE  F AY1 N
+FINEBERG  F AY1 N - B ER0 G
+FINED  F AY1 N D
+FINEFROCK  F AY1 N - F R AA2 K
+FINEGAN  F IH1 - N IH0 - G AE0 N
+FINEGOLD  F AY1 N - G OW2 L D
+FINELLA  F IH0 - N EH1 - L AH0
+FINELLI  F IH0 - N EH1 - L IY0
+FINELY  F AY1 N - L IY0
+FINEMAN  F AY1 N - M AH0 N
+FINER  F AY1 - N ER0
+FINERAN  F IH1 - N ER0 - AE0 N
+FINERTY  F IH1 - N ER0 - T IY0
+FINERY  F AY1 - N ER0 - IY0
+FINES  F AY1 N Z
+FINESSE  F IH0 - N EH1 S
+FINESSED  F IH0 - N EH1 S T
+FINEST  F AY1 - N AH0 S T
+FINESTONE  F AY1 N - S T OW2 N
+FINEVEST  F AY1 N - V EH2 S T
+FINEX  F IH1 - N EH2 K S
+FINFROCK  F IH1 N - F R AA2 K
+FINGER  F IH1 NG - G ER0
+FINGERBOARD  F IH1 NG - G ER0 - B AO2 R D
+FINGERED  F IH1 NG - G ER0 D
+FINGERHUT  F IH1 NG - G ER0 - HH AH2 T
+FINGERING  F IH1 NG - G ER0 - IH0 NG
+FINGERNAIL  F IH1 NG - G ER0 - N EY2 L
+FINGERNAILS  F IH1 NG - G ER0 - N EY2 L Z
+FINGERPAINT  F IH1 NG - G ER0 - P EY2 N T
+FINGERPAINTED  F IH1 NG - G ER0 - P EY2 N - T IH0 D
+FINGERPAINTED(2)  F IH1 NG - G ER0 - P EY2 - N IH0 D
+FINGERPOINTING  F IH1 NG - G ER0 - P OY2 N - T IH0 NG
+FINGERPRINT  F IH1 NG - G ER0 - P R IH2 N T
+FINGERPRINTED  F IH1 NG - G ER0 - P R IH2 N - T IH0 D
+FINGERPRINTING  F IH1 NG - G ER0 - P R IH2 N - T IH0 NG
+FINGERPRINTS  F IH1 NG - G ER0 - P R IH2 N T S
+FINGERS  F IH1 NG - G ER0 Z
+FINGERTIP  F IH1 NG - G ER0 - T IH2 P
+FINGERTIPS  F IH1 NG - G ER0 - T IH2 P S
+FINI  F IY1 - N IY0
+FINIAN  F IH1 - N IY0 - AH0 N
+FINIAN'S  F IH1 - N IY0 - AH0 N Z
+FINICKY  F IH1 - N AH0 - K IY0
+FINIGAN  F IH1 - N IH0 - G AH0 N
+FINING  F AY1 - N IH0 NG
+FININVEST  F IH1 - N IH2 N - V EH2 S T
+FININVEST'S  F IH1 - N IH2 N - V EH2 S T S
+FINISH  F IH1 - N IH0 SH
+FINISHED  F IH1 - N IH0 SH T
+FINISHER  F IH1 - N IH0 - SH ER0
+FINISHERS  F IH1 - N IH0 - SH ER0 Z
+FINISHES  F IH1 - N IH0 - SH IH0 Z
+FINISHING  F IH1 - N IH0 - SH IH0 NG
+FINITE  F AY1 - N AY2 T
+FINK  F IH1 NG K
+FINK'S  F IH1 NG K S
+FINKBEINER  F IH1 NG K - B AY2 - N ER0
+FINKBINER  F IH1 NG K - B AY2 - N ER0
+FINKE  F IH1 NG K
+FINKEL  F IH1 NG - K AH0 L
+FINKELMAN  F IH1 NG - K AH0 L - M AH0 N
+FINKELSON  F IH1 NG - K AH0 L - S AH0 N
+FINKELSTEIN  F IH1 NG - K AH0 L - S T IY2 N
+FINKELSTEIN'S  F IH1 NG - K AH0 L - S T IY2 N Z
+FINKELSTEIN'S(2)  F IH1 NG - K AH0 L - S T AY2 N Z
+FINKELSTEIN(2)  F IH1 NG - K AH0 L - S T AY2 N
+FINKEN  F IH1 NG - K AH0 N
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+FINKIELSTAIN  F IH0 NG - K IY1 L - S T IH0 N
+FINKIELSTAIN(2)  F IH2 NG - K IY1 L - S T EY0 N
+FINKLE  F IH1 NG - K AH0 L
+FINKLEA  F IH1 NG - K L IY0 - AH0
+FINKLER  F IH1 NG - K L ER0
+FINKLEY  F IH1 NG - K L IY0
+FINKS  F IH1 NG K S
+FINLAND  F IH1 N - L AE2 N D
+FINLAND'S  F IH1 N - L AH0 N D Z
+FINLAND(2)  F IH1 N - L AH0 N D
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+FINLAY  F IH1 N - L IY0
+FINLAYSON  F IH1 N - L IY0 - S AH0 N
+FINLETS  F IH1 N - L AH0 T S
+FINLEY  F IH1 N - L IY0
+FINLEY'S  F IH1 N - L IY0 Z
+FINMECCANICA  F IH2 N - M AH0 - K AE1 - N IH0 - K AH0
+FINN  F IH1 N
+FINN'S  F IH1 N Z
+FINNAIR  F IH1 - N EH1 R
+FINNAN  F IH1 - N AH0 N
+FINNE  F IH1 N
+FINNEGAN  F IH1 - N AH0 - G AH0 N
+FINNELL  F IH1 - N AH0 L
+FINNERAN  F IH1 - N ER0 - AE0 N
+FINNERTY  F IH1 - N ER0 - T IY0
+FINNEY  F IH1 - N IY0
+FINNICK  F IH1 - N IH0 K
+FINNICUM  F IH1 - N IH0 - K AH0 M
+FINNIE  F IH1 - N IY0
+FINNIGAN  F IH1 - N IH0 - G AH0 N
+FINNING  F IH1 - N IH0 NG
+FINNISH  F IH1 - N IH0 SH
+FINNS  F IH1 N Z
+FINO  F IY1 - N OW0
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+FINOCCHIO  F IY0 - N OW1 - K IY0 - OW0
+FINS  F IH1 N Z
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+FINSIDER  F IH1 N - S AY0 - D ER0
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+FIOLA  F IY0 - OW1 - L AH0
+FIONA  F IY0 - OW1 - N AH0
+FIORAVANTI  F IY0 - AO0 - R AA0 - V AA1 N - T IY0
+FIORE  F IY0 - AO1 - R IY0
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+FIORELLI  F IY0 - AO0 - R EH1 - L IY0
+FIORELLO  F IY0 - AO0 - R EH1 - L OW0
+FIORENTINO  F IY0 - AO0 - R EH0 N - T IY1 - N OW0
+FIORENZA  F IY0 - AO0 - R EH1 N - Z AH0
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+FIORI  F IY0 - AO1 - R IY0
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+FIORINA  F IY0 - AO0 - R IY1 - N AH0
+FIORINI  F IY0 - AO0 - R IY1 - N IY0
+FIORINO  F IY0 - AO0 - R IY1 - N OW0
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+FIORUCCI  F IY0 - AO0 - R UW1 - CH IY0
+FIPPLE  F IH1 - P AH0 L
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+FIRE'S  F AY1 - ER0 Z
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+FIREARMS  F AY1 - ER0 - AA2 R M Z
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+FIREBALL  F AY1 - ER0 - B AO2 L
+FIREBALL(2)  F AY1 R - B AO2 L
+FIREBALLS  F AY1 - ER0 - B AO2 L Z
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+FIREBIRD  F AY1 - ER0 - B ER2 D
+FIREBIRDS  F AY1 - ER0 - B ER2 D Z
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+FIREBOMBED  F AY1 R - B AA2 M D
+FIREBOMBING  F AY1 - ER0 - B AA2 - M IH0 NG
+FIREBOMBS  F AY1 R - B AA2 M Z
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+FIREBRICK  F AY1 - ER0 - B R IH2 K
+FIREBUG  F AY1 - ER0 - B AH2 G
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+FIRECRACKER  F AY1 - ER0 - K R AE2 - K ER0
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+FIRED  F AY1 - ER0 D
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+FIREFIGHT  F AY1 R - F AY2 T
+FIREFIGHTER  F AY1 R - F AY2 - T ER0
+FIREFIGHTERS  F AY1 - ER0 - F AY2 - T ER0 Z
+FIREFIGHTING  F AY1 R - F AY2 - T IH0 NG
+FIREFIGHTS  F AY1 R - F AY2 T S
+FIREFLIES  F AY1 - ER0 - F L AY2 Z
+FIREFLY  F AY1 - ER0 - F L AY2
+FIREHOUSE  F AY1 - ER0 - HH AW2 S
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+FIREMAN  F AY1 R - M AH0 N
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+FIRENZA  F IH0 - R EH1 N - Z AH0
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+FLING  F L IH1 NG
+FLINGING  F L IH1 - NG IH0 NG
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+FLINK  F L IH1 NG K
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+FUNCTIONAL  F AH1 NG K - SH AH0 - N AH0 L
+FUNCTIONALITY  F AH2 NG K - SH AH0 - N AE1 - L IH0 - T IY0
+FUNCTIONALLY  F AH1 NG K - SH AH0 N - AH0 L - IY0
+FUNCTIONALLY(2)  F AH1 NG K SH - N AH0 - L IY0
+FUNCTIONARIES  F AH1 NG K - SH AH0 N - EH2 - R IY0 Z
+FUNCTIONARY  F AH1 NG K - SH AH0 N - EH2 - R IY0
+FUNCTIONED  F AH1 NG K - SH AH0 N D
+FUNCTIONING  F AH1 NG K - SH AH0 N - IH0 NG
+FUNCTIONS  F AH1 NG K - SH AH0 N Z
+FUND  F AH1 N D
+FUND'S  F AH1 N D Z
+FUNDAMENTAL  F AH2 N - D AH0 - M EH1 N - T AH0 L
+FUNDAMENTAL(2)  F AH2 N - D AH0 - M EH1 - N AH0 L
+FUNDAMENTALISM  F AH2 N - D AH0 - M EH1 N - T AH0 - L IH2 - Z AH0 M
+FUNDAMENTALISM(2)  F AH2 N - D AH0 - M EH1 - N AH0 - L IH2 - Z AH0 M
+FUNDAMENTALIST  F AH2 N - D AH0 - M EH1 N - T AH0 - L IH0 S T
+FUNDAMENTALIST(2)  F AH2 N - D AH0 - M EH1 - N AH0 - L IH0 S T
+FUNDAMENTALISTS  F AH2 N - D AH0 - M EH1 N - T AH0 - L IH0 S T S
+FUNDAMENTALISTS(2)  F AH2 N - D AH0 - M EH1 N - T AH0 - L IH0 S S
+FUNDAMENTALISTS(3)  F AH2 N - D AH0 - M EH1 - N AH0 - L IH0 S T S
+FUNDAMENTALISTS(4)  F AH2 N - D AH0 - M EH1 - N AH0 - L IH0 S S
+FUNDAMENTALISTS(5)  F AH2 N - D AH0 - M EH1 N - T AH0 - L IH0 S
+FUNDAMENTALISTS(6)  F AH2 N - D AH0 - M EH1 - N AH0 - L IH0 S
+FUNDAMENTALLY  F AH2 N - D AH0 - M EH1 N - T AH0 - L IY0
+FUNDAMENTALLY(2)  F AH2 N - D AH0 - M EH1 - N AH0 - L IY0
+FUNDAMENTALS  F AH2 N - D AH0 - M EH1 N - T AH0 L Z
+FUNDAMENTALS(2)  F AH2 N - D AH0 - M EH1 - N AH0 L Z
+FUNDED  F AH1 N - D AH0 D
+FUNDED(2)  F AH1 N - D IH0 D
+FUNDER  F AH1 N - D ER0
+FUNDERBURG  F AH1 N - D ER0 - B ER0 G
+FUNDERBURK  F AH1 N - D ER0 - B ER0 K
+FUNDERBURKE  F AH1 N - D ER0 - B ER2 K
+FUNDERS  F AH1 N - D ER0 Z
+FUNDING  F AH1 N - D IH0 NG
+FUNDORA  F UW0 N - D AO1 - R AH0
+FUNDRAISER  F AH1 N - D R EY2 - Z ER0
+FUNDRAISER'S  F AH1 N - D R EY2 - Z ER0 Z
+FUNDRAISERS  F AH1 N - D R EY2 - Z ER0 Z
+FUNDRAISING  F AH1 N - D R EY2 - S IH0 NG
+FUNDS  F AH1 N D Z
+FUNDS'  F AH1 N D Z
+FUNERAL  F Y UW1 - N ER0 - AH0 L
+FUNERALS  F Y UW1 - N ER0 - AH0 L Z
+FUNERARY  F Y UW1 - N ER0 - EH2 - R IY0
+FUNES  F Y UW1 N Z
+FUNG  F AH1 NG
+FUNG'S  F AH1 NG Z
+FUNGAL  F AH1 NG - G AH0 L
+FUNGI  F AH1 N - JH AY0
+FUNGIBLE  F AH1 N - JH IH0 - B AH0 L
+FUNGICIDE  F AH1 N - JH AH0 - S AY2 D
+FUNGICIDES  F AH1 N - JH AH0 - S AY2 D Z
+FUNGUS  F AH1 NG - G AH0 S
+FUNICELLO  F AH2 - N AH0 - CH EH1 - L OW0
+FUNK  F AH1 NG K
+FUNKE  F AH1 NG K
+FUNKHOUSER  F AH1 NG K - HH AW2 - S ER0
+FUNKS  F AH1 NG K S
+FUNKY  F AH1 NG - K IY0
+FUNNEL  F AH1 - N AH0 L
+FUNNELED  F AH1 - N AH0 L D
+FUNNELING  F AH1 - N AH0 L - IH0 NG
+FUNNELING(2)  F AH1 N - L IH0 NG
+FUNNELL  F AH1 - N AH0 L
+FUNNELS  F AH1 - N AH0 L Z
+FUNNEST  F AH1 - N IH0 S T
+FUNNIER  F AH1 - N IY0 - ER0
+FUNNIEST  F AH1 - N IY0 - AH0 S T
+FUNNINESS  F AH1 - N IY0 - N AH0 S
+FUNNY  F AH1 - N IY0
+FUNS  F AH1 N Z
+FUNSTON  F AH1 N - S T AH0 N
+FUNTIME  F AH1 N - T AY2 M
+FUOCO  F UW0 - OW1 - K OW0
+FUOSS  F UW1 S
+FUQUA  F UW1 - K W AH0
+FUQUAY  F UW1 - K EY0
+FUR  F ER1
+FURASH  F Y ER0 - AE1 SH
+FURBEE  F ER1 - B IY2
+FURBER  F ER1 - B ER0
+FURBISH  F ER1 - B IH0 SH
+FURBISHING  F ER1 - B IH0 - SH IH0 NG
+FURBUSH  F ER1 - B UH2 SH
+FURBY  F ER1 - B IY0
+FURCHES  F ER0 - SH IY1 Z
+FURER  F Y UH1 - R ER0
+FUREY  F Y UH1 - R IY0
+FURFARO  F UH0 R - F AA1 - R OW0
+FURGASON  F ER1 - G AH0 - S AH0 N
+FURGERSON  F ER1 - G ER0 - S AH0 N
+FURGESON  F ER1 - G IH0 - S AH0 N
+FURIA  F Y UH1 - R IY0 - AH0
+FURINI  F UH0 - R IY1 - N IY0
+FURINI'S  F UH0 - R IY1 - N IY0 Z
+FURINO  F UH0 - R IY1 - N OW0
+FURIOUS  F Y UH1 - R IY0 - AH0 S
+FURIOUSER  F Y UH1 - R IY0 - AH0 - S ER0
+FURIOUSLY  F Y UH1 - R IY0 - AH0 S - L IY0
+FURLAN  F ER1 - L AH0 N
+FURLAUD  F ER0 - L OW1
+FURLETT  F ER0 - L EH1 T
+FURLETT(2)  F ER1 - L AH0 T
+FURLONG  F ER1 - L AO2 NG
+FURLOUGH  F ER1 - L OW0
+FURLOUGHED  F ER1 - L OW0 D
+FURLOUGHS  F ER1 - L OW0 Z
+FURLOW  F ER1 - L OW2
+FURMAN  F ER1 - M AE2 N
+FURMARK  F ER1 - M AA2 R K
+FURNACE  F ER1 - N AH0 S
+FURNACES  F ER1 - N AH0 - S AH0 Z
+FURNACES(2)  F ER1 - N AH0 - S IH0 Z
+FURNARI  F UH0 R - N AA1 - R IY0
+FURNAS  F ER1 - N AH0 S
+FURNER  F ER1 - N ER0
+FURNESS  F ER1 - N IH0 S
+FURNEY  F ER1 - N IY0
+FURNISH  F ER1 - N IH0 SH
+FURNISHED  F ER1 - N IH0 SH T
+FURNISHES  F ER1 - N IH0 - SH AH0 Z
+FURNISHES(2)  F ER1 - N IH0 - SH IH0 Z
+FURNISHING  F ER1 - N IH0 - SH IH0 NG
+FURNISHINGS  F ER1 - N IH0 - SH IH0 NG Z
+FURNISS  F ER0 - N IH1 S
+FURNITURE  F ER1 - N IH0 - CH ER0
+FURNITURE'S  F ER1 - N IH0 - CH ER0 Z
+FURNO  F UH1 R - N OW0
+FUROR  F Y UH1 - R AO0 R
+FURR  F ER1
+FURR'S  F ER1 Z
+FURRER  F ER1 - ER0
+FURRH  F ER1
+FURRIER  F ER1 - IY0 - ER0
+FURRIERS  F ER1 - IY0 - ER0 Z
+FURROW  F ER1 - OW0
+FURROWED  F ER1 - OW0 D
+FURRY  F ER1 - IY0
+FURS  F ER1 Z
+FURSE  F ER1 S
+FURST  F ER1 S T
+FURSTENBERG  F ER1 - S T AH0 N - B ER0 G
+FURTADO  F UH0 R - T AA1 - D OW0
+FURTAK  F ER1 - T AH0 K
+FURTAW  F ER1 - T AO0
+FURTH  F ER1 TH
+FURTHER  F ER1 - DH ER0
+FURTHERANCE  F ER1 - TH ER0 - AH0 N S
+FURTHERED  F ER1 - DH ER0 D
+FURTHERING  F ER1 - DH ER0 - IH0 NG
+FURTHERMORE  F ER1 - DH ER0 - M AO2 R
+FURTHERS  F ER1 - DH ER0 Z
+FURTHEST  F ER1 - TH AH0 S T
+FURTICK  F ER1 - T IH0 K
+FURTIVE  F ER1 - T IH0 V
+FURTIVELY  F ER1 - T IH0 V - L IY0
+FURUKAWA  F UH0 - R UW0 - K AA1 - W AH0
+FURUTA  F ER0 - UW1 - T AH0
+FURUYA  F UH0 - R UW1 - Y AH0
+FURY  F Y UH1 - R IY0
+FUSARO  F UW0 - S AA1 - R OW0
+FUSCO  F UW1 - S K OW0
+FUSE  F Y UW1 Z
+FUSED  F Y UW1 Z D
+FUSELAGE  F Y UW1 - S AH0 - L AA2 JH
+FUSELAGE(2)  F Y UW1 - S AH0 - L IH0 JH
+FUSELAGES  F Y UW1 - S AH0 - L AA0 - JH IH0 Z
+FUSELAGES(3)  F Y UW1 - S AH0 - L IH0 - JH IH0 Z
+FUSELIER  F Y UW1 S - L IY0 - ER0
+FUSES  F Y UW1 - Z AH0 Z
+FUSES(2)  F Y UW1 - Z IH0 Z
+FUSIBLE  F Y UW1 - Z AH0 - B AH0 L
+FUSILLADE  F Y UW1 - S IH0 - L EY2 D
+FUSILLI  F Y UW0 - S IH1 - L IY0
+FUSING  F Y UW1 - Z IH0 NG
+FUSION  F Y UW1 - ZH AH0 N
+FUSON  F UW1 - S AH0 N
+FUSS  F AH1 S
+FUSSED  F AH1 S T
+FUSSELL  F AH1 - S AH0 L
+FUSSELMAN  F AH1 - S AH0 L - M AH0 N
+FUSSES  F AH1 - S IH0 Z
+FUSSING  F AH1 - S IH0 NG
+FUSSNER  F AH1 S - N ER0
+FUSSY  F AH1 - S IY0
+FUST  F AH1 S T
+FUSTAT  F AH1 - S T AE0 T
+FUSTOK  F AH1 - S T AA0 K
+FUSTON  F AH1 - S T AH0 N
+FUTCH  F AH1 CH
+FUTHER  F AH1 - DH ER0
+FUTILE  F Y UW1 - T AH0 L
+FUTILITY  F Y UW0 - T IH1 - L AH0 - T IY0
+FUTRAL  F AH1 - T R AH0 L
+FUTRELL  F Y UW0 - T R EH1 L
+FUTTERMAN  F AH1 - T ER0 - M AH0 N
+FUTURE  F Y UW1 - CH ER0
+FUTURE'S  F Y UW1 - CH ER0 Z
+FUTURES  F Y UW1 - CH ER0 Z
+FUTURES'  F Y UW1 - CH ER0 Z
+FUTURISM  F Y UW1 - CH ER0 - IH2 - Z AH0 M
+FUTURIST  F Y UW1 - CH ER0 - IH0 S T
+FUTURISTIC  F Y UW2 - CH ER0 - IH1 - S T IH0 K
+FUTURISTS  F Y UW1 - CH ER0 - IH0 S T S
+FUTURISTS(2)  F Y UW1 - CH ER0 - IH0 S S
+FUTURISTS(3)  F Y UW1 - CH ER0 - IH0 S
+FUZES  F Y UW1 - Z IH0 Z
+FUZZ  F AH1 Z
+FUZZIER  F AH1 - Z IY0 - ER0
+FUZZY  F AH1 - Z IY0
+FYE  F AY1
+FYFE  F AY1 F
+FYFFE  F AY1 F
+FYFFES  F IH1 F S
+FYFFES(2)  F AY1 F S
+FYKE  F AY1 K
+FYOCK  F Y AA1 K
+FYODOR  F Y OW1 - D ER0
+FYODOR'S  F Y OW1 - D ER0 Z
+FYODOROV  F Y OW1 - D ER0 - AO2 V
+FYODOROV'S  F Y OW1 - D ER0 - AO2 V Z
+G  JH IY1
+G'S  JH IY1 Z
+G'VANNI'S  JH IY2 - OW0 - V AA1 - N IY0 Z
+G.  JH IY1
+G.'S  JH IY1 Z
+G.S  JH IY1 Z
+GA  G AA1
+GA(2)  JH IY1 - EY1
+GA(3)  JH AO1 R - JH AH0
+GAAL  G AA1 L
+GAAR  G AA1 R
+GAARDER  G AA1 R - D ER0
+GAB  G AE1 B
+GABA  G AA1 - B AH0
+GABALDON  G AA0 - B AA0 L - D AO1 N
+GABARDINE  G AE1 - B ER0 - D IY2 N
+GABARDINES  G AE1 - B ER0 - D IY2 N Z
+GABAY  G AE1 - B EY0
+GABBARD  G AH0 - B AA1 R D
+GABBERT  G AE1 - B ER0 T
+GABBING  G AE1 - B IH0 NG
+GABBRO  G AE1 - B R OW0
+GABBROIC  G AE0 - B R OW1 - IH0 K
+GABBY  G AE1 - B IY0
+GABE  G EY1 B
+GABEHART  G EY1 B - HH AA2 R T
+GABEL  G AH0 - B EH1 L
+GABELE  G AH0 - B EH1 - L EY0
+GABELLI  G AH0 - B EH1 - L IY0
+GABER  G EY1 - B ER0
+GABERT  G AE1 - B ER0 T
+GABHART  G AE1 B - HH AA2 R T
+GABIE  G AE1 - B IY0
+GABLE  G EY1 - B AH0 L
+GABLER  G EY1 - B AH0 L - ER0
+GABLER(2)  G EY1 - B L ER0
+GABLES  G EY1 - B AH0 L Z
+GABON  G AH0 - B AA1 N
+GABOR  G AH0 - B AO1 R
+GABORIAULT  G AE1 - B ER0 - IY0 - OW0
+GABOURY  G AE1 - B UH0 - R IY0
+GABRALL  G EY1 - B R AH0 L
+GABRALL'S  G EY1 - B R AH0 L Z
+GABRIEL  G EY1 - B R IY0 - AH0 L
+GABRIEL'S  G EY1 - B R IY0 - AH0 L Z
+GABRIELA  G AA0 - B R IY0 - EH1 - L AH0
+GABRIELE  G AA0 - B R IY0 - EH1 L
+GABRIELLA  G AA0 - B R IY0 - EH1 - L AH0
+GABRIELLE  G AE1 - B R IY0 - EH0 L
+GABRIELLI  G AA0 - B R IY0 - EH1 - L IY0
+GABRIELSEN  G AE1 - B R IY0 L - S AH0 N
+GABRIELSEN(2)  G EY1 - B R IY0 - EH0 L - S AH0 N
+GABRIELSON  G AE1 - B R IY0 L - S AH0 N
+GABRIELSON(2)  G EY1 - B R IY0 - EH0 L - S AH0 N
+GABROWNY  G AH0 - B R AW1 - N IY0
+GABRYS  G AE1 - B ER0 - IY0 Z
+GABY  G AE1 - B IY0
+GACCIONE  G AA0 K - CH OW1 - N IY0
+GACEK  G AA1 - CH EH2 K
+GACH  G AE1 CH
+GACY  G EY1 - S IY0
+GACY'S  G EY1 - S IY0 Z
+GAD  G AE1 D
+GADBERRY  G AE1 D - B EH2 - R IY0
+GADBOIS  G AE1 D - B W AA2
+GADD  G AE1 D
+GADDIE  G AE1 - D IY0
+GADDING  G AE1 - D IH0 NG
+GADDIS  G AE1 - D IH0 S
+GADDUM  G AE1 - D AH0 M
+GADDY  G AE1 - D IY0
+GADE  G EY1 D
+GADFLIES  G AE1 D - F L AY2 Z
+GADFLY  G AE1 D - F L AY2
+GADGET  G AE1 - JH AH0 T
+GADGETRY  G AE1 - JH AH0 - T R IY0
+GADGETS  G AE1 - JH AH0 T S
+GADHAFI  G AH0 - D AA1 - F IY0
+GADHAFI'S  G AH0 - D AA1 - F IY0 Z
+GADHAFI'S(2)  G AH0 D - HH AA1 - F IY0 Z
+GADHAFI(2)  G AH0 D - HH AA1 - F IY0
+GADOMSKI  G AH0 - D AA1 M S - K IY0
+GADOURY  G AE1 - D UH0 - R IY0
+GADS  G AE1 D Z
+GADSBY  G AE1 D Z - B IY0
+GADSDEN  G AE1 D Z - D AH0 N
+GADSON  G AE1 D - S AH0 N
+GADWAY  G AE1 D - W EY2
+GADZINSKI  G AH0 - JH IH1 N - S K IY0
+GAE  G AY1
+GAEA  G IY1 - AH0
+GAEBEL  G EH1 - B AH0 L
+GAEDE  G IY1 D
+GAEL  G EY1 L
+GAELIC  G EY1 - L IH0 K
+GAERTNER  G EH1 R T - N ER0
+GAETA  G IY1 - T AH0
+GAETANO  G AY0 - T AA1 - N OW0
+GAETH  G IY1 TH
+GAETZ  G IY1 T S
+GAF  G AE1 F
+GAFF  G AE1 F
+GAFFE  G AE1 F
+GAFFES  G AE1 F S
+GAFFEY  G AE1 - F IY0
+GAFFIN  G AE1 - F IH0 N
+GAFFNEY  G AE1 F - N IY0
+GAFFORD  G AE1 - F ER0 D
+GAG  G AE1 G
+GAGAN  G EY1 - G AH0 N
+GAGARIN  G AH0 - G AE1 - R AH0 N
+GAGE  G EY1 JH
+GAGEL  G AE1 - G AH0 L
+GAGEN  G AE1 - G AH0 N
+GAGER  G EY1 - G ER0
+GAGGED  G AE1 G D
+GAGGLE  G AE1 - G AH0 L
+GAGLIANO  G AA0 - G L IY0 - AA1 - N OW0
+GAGLIANO(2)  G AE0 G - L IY0 - AA1 - N OW0
+GAGLIARDI  G AA0 - G L IY0 - AA1 R - D IY0
+GAGLIARDI(2)  G AE0 G - L IY0 - AA1 R - D IY0
+GAGLIARDO  G AA0 - G L IY0 - AA1 R - D OW0
+GAGLIARDO(2)  G AE0 G - L IY0 - AA1 R - D OW0
+GAGLIO  G AE1 G - L IY0 - OW0
+GAGLIONE  G AA0 - G L IY0 - OW1 - N IY0
+GAGLIONE(2)  G AE0 G - L IY0 - OW1 - N IY0
+GAGNE  G EY1 - N IY0
+GAGNE(2)  G AE1 G - N IY0
+GAGNER  G AE1 G - N ER0
+GAGNIER  G AE1 G - N IY0 - ER0
+GAGNON  G AE1 - N Y AH0 N
+GAGS  G AE1 G Z
+GAHAGAN  G AA0 - HH AA1 - G AA0 N
+GAHAN  G AE1 - HH AH0 N
+GAHLI  G AA1 - L IY0
+GAHM  G AE1 M
+GAHN  G AE1 N
+GAHR  G AA1 R
+GAIA  G AY1 - AH0
+GAIDAR  G AY1 - D AA2 R
+GAIDAR'S  G AY1 - D AA2 R Z
+GAIER  G EY1 - ER0
+GAIGE  G EY1 JH
+GAIL  G EY1 L
+GAIL'S  G EY1 L Z
+GAILE  G EY1 L
+GAILEY  G EY1 - L IY0
+GAILLARD  G EY1 - L ER0 D
+GAILY  G EY1 - L IY0
+GAIN  G EY1 N
+GAINED  G EY1 N D
+GAINER  G EY1 - N ER0
+GAINERS  G EY1 - N ER0 Z
+GAINES  G EY1 N Z
+GAINESVILLE  G EY1 N Z - V IH2 L
+GAINEY  G EY1 - N IY0
+GAINFUL  G EY1 N - F AH0 L
+GAINFULLY  G EY1 N - F AH0 - L IY0
+GAINING  G EY1 - N IH0 NG
+GAINOR  G EY1 - N ER0
+GAINOUS  G EY1 - N AH0 S
+GAINS  G EY1 N Z
+GAINSAY  G EY1 N - S EY2
+GAINSBORO  G EY1 N Z - B ER0 - OW0
+GAINSCO  G EY1 N - S K OW0
+GAIR  G EH1 R
+GAISER  G EY1 - Z ER0
+GAIT  G EY1 T
+GAITAN  G AY2 - T AA1 N
+GAITER  G EY1 - T ER0
+GAITHER  G EY1 - TH ER0
+GAITHERSBURG  G EY1 - TH ER0 Z - B ER0 G
+GAITSKILL  G EY1 T - S K IH1 L
+GAJDA  G AY1 - D AH0
+GAJEWSKI  G AY0 - EH1 F S - K IY0
+GAL  G AE1 L
+GALA  G AE1 - L AH0
+GALA(2)  G EY1 - L AH0
+GALACTIC  G AH0 - L AE1 K - T IH0 K
+GALACTOSE  G AH0 - L AE1 K - T OW0 S
+GALAHAD  G AE1 - L AH0 - HH AE2 D
+GALAMBOS  G AA0 - L AA1 M - B OW0 Z
+GALAN  G EY1 - L AH0 N
+GALANE  G AH0 - L EY1 N
+GALANG  G AE1 - L AH0 NG
+GALANIS  G AH0 - L AA1 - N IH0 S
+GALANT  G AA1 - L AH0 N T
+GALANTE  G AA0 - L AA1 N - T IY0
+GALANTER  G AH0 - L AE1 N - T ER0
+GALANTI  G AH0 - L AE1 N - T IY0
+GALAPAGOS  G AH0 - L AA1 - P AH0 - G OW0 Z
+GALARNEAU  G AE1 - L AA0 R - N OW2
+GALARZA  G AH0 - L AA1 R - Z AH0
+GALAS  G EY1 - L AH0 Z
+GALASIE  G AE1 - L AH0 - S IY0
+GALASSI  G AA0 - L AA1 - S IY0
+GALASSO  G AA0 - L AA1 - S OW0
+GALATAS  G AA0 - L AA1 - T AA0 Z
+GALATEA  G AE2 - L AH0 - T IY1 - AH0
+GALATI  G AA0 - L AA1 - T IY0
+GALAVIZ  G AE1 - L AH0 - V IH0 Z
+GALAX  G AE1 - L AE2 K S
+GALAXIES  G AE1 - L AH0 K - S IY0 Z
+GALAXY  G AE1 - L AH0 K - S IY0
+GALAXY'S  G AE1 - L AH0 K - S IY0 Z
+GALBAN  G AE1 L - B AH0 N
+GALBO  G AE1 L - B OW0
+GALBRAITH  G AE1 L - B R EY2 TH
+GALBREATH  G AE1 L - B R EH2 TH
+GALE  G EY1 L
+GALE'S  G EY1 L Z
+GALEA  G EY1 - L IY0 - AH0
+GALEANO  G AA0 - L IY1 - N OW0
+GALEB  G AE1 - L AH0 B
+GALEB(2)  G EY1 - L AH0 B
+GALEBS  G AE1 - L AH0 B Z
+GALEBS(2)  G EY1 - L AH0 B Z
+GALEF  G AE1 - L AH0 F
+GALEN  G EY1 - L AH0 N
+GALEN'S  G EY1 - L AH0 N Z
+GALENA  G AH0 - L IY1 - N AH0
+GALENICAL  G AH0 - L EH1 - N IH0 - K AH0 L
+GALENTINE  G AA0 - L EH0 N - T IY1 - N IY0
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+HERTENSTEIN  HH ER1 - T AH0 N - S T AY0 N
+HERTENSTEIN(2)  HH ER1 - T AH0 N - S T IY0 N
+HERTER  HH ER1 - T ER0
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+HERTING  HH ER1 - T IH0 NG
+HERTLEIN  HH ER1 T - L AY0 N
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+HERTZBERG  HH ER1 T S - B ER0 G
+HERTZENLEBEN  HH ER1 - T AH0 N - L EY2 - B AH0 N
+HERTZENLEBEN'S  HH ER1 - T AH0 N - L EY2 - B AH0 N Z
+HERTZLER  HH ER1 T - Z AH0 L - ER0
+HERTZLER(2)  HH ER1 T Z - L ER0
+HERTZOG  HH ER1 T - Z AH0 G
+HERTZOG(2)  HH ER1 T - Z AA2 G
+HERVE  HH ER1 V
+HERVE(2)  HH ER1 - V EY0
+HERVEY  HH ER0 - V EY1
+HERWICK  HH ER1 - W IH2 K
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+HERYANA  HH ER0 - Y AE1 - N AH0
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+HERZBERGER  HH ER1 Z - B ER0 - G ER0
+HERZEGOVINA  HH EH2 R T - S AH0 - G OW0 - V IY1 - N AH0
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+HERZEGOVINA'S(2)  HH ER2 T - S AH0 - G OW0 - V IY1 - N AH0 Z
+HERZEGOVINA(2)  HH ER2 T - S AH0 - G OW0 - V IY1 - N AH0
+HERZER  HH ER1 - Z ER0
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+HESCH  HH EH1 SH
+HESELTINE  HH EH1 - S AH0 L - T IY2 N
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+HESITANT  HH EH1 - Z IH0 - T AH0 N T
+HESITANTLY  HH EH1 - Z IH0 - T AH0 N T - L IY0
+HESITATE  HH EH1 - Z AH0 - T EY2 T
+HESITATED  HH EH1 - Z IH0 - T EY2 - T IH0 D
+HESITATES  HH EH1 - Z AH0 - T EY2 T S
+HESITATING  HH EH1 - Z AH0 - T EY2 - T IH0 NG
+HESITATION  HH EH2 - Z AH0 - T EY1 - SH AH0 N
+HESITATIONS  HH EH2 - Z AH0 - T EY1 - SH AH0 N Z
+HESKETH  HH EH1 - S K IH0 TH
+HESKETT  HH EH1 - S K IH0 T
+HESLEP  HH EH1 S - L IH0 P
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+HESSE'S  HH EH1 - S IH0 Z
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+HESSER  HH EH1 - S ER0
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+HESSINGER  HH EH1 - S IH0 N - JH ER0
+HESSION  HH EH1 - SH IH0 N
+HESSITE  HH EH1 - S AY0 T
+HESSLER  HH EH1 S - L ER0
+HESSLING  HH EH1 - S AH0 - L IH0 NG
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+HESSTON  HH EH1 - S T AH0 N
+HESSTON'S  HH EH1 - S T AH0 N Z
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+HETERODOXY  HH EH1 - T ER0 - AH0 - D AA2 K - S IY0
+HETERODYNE  HH EH1 - T ER0 - AH0 - D AY2 N
+HETEROGENEITY  HH EH2 - T ER0 - AH0 - JH IH0 - N IY1 - AH0 - T IY0
+HETEROGENEITY(2)  HH EH2 - T ER0 - AH0 - JH IH0 - N EY1 - AH0 - T IY0
+HETEROGENEOUS  HH EH2 - T ER0 - AH0 - JH IY1 - N Y AH0 S
+HETEROSEXUAL  HH EH2 - T ER0 - OW0 - S EH1 K - SH AH0 - W AH0 L
+HETEROSEXUALITY  HH EH2 - T ER0 - OW0 - S EH0 K - SH AH0 W - AE1 - L IH0 - T IY0
+HETEROSEXUALS  HH EH2 - T ER0 - OW0 - S EH1 K - SH AH0 - W AH0 L Z
+HETEROSIS  HH EH2 - T ER0 - OW1 - S AH0 S
+HETEROSPOROUS  HH EH2 - T ER0 - AA1 - S P ER0 - AH0 S
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+HETH  HH EH1 TH
+HETHERINGTON  HH EH1 - DH ER0 - IH0 NG - T AH0 N
+HETLAND  HH EH1 T - L AH0 N D
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+HEUBLEIN  HH Y UW1 - B L AY2 N
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+HEUERMAN  HH OY1 - ER0 - M AH0 N
+HEUERMANN  HH OY1 - ER0 - M AH0 N
+HEUMAN  HH Y UW1 - M AH0 N
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+HEUN  HH Y UW1 N
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+HEUSEN  HH Y UW1 - S AH0 N
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+HEUSSER  HH Y UW1 - S ER0
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+HEVERLY  HH EH1 - V ER0 - L IY0
+HEVEY  HH IH0 - V EY1
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+HEW  HH Y UW1
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+HEWETT  HH Y UW1 - IH0 T
+HEWEY  HH Y UW1 - IY0
+HEWING  HH Y UW1 - IH0 NG
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+HEWITT'S  HH Y UW1 - IH0 T S
+HEWLER  HH Y UW1 - L ER0
+HEWLER'S  HH Y UW1 - L ER0 Z
+HEWLETT  HH Y UW1 - L IH0 T
+HEWN  HH Y UW1 N
+HEWS  HH Y UW1 Z
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+HEXACHLOROPHENE  HH EH2 K - S AH0 - K L AO1 - R AH0 - F IY2 N
+HEXAGON  HH EH1 K - S AH0 - G AA2 N
+HEXAGONAL  HH EH0 K - S AE1 - G AH0 - N AH0 L
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+HEYDE  HH EY1 D
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+HEYMANN  HH EY1 - M AH0 N
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+HEYN  HH EY1 N
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+HEZBOLLAH  HH EH0 Z - B AA1 - L AH0
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+HEZBOLLAH'S(2)  HH EH1 Z - B AH0 - L AH0 Z
+HEZBOLLAH(2)  HH EH1 Z - B AH0 - L AH0
+HEZBULLAH  HH EH0 Z - B AA1 - L AH0
+HEZBULLAH(2)  HH EH1 Z - B AH0 - L AH0
+HFDF  EY1 - CH EH1 F - D IY1 - EH1 F
+HGH  EY1 CH - JH IY1 - EY1 CH
+HI  HH AY1
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+HIAASEN  HH AY1 - AA0 - S IH0 N
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+HIBDON  HH IH1 B - D AH0 N
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+HIBERNATION  HH AY2 - B ER0 - N EY1 - SH AH0 N
+HIBERNIA  HH AY2 - B ER1 - N IY0 - AH0
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+HIBLER  HH IH1 - B L ER0
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+HICKMAN  HH IH1 K - M AH0 N
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+HICKORY  HH IH1 - K ER0 - IY0
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+HIERARCHY  HH AY1 - ER0 - AA2 R - K IY0
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+HIERHOLZER  HH AY1 R - HH OW0 L - Z ER0
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+HIGGINBOTTOM  HH IH0 - G IH0 N - B AA1 - T AH0 M
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+HIGHLAND  HH AY1 - L AH0 N D
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+HIGHLANDERS  HH AY1 - L AE2 N - D ER0 Z
+HIGHLANDS  HH AY1 - L AH0 N D Z
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+HIGHLIGHT  HH AY1 - L AY2 T
+HIGHLIGHTED  HH AY1 - L AY2 - T IH0 D
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+HIGHTOWER'S  HH AY1 - T AW2 - ER0 Z
+HIGHWAY  HH AY1 - W EY2
+HIGHWAY'S  HH AY1 - W EY2 Z
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+HIGMAN  HH IH1 G - M AH0 N
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+HIGUERA  HH IY0 - G EH1 - R AH0
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+HIJACKED  HH AY1 - JH AE2 K T
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+HIJACKING  HH AY1 - JH AE2 - K IH0 NG
+HIJACKINGS  HH AY1 - JH AE2 - K IH0 NG Z
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+HILARIOUS  HH IH0 - L EH1 - R IY0 - AH0 S
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+IMMORTAL  IH0 - M AO1 R - T AH0 L
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+IMMUNOTHERAPY  IH2 - M Y UW0 - N OW0 - TH EH1 - R AH0 - P IY0
+IMMUTABLE  IH0 - M Y UW1 - T AH0 - B AH0 L
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+IMPORTANT  IH0 M - P AO1 R - T AH0 N T
+IMPORTANTLY  IH0 M - P AO1 R - T AH0 N T - L IY0
+IMPORTATION  IH2 M - P AO0 R - T EY1 - SH AH0 N
+IMPORTED  IH0 M - P AO1 R - T IH0 D
+IMPORTER  IH0 M - P AO1 R - T ER0
+IMPORTERS  IH0 M - P AO1 R - T ER0 Z
+IMPORTERS'  IH0 M - P AO1 R - T ER0 Z
+IMPORTING  IH0 M - P AO1 R - T IH0 NG
+IMPORTS  IH0 M - P AO1 R T S
+IMPORTS'  IH1 M - P AO0 R T S
+IMPORTS(2)  IH1 M - P AO0 R T S
+IMPOSE  IH0 M - P OW1 Z
+IMPOSED  IH0 M - P OW1 Z D
+IMPOSES  IH0 M - P OW1 - Z AH0 Z
+IMPOSES(2)  IH0 M - P OW1 - Z IH0 Z
+IMPOSING  IH0 M - P OW1 - Z IH0 NG
+IMPOSITION  IH2 M - P AH0 - Z IH1 - SH AH0 N
+IMPOSSIBILITY  IH0 M - P AO2 - S IH0 - B IH1 - L IH0 - T IY0
+IMPOSSIBLE  IH0 M - P AA1 - S AH0 - B AH0 L
+IMPOSSIBLE'S  IH0 M - P AA1 - S AH0 - B AH0 L Z
+IMPOSSIBLY  IH0 M - P AA1 - S AH0 - B L IY0
+IMPOSTOR  IH0 M - P AO1 - S T ER0
+IMPOSTORS  IH0 M - P AO1 - S T ER0 Z
+IMPOTENCE  IH1 M - P AH0 - T AH0 N S
+IMPOTENT  IH1 M - P AH0 - T AH0 N T
+IMPOUND  IH0 M - P AW1 N D
+IMPOUNDED  IH0 M - P AW1 N - D IH0 D
+IMPOUNDMENT  IH0 M - P AW1 N D - M AH0 N T
+IMPOUNDMENTS  IH0 M - P AW1 N D - M AH0 N T S
+IMPOVERISH  IH0 M - P AA1 - V R IH0 SH
+IMPOVERISHED  IH0 M - P AA1 - V R IH0 SH T
+IMPOVERISHES  IH0 M - P AA1 - V R IH0 - SH AH0 Z
+IMPOVERISHMENT  IH0 M - P AA1 - V R IH0 SH - M AH0 N T
+IMPRACTICABLE  IH0 M - P R AE1 K - T IH0 - K AH0 - B AH0 L
+IMPRACTICAL  IH0 M - P R AE1 K - T AH0 - K AH0 L
+IMPRACTICAL(2)  IH0 M - P R AE1 K - T IH0 - K AH0 L
+IMPRECISE  IH1 M - P R AH0 - S AY2 S
+IMPREGNABLE  IH0 M - P R EH1 G - N AH0 - B AH0 L
+IMPREGNATED  IH0 M - P R EH1 G - N EY2 - T AH0 D
+IMPREGNATION  IH0 M - P R EH1 G - N EY1 - SH AH0 N
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+IMPRESS  IH0 M - P R EH1 S
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+IMPRESSED  IH0 M - P R EH1 S T
+IMPRESSES  IH0 M - P R EH1 - S IH0 Z
+IMPRESSING  IH0 M - P R EH1 - S IH0 NG
+IMPRESSION  IH0 M - P R EH1 - SH AH0 N
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+IMPRESSIONISTIC  IH0 M - P R EH2 - SH AH0 - N IH1 - S T IH0 K
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+IMPRESSIONISTS(3)  IH0 M - P R EH1 - SH AH0 N - IH0 S
+IMPRESSIONS  IH0 M - P R EH1 - SH AH0 N Z
+IMPRESSIVE  IH0 M - P R EH1 - S IH0 V
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+IMPRESSMENT  IH0 M - P R EH1 S - M AH0 N T
+IMPRIMATUR  IH2 M - P R IH0 - M AA1 - T ER0
+IMPRIMIS  IH0 M - P R IY1 - M IH0 S
+IMPRINT  IH0 M - P R IH1 N T
+IMPRINT(2)  IH1 M - P R IH0 N T
+IMPRINTED  IH0 M - P R IH1 N - T IH0 D
+IMPRINTED(2)  IH0 M - P R IH1 - N IH0 D
+IMPRINTING  IH0 M - P R IH1 N - T IH0 NG
+IMPRINTING(2)  IH0 M - P R IH1 - N IH0 NG
+IMPRINTS  IH0 M - P R IH1 N T S
+IMPRISON  IH0 M - P R IH1 - Z AH0 N
+IMPRISONED  IH0 M - P R IH1 - Z AH0 N D
+IMPRISONING  IH0 M - P R IH1 - Z AH0 N - IH0 NG
+IMPRISONMENT  IH0 M - P R IH1 - Z AH0 N - M AH0 N T
+IMPROBABLE  IH0 M - P R AA1 - B AH0 - B AH0 L
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+IMPROMPTU  IH0 M - P R AA1 M P - T UW0
+IMPROPER  IH0 M - P R AA1 - P ER0
+IMPROPERLY  IH0 M - P R AA1 - P ER0 - L IY0
+IMPROPRIETIES  IH2 M - P R AH0 - P R AY1 - AH0 - T IY0 Z
+IMPROPRIETY  IH2 M - P R AH0 - P R AY1 - AH0 - T IY0
+IMPROV  IH1 M - P R AA2 V
+IMPROVE  IH0 M - P R UW1 V
+IMPROVED  IH0 M - P R UW1 V D
+IMPROVEMENT  IH0 M - P R UW1 V - M AH0 N T
+IMPROVEMENTS  IH0 M - P R UW1 V - M AH0 N T S
+IMPROVES  IH0 M - P R UW1 V Z
+IMPROVING  IH0 M - P R UW1 - V IH0 NG
+IMPROVISATION  IH2 M - P R AA0 - V IH0 - Z EY1 - SH AH0 N
+IMPROVISATIONAL  IH2 M - P R AA0 - V IH0 - Z EY1 - SH AH0 - N AH0 L
+IMPROVISATIONS  IH2 M - P R AA0 - V IH0 - Z EY1 - SH AH0 N Z
+IMPROVISE  IH1 M - P R AH0 - V AY2 Z
+IMPROVISE(2)  IH2 M - P R AH0 - V AY1 Z
+IMPROVISED  IH1 M - P R AH0 - V AY2 Z D
+IMPROVISING  IH1 M - P R AH0 - V AY2 - Z IH0 NG
+IMPRUDENCE  IH0 M - P R UW1 - D AH0 N S
+IMPRUDENT  IH0 M - P R UW1 - D AH0 N T
+IMPRUDENTLY  IH0 M - P R UW1 - D AH0 N T - L IY0
+IMPSON  IH1 M P - S AH0 N
+IMPUGN  IH0 M - P Y UW1 N
+IMPUGNED  IH0 M - P Y UW1 N D
+IMPUGNING  IH0 M - P Y UW1 - N IH0 NG
+IMPULSE  IH1 M - P AH0 L S
+IMPULSE(2)  IH0 M - P AH1 L S
+IMPULSES  IH1 M - P AH0 L - S IH0 Z
+IMPULSES(2)  IH0 M - P AH1 L - S IH0 Z
+IMPULSIVE  IH0 M - P AH1 L - S IH0 V
+IMPULSIVELY  IH0 M - P AH1 L - S IH0 V - L IY0
+IMPUNITY  IH0 M - P Y UW1 - N IH0 - T IY0
+IMPURE  IH0 M - P Y UH1 R
+IMPURITIES  IH0 M - P Y UH1 - R AH0 - T IY0 Z
+IMPURITY  IH0 M - P Y UH1 - R AH0 - T IY0
+IMPUTATION  IH2 M - P Y AH0 - T EY1 - SH AH0 N
+IMPUTE  IH0 M - P Y UW1 T
+IMPUTED  IH0 M - P Y UW1 - T IH0 D
+IMRE  IH1 - M R AH0
+IMREG  IH2 M - R EH1 G
+IMREG'S  IH2 M - R EH1 G Z
+IMRIE  IH1 - M ER0 - IY0
+IMUS  AY1 - M AH0 S
+IN  IH0 N
+IN(2)  IH1 N
+IN.  IH1 N
+IN.(2)  IH1 N CH
+INA  IY1 - N AH0
+INABILITY  IH2 N - AH0 - B IH1 - L IH0 - T IY0
+INABINET  IH0 N - AH0 - B IH1 - N IH0 T
+INACCESSIBILITY  IH2 N - AH0 K - S EH2 - S AH0 - B IH1 - L AH0 - T IY0
+INACCESSIBLE  IH2 N - AH0 K - S EH1 - S AH0 - B AH0 L
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+INACCURACY  IH0 N - AE1 - K Y ER0 - AH0 - S IY0
+INACCURATE  IH0 N - AE1 - K Y ER0 - AH0 T
+INACCURATELY  IH0 N - AE1 - K Y ER0 - AH0 T - L IY0
+INACOM  IH1 N - AH0 - K AA2 M
+INACOM(2)  AY1 - N AH0 - K AA2 M
+INACOMP  AY1 - N AH0 - K AA2 M P
+INACOMP(2)  IH1 N - AH0 - K AA2 M P
+INACTION  IH0 N - AE1 K - SH AH0 N
+INACTIVATE  IH0 N - AE1 K - T IH0 - V EY2 T
+INACTIVATED  IH0 N - AE1 K - T IH0 - V EY2 - T IH0 D
+INACTIVATION  IH0 N - AE2 K - T IH0 - V EY1 - SH AH0 N
+INACTIVE  IH0 N - AE1 K - T IH0 V
+INACTIVITY  IH0 N - AE0 K - T IH1 - V IH0 - T IY0
+INADEQUACIES  IH0 N - AE1 - D AH0 - K W AH0 - S IY0 Z
+INADEQUACY  IH0 N - AE1 - D IH0 - K W AH0 - S IY0
+INADEQUATE  IH0 N - AE1 - D AH0 - K W AH0 T
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+INADEQUATELY  IH0 N - AE1 - D AH0 - K W AH0 T - L IY0
+INADMISSIBLE  IH0 N - AH0 D - M IH1 - S AH0 - B AH0 L
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+INADVERTENCE  IH2 N - AH0 D - V ER1 - T AH0 N S
+INADVERTENCE(2)  IH2 N - AE0 D - V ER1 - T AH0 N S
+INADVERTENT  IH2 N - AH0 D - V ER1 - T AH0 N T
+INADVERTENT(2)  IH2 N - AE0 D - V ER1 - T AH0 N T
+INADVERTENTLY  IH2 N - AH0 D - V ER1 - T AH0 N T - L IY0
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+INADVISABLE  IH0 N - AH0 D - V AY1 - Z AH0 - B AH0 L
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+INAEZ  IH2 N - AE1 Z
+INAEZ(2)  IH2 - N EY1 Z
+INALIENABLE  IH0 N - EY1 - L Y AH0 - N AH0 - B AH0 L
+INAMURA  IH2 - N AH0 - M UH1 - R AH0
+INANE  IH0 N - EY1 N
+INANIMATE  IH0 N - AE1 - N AH0 - M AH0 T
+INAPPLICABLE  IH0 N - AE1 - P L IH0 - K AH0 - B AH0 L
+INAPPROPRIATE  IH2 N - AH0 - P R OW1 - P R IY0 - IH0 T
+INAPPROPRIATELY  IH0 N - AH0 - P R AA1 - P R IY0 - AH0 T - L IY0
+INARTICULATE  IH0 N - AA0 R - T IH1 - K Y AH0 - L AH0 T
+INASMUCH  IH0 N - AE1 S - M AH0 K
+INATTENTION  IH0 N - AH0 - T EH1 N - CH AH0 N
+INATTENTIVE  IH2 N - AH0 - T EH1 N - T IH0 V
+INAUDIBLE  IH0 N - AO1 - D AH0 - B AH0 L
+INAUGURAL  IH0 - N AO1 - G ER0 - AH0 L
+INAUGURAL(2)  IH0 - N AO1 - G Y ER0 - AH0 L
+INAUGURATE  IH0 - N AO1 - G Y ER0 - IH0 T
+INAUGURATE(2)  IH0 - N AO1 - G Y ER0 - EY0 T
+INAUGURATED  IH0 - N AO1 - G ER0 - EY2 - T IH0 D
+INAUGURATED(2)  IH0 - N AO1 - G Y ER0 - EY2 - T IH0 D
+INAUGURATES  IH0 - N AO1 - G Y ER0 - IH0 T S
+INAUGURATING  IH0 - N AO1 - G Y ER0 - EY2 - T IH0 NG
+INAUGURATION  IH0 - N AO2 - G Y ER0 - EY1 - SH AH0 N
+INAUGURATIONS  IH0 - N AO2 - G Y ER0 - EY1 - SH AH0 N Z
+INAUSPICIOUS  IH0 N - AW2 - S P IH1 - SH IH0 S
+INBIO  IH0 N - B AY1 - OW0
+INBOARD  IH1 N - B AO2 R D
+INBODEN  IH1 N - B OW0 - D AH0 N
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+INBOUND  IH0 N - B AW1 N D
+INBOUND(2)  IH1 N - B AW0 N D
+INBRED  IH1 N - B R EH2 D
+INBREED  IH1 N - B R IY2 D
+INBREEDING  IH2 N - B R IY1 - D IH0 NG
+INC  IH1 NG K
+INC.  IH1 NG K
+INC.'S  IH1 NG K S
+INC.(2)  IH0 NG - K AO1 R - P AO0 - R EY0 - T AH0 D
+INCA  IH1 NG - K AH0
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+INCANDESCENT  IH2 N - K AH0 N - D EH1 - S AH0 N T
+INCANT  IH0 N - K AE1 N T
+INCANTATION  IH0 N - K AE1 N - T EY1 - SH AH0 N
+INCANTATORY  IH0 N - K AE1 N - T AH0 - T AO2 - R IY0
+INCAPABLE  IH0 N - K EY1 - P AH0 - B AH0 L
+INCAPACITATE  IH0 N - K AH0 - P AE1 - S IH0 - T EY2 T
+INCAPACITATED  IH0 N - K AH0 - P AE1 - S IH0 - T EY2 - T IH0 D
+INCAPACITATING  IH0 N - K AH0 - P AE1 - S IH0 - T EY2 - T IH0 NG
+INCAPACITATION  IH0 N - K AH0 - P AE2 - S IH0 - T EY1 - SH AH0 N
+INCAPACITY  IH0 N - K AH0 - P AE1 - S AH0 - T IY0
+INCARCERATE  IH0 N - K AA1 R - S ER0 - EY2 T
+INCARCERATED  IH0 N - K AA1 R - S ER0 - EY2 - T IH0 D
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+INCARCERATION  IH0 N - K AA2 R - S ER0 - EY1 - SH AH0 N
+INCARNATE  IH0 N - K AA1 R - N AH0 T
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+INCARNATION  IH0 N - K AA1 R - N EY1 - SH AH0 N
+INCARNATIONS  IH0 N - K AA0 R - N EY1 - SH AH0 N Z
+INCAS  IH1 NG - K AH0 Z
+INCASE  IH0 N - K EY1 S
+INCATA  IH0 NG - K AA1 - T AH0
+INCATA'S  IH0 NG - K AA1 - T AH0 Z
+INCE  IH1 N S
+INCENDIARY  IH0 N - S EH1 N - D IY0 - EH0 - R IY0
+INCENSE  IH0 N - S EH1 N S
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+INCENSED  IH1 N - S EH2 N S T
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+INCENTIVES  IH0 N - S EH1 N - T IH0 V Z
+INCENTIVES(2)  IH0 N - S IH1 - N IH0 V Z
+INCEPTION  IH0 N - S EH1 P - SH AH0 N
+INCESSANT  IH0 N - S EH1 - S AH0 N T
+INCESSANTLY  IH0 N - S EH1 - S AH0 N T - L IY0
+INCEST  IH1 N - S EH2 S T
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+INCHED  IH1 N CH T
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+INCHES'  IH1 N - CH AH0 Z
+INCHES'(2)  IH1 N - CH IH0 Z
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+INCHING  IH1 N - CH IH0 NG
+INCHON  IH1 N - CH AH0 N
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+INCIDENCE  IH1 N - S AH0 - D AH0 N S
+INCIDENCE(2)  IH1 N - S IH0 - D AH0 N S
+INCIDENCES  IH1 N - S AH0 - D AH0 N - S IH0 Z
+INCIDENT  IH1 N - S AH0 - D AH0 N T
+INCIDENTAL  IH2 N - S IH0 - D EH1 N - T AH0 L
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+INCIDENTALLY(2)  IH2 N - S IH0 - D EH1 N T - L IY0
+INCIDENTALS  IH2 N - S IH0 - D EH1 N - T AH0 L Z
+INCIDENTS  IH1 N - S AH0 - D AH0 N T S
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+INCINERATED  IH0 N - S IH1 - N ER0 - EY2 - T IH0 D
+INCINERATING  IH0 N - S IH1 - N ER0 - EY2 - T IH0 NG
+INCINERATION  IH0 N - S IH1 - N ER0 - EY2 - SH AH0 N
+INCINERATOR  IH0 N - S IH1 - N ER0 - EY2 - T ER0
+INCINERATORS  IH0 N - S IH1 - N ER0 - EY2 - T ER0 Z
+INCIPIENT  IH0 N - S IH1 - P IY0 - AH0 N T
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+INCISE  IH0 N - S AY1 Z
+INCISED  IH0 N - S AY1 Z D
+INCISION  IH0 N - S IH1 - ZH AH0 N
+INCISIONS  IH0 N - S IH1 - ZH AH0 N Z
+INCISIVE  IH0 N - S AY1 - S IH0 V
+INCISOR  IH0 N - S AY1 - Z ER0
+INCISORS  IH0 N - S AY1 - Z ER0 Z
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+INCITES  IH0 N - S AY1 T S
+INCITING  IH0 N - S AY1 - T IH0 NG
+INCIVILITY  IH0 N - S IH0 - V IH1 - L IH0 - T IY0
+INCLEMENT  IH0 N - K L EH1 - M AH0 N T
+INCLEMENT(2)  IH1 N - K L IH0 - M AH0 N T
+INCLINATION  IH2 N - K L AH0 - N EY1 - SH AH0 N
+INCLINATIONS  IH2 N - K L AH0 - N EY1 - SH AH0 N Z
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+INCLINED  IH0 N - K L AY1 N D
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+INCLINES(2)  IH1 N - K L AY0 N Z
+INCLOSURE  IH0 N - K L OW1 - ZH ER0
+INCLUDE  IH0 N - K L UW1 D
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+INCLUDES  IH0 N - K L UW1 D Z
+INCLUDING  IH0 N - K L UW1 - D IH0 NG
+INCLUSION  IH0 N - K L UW1 - ZH AH0 N
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+INCO  IH2 N - K OW1
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+INCOHERENCE  IH0 N - K OW0 - HH IH1 - R AH0 N S
+INCOHERENT  IH0 N - K OW0 - HH IH1 - R AH0 N T
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+INCOM  IH1 NG - K AA0 M
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+INCOMES  IH1 N - K AH2 M Z
+INCOMING  IH1 N - K AH2 - M IH0 NG
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+INCOMPARABLE  IH0 N - K AA1 M - P ER0 - AH0 - B AH0 L
+INCOMPARABLY  IH0 N - K AA1 M - P ER0 - AH0 - B L IY0
+INCOMPATIBILITY  IH0 N - K AA2 M - P AH0 - T IH0 - B IH1 - L IH0 - T IY0
+INCOMPATIBLE  IH0 N - K AH0 M - P AE1 - T AH0 - B AH0 L
+INCOMPETENCE  IH0 N - K AA1 M - P AH0 - T AH0 N S
+INCOMPETENCY  IH0 N - K AA1 M - P AH0 - T AH0 N - S IY0
+INCOMPETENT  IH0 N - K AA1 M - P AH0 - T AH0 N T
+INCOMPETENTLY  IH0 N - K AA1 M - P AH0 - T AH0 N T - L IY0
+INCOMPETENTS  IH0 NG - K AA1 M - P AH0 - T AH0 N T S
+INCOMPLETE  IH0 N - K AH0 M - P L IY1 T
+INCOMPREHENSIBLE  IH0 NG - K AA2 M - P R AH0 - HH EH1 N - S IH0 - B AH0 L
+INCOMPRESSIBLE  IH0 N - K AH0 M - P R EH1 - S AH0 - B AH0 L
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+INCONSISTENCIES  IH0 NG - K AA1 N - S IH0 - S T EH2 N - S IY0 Z
+INCONSISTENCY  IH2 N - K AH0 N - S IH1 - S T AH0 N - S IY0
+INCONSISTENT  IH2 N - K AH0 N - S IH1 - S T AH0 N T
+INCONSPICUOUS  IH0 NG - K AA1 N - S P IH0 - K W AH0 S
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+INCONTINENCE  IH0 N - K AA1 N - T AH0 - N AH0 N S
+INCONTINENT  IH0 N - K AA1 N - T AH0 - N AH0 N T
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+INCONVENIENT  IH2 N - K AH0 N - V IY1 - N Y AH0 N T
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+INCORPORATED  IH0 N - K AO1 R - P ER0 - EY2 - T AH0 D
+INCORPORATED'S  IH0 N - K AO1 R - P ER0 - EY2 - T IH0 D Z
+INCORPORATED(2)  IH0 N - K AO1 R - P ER0 - EY2 - T IH0 D
+INCORPORATES  IH0 N - K AO1 R - P ER0 - EY2 T S
+INCORPORATING  IH0 N - K AO1 R - P ER0 - EY2 - T IH0 NG
+INCORPORATION  IH0 N - K AO2 R - P ER0 - EY1 - SH AH0 N
+INCORPORATION'S  IH0 N - K AO2 R - P ER0 - EY1 - SH AH0 N Z
+INCORPORATIONS  IH0 N - K AO2 R - P ER0 - EY1 - SH AH0 N Z
+INCORRECT  IH0 N - K ER0 - EH1 K T
+INCORRECTLY  IH0 N - K ER0 - EH1 K T - L IY0
+INCORRIGIBLE  IH0 N - K AA1 - R AH0 - JH AH0 - B AH0 L
+INCORVAIA  IH0 N - K AO0 R - V AA1 - Y AH0
+INCREASE  IH0 N - K R IY1 S
+INCREASE(2)  IH1 N - K R IY2 S
+INCREASED  IH0 N - K R IY1 S T
+INCREASED(2)  IH1 N - K R IY2 S T
+INCREASES  IH0 N - K R IY1 - S AH0 Z
+INCREASES(2)  IH0 N - K R IY1 - S IH0 Z
+INCREASES(3)  IH1 N - K R IY0 - S AH0 Z
+INCREASING  IH0 N - K R IY1 - S IH0 NG
+INCREASINGLY  IH0 N - K R IY1 - S IH0 NG - L IY0
+INCREASINGLY(2)  IH0 N - K R IY1 - S IH0 NG - G L IY0
+INCREDIBLE  IH0 N - K R EH1 - D AH0 - B AH0 L
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+INCREMENT  IH1 N - K R AH0 - M AH0 N T
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+INCREMENTALISM  IH2 N - K R AH0 - M EH1 N - T AH0 - L IH0 Z M
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+INCRUSTATION  IH2 N - K R AH0 - S T EY1 - SH AH0 N
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+INCULCATE  IH1 NG - K AH0 L - K EY2 T
+INCULCATED  IH1 NG - K AH0 L - K EY2 - T AH0 D
+INCULCATES  IH1 NG - K AH0 L - K EY2 T S
+INCUMBENCY  IH0 N - K AH1 M - B AH0 N - S IY0
+INCUMBENT  IH0 N - K AH1 M - B AH0 N T
+INCUMBENT'S  IH0 N - K AH1 M - B AH0 N T S
+INCUMBENTS  IH0 N - K AH1 M - B AH0 N T S
+INCUR  IH0 N - K ER1
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+INCURRING  IH0 N - K ER1 - IH0 NG
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+INCURSION  IH0 N - K ER1 - ZH AH0 N
+INCURSIONS  IH0 N - K ER1 - ZH AH0 N Z
+INDA  IY1 N - D AH0
+INDABA  IH0 N - D AA1 - B AH0
+INDAL  IH1 N - D AH0 L
+INDATA  IH0 N - D AA1 - T AH0
+INDEBTED  IH0 N - D EH1 - T AH0 D
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+INDECENT  IH0 N - D IY1 - S AH0 N T
+INDECISION  IH0 N - D IH0 - S IH1 - ZH AH0 N
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+INDECISIVENESS  IH0 N - D EH1 - S IH0 - S IH0 V - N AH0 S
+INDEED  IH0 N - D IY1 D
+INDEFATIGABLE  IH2 N - D IH0 - F AE1 - T IH0 - G AH0 - B AH0 L
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+INDEFINITELY  IH0 N - D EH1 - F AH0 - N AH0 T - L IY0
+INDELIBLE  IH0 N - D EH1 - L IH0 - B AH0 L
+INDELIBLY  IH0 N - D EH1 - L AH0 - B L IY0
+INDELICATE  IH0 N - D EH1 - L IH0 - K AH0 T
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+INDENT  IH0 N - D EH1 N T
+INDENTATION  IH0 N - D EH2 N - T EY1 - SH AH0 N
+INDENTURE  IH0 N - D EH1 N - CH ER0
+INDENTURED  IH0 N - D EH1 N - CH ER0 D
+INDENTURES  IH0 N - D EH1 N - CH ER0 Z
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+INDEPENDENT  IH2 N - D IH0 - P EH1 N - D AH0 N T
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+INDERAL  IH1 N - D ER0 - AH0 L
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+INDEX  IH1 N - D EH0 K S
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+INDEXATION  IH1 N - D EH2 K - S EY1 - SH AH0 N
+INDEXED  IH1 N - D EH0 K S T
+INDEXER  IH1 N - D EH2 K - S ER0
+INDEXERS  IH1 N - D EH2 K - S ER0 Z
+INDEXES  IH1 N - D EH0 K - S IH0 Z
+INDEXING  IH1 N - D EH0 K - S IH0 NG
+INDIA  IH1 N - D IY0 - AH0
+INDIA'S  IH1 N - D IY0 - AH0 Z
+INDIAN  IH1 N - D IY0 - AH0 N
+INDIAN'S  IH1 N - D IY0 - AH0 N Z
+INDIANA  IH2 N - D IY0 - AE1 - N AH0
+INDIANA'S  IH2 N - D IY0 - AE1 - N AH0 Z
+INDIANAPOLIS  IH2 N - D IY0 - AH0 N - AE1 - P AH0 - L IH0 S
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+INDIANIAN  IH2 N - D IY0 - AE1 - N IY0 - AH0 N
+INDIANIANS  IH2 N - D IY0 - AE1 - N IY0 - AH0 N Z
+INDIANOLA  IH1 N - D IY0 - AH0 - N OW1 - L AH0
+INDIANS  IH1 N - D IY0 - AH0 N Z
+INDIANS'  IH1 N - D IY0 - AH0 N Z
+INDIC  IH1 N - D IH0 K
+INDICATE  IH1 N - D AH0 - K EY2 T
+INDICATED  IH1 N - D AH0 - K EY2 - T AH0 D
+INDICATED(2)  IH1 N - D AH0 - K EY2 - T IH0 D
+INDICATES  IH1 N - D IH0 - K EY2 T S
+INDICATING  IH1 N - D AH0 - K EY2 - T IH0 NG
+INDICATION  IH2 N - D AH0 - K EY1 - SH AH0 N
+INDICATIONS  IH2 N - D AH0 - K EY1 - SH AH0 N Z
+INDICATIVE  IH0 N - D IH1 - K AH0 - T IH0 V
+INDICATOR  IH1 N - D AH0 - K EY2 - T ER0
+INDICATORS  IH1 N - D AH0 - K EY2 - T ER0 Z
+INDICES  IH1 N - D IH0 - S IY2 Z
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+INDICIA  IH0 N - D IH1 S - Y AH0
+INDICT  IH0 N - D AY1 T
+INDICTED  IH0 N - D AY1 - T AH0 D
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+INDICTING  IH0 N - D AY1 - T IH0 NG
+INDICTMENT  IH0 N - D AY1 T - M AH0 N T
+INDICTMENTS  IH0 N - D AY1 T - M AH0 N T S
+INDIES  IH1 N - D IY0 Z
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+INDIFFERENT  IH0 N - D IH1 - F R AH0 N T
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+INDIGENT  IH1 N - D IH0 - JH AH0 N T
+INDIGENTS  IH1 N - D IH0 - JH AH0 N T S
+INDIGEST  IH0 N - D AY0 - JH EH1 S T
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+INDIGESTION  IH2 N - D AY0 - JH EH1 S - CH AH0 N
+INDIGNANT  IH0 N - D IH1 G - N AH0 N T
+INDIGNANTLY  IH0 N - D IH1 G - N AH0 N T - L IY0
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+INDIGNITY  IH0 N - D IH1 G - N AH0 - T IY0
+INDIGO  IH1 N - D AH0 - G OW2
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+INDIRA  IH0 N - D IH1 - R AH0
+INDIRECT  IH0 N - D ER0 - EH1 K T
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+INDISCREET  IH0 N - D IH0 - S K R IY1 T
+INDISCRETION  IH2 N - D IH0 - S K R EH1 - SH AH0 N
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+INDISCRIMINATE  IH0 N - D IH0 - S K R IH1 - M AH0 - N AH0 T
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+INDISTINCT  IH0 N - D IH0 - S T IH1 NG K T
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+INDITE  IH0 N - D AY1 T
+INDITED  IH0 N - D AY1 - T IH0 D
+INDIUM  IH1 N - D IY0 - AH0 M
+INDIVIDUAL  IH2 N - D AH0 - V IH1 - JH AH0 - W AH0 L
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+INDIVIDUALISM  IH2 N - D IH0 - V IH0 - D UW1 - AH0 - L IH2 - Z AH0 M
+INDIVIDUALIST  IH2 N - D IH0 - V IH0 - D UW1 - AH0 - L IH0 S T
+INDIVIDUALISTIC  IH2 N - D IH0 - V IH2 - JH UW0 - AH0 - L IH1 - S T IH0 K
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+INDIVIDUALISTS(3)  IH2 N - D AH0 - V IH1 - JH UW0 - AH0 - L IH0 S
+INDIVIDUALITY  IH0 N - D IH2 - V IH0 - JH UW0 - AE1 - L IH0 - T IY0
+INDIVIDUALIZE  IH2 N - D IH0 - V IH1 - JH UW0 - AH0 - L AY0 Z
+INDIVIDUALIZE(2)  IH2 N - D IH0 - V IH1 - JH AH0 - L AY0 Z
+INDIVIDUALIZED  IH2 N - D IH0 - V IH1 - JH UW0 - AH0 - L AY0 Z D
+INDIVIDUALIZED(2)  IH2 N - D IH0 - V IH1 - JH AH0 - L AY0 Z D
+INDIVIDUALLY  IH2 N - D IH0 - V IH1 - JH UW0 - AH0 - L IY0
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+INDIVISIBLE  IH2 N - D IH0 - V IH1 - S IH0 - B AH0 L
+INDO  IH1 N - D OW0
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+INDOCHINA  IH2 N - D OW0 - CH AY1 - N AH0
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+INDOLENT  IH1 N - D AH0 - L AH0 N T
+INDOMITABLE  IH0 N - D AA1 - M AH0 - T AH0 - B AH0 L
+INDONESIA  IH2 N - D OW0 - N IY1 - ZH AH0
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+INDUCES  IH0 N - D UW1 - S IH0 Z
+INDUCING  IH0 N - D UW1 - S IH0 NG
+INDUCT  IH0 N - D AH1 K T
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+INDUCTION  IH0 N - D AH1 K - SH AH0 N
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+INDULGES  IH0 N - D AH1 L - JH IH0 Z
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+MINIT  M IH1 - N IH0 T
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+MINK  M IH1 NG K
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+MINNEY  M IH1 - N IY0
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+MINNIX  M IH1 - N IH0 K S
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+MINNOWS  M IH1 - N OW0 Z
+MINNS  M IH1 N Z
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+MINNY  M IH1 - N IY0
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+MINOAN  M AH0 - N OW1 - AH0 N
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+MINOLI  M IH0 - N OW1 - L IY0
+MINOLTA  M IH0 - N AA1 L - T AH2
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+MINOR  M AY1 - N ER0
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+MINORITY  M AY0 - N AO1 - R AH0 - T IY0
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+MINPECO  M IH0 N - P EH1 - K OW0
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+MINSTREL  M IH1 N - S T R AH0 L
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+MINUET  M IH2 - N Y AH0 W - EH1 T
+MINUS  M AY1 - N AH0 S
+MINUSCULE  M IH1 - N AH0 - S K Y UW2 L
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+MINUTEMAN  M IH1 - N AH0 T - M AE2 N
+MINUTEMEN  M IH1 - N AH0 T - M EH2 N
+MINUTES  M IH1 - N AH0 T S
+MINUTES'  M IH1 - N AH0 T S
+MINUTIA  M IH0 - N UW1 - SH IY0 - AH0
+MINUTIAE  M IH0 - N UW1 - SH IY0 - AH0
+MINYARD  M IH1 N - Y AA0 R D
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+MIOT  M AY1 - AH0 T
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+MIRABEL  M IH0 - R AA0 - B EH1 L
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+MIRABELLE  M AO1 - R AH0 - B AH0 L
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+MIRACID  M IH2 - R AE1 - S AH0 D
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+MIRACLE  M IH1 - R AH0 - K AH0 L
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+MIRACULOUS  M ER0 - AE1 - K Y AH0 - L AH0 S
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+MIRON  M IH0 - R AO1 N
+MIRONENKO  M IH2 - R OW0 - N EH1 NG - K OW0
+MIROSLAV  M IH1 - R AH0 - S L AA0 V
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+MIS  M IH1 S
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+MISALLOCATED  M IH0 S - AE1 - L AH0 - K EY2 - T IH0 D
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+MISAPPLY  M IH0 S - AH0 - P L AY1
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+MISAPPROPRIATED  M IH2 S - AH0 - P R OW1 - P R IY0 - EY2 - T IH0 D
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+MISATER  M IH0 S - EY1 - T ER0
+MISAWA  M IH0 - S AA1 - W AH0
+MISBEGOTTEN  M IH2 S - B AH0 - G AA1 - T AH0 N
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+MISBEHAVED  M IH2 S - B AH0 - HH EY1 V D
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+MISBEHAVING  M IH2 S - B AH0 - HH EY1 - V IH0 NG
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+MISCALCULATE  M IH0 S - K AE1 L - K Y AH0 - L EY2 T
+MISCALCULATED  M IH0 S - K AE1 L - K Y AH0 - L EY2 - T IH0 D
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+MISCARRIAGE  M IH0 S - K EH1 - R AH0 JH
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+MISCAST  M IH0 S - K AE1 S T
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+MISCAYUNA  M IH2 S - K AY0 - Y UW1 - N AH0
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+MISCHARACTERIZED  M IH0 S - K AE1 - R AH0 K - T ER0 - AY2 Z D
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+MISCIBLE  M IH1 - S AH0 - B AH0 L
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+MISCOMMUNICATION  M IH0 S - K AH0 - M Y UW2 - N AH0 - K EY1 - SH AH0 N
+MISCONCEIVE  M IH0 S - K AH0 N - S IY1 V
+MISCONCEIVED  M IH0 S - K AH0 N - S IY1 V D
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+MISCONDUCT  M IH0 S - K AA1 N - D AH0 K T
+MISCONSTRUE  M IH0 S - K AH0 N - S T R UW1
+MISCONSTRUED  M IH2 S - K AH0 N - S T R UW1 D
+MISCOUNT  M IH1 S - K AW1 N T
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+MISDEED  M IH1 S - D IY1 D
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+MISDIAGNOSE  M IH0 S - D AY2 - IH0 G - N OW1 Z
+MISDIAGNOSED  M IH0 S - D AY2 - IH0 G - N OW1 Z D
+MISDIAGNOSES  M IH0 S - D AY2 - IH0 G - N OW1 - S IY0 Z
+MISDIAGNOSES(2)  M IH0 S - D AY2 - IH0 G - N OW1 - S AH0 Z
+MISDIAGNOSIS  M IH0 S - D AY2 - IH0 G - N OW1 - S AH0 S
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+MISEK  M IH1 - S EH0 K
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+MISER  M AY1 - Z ER0
+MISERABLE  M IH1 - Z ER0 - AH0 - B AH0 L
+MISERABLE(2)  M IH1 Z - R AH0 - B AH0 L
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+MISERABLES(3)  M IH2 - Z ER0 - AA1 B
+MISERABLY  M IH1 - Z ER0 - AH0 - B L IY0
+MISERABLY(2)  M IH1 Z - R AH0 - B L IY0
+MISERATION  M IH0 - Z ER0 - EY1 - SH AH0 N
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+MISFORTUNE  M IH0 S - F AO1 R - CH AH0 N
+MISFORTUNES  M IH0 S - F AO1 R - CH AH0 N Z
+MISGIVE  M IH0 S - G IH1 V
+MISGIVING  M IH0 S - G IH1 - V IH0 NG
+MISGIVINGS  M IH0 S - G IH1 - V IH0 NG Z
+MISGOVERNMENT  M IH0 S - G AH1 - V ER0 N - M AH0 N T
+MISGUIDE  M IH0 S - G AY1 D
+MISGUIDED  M IH0 S - G AY1 - D IH0 D
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+MISIDENTIFICATION  M IH2 - S AY0 - D EH2 N - T IH0 - F IH0 - K EY1 - SH AH0 N
+MISIDENTIFICATION(2)  M IH2 - S AY0 - D EH2 - N IH0 - F IH0 - K EY1 - SH AH0 N
+MISIDENTIFIED  M IH0 - S AY0 - D EH1 N - T IH0 - F AY2 D
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+MISIDENTIFY  M IH0 - S AY0 - D EH1 N - T AH0 - F AY2
+MISIDENTIFY(2)  M IH0 - S AY0 - D EH1 - N AH0 - F AY2
+MISIEWICZ  M IH1 S - AH0 - V IH0 CH
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+MISINFORM  M IH0 S - IH2 N - F AO1 R M
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+MISINFORMED  M IH0 S - IH0 N - F AO1 R M D
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+MISJUDGMENT  M IH0 S - JH AH1 JH - M AH0 N T
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+MISKA  M IH1 S - K AH0
+MISKE  M IH1 S K
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+MISMANAGING  M IH0 S - M AE1 - N IH0 - JH IH0 NG
+MISMATCH  M IH0 S - M AE1 CH
+MISMATCH(2)  M IH1 S - M AE2 CH
+MISMATCHED  M IH0 S - M AE1 CH T
+MISMATCHES  M IH0 S - M AE1 - CH IH0 Z
+MISNER  M IH1 Z - N ER0
+MISNOMER  M IH0 S - N OW1 - M ER0
+MISOGYNE  M IH1 - Z AH0 - JH IH0 N
+MISOGYNIST  M IH1 - Z AH0 - JH IH0 - N IH0 S T
+MISOGYNY  M IH1 - Z AH0 - JH IH0 - N IY0
+MISPERCEIVE  M IH0 - S P ER0 - S IY1 V
+MISPERCEIVES  M IH0 - S P ER0 - S IY1 V Z
+MISPERCEPTION  M IH2 S - P ER0 - S EH1 P - SH AH0 N
+MISPERCEPTIONS  M IH0 S - P ER0 - S EH1 P - SH AH0 N Z
+MISPLACE  M IH0 S - P L EY1 S
+MISPLACED  M IH0 S - P L EY1 S T
+MISPRICE  M IH0 S - P R AY1 S
+MISPRICED  M IH0 S - P R AY1 S T
+MISPRINT  M IH1 S - P R IH1 N T
+MISPRISION  M IH0 S - P R IH1 - ZH AH0 N
+MISPRONOUNCE  M IH0 S - P R AH0 - N AW1 N S
+MISPRONOUNCED  M IH0 S - P R AH0 - N AW1 N S T
+MISQUOTE  M IH0 S - K W OW1 T
+MISQUOTED  M IH0 S - K W OW1 - T IH0 D
+MISREAD  M IH0 S - R IY1 D
+MISREADING  M IH0 S - R IY1 - D IH0 NG
+MISREMEMBER  M IH0 S - R IY0 - M EH1 M - B ER0
+MISREMEMBERED  M IH0 S - R IY0 - M EH1 M - B ER0 D
+MISREPORT  M IH0 S - R IH0 - P AO1 R T
+MISREPORTED  M IH0 S - R IH0 - P AO1 R - T IH0 D
+MISREPRESENT  M IH0 S - R EH2 - P R AH0 - Z EH1 N T
+MISREPRESENTATION  M IH0 S - R EH2 - P R IH0 - Z EH0 N - T EY1 - SH AH0 N
+MISREPRESENTATIONS  M IH0 S - R EH2 - P R IH0 - Z EH0 N - T EY1 - SH AH0 N Z
+MISREPRESENTED  M IH2 S - R EH0 - P R IH0 - Z EH1 N - T IH0 D
+MISREPRESENTED(2)  M IH2 S - R EH0 - P R IH0 - Z EH1 - N IH0 D
+MISREPRESENTING  M IH0 S - R EH2 - P R AH0 - Z EH1 N - T IH0 NG
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+MISREPRESENTS  M IH0 S - R EH2 - P R AH0 - Z EH1 N T S
+MISREPRESENTS(2)  M IH0 S - R EH2 - P R AH0 - Z EH1 N S
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+MISS  M IH1 S
+MISSAL  M IH1 - S AH0 L
+MISSED  M IH1 S T
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+MISSES  M IH1 - S AH0 Z
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+MISSETT  M IH1 - S AH0 T
+MISSEY  M IH1 - S IY0
+MISSHAPEN  M IH0 S - SH EY1 - P AH0 N
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+MISSIE  M IH1 - S IY0
+MISSILDINE  M IH0 - S IY0 L - D IY1 - N IY0
+MISSILDINE(2)  M IH0 S - IH0 L - D AY1 N
+MISSILE  M IH1 - S AH0 L
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+MISSILES'  M IH1 - S AH0 L Z
+MISSIMER  M IH1 - S IH0 - M ER0
+MISSING  M IH1 - S IH0 NG
+MISSION  M IH1 - SH AH0 N
+MISSION'S  M IH1 - SH AH0 N Z
+MISSIONARIES  M IH1 - SH AH0 N - EH2 - R IY0 Z
+MISSIONARY  M IH1 - SH AH0 N - EH2 - R IY0
+MISSIONS  M IH1 - SH AH0 N Z
+MISSISSAUGA  M IH2 - S IH0 - S AO1 - G AH0
+MISSISSIPPI  M IH2 - S IH0 - S IH1 - P IY0
+MISSISSIPPI'S  M IH2 - S IH0 - S IH1 - P IY0 Z
+MISSISSIPPIAN  M IH2 - S IH0 - S IH1 - P IY0 - AH0 N
+MISSISSIPPIANS  M IH2 - S IH0 - S IH1 - P IY0 - AH0 N Z
+MISSISSIPPIS  M IH2 - S IH0 - S IH1 - P IY0 Z
+MISSIVE  M IH1 - S IH0 V
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+MISSOULA  M IH0 - Z UW1 - L AH0
+MISSOURI  M AH0 - Z UH1 - R IY0
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+MISSOURI'S(2)  M AH0 - Z ER1 - AH0 Z
+MISSOURI(2)  M AH0 - Z ER1 - AH0
+MISSPEAK  M IH0 S - S P IY1 K
+MISSPEAK(2)  M IH0 - S P IY1 K
+MISSPELL  M IH0 S - S P EH1 L
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+MISSPELLING  M IH0 S - S P EH1 - L IH0 NG
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+MISSPEND  M IH0 S - S P EH1 N D
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+MISSPENDING  M IH0 S - S P EH1 N - D IH0 NG
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+MISSTATE  M IH0 S - S T EY1 T
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+MISSTATEMENT  M IH0 S - T EY1 T - M AH0 N T
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+MISTREATMENT  M IH0 S - T R IY1 T - M AH0 N T
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+MISTRETTA  M IH0 - S T R EH1 - T AH0
+MISTRIAL  M IH0 S - T R AY1 - AH0 L
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+MISTRIALS  M IH0 S - T R AY1 - AH0 L Z
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+MISTRUSTFUL  M IH0 S - T R AH1 S T - F AH0 L
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+MISUNDERSTAND  M IH2 S - AH0 N - D ER0 - S T AE1 N D
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+MISUNDERSTANDINGS  M IH2 S - AH0 N - D ER0 - S T AE1 N - D IH0 NG Z
+MISUNDERSTANDS  M IH2 S - AH0 N - D ER0 - S T AE1 N D Z
+MISUNDERSTOOD  M IH2 S - AH0 N - D ER0 - S T UH1 D
+MISURACA  M IH0 - S UH0 - R AA1 - K AH0
+MISUSE  M IH0 S - Y UW1 Z
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+MITCHEM  M IH1 - CH IH0 M
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+MITIGATE  M IH1 - T AH0 - G EY2 T
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+MITIGATING  M IH1 - T AH0 - G EY2 - T IH0 NG
+MITIGATION  M IH2 - T IH0 - G EY1 - SH AH0 N
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+MITTLEMAN  M IH1 - T AH0 L - M AH0 N
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+NEFT  N EH1 F T
+NEFTEGORSK  N EH1 F - T IH0 - G AO2 R S K
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+NEGATING  N IH0 - G EY1 - T IH0 NG
+NEGATION  N AH0 - G EY1 - SH AH0 N
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+NEGATIVES  N EH1 - G AH0 - T IH0 V Z
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+NEIGHBORHOODS  N EY1 - B ER0 - HH UH2 D Z
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+NOMENKLATURA  N OW0 - M EH2 NG - K L AH0 - CH UH1 - R AH0
+NOMI  N OW1 - M IY0
+NOMINAL  N AA1 - M AH0 - N AH0 L
+NOMINALLY  N AA1 - M AH0 - N AH0 - L IY0
+NOMINATE  N AA1 - M AH0 - N AH0 T
+NOMINATE(2)  N AA1 - M AH0 - N EY2 T
+NOMINATED  N AA1 - M AH0 - N EY2 - T AH0 D
+NOMINATES  N AA1 - M AH0 - N EY2 T S
+NOMINATES(2)  N AA1 - M AH0 - N AH0 T S
+NOMINATING  N AA1 - M AH0 - N EY2 - T IH0 NG
+NOMINATION  N AA2 - M AH0 - N EY1 - SH AH0 N
+NOMINATIONS  N AA2 - M AH0 - N EY1 - SH AH0 N Z
+NOMINEE  N AA2 - M AH0 - N IY1
+NOMINEE'S  N AA2 - M AH0 - N IY1 Z
+NOMINEES  N AA2 - M AH0 - N IY1 Z
+NOMO  N OW1 - M OW0
+NOMURA  N OW0 - M UH1 - R AH0
+NOMURA'S  N OW0 - M UH1 - R AH0 Z
+NOMURA'S(2)  N UW1 - M ER0 - AH0 Z
+NON  N AA1 N
+NON-CATHOLIC  N AA0 N - K AE1 TH - L IH0 K
+NON-CATHOLICS  N AA0 N - K AE1 TH - L IH0 K S
+NON-NONSENSE  N AA1 N - N AA1 N - S EH2 N S
+NON-SUPERVISORY  N AA1 N - S UW2 - P ER0 - V AY1 - Z ER0 - IY0
+NONA  N AA1 - N AH0
+NONACADEMIC  N AA0 N - AE2 - K AH0 - D EH1 - M IH0 K
+NONACCRUAL  N AA2 N - AH0 - K R UW1 - AH0 L
+NONACCRUING  N AA2 N - AH0 - K R UW1 - IH0 NG
+NONAGGRESSION  N AA2 N - AH0 - G R EH1 - SH AH0 N
+NONAGRICULTURAL  N AA2 N - AE2 - G R IH0 - K AH1 L - CH ER0 - AH0 L
+NONALCOHOLIC  N AA2 N - AE2 L - K AH0 - HH AA1 - L IH0 K
+NONALIGN  N AA1 N - AH0 - L AY2 N
+NONALIGNED  N AA1 N - AH0 - L AY2 N D
+NONAUTOMOTIVE  N AA2 N - AO2 - T OW0 - M OW1 - T IH0 V
+NONBANK  N AA1 N - B AE1 NG K
+NONBANKING  N AA1 N - B AE1 NG - K IH0 NG
+NONBELIEVER  N AA2 N - B AH0 - L IY1 - V ER0
+NONBELIEVERS  N AA2 N - B AH0 - L IY1 - V ER0 Z
+NONBINDING  N AA1 N - B AY1 N - D IH0 NG
+NONBITING  N AA0 N - B AY1 - T IH0 NG
+NONBUILDING  N AA1 N - B IH1 L - D IH0 NG
+NONBUSINESS  N AA1 N - B IH1 Z - N AH0 S
+NONCALLABLE  N AA0 N - K AO1 - L AH0 - B AH0 L
+NONCASH  N AA1 N - K AE1 SH
+NONCHALANCE  N AA1 N - SH AH0 - L AA1 N S
+NONCHALANT  N AA2 N - SH AH0 - L AA1 N T
+NONCHALANTLY  N AA1 N - SH AH0 - L AA1 N T - L IY0
+NONCOLOR  N AA0 N - K AH1 - L ER0
+NONCOMBATANT  N AA2 N - K AH0 M - B AE1 - T AH0 N T
+NONCOMBATANTS  N AA2 N - K AH0 M - B AE1 - T AH0 N T S
+NONCOMMERCIAL  N AA1 N - K AH0 - M ER1 - SH AH0 L
+NONCOMMITTAL  N AA1 N - K AH0 - M IH1 - T AH0 L
+NONCOMMUNIST  N AA1 N - K AA1 - M Y UW0 - N IH0 S T
+NONCOMPETE  N AA0 N - K AH0 M - P IY1 T
+NONCOMPETITIVE  N AA2 N - K AH0 M - P EH1 - T AH0 - T IH0 V
+NONCOMPLIANCE  N AA2 N - K AH0 M - P L AY1 - AH0 N S
+NONCONFORMIST  N AA2 N - K AH0 N - F AO1 R - M IH0 S T
+NONCONFORMISTS  N AA2 N - K AH0 N - F AO1 R - M AH0 S T S
+NONCONFORMISTS(2)  N AA2 N - K AH0 N - F AO1 R - M AH0 S S
+NONCONFORMISTS(3)  N AA2 N - K AH0 N - F AO1 R - M AH0 S
+NONCONFORMITY  N AA2 N - K AH0 N - F AO1 R - M AH0 - T IY0
+NONCONTRACT  N AA0 N - K AA1 N - T R AE2 K T
+NONCONTROVERSIAL  N AA0 N - K AA2 N - T R AH0 - V ER1 - SH AH0 L
+NONCONVERTIBLE  N AA0 N - K AH0 N - V ER1 - T AH0 - B AH0 L
+NONCORE  N AA1 N - K AO1 R
+NONCORPORATE  N AA0 N - K AO1 R - P R AH0 T
+NONCRIMINAL  N AA0 N - K R IH1 - M IH0 - N AH0 L
+NONCUMULATIVE  N AA0 N - K Y UW1 - M Y AH0 - L AH0 - T IH0 V
+NONDEDUCTIBLE  N AA0 N - D IH0 - D AH1 K - T IH0 - B AH0 L
+NONDEFENSE  N AA0 N - D IH0 - F EH1 N S
+NONDESCRIPT  N AA1 N - D IH0 - S K R IH1 P T
+NONDIRECT  N AA1 N - D ER0 - EH1 K T
+NONDISCRIMINATE  N AA0 N - D IH2 - S K R IH1 - M AH0 - N AH0 T
+NONDISCRIMINATION  N AA0 N - D IH2 - S K R IH0 - M IH0 - N EY1 - SH AH0 N
+NONDISCRIMINATORY  N AA1 N - D IH0 - S K R IH1 - M AH0 - N AH0 - T AO2 - R IY0
+NONDOLLAR  N AA1 N - D AA1 - L ER0
+NONDURABLE  N AA0 N - D UH1 - R AH0 - B AH0 L
+NONDURABLES  N AA0 N - D UH1 - R AH0 - B AH0 L Z
+NONE  N AH1 N
+NONECONOMIC  N AA2 N - EH2 - K AH0 - N AA1 - M IH0 K
+NONELECTRICAL  N AA0 N - IH0 - L EH1 K - T R IH0 - K AH0 L
+NONEMERGENCY  N AA2 - N IH0 - M ER1 - JH AH0 N - S IY0
+NONENTITY  N AA0 N - EH1 N - T AH0 - T IY0
+NONESSENTIAL  N AA2 N - IH0 - S EH1 N - CH AH0 L
+NONESUCH  N AH1 N - S AH1 CH
+NONETHELESS  N AH2 N - DH AH0 - L EH1 S
+NONEVENT  N AA1 N - IH0 - V EH1 N T
+NONEXCLUSIVE  N AA2 N - IH0 K - S K L UW1 - S IH0 V
+NONEXECUTIVE  N AA1 N - IH0 G - Z EH1 - K Y AH0 - T IH0 V
+NONEXISTENT  N AA2 N - AH0 G - Z IH1 - S T AH0 N T
+NONFARM  N AA1 N - F AA1 R M
+NONFAT  N AA1 N - F AE1 T
+NONFATAL  N AA1 N - F EY1 - T AH0 L
+NONFERROUS  N AA0 N - F EH1 - R AH0 S
+NONFICTION  N AA0 N - F IH1 K - SH AH0 N
+NONFINANCIAL  N AA0 N - F AH0 - N AE1 N - SH AH0 L
+NONFINANCIAL(2)  N AA0 N - F AY0 - N AE1 N - SH AH0 L
+NONFOOD  N AA1 N - F UW1 D
+NONGOVERNMENT  N AA0 N - G AH1 - V ER0 N - M AH0 N T
+NONGOVERNMENTAL  N AA0 N - G AH2 - V ER0 N - M EH1 N - T AH0 L
+NONGREEK  N AA2 N - G R IY1 K
+NONHUMAN  N AA0 N - HH Y UW1 - M AH0 N
+NONIE  N AA1 - N IY0
+NONINFLATIONARY  N AA2 N - IH0 N - F L EY1 - SH AH0 N - EH2 - R IY0
+NONINTEREST  N AA0 N - IH1 N - T R AH0 S T
+NONINTERFERENCE  N AA2 N - IH2 N - T ER0 - F IH1 - R AH0 N S
+NONINTERVENTION  N AA2 N - IH2 N - T ER0 - V EH1 N - CH AH0 N
+NONJET  N AA1 N - JH EH1 T
+NONJETS  N AA1 N - JH EH1 T S
+NONLETHAL  N AA0 N - L IY1 - TH AH0 L
+NONLINEAR  N AA0 N - L IH1 - N IY2 - ER0
+NONLITURGICAL  N AA0 N - L AH0 - T ER1 - JH IH0 - K AH0 L
+NONMANAGEMENT  N AA0 N - M AE1 - N IH0 JH - M AH0 N T
+NONMANUFACTURING  N AA2 N - M AE2 - N Y AH0 - F AE1 K - CH ER0 - IH0 NG
+NONMEMBER  N AA0 N - M EH1 M - B ER0
+NONMEMBERS  N AA0 N - M EH1 M - B ER0 Z
+NONMILITARY  N AA0 N - M IH1 - L AH0 - T EH2 - R IY0
+NONNATIVE  N AA1 - N EY1 - T IH0 V
+NONNEMACHER  N AA1 - N IH0 - M AH0 - K ER0
+NONNUCLEAR  N AA1 N - UW1 - K L IY2 - ER0
+NONOPERATING  N AA1 N - AO1 - P ER0 - EY2 - T IH0 NG
+NONPACIFIST  N AA2 N - P AE1 - S IH0 - F IH0 S T
+NONPACIFISTS  N AA2 N - P AE1 - S IH0 - F IH0 S T S
+NONPACIFISTS(2)  N AA2 N - P AE1 - S IH0 - F IH0 S S
+NONPACIFISTS(3)  N AA2 N - P AE1 - S IH0 - F IH0 S
+NONPARTISAN  N AA0 N - P AA1 R - T AH0 - Z AH0 N
+NONPAYING  N AA0 N - P EY1 - IH0 NG
+NONPAYMENT  N AA0 N - P EY1 - M AH0 N T
+NONPERFORMING  N AA0 N - P ER0 - F AO1 R - M IH0 NG
+NONPLANAR  N AA0 N - P L EY1 - N ER0
+NONPLUSS  N AA0 N - P L AH1 S
+NONPLUSSED  N AA0 N - P L AH1 S T
+NONPOISONOUS  N AA0 N - P OY1 - Z AH0 - N AH0 S
+NONPOLITICAL  N AA2 N - P AH0 - L IH1 - T IH0 - K AH0 L
+NONPRESCRIPTION  N AA2 N - P R AH0 - S K R IH1 P - SH AH0 N
+NONPRODUCTIVE  N AA2 N - P R AH0 - D AH1 K - T IH0 V
+NONPROFESSIONAL  N AA2 N - P R AH0 - F EH1 - SH AH0 - N AH0 L
+NONPROFESSIONALS  N AA0 N - P R AH0 - F EH1 - SH AH0 - N AH0 L Z
+NONPROFIT  N AA0 N - P R AA1 - F AH0 T
+NONPROFITS  N AA1 N - P R AA1 - F IH0 T S
+NONPROLIFERATION  N AA0 N - P R AH0 - L IH2 - F ER0 - EY1 - SH AH0 N
+NONPUBLIC  N AA0 N - P AH1 - B L IH0 K
+NONQUALIFIED  N AA0 N - K W AA1 - L AH0 - F AY2 D
+NONRACIAL  N AA0 N - R EY1 - SH AH0 L
+NONRECURRING  N AA0 N - R IH0 - K ER1 - IH0 NG
+NONREFUNDABLE  N AA0 N - R IH0 - F AH1 N - D AH0 - B AH0 L
+NONREGULATED  N AA0 N - R EH1 - G Y AH0 - L EY2 - T IH0 D
+NONRELIGIOUS  N AA2 N - R IH0 - L IH1 - JH AH0 S
+NONRENEWABLE  N AA0 N - R IY0 - N UW1 - AH0 - B AH0 L
+NONRESIDENT  N AA0 N - R EH1 - Z AH0 - D AH0 N T
+NONRESIDENTIAL  N AA2 N - R EH2 - Z AH0 - D EH1 N - SH AH0 L
+NONRESIDENTIAL(2)  N AA2 N - R EH2 - Z AH0 - D EH1 N - CH AH0 L
+NONRESIDENTS  N AA0 N - R EH1 - Z AH0 - D AH0 N T S
+NONRESPONSIVE  N AA0 N - R IH0 - S P AA1 N - S IH0 V
+NONROMAN  N AA0 N - R OW1 - M AH0 N
+NONRULING  N AA0 N - R UW1 - L IH0 NG
+NONSECTARIAN  N AA2 N - S EH0 K - T EH1 - R IY0 - AH0 N
+NONSENSE  N AA1 N - S EH0 N S
+NONSENSICAL  N AA0 N - S EH1 N - S IH0 - K AH0 L
+NONSMOKER  N AA0 N - S M OW1 - K ER0
+NONSMOKERS  N AA0 N - S M OW1 - K ER0 Z
+NONSMOKERS'  N AA1 N - S M OW1 - K ER0 Z
+NONSMOKING  N AA1 N - S M OW1 - K IH0 NG
+NONSPECIFIC  N AA2 N - S P AH0 - S IH1 - F IH0 K
+NONSPORTING  N AA0 N - S P AO1 R - T IH0 NG
+NONSTANDARD  N AA0 N - S T AE1 N - D ER0 D
+NONSTARTER  N AA0 N - S T AA1 R - T ER0
+NONSTICK  N AA0 N - S T IH1 K
+NONSTOP  N AA2 N - S T AA1 P
+NONSTOPS  N AA2 N - S T AA1 P S
+NONSTRATEGIC  N AA0 N - S T R AH0 - T IY1 - JH IH0 K
+NONSURGICAL  N AA0 N - S ER1 - JH IH0 - K AH0 L
+NONTAXABLE  N AA0 N - T AE1 K - S AH0 - B AH0 L
+NONTECHNICAL  N AA0 N - T EH1 K - N IH0 - K AH0 L
+NONTOXIC  N AA0 N - T AA1 K - S IH0 K
+NONTRADITIONAL  N AA2 N - T R AH0 - D IH1 - SH AH0 - N AH0 L
+NONTRANSFERABLE  N AA2 N - T R AE2 N S - F ER1 - AH0 - B AH0 L
+NONTURBO  N AO0 N - T ER1 - B OW0
+NONTURBOS  N AO0 N - T ER1 - B OW0 Z
+NONUNION  N AA0 N - Y UW1 - N Y AH0 N
+NONUNIONIZED  N AA0 N - Y UW1 - N Y AH0 - N AY2 Z D
+NONUTILITY  N AA2 N - Y UW0 - T IH1 - L IH0 - T IY0
+NONVERBAL  N AA0 N - V ER1 - B AH0 L
+NONVIOLENCE  N AA0 N - V AY1 - AH0 - L AH0 N S
+NONVIOLENT  N AA0 N - V AY1 - AH0 - L AH0 N T
+NONVOLATILE  N AA0 N - V AA1 - L AH0 - T AH0 L
+NONVOTING  N AA0 N - V OW1 - T IH0 NG
+NONWHITE  N AA1 N - W AY1 T
+NONWHITE(2)  N AA1 N HH - W AY1 T
+NONWHITES  N AA1 N - W AY1 T S
+NONWHITES(2)  N AA1 N HH - W AY1 T S
+NONWOVEN  N AA1 N - W OW1 - V IH0 N
+NONWOVENS  N AA1 N - W OW1 - V AH0 N Z
+NOODLE  N UW1 - D AH0 L
+NOODLES  N UW1 - D AH0 L Z
+NOOK  N UH1 K
+NOOKS  N UH1 K S
+NOON  N UW1 N
+NOONAN  N UW1 - N AH0 N
+NOONE  N UW1 N
+NOONEY  N UW1 - N IY0
+NOONS  N UW1 N Z
+NOONTIME  N UW1 N - T AY2 M
+NOORDA  N UH1 R - D AH0
+NOOSE  N UW1 S
+NOPE  N OW1 P
+NOR  N AO1 R
+NORA  N AO1 - R AH0
+NORA'S  N AO1 - R AH0 Z
+NORAH  N AO1 - R AH0
+NORAMCO  N AO0 - R AE1 M - K OW0
+NORANDA  N AO0 - R AE1 N - D AH0
+NORANDA'S  N AO0 - R AE1 N - D AH0 Z
+NORBECK  N AO1 R - B EH0 K
+NORBERG  N AO1 R - B ER0 G
+NORBERT  N AO1 R - B ER0 T
+NORBERTA  N AO0 R - B EH1 R - T AH0
+NORBERTO  N AO0 R - B ER1 - T OW2
+NORBURY  N AO1 R - B EH0 - R IY0
+NORBY  N AO1 R - B IY0
+NORCEN  N AO1 R - S AH0 N
+NORCEN'S  N AO1 R - S AH0 N Z
+NORCIA  N AO1 R - CH AH0
+NORCO  N AO1 R - K OW0
+NORCROSS  N AO1 R - K R AO0 S
+NORD  N AO1 R D
+NORDAHL  N AO1 R - D AA0 L
+NORDAN  N AO1 R - D AH0 N
+NORDBANKEN  N AO1 R D - B AE2 NG - K AH0 N
+NORDBERG  N AO1 R D - B ER0 G
+NORDBY  N AO1 R D - B IY0
+NORDEEN  N ER0 - D IY1 N
+NORDELL  N AO1 R - D AH0 L
+NORDEN  N AO1 R - D AH0 N
+NORDER  N AO1 R - D ER0
+NORDGREN  N AO1 R D - G R EH0 N
+NORDIC  N AO1 R - D IH0 K
+NORDICA  N AO1 R - D IH0 - K AH0
+NORDICTRACK  N AO1 R - D IH0 K - T R AE2 K
+NORDIN  N AO1 R - D IH0 N
+NORDINE  N AO0 R - D IY1 - N IY0
+NORDIO  N AO1 R - D IY0 - OW0
+NORDISK  N AO1 R - D IH2 S K
+NORDLING  N AO1 R - D AH0 L - IH0 NG
+NORDLING(2)  N AO1 R D - L IH0 NG
+NORDLUND  N AO1 R D - L AH0 N D
+NORDMAN  N AO1 R D - M AH0 N
+NORDMANN  N AO1 R D - M AH0 N
+NORDMEYER  N AO1 R D - M AY0 - ER0
+NORDQUIST  N AO1 R D - K W IH0 S T
+NORDSON  N AO1 R D - S AH0 N
+NORDSTROM  N AO1 R D - S T R AH0 M
+NORDSTROM'S  N AO1 R D - S T R AH0 M Z
+NORDSTROMS  N AO1 R D - S T R AH0 M Z
+NORDYKE  N AO1 R - D AY2 K
+NORECO  N AO0 - R EH1 - K OW0
+NORED  N AO1 R D
+NOREEN  N ER0 - IY1 N
+NOREIGA  N AO0 - R EY1 - G AH0
+NORELCO  N ER0 - EH1 L - K OW0
+NORELL  N AO1 - R AH0 L
+NOREM  N AO1 - R IH0 M
+NOREN  N AO1 - R AH0 N
+NORENBERG  N AO1 - R AH0 N - B ER0 G
+NORENCO  N AO1 - R AH0 N - K OW0
+NORENKO  N AH0 - R EH1 N - K OW0
+NOREX  N AO1 - R AH0 K S
+NORFLEET  N AO1 R F - L IY2 T
+NORFOLK  N AO1 R - F AH0 K
+NORFOLK'S  N AO1 R - F AH0 K S
+NORGAARD  N AO1 R - G AA0 R D
+NORGARD  N AO1 R - G ER0 D
+NORGE  N AO1 R JH
+NORGREN  N AO1 R - G R AH0 N
+NORICK  N AO1 - R IH0 K
+NORIEGA  N AO2 - R IY0 - EY1 - G AH0
+NORIEGA'S  N AO2 - R IY0 - EY1 - G AH0 Z
+NORIEGAS  N AO2 - R IY0 - EY1 - G AH0 S
+NORILSK  N AO2 - R IH1 L S K
+NORIMATSU  N AO2 - R IY0 - M AA1 T - S UW0
+NORINCHUKIN  N AO2 - R IH0 N - CH UW1 - K IH0 N
+NORINKO  N AO2 - R IH1 NG - K OW0
+NORK  N AO1 R K
+NORKUS  N AO1 R - K IH0 S
+NORLAND  N AO1 R - L AH0 N D
+NORLANDER  N AO1 R - L AH0 N - D ER0
+NORLIN  N AO1 R - L IH0 N
+NORLING  N AO1 R - L IH0 NG
+NORM  N AO1 R M
+NORMA  N AO1 R - M AH0
+NORMAL  N AO1 R - M AH0 L
+NORMALCY  N AO1 R - M AH0 L - S IY0
+NORMALITY  N AO2 R - M AE1 - L AH0 - T IY0
+NORMALIZATION  N AO2 R - M AH0 - L IH0 - Z EY1 - SH AH0 N
+NORMALIZE  N AO1 R - M AH0 - L AY2 Z
+NORMALIZED  N AO1 R - M AH0 - L AY2 Z D
+NORMALIZING  N AO1 R - M AH0 - L AY2 - Z IH0 NG
+NORMALLY  N AO1 R - M AH0 - L IY0
+NORMALLY(2)  N AO1 R M - L IY0
+NORMAN  N AO1 R - M AH0 N
+NORMAN'S  N AO1 R - M AH0 N Z
+NORMAND  N AO1 R - M AH0 N D
+NORMANDIE  N AO1 R - M AH0 N - D IY0
+NORMANDIN  N AO1 R - M AH0 N - D IH0 N
+NORMANDY  N AO1 R - M AH0 N - D IY0
+NORMATIVE  N AO1 R - M AH0 - T IH0 V
+NORMENT  N AO1 R - M AH0 N T
+NORMICK  N AO1 R - M IH0 K
+NORMIE  N AO1 R - M IY0
+NORMILE  N AO1 R - M AY0 L
+NORMING  N AO1 R - M IH0 NG
+NORMINGTON  N AO1 R - M IH0 NG - T AH0 N
+NORMOYLE  N AO1 R - M OY0 L
+NORMS  N AO1 R M Z
+NORMY  N AO1 R - M IY0
+NORNA  N AO1 R - N AH0
+NORODOM  N AO1 - R OW0 - D AH2 M
+NORODOM(2)  N AO1 - R AH0 - D AH2 M
+NORPLANT  N AO1 R - P L AE2 N T
+NORPLANT'S  N AO1 R - P L AE2 N T S
+NORQUIST  N AO1 R - K W IH0 S T
+NORRED  N AO1 R D
+NORRELL  N AO1 - R AH0 L
+NORRIE  N AO1 - R IY0
+NORRINGTON  N AO1 - R IH0 NG - T AH0 N
+NORRIS  N AO1 - R IH0 S
+NORRIS'S  N AO1 - R AH0 - S IH0 Z
+NORROD  N AO1 - R AH0 D
+NORRY  N AO1 - R IY0
+NORSE  N AO1 R S
+NORSEMEN  N AO1 R S - M IH0 N
+NORSK  N AO1 R S K
+NORSKE  N AO1 R S - K IY0
+NORSTAN  N AO1 R - S T AH0 N
+NORSTAR  N AO1 R - S T AA2 R
+NORSTROM  N AO1 R S - T R AH0 M
+NORSWORTHY  N AO1 R S - W ER0 - DH IY0
+NORTE  N AO1 R T
+NORTEK  N AO1 R - T EH2 K
+NORTEL  N AO1 R - T EH0 L
+NORTH  N AO1 R TH
+NORTH'S  N AO1 R TH S
+NORTHAM  N AO1 R - TH AH0 M
+NORTHAMPTON  N AO2 R - TH AE1 M P - T AH0 N
+NORTHBOUND  N AO1 R TH - B AW0 N D
+NORTHBROOK  N AO1 R TH - B R UH2 K
+NORTHCLIFF  N AO1 R TH - K L IH0 F
+NORTHCOTT  N AO1 R TH - K AH0 T
+NORTHCRAFT  N AO1 R TH - K R AE2 F T
+NORTHCUTT  N AO1 R TH - K AH0 T
+NORTHEAST  N AO2 R TH - IY1 S T
+NORTHEAST'S  N AO2 R TH - IY1 S T S
+NORTHEASTERLY  N AO2 R TH - IY1 - S T ER0 - L IY0
+NORTHEASTERN  N AO2 R TH - IY1 - S T ER0 N
+NORTHEASTERNER  N AO0 R TH - IY1 - S T ER0 - N ER0
+NORTHEASTERNERS  N AO0 R TH - IY1 S - T ER0 - N ER0 Z
+NORTHEASTWARD  N AO2 R TH - IY1 S T - W ER0 D
+NORTHER  N AO1 R - DH ER0
+NORTHERLY  N AO1 R - DH ER0 - L IY0
+NORTHERN  N AO1 R - DH ER0 N
+NORTHERN'S  N AO1 R - DH ER0 N Z
+NORTHERNER  N AO1 R - DH ER0 - N ER0
+NORTHERNERS  N AO1 R - DH ER0 - N ER0 Z
+NORTHERNMOST  N AO1 R - DH ER0 N - M OW2 S T
+NORTHEY  N AO1 R - DH IY0
+NORTHFIELD  N AO1 R TH - F IY2 L D
+NORTHGATE  N AO1 R TH - G EY2 T
+NORTHINGTON  N AO1 R - TH IH0 NG - T AH0 N
+NORTHLAND  N AO1 R TH - L AE2 N D
+NORTHRIDGE  N AO1 R - TH R IH2 JH
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+PRAVDA'S  P R AE1 V - D AH0 Z
+PRAWN  P R AO1 N
+PRAWNS  P R AO1 N Z
+PRAXAIR  P R AE1 K - S EH1 R
+PRAXIS  P R AE1 K - S IH0 S
+PRAY  P R EY1
+PRAYED  P R EY1 D
+PRAYER  P R EH1 R
+PRAYER(2)  P R EY1 - ER0
+PRAYERFUL  P R EH1 R - F AH0 L
+PRAYERFUL(2)  P R EY1 - ER0 - F AH0 L
+PRAYERS  P R EH1 R Z
+PRAYERS(2)  P R EY1 - ER0 Z
+PRAYING  P R EY1 - IH0 NG
+PRAYS  P R EY1 Z
+PRAYTOR  P R EY1 - T ER0
+PRAZAK  P R AA1 - Z AH0 K
+PRCHAL  P ER0 - SH AE1 L
+PRE  P R IY1
+PREACH  P R IY1 CH
+PREACHED  P R IY1 CH T
+PREACHER  P R IY1 - CH ER0
+PREACHER'S  P R IY1 - CH ER0 Z
+PREACHERS  P R IY1 - CH ER0 Z
+PREACHES  P R IY1 - CH IH0 Z
+PREACHING  P R IY1 - CH IH0 NG
+PREACHY  P R IY1 - CH IY0
+PREADOLESCENCE  P R IY2 - AE0 - D AH0 - L EH1 - S IH0 S
+PREADOLESCENT  P R IY2 - AE0 - D AH0 - L EH1 - S IH0 N T
+PREAKNESS  P R IY1 K - N AH0 S
+PREAMBLE  P R IY0 - AE1 M - B AH0 L
+PREARRANGE  P R IY2 - ER0 - EY1 N JH
+PREARRANGED  P R IY2 - ER0 - EY1 N JH D
+PREAS  P R IY1 Z
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+PREBE  P R IY1 B
+PREBLE  P R EH1 - B AH0 L
+PREBON  P R IY1 - B AA0 N
+PRECAMBRIAN  P R IY0 - K AE1 M - B R IY0 - AH0 N
+PRECANCEROUS  P R IY0 - K AE1 N - S ER0 - AH0 S
+PRECARIOUS  P R IY0 - K EH1 - R IY0 - AH0 S
+PRECARIOUSLY  P R IH0 - K EH1 - R IY0 - AH0 S - L IY0
+PRECAST  P R IY0 - K AE1 S T
+PRECAUTION  P R IY0 - K AO1 - SH AH0 N
+PRECAUTIONARY  P R IH0 - K AO1 - SH AH0 N - EH0 - R IY0
+PRECAUTIONS  P R IY0 - K AO1 - SH AH0 N Z
+PRECEDE  P R IH0 - S IY1 D
+PRECEDED  P R IH0 - S IY1 - D IH0 D
+PRECEDED(2)  P R IY0 - S IY1 - D AH0 D
+PRECEDED(3)  P R IY0 - S IY1 - D IH0 D
+PRECEDENCE  P R EH1 - S AH0 - D AH0 N S
+PRECEDENT  P R EH1 - S IH0 - D AH0 N T
+PRECEDENTS  P R EH1 - S AH0 - D AH0 N T S
+PRECEDENTS(2)  P R EH1 - S AH0 - D EH2 N T S
+PRECEDES  P R IH0 - S IY1 D Z
+PRECEDING  P R IY0 - S IY1 - D IH0 NG
+PRECEEDING  P R IH0 - S IY1 - D IH0 NG
+PRECEPT  P R IY1 - S EH2 P T
+PRECEPTS  P R IY1 - S EH2 P T S
+PRECESSION  P R IY0 - S EH1 - SH AH0 N
+PRECHT  P R EH1 K T
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+PRECHTL  P R EH1 K - T AH0 L
+PRECIADO  P R EH0 - CH AA1 - D OW0
+PRECINCT  P R IY1 - S IH2 NG K T
+PRECINCT(2)  P R IY1 - S IH2 NG K
+PRECINCTS  P R IY1 - S IH2 NG K T S
+PRECINCTS(2)  P R IY1 - S IH2 NG K S
+PRECIOUS  P R EH1 - SH AH0 S
+PRECIPICE  P R EH1 - S AH0 - P AH0 S
+PRECIPITATE  P R IH0 - S IH1 - P IH0 - T EY2 T
+PRECIPITATED  P R IH0 - S IH1 - P IH0 - T EY2 - T IH0 D
+PRECIPITATING  P R IH0 - S IH1 - P AH0 - T EY2 - T IH0 NG
+PRECIPITATION  P R IH0 - S IH2 - P IH0 - T EY1 - SH AH0 N
+PRECIPITOUS  P R IH0 - S IH1 - P IH0 - T AH0 S
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+PRECISE  P R IH0 - S AY1 S
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+PRECISION  P R IY0 - S IH1 - ZH AH0 N
+PRECLINICAL  P R IY0 K - L IH1 - N IH0 - K AH0 L
+PRECLUDE  P R IH0 - K L UW1 D
+PRECLUDE(2)  P R IY0 - K L UW1 D
+PRECLUDED  P R IH0 - K L UW1 - D IH0 D
+PRECLUDED(2)  P R IY0 - K L UW1 - D IH0 D
+PRECLUDES  P R IH0 - K L UW1 D Z
+PRECLUDES(2)  P R IY0 - K L UW1 D Z
+PRECLUDING  P R IH0 - K L UW1 - D IH0 NG
+PRECLUSION  P R IH0 - K L UW1 - ZH AH0 N
+PRECLUSION(2)  P R IY0 - K L UW1 - ZH AH0 N
+PRECOCIOUS  P R IH0 - K OW1 - SH AH0 S
+PRECOCIOUS(2)  P R IY0 - K OW1 - SH AH0 S
+PRECONCEIVE  P R IY2 - K AH0 N - S IY1 V
+PRECONCEIVED  P R IY2 - K AH0 N - S IY1 V D
+PRECONCEPTION  P R IY0 - K AH0 N - S EH1 P - SH AH0 N
+PRECONCEPTIONS  P R IY0 - K AH0 N - S EH1 P - SH AH0 N Z
+PRECONDITION  P R IY2 - K AH0 N - D IH1 - SH AH0 N
+PRECONDITIONS  P R IY2 - K AH0 N - D IH1 - SH AH0 N Z
+PRECOOK  P R IY1 - K UH1 K
+PRECOOKED  P R IY0 - K UH1 K T
+PRECOURT  P R IH0 - K AO1 R T
+PRECRASH  P R IY0 - K R AE1 SH
+PRECURSOR  P R IY0 - K ER1 - S ER0
+PRECURSORS  P R IY0 - K ER1 - S ER0 Z
+PREDACEOUS  P R IY0 - D EY1 - SH AH0 S
+PREDATE  P R IY0 - D EY1 T
+PREDATE(2)  P R IY1 - D EY1 T
+PREDATED  P R IY0 - D EY1 - T IH0 D
+PREDATED(2)  P R IY1 - D EY1 - T IH0 D
+PREDATES  P R IY1 - D EY1 T S
+PREDATOR  P R EH1 - D AH0 - T ER0
+PREDATORS  P R EH1 - D AH0 - T ER0 Z
+PREDATORY  P R EH1 - D AH0 - T AO2 - R IY0
+PREDAWN  P R IY0 - D AO1 N
+PREDDY  P R EH1 - D IY0
+PREDECESSOR  P R EH1 - D AH0 - S EH2 - S ER0
+PREDECESSOR'S  P R EH1 - D AH0 - S EH2 - S ER0 Z
+PREDECESSORS  P R EH1 - D AH0 - S EH2 - S ER0 Z
+PREDECESSORS'  P R EH2 - D AH0 - S EH1 - S ER0 Z
+PREDESTINATION  P R IY2 - D EH2 - S T AH0 - N EY1 - SH AH0 N
+PREDESTINE  P R IY2 - D EH1 - S T AH0 N
+PREDESTINED  P R IY2 - D EH1 - S T AH0 N D
+PREDETERMINE  P R IY2 - D IH0 - T ER1 - M AH0 N
+PREDETERMINE(2)  P R IY2 - D IY0 - T ER1 - M AH0 N
+PREDETERMINED  P R IY2 - D IY0 - T ER1 - M IH0 N D
+PREDICAMENT  P R IH0 - D IH1 - K AH0 - M AH0 N T
+PREDICAMENT(2)  P R IY0 - D IH1 - K AH0 - M AH0 N T
+PREDICAMENTS  P R IH0 - D IH1 - K AH0 - M AH0 N T S
+PREDICATE  P R EH1 - D AH0 - K EY2 T
+PREDICATE(2)  P R EH1 - D IH0 - K AH0 T
+PREDICATED  P R EH1 - D AH0 - K EY2 - T IH0 D
+PREDICATES  P R EH1 - D AH0 - K EY2 T S
+PREDICATES(2)  P R EH1 - D IH0 - K AH0 T S
+PREDICT  P R IH0 - D IH1 K T
+PREDICT(2)  P R IY0 - D IH1 K T
+PREDICTABILITY  P R IH0 - D IH2 K - T AH0 - B IH1 - L IH0 - T IY0
+PREDICTABLE  P R IH0 - D IH1 K - T AH0 - B AH0 L
+PREDICTABLE(2)  P R IY0 - D IH1 K - T AH0 - B AH0 L
+PREDICTABLY  P R IH0 - D IH1 K - T AH0 - B L IY0
+PREDICTED  P R IH0 - D IH1 K - T IH0 D
+PREDICTED(2)  P R IY0 - D IH1 K - T AH0 D
+PREDICTED(3)  P R IY0 - D IH1 K - T IH0 D
+PREDICTING  P R IH0 - D IH1 K - T IH0 NG
+PREDICTING(2)  P R IY0 - D IH1 K - T IH0 NG
+PREDICTION  P R IY0 - D IH1 K - SH AH0 N
+PREDICTIONS  P R IY0 - D IH1 K - SH AH0 N Z
+PREDICTIVE  P R IH0 - D IH1 K - T IH0 V
+PREDICTIVE(2)  P R IY0 - D IH1 K - T IH0 V
+PREDICTOR  P R IH0 - D IH1 K - T ER0
+PREDICTORS  P R IH0 - D IH1 K - T ER0 Z
+PREDICTORS(2)  P R IY0 - D IH1 K - T ER0 Z
+PREDICTS  P R IH0 - D IH1 K T S
+PREDICTS(2)  P R IY0 - D IH1 K T S
+PREDICTS(3)  P R IH0 - D IH1 K S
+PREDICTS(4)  P R IY0 - D IH1 K S
+PREDILECTION  P R EH2 - D AH0 - L EH1 K - SH AH0 N
+PREDILECTIONS  P R EH2 - D AH0 - L EH1 K - SH AH0 N Z
+PREDISPOSE  P R IY2 - D IH0 - S P OW1 Z
+PREDISPOSED  P R IY2 - D IH0 - S P OW1 Z D
+PREDISPOSING  P R IY2 - D IH0 - S P OW1 - Z IH0 NG
+PREDISPOSITION  P R IY2 - D IH0 S - P AH0 - Z IH1 - SH AH0 N
+PREDISPOSITIONS  P R IY2 - D IH0 S - P AH0 - Z IH1 - SH AH0 N Z
+PREDMORE  P R EH1 D - M AO0 R
+PREDOMINANCE  P R IH0 - D AA1 - M AH0 - N AH0 N S
+PREDOMINANT  P R IH0 - D AA1 - M AH0 - N AH0 N T
+PREDOMINANTLY  P R IH0 - D AA1 - M AH0 - N AH0 N T - L IY0
+PREDOMINATE  P R IH0 - D AA1 - M AH0 - N EY2 T
+PREDOMINATE(2)  P R IH0 - D AA1 - M AH0 - N AH0 T
+PREDOMINATED  P R IH0 - D AA1 - M AH0 - N EY2 - T IH0 D
+PREDOMINATES  P R IH0 - D AA1 - M AH0 - N EY2 T S
+PREDOMINATING  P R IH0 - D AA1 - M AH0 - N EY2 - T IH0 NG
+PREE  P R IY1
+PREECE  P R IY1 S
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+PREEMINENCE  P R IY0 - EH1 - M AH0 - N AH0 N S
+PREEMINENT  P R IY0 - EH1 - M AH0 - N AH0 N T
+PREEMPT  P R IY1 - EH2 M P T
+PREEMPTED  P R IY0 - EH1 M P - T IH0 D
+PREEMPTING  P R IY0 - EH1 M P - T IH0 NG
+PREEMPTION  P R IY2 - EH1 M P - SH AH0 N
+PREEMPTIVE  P R IY0 - EH1 M P - T IH0 V
+PREEMPTIVELY  P R IY0 - EH1 M P - T IH0 V - L IY0
+PREEN  P R IY1 N
+PREENED  P R IY1 N D
+PREENING  P R IY1 - N IH0 NG
+PREEXIST  P R IY1 - IH0 G - Z IH1 S T
+PREEXISTED  P R IY1 - IH0 G - Z IH1 - S T IH0 D
+PREEXISTING  P R IY1 - IH0 G - Z IH1 - S T IH0 NG
+PREEXISTS  P R IY1 - IH0 G - Z IH1 S T S
+PREEXISTS(2)  P R IY1 - IH0 G - Z IH1 S S
+PREEXISTS(3)  P R IY1 - IH0 G - Z IH1 S
+PREFAB  P R IY1 - F AE1 B
+PREFABRICATE  P R IY0 - F AE1 - B R IH0 - K EY2 T
+PREFABRICATED  P R IY0 - F AE1 - B R IH0 - K EY2 - T IH0 D
+PREFABRICATION  P R IY2 - F AE2 - B R AH0 - K EY1 - SH AH0 N
+PREFABS  P R IY1 - F AE1 B Z
+PREFACE  P R EH1 - F AH0 S
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+PREFECT  P R IY1 - F EH2 K T
+PREFECTURAL  P R IY0 - F EH1 K - CH ER0 - AH0 L
+PREFECTURE  P R IY1 - F EH2 K - CH ER0
+PREFER  P R AH0 - F ER1
+PREFER(2)  P R IH0 - F ER1
+PREFER(3)  P R IY0 - F ER1
+PREFERABLE  P R EH1 - F ER0 - AH0 - B AH0 L
+PREFERABLE(2)  P R EH1 - F R AH0 - B AH0 L
+PREFERABLY  P R EH1 - F ER0 - AH0 - B L IY0
+PREFERABLY(2)  P R EH1 - F R AH0 - B L IY0
+PREFERED  P R IH0 - F ER1 D
+PREFERENCE  P R EH1 - F ER0 - AH0 N S
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+PREFERENCED  P R EH1 - F ER0 - AH0 N S T
+PREFERENCED(2)  P R EH1 - F R AH0 N S T
+PREFERENCES  P R EH1 - F ER0 - AH0 N - S IH0 Z
+PREFERENTIAL  P R EH2 - F ER0 - EH1 N - CH AH0 L
+PREFERENTIAL(2)  P R EH2 - F ER0 - EH1 N - SH AH0 L
+PREFERENTIALLY  P R EH2 - F ER0 - EH1 N - CH AH0 - L IY0
+PREFERENTIALLY(2)  P R EH2 - F ER0 - EH1 N - SH AH0 - L IY0
+PREFERRED  P R AH0 - F ER1 D
+PREFERRED(2)  P R IH0 - F ER1 D
+PREFERRED(3)  P R IY0 - F ER1 D
+PREFERREDS  P R IY0 - F ER1 - AH0 D Z
+PREFERRING  P R IH0 - F ER1 - IH0 NG
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+PREFERS(3)  P R IY0 - F ER1 Z
+PREFIX  P R IY1 - F IH0 K S
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+PREGLER  P R EH1 G - L ER0
+PREGNANCIES  P R EH1 G - N AH0 N - S IY0 Z
+PREGNANCY  P R EH1 G - N AH0 N - S IY0
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+PREIGNITION  P R IY2 - AH0 G - N IH1 - SH AH0 N
+PREIS  P R IY1 Z
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+PREJUDGE  P R IY0 - JH AH1 JH
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+PREJUDGMENT  P R IY0 - JH AH1 JH - M AH0 N T
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+PREJUDICIAL  P R EH2 - JH AH0 - D IH1 - SH AH0 L
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+PREKINDERGARTEN  P R IY0 - K IH1 N - D ER0 - G AA2 - D AH0 N
+PRELATE  P R EH1 - L IH0 T
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+PRELIMINARIES(2)  P R IY0 - L IH1 - M AH0 - N EH2 - R IY0 Z
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+PRELIMINARY(2)  P R IY0 - L IH1 - M AH0 - N EH2 - R IY0
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+PREMEDITATE  P R IY0 - M EH1 - D AH0 - T EY2 T
+PREMEDITATED  P R IY0 - M EH1 - D AH0 - T EY2 - T IH0 D
+PREMEDITATION  P R IY0 - M EH2 - D AH0 - T EY1 - SH AH0 N
+PREMIER  P R EH0 - M IH1 R
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+PREMIER(2)  P R IY0 - M IH1 R
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+PREMIERS  P R EH0 - M IH1 R Z
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+PRENATAL  P R IY0 - N EY1 - T AH0 L
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+PRENGER  P R EH1 N - JH ER0
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+PRENUPTIAL  P R IY0 - N AH1 P - SH AH0 L
+PREOCCUPATION  P R IY0 - AA2 - K Y AH0 - P EY1 - SH AH0 N
+PREOCCUPATIONS  P R IY0 - AA2 - K Y AH0 - P EY1 - SH AH0 N Z
+PREOCCUPIED  P R IY0 - AA1 - K Y AH0 - P AY2 D
+PREOCCUPIES  P R IY0 - AA1 - K Y AH0 - P AY2 Z
+PREOCCUPY  P R IY0 - AA1 - K Y AH0 - P AY2
+PREORDAIN  P R IY2 - AO0 R - D EY1 N
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+PREP  P R EH1 P
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+PREPACKAGED  P R IY0 - P AE1 - K AH0 JH D
+PREPAID  P R IY0 - P EY1 D
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+PREPARATIONS  P R EH2 - P ER0 - EY1 - SH AH0 N Z
+PREPARATORY  P R IH0 - P EH1 - R AH0 - T AO2 - R IY0
+PREPARATORY(2)  P R EH1 - P R AH0 - T AO2 - R IY0
+PREPARE  P R IY0 - P EH1 R
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+PROFOUND  P R OW0 - F AW1 N D
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+PROGNOSTICATER  P R AA2 G - N AA1 - S T AH0 - K EY2 - T ER0
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+PROGNOSTICATION  P R AA0 G - N AA2 - S T AH0 - K EY1 - SH AH0 N
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+PROGRESS  P R AA1 - G R EH2 S
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+PROHIBITION  P R OW2 - AH0 - B IH1 - SH AH0 N
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+PROHIBITIONS  P R OW2 - AH0 - B IH1 - SH AH0 N Z
+PROHIBITIVE  P R OW0 - HH IH1 - B AH0 - T IH0 V
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+PROHIBITORY  P R OW0 - HH IH1 - B AH0 - T AO2 - R IY0
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+PROIA  P R OW1 - Y AH0
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+PROJECT  P R AA1 - JH EH0 K T
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+PROJECT(2)  P R AH0 - JH EH1 K T
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+RECOMMENDED(2)  R EH2 - K AH0 - M EH1 N - D IH0 D
+RECOMMENDING  R EH2 - K AH0 - M EH1 N - D IH0 NG
+RECOMMENDS  R EH2 - K AH0 - M EH1 N D Z
+RECOMMIT  R IH0 - K AA1 - M IH0 T
+RECOMMIT(2)  R IY2 - K AH0 - M IH1 T
+RECOMMITED  R IH0 - K AA1 - M IH2 - T IH0 D
+RECOMMITED(2)  R IY2 - K AH0 - M IH1 - T IH0 D
+RECOMPENSE  R EH1 - K AH0 M - P EH2 N S
+RECON  R IY1 - K AO0 N
+RECONCILE  R EH1 - K AH0 N - S AY2 L
+RECONCILED  R EH1 - K AH0 N - S AY2 L D
+RECONCILES  R EH1 - K AH0 N - S AY2 L Z
+RECONCILIATION  R EH2 - K AH0 N - S IH2 - L IY0 - EY1 - SH AH0 N
+RECONCILIATIONS  R EH2 - K AH0 N - S IH2 - L IY0 - EY1 - SH AH0 N Z
+RECONCILING  R EH1 - K AH0 N - S AY2 - L IH0 NG
+RECONDITE  R EH1 - K AH0 N - D AY2 T
+RECONDITION  R IY2 - K AH0 N - D IH1 - SH AH0 N
+RECONDITIONED  R IY2 - K AH0 N - D IH1 - SH AH0 N D
+RECONDITIONING  R IY2 - K AH0 N - D IH1 - SH AH0 N - IH0 NG
+RECONDITIONING(2)  R IY2 - K AH0 N - D IH1 SH - N IH0 NG
+RECONFIGURATION  R IY0 - K AH0 N - F IH2 - G Y ER0 - EY1 - SH AH0 N
+RECONFIGURE  R IY2 - K AH0 N - F IH1 - G Y ER0
+RECONFIGURED  R IY2 - K AH0 N - F IH1 - G Y ER0 D
+RECONFIRM  R IY2 - K AH0 N - F ER1 M
+RECONFIRMATION  R IY2 - K AA2 N - F ER0 - M EY1 - SH AH0 N
+RECONFIRMED  R IY2 - K AH0 N - F ER1 M D
+RECONNAISSANCE  R IY0 - K AA1 - N AH0 - S AH0 N S
+RECONNECT  R IY0 - K AH0 - N EH1 K T
+RECONNECTED  R IY0 - K AH0 - N EH1 K - T IH0 D
+RECONNECTING  R IY0 - K AH0 - N EH1 K - T IH0 NG
+RECONNECTS  R IY0 - K AH0 - N EH1 K T S
+RECONQUER  R IY0 - K AO1 NG - K ER0
+RECONQUERED  R IY0 - K AO1 NG - K ER0 D
+RECONSIDER  R IY2 - K AH0 N - S IH1 - D ER0
+RECONSIDERATION  R IY0 - K AH0 N - S IH2 - D ER0 - EY1 - SH AH0 N
+RECONSIDERED  R IY2 - K AH0 N - S IH1 - D ER0 D
+RECONSIDERING  R IY2 - K AH0 N - S IH1 - D ER0 - IH0 NG
+RECONSTITUTE  R IY0 - K AA1 N - S T AH0 - T UW2 T
+RECONSTITUTED  R IY0 - K AA1 N - S T AH0 - T UW2 - T IH0 D
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+RECONSTRUCT  R IY2 - K AH0 N - S T R AH1 K T
+RECONSTRUCTED  R IY2 - K AH0 N - S T R AH1 K - T IH0 D
+RECONSTRUCTING  R IY2 - K AH0 N - S T R AH1 K - T IH0 NG
+RECONSTRUCTION  R IY2 - K AH0 N - S T R AH1 K - SH AH0 N
+RECONSTRUCTIONS  R IY2 - K AH0 N - S T R AH1 K - SH AH0 N Z
+RECONSTRUCTIVE  R IY2 - K AH0 N - S T R AH1 K - T IH0 V
+RECONVENE  R IY0 - K AH0 N - V IY1 N
+RECONVENED  R IY0 - K AH0 N - V IY1 N D
+RECONVENES  R IY0 - K AH0 N - V IY1 N Z
+RECORD  R AH0 - K AO1 R D
+RECORD'S  R EH1 - K ER0 D Z
+RECORD(2)  R EH1 - K ER0 D
+RECORD(3)  R IH0 - K AO1 R D
+RECORDABLE  R IH0 - K AO1 R - D AH0 - B AH0 L
+RECORDED  R AH0 - K AO1 R - D AH0 D
+RECORDED(2)  R IH0 - K AO1 R - D IH0 D
+RECORDER  R IH0 - K AO1 R - D ER0
+RECORDER(2)  R IY0 - K AO1 R - D ER0
+RECORDERS  R IH0 - K AO1 R - D ER0 Z
+RECORDERS(2)  R IY0 - K AO1 R - D ER0 Z
+RECORDING  R AH0 - K AO1 R - D IH0 NG
+RECORDING(2)  R IH0 - K AO1 R - D IH0 NG
+RECORDINGS  R IH0 - K AO1 R - D IH0 NG Z
+RECORDKEEPING  R EH1 - K ER0 D - K IY2 - P IH0 NG
+RECORDS  R AH0 - K AO1 R D Z
+RECORDS'  R EH1 - K ER0 D Z
+RECORDS(2)  R EH1 - K ER0 D Z
+RECORDS(3)  R IH0 - K AO1 R D Z
+RECORE  R EH0 - K AO1 - R IY0
+RECOTON  R IH0 - K AO1 - T IH0 N
+RECOUNT  R IH0 - K AW1 N T
+RECOUNT(2)  R IY2 - K AW1 N T
+RECOUNTED  R IH0 - K AW1 N - T IH0 D
+RECOUNTED(2)  R IY2 - K AW1 N - T IH0 D
+RECOUNTING  R IH0 - K AW1 N - T IH0 NG
+RECOUNTING(2)  R IY2 - K AW1 N - T IH0 NG
+RECOUNTS  R IY2 - K AW1 N T S
+RECOUNTS(2)  R IH0 - K AW1 N T S
+RECOUP  R IH0 - K UW1 P
+RECOUPED  R IH0 - K UW1 P T
+RECOUPING  R IH0 - K UW1 - P IH0 NG
+RECOUPS  R IH0 - K UW1 P S
+RECOURSE  R IY1 - K AO0 R S
+RECOVER  R IH0 - K AH1 - V ER0
+RECOVERABLE  R IH0 - K AH1 - V ER0 - AH0 - B AH0 L
+RECOVERED  R AH0 - K AH1 - V ER0 D
+RECOVERED(2)  R IH0 - K AH1 - V ER0 D
+RECOVERIES  R IH0 - K AH1 - V ER0 - IY0 Z
+RECOVERING  R AH0 - K AH1 - V ER0 - IH0 NG
+RECOVERING(2)  R IH0 - K AH1 - V ER0 - IH0 NG
+RECOVERS  R IH0 - K AH1 - V ER0 Z
+RECOVERY  R IH0 - K AH1 - V R IY0
+RECOVERY'S  R IH0 - K AH1 - V ER0 - IY0 Z
+RECOVERY'S(2)  R IH0 - K AH1 - V R IY0 Z
+RECOVERY(2)  R IH0 - K AH1 - V ER0 - IY0
+RECREATE  R EH1 - K R IY0 - EY2 T
+RECREATE(2)  R IY0 - K R IY0 - EY1 T
+RECREATED  R EH1 - K R IY0 - EY2 - T IH0 D
+RECREATED(2)  R IY0 - K R IY0 - EY1 - T IH0 D
+RECREATES  R EH1 - K R IY0 - EY2 T S
+RECREATES(2)  R IY0 - K R IY0 - EY1 T S
+RECREATING  R EH1 - K R IY0 - EY2 - T IH0 NG
+RECREATING(2)  R IY0 - K R IY0 - EY1 - T IH0 NG
+RECREATION  R EH2 - K R IY0 - EY1 - SH AH0 N
+RECREATIONAL  R EH2 - K R IY0 - EY1 - SH AH0 N - AH0 L
+RECRIMINATE  R IH0 - K R IH2 - M IH0 - N EY1 T
+RECRIMINATION  R IH0 - K R IH2 - M IH0 - N EY1 - SH AH0 N
+RECRIMINATIONS  R IH0 - K R IH2 - M IH0 - N EY1 - SH AH0 N Z
+RECROSS  R IY0 - K R AO1 S
+RECRUIT  R AH0 - K R UW1 T
+RECRUIT'S  R IY0 - K R UW1 T S
+RECRUIT(2)  R IH0 - K R UW1 T
+RECRUIT(3)  R IY0 - K R UW1 T
+RECRUITED  R IH0 - K R UW1 - T IH0 D
+RECRUITED(2)  R IY0 - K R UW1 - T IH0 D
+RECRUITER  R IH0 - K R UW1 - T ER0
+RECRUITERS  R IH0 - K R UW1 - T ER0 Z
+RECRUITING  R AH0 - K R UW1 - T IH0 NG
+RECRUITING(2)  R IH0 - K R UW1 - T IH0 NG
+RECRUITING(3)  R IY0 - K R UW1 - T IH0 NG
+RECRUITMENT  R AH0 - K R UW1 T - M AH0 N T
+RECRUITMENT(2)  R IH0 - K R UW1 T - M AH0 N T
+RECRUITMENT(3)  R IY0 - K R UW1 T - M AH0 N T
+RECRUITS  R AH0 - K R UW1 T S
+RECRUITS(2)  R IH0 - K R UW1 T S
+RECRUITS(3)  R IY0 - K R UW1 T S
+RECTAL  R EH1 K - T AH0 L
+RECTANGLE  R EH1 K - T AE0 NG - G AH0 L
+RECTANGLES  R EH1 K - T AE2 NG - G AH0 L Z
+RECTANGULAR  R EH0 K - T AE1 NG - G Y AH0 - L ER0
+RECTENWALD  R EH1 K - T IH0 N - W AO0 L D
+RECTIFICATION  R EH2 K - T AH0 - F AH0 - K EY1 - SH AH0 N
+RECTIFIED  R EH1 K - T AH0 - F AY2 D
+RECTIFIER  R EH1 K - T AH0 - F AY2 - ER0
+RECTIFIERS  R EH1 K - T AH0 - F AY2 - ER0 Z
+RECTIFY  R EH1 K - T AH0 - F AY2
+RECTIFYING  R EH1 K - T AH0 - F AY2 - IH0 NG
+RECTITUDE  R EH1 K - T IH0 - T UW2 D
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+RECTORY  R EH1 K - T ER0 - IY0
+RECTUM  R EH1 K - T AH0 M
+RECUPERATE  R IH0 - K UW1 - P ER0 - EY2 T
+RECUPERATING  R IH0 - K UW1 - P ER0 - EY2 - T IH0 NG
+RECUPERATING(2)  R IY0 - K UW1 - P ER0 - EY2 - T IH0 NG
+RECUPERATION  R IH0 - K UW2 - P ER0 - EY1 - SH AH0 N
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+RECUPERO  R EY0 - K UW0 - P EH1 - R OW0
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+RECURRENCE(2)  R IY0 - K ER1 - AH0 N S
+RECURRENT  R IH0 - K ER1 - AH0 N T
+RECURRENT(2)  R IY0 - K ER1 - AH0 N T
+RECURRING  R IH0 - K ER1 - IH0 NG
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+RECUSAL  R IH0 - K Y UW1 - Z AH0 L
+RECUSE  R IH2 - K Y UW1 Z
+RECUSED  R IH0 - K Y UW1 Z D
+RECUSING  R IH2 - K Y UW1 - Z IH0 NG
+RECYCLABLE  R IY0 - S AY1 - K L AH0 - B AH0 L
+RECYCLABLES  R IY0 - S AY1 - K L AH0 - B AH0 L Z
+RECYCLE  R IY0 - S AY1 - K AH0 L
+RECYCLED  R IY0 - S AY1 - K AH0 L D
+RECYCLER  R IY0 - S AY1 - K L ER0
+RECYCLERS  R IY0 - S AY1 - K L ER0 Z
+RECYCLES  R IY0 - S AY1 - K AH0 L Z
+RECYCLING  R IY0 - S AY1 - K AH0 L - IH0 NG
+RECYCLING(2)  R IY0 - S AY1 - K L IH0 NG
+RECZEK  R EH1 - CH EH0 K
+RED  R EH1 D
+RED-HANDED  R EH1 D - HH AE1 N - D AH0 D
+RED-HANDED(2)  R EH1 D - HH AE1 N - D IH0 D
+REDA  R EY1 - D AH0
+REDACT  R IH0 - D AE1 K T
+REDACTED  R IH0 - D AE1 K - T IH0 D
+REDACTES  R IH0 - D AE1 K T S
+REDACTING  R IH0 - D AE1 K - T IH0 NG
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+REDBUD  R EH1 D - B AH2 D
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+REDCAY  R EH1 D - K EY0
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+REDDELL  R EH1 - D AH0 L
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+REDDING  R EH1 - D IH0 NG
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+REDDINGTON  R EH1 - D IH0 NG - T AH0 N
+REDDISH  R EH1 - D IH0 SH
+REDDITT  R EH1 - D IH0 T
+REDDOCH  R EH1 - D AH0 K
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+REDECORATED  R IY0 - D EH1 - K ER0 - EY2 - T IH0 D
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+REDECORATION  R IY2 - D EH0 - K ER0 - EY1 - SH AH0 N
+REDEDICATE  R IY2 - D EH1 - D AH0 - K EY2 T
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+REDEFINE  R IY0 - D IH0 - F AY1 N
+REDEFINED  R IY0 - D IH0 - F AY1 N D
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+REDEMPTION  R IH0 - D EH1 M P - SH AH0 N
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+REDER  R EH1 - D ER0
+REDESIGN  R IY2 - D IH0 Z - AY1 N
+REDESIGNED  R IY2 - D IH0 Z - AY1 N D
+REDESIGNING  R IY2 - D IH0 - Z AY1 - N IH0 NG
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+REDEVELOPMENT  R IY2 - D IH0 V - EH1 - L AH0 P - M AH0 N T
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+REDFORD'S  R EH1 D - F ER0 D Z
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+REDHEAD  R EH1 D - HH EH2 D
+REDHEADED  R EH1 D - HH EH2 - D IH0 D
+REDHEADS  R EH1 D - HH EH1 D Z
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+REDIFER(2)  R IY0 - D AH0 - F ER1
+REDIFFUSION  R IY0 - D IH0 - F Y UW1 - ZH AH0 N
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+REDIKER  R EH1 - D IH0 - K ER0
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+REDIRECTED  R IY2 - D ER0 - EH1 K - T IH0 D
+REDIRECTING  R IY2 - D ER0 - EH1 K - T IH0 NG
+REDIRECTION  R IY2 - D ER0 - EH1 K - SH AH0 N
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+REDISCOVERED  R IY0 - D IH0 S - K AH1 - V ER0 D
+REDISCOVERING  R IY0 - D IH0 S - K AH1 - V ER0 - IH0 NG
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+REDISTRIBUTING  R IY2 - D IH0 S - T R IH1 - B Y UW0 - T IH0 NG
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+REDOUBLED  R IY0 - D AH1 - B AH0 L D
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+REDRAFT  R IY0 - D R AE1 F T
+REDRAFTED  R IY0 - D R AE1 F - T IH0 D
+REDRAW  R IY0 - D R AO1
+REDRAWING  R IY0 - D R AO1 - IH0 NG
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+REDRESSING  R IY0 - D R EH1 - S IH0 NG
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+REDSKIN  R EH1 D - S K IH2 N
+REDSKINS  R EH1 D - S K IH2 N Z
+REDSKINS'  R EH1 D - S K IH2 N Z
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+REDSTONE'S  R EH1 D - S T OW2 N Z
+REDUCE  R AH0 - D UW1 S
+REDUCE(2)  R IH0 - D UW1 S
+REDUCE(3)  R IY0 - D UW1 S
+REDUCED  R AH0 - D UW1 S T
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+REDUCES(3)  R IY0 - D UW1 - S IH0 Z
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+REDUCTION  R AH0 - D AH1 K - SH AH0 N
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+REDUCTIONS  R IY0 - D AH1 K - SH AH0 N Z
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+REDUNDANT  R IH0 - D AH1 N - D AH0 N T
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+RELATED  R IH0 - L EY1 - T IH0 D
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+RELATES(2)  R IY0 - L EY1 T S
+RELATING  R IH0 - L EY1 - T IH0 NG
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+RELATION  R IY0 - L EY1 - SH AH0 N
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+RELATIONS  R IY0 - L EY1 - SH AH0 N Z
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+RELATIVELY  R EH1 - L AH0 - T IH0 V - L IY0
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+RELATIVISM  R EH1 - L AH0 - T IH0 - V IH2 - Z AH0 M
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+RENK  R EH1 NG K
+RENKEN  R EH1 NG - K AH0 N
+RENKO  R EH1 NG - K OW0
+RENMINBI  R EH0 N - M IH1 N - B IY0
+RENN  R EH1 N
+RENNA  R EH1 - N AH0
+RENNARD  R IH0 - N AA1 R D
+RENNE  R EH1 N
+RENNELS  R EH1 - N AH0 L Z
+RENNER  R EH1 - N ER0
+RENNERT  R EH1 - N ER0 T
+RENNET  R EH1 - N AH0 T
+RENNICK  R EH1 - N IH0 K
+RENNIE  R EH1 - N IY0
+RENNINGER  R EH1 - N IH0 - NG ER0
+RENNO  R EH1 - N OW0
+RENNY  R EH1 - N IY0
+RENO  R IY1 - N OW0
+RENO'S  R IY1 - N OW0 Z
+RENOIR  R EH0 N - W AA1 R
+RENOIRS  R EH0 N - W AA1 R Z
+RENOMINATE  R IY0 - N AO1 - M IH0 - N EY2 T
+RENOMINATED  R IY0 - N AA1 - M AH0 - N EY2 - T IH0 D
+RENOMINATION  R IY1 - N AA2 - M AH0 - N EY1 - SH AH0 N
+RENOUF  R AH0 - N UW1 F
+RENOUNCE  R IH0 - N AW1 N S
+RENOUNCED  R IH0 - N AW1 N S T
+RENOUNCES  R IH0 - N AW1 N - S IH0 Z
+RENOUNCING  R IH0 - N AW1 N - S IH0 NG
+RENOVATABLE  R EH1 - N AH0 - V EY2 - T AH0 - B AH0 L
+RENOVATE  R EH1 - N AH0 - V EY2 T
+RENOVATED  R EH1 - N AH0 - V EY2 - T IH0 D
+RENOVATING  R EH1 - N AH0 - V EY2 - T IH0 NG
+RENOVATION  R EH2 - N AH0 - V EY1 - SH AH0 N
+RENOVATIONS  R EH1 - N AH0 - V EY2 - SH AH0 N Z
+RENOVATOR  R EH1 - N AH0 - V EY2 - T ER0
+RENOVATORS  R EH1 - N AH0 - V EY2 - T ER0 Z
+RENOWN  R IH0 - N AW1 N
+RENOWNED  R IH0 - N AW1 N D
+RENQUIST  R EH1 N - K W IH0 S T
+RENS  R EH1 N Z
+RENSBERGER  R EH1 N S - B ER0 - G ER0
+RENSCH  R EH1 N SH
+RENSCHLER  R EH1 N - SH AH0 - L ER0
+RENSCHLER(2)  R EH1 N SH - L ER0
+RENSHAW  R EH1 N - SH AO2
+RENSING  R EH1 N - S IH0 NG
+RENSSELAER  R EH2 N - S AH0 - L IH1 R
+RENSTROM  R EH1 N S - T R AH0 M
+RENT  R EH1 N T
+RENTA  R EH1 N - T AH0
+RENTABLE  R EH1 N - T AH0 - B AH0 L
+RENTAL  R EH1 N - T AH0 L
+RENTALS  R EH1 N - T AH0 L Z
+RENTAS  R EH1 N - T AH0 Z
+RENTED  R EH1 N - T AH0 D
+RENTED(2)  R EH1 N - T IH0 D
+RENTER  R EH1 N - T ER0
+RENTERIA  R EH0 N - T EH1 - R IY0 - AH0
+RENTERS  R EH1 N - T ER0 Z
+RENTFRO  R EH1 N T - F R OW0
+RENTFROW  R EH1 N T - F R AW0
+RENTING  R EH1 N - T IH0 NG
+RENTMEESTER  R EH1 N T - M IY2 - S T ER0
+RENTON  R EH1 N - T AH0 N
+RENTS  R EH1 N T S
+RENTSCH  R EH1 N CH
+RENTSCHLER  R EH1 N CH - L ER0
+RENTZ  R EH1 N T S
+RENUNCIATE  R IH0 - N AH1 N - S IY0 - EY2 T
+RENUNCIATION  R IH0 - N AH2 N - S IY0 - EY1 - SH AH0 N
+RENUZIT  R IY0 - N UW1 - Z IH0 T
+RENWICK  R EH1 N - W IH0 K
+RENY  R IY1 - N IY0
+RENZ  R EH1 N Z
+RENZI  R EH1 N - Z IY0
+RENZULLI  R EH0 N - Z UW1 - L IY0
+REO  R IY1 - OW0
+REOCCUPY  R IY0 - AO1 - K Y UW2 - P AY0
+REOCCUR  R IY0 - AH0 - K ER1
+REOFFEND  R IY0 - AO0 - F EH1 N D
+REOFFENDED  R IY0 - AO0 - F EH1 N - D IH0 D
+REOFFER  R IY0 - AO1 - F ER0
+REOFFERED  R IY0 - AO1 - F ER0 D
+REOFFERING  R IY0 - AO1 - F ER0 - IH0 NG
+REOPEN  R IY0 - OW1 - P AH0 N
+REOPENED  R IY0 - OW1 - P AH0 N D
+REOPENING  R IY0 - OW1 - P AH0 - N IH0 NG
+REOPENS  R IY0 - OW1 - P AH0 N Z
+REORDER  R IY0 - AO1 R - D ER0
+REORDERING  R IY0 - AO1 R - D ER0 - IH0 NG
+REORGANIZATION  R IY2 - AO0 R - G AH0 - N AH0 - Z EY1 - SH AH0 N
+REORGANIZATIONS  R IY0 - AO2 R - G AH0 - N IH0 - Z EY1 - SH AH0 N Z
+REORGANIZE  R IY0 - AO1 R - G AH0 - N AY2 Z
+REORGANIZED  R IY0 - AO1 R - G AH0 - N AY2 Z D
+REORGANIZES  R IY0 - AO1 R - G AH0 - N AY2 - Z IH0 Z
+REORGANIZING  R IY0 - AO1 R - G AH0 - N AY2 - Z IH0 NG
+REORIENT  R IY0 - AO1 - R IY0 - EH0 N T
+REP  R EH1 P
+REP'S  R EH1 P S
+REP(2)  R EH0 - P R IY0 - Z EH1 - T AH0 - T IH0 V
+REP.(2)  R EH0 - P R IY0 - Z EH1 - T AH0 - T IH0 V
+REPACK  R IY0 - P AE1 K
+REPACKAGE  R IY2 - P AE1 - K IH0 JH
+REPACKAGED  R IY1 - P AE0 - K IH0 JH D
+REPACKAGES  R IY2 - P AE1 - K IH0 - JH IH0 Z
+REPACKAGING  R IY1 - P AE0 - K IH0 - JH IH0 NG
+REPACKED  R IY0 - P AE1 K T
+REPAID  R IY0 - P EY1 D
+REPAINT  R IY0 - P EY1 N T
+REPAINTED  R IY0 - P EY1 N - T AH0 D
+REPAINTING  R IY0 - P EY1 N - T IH0 NG
+REPAIR  R IH0 - P EH1 R
+REPAIRABLE  R IH0 - P EH1 - R AH0 - B AH0 L
+REPAIRED  R IH0 - P EH1 R D
+REPAIRED(2)  R IY0 - P EH1 R D
+REPAIRING  R IH0 - P EH1 - R IH0 NG
+REPAIRING(2)  R IY0 - P EH1 - R IH0 NG
+REPAIRMAN  R IH0 - P EH1 R - M AE2 N
+REPAIRMAN(2)  R IY0 - P EH1 R - M AE2 N
+REPAIRMEN  R IH0 - P EH1 R - M EH2 N
+REPAIRS  R IH0 - P EH1 R Z
+REPAIRS(2)  R IY0 - P EH1 R Z
+REPAP  R IY1 - P AE0 P
+REPARATION  R EH2 - P ER0 - EY1 - SH AH0 N
+REPARATIONS  R EH2 - P ER0 - EY1 - SH AH0 N Z
+REPARTEE  R EH2 - P ER0 - T IY1
+REPASKY  R IH0 - P AA1 S - K IY0
+REPASS  R IY0 - P AE1 S
+REPATRIATE  R IY0 - P EY1 - T R IY0 - EY2 T
+REPATRIATED  R IY0 - P EY1 - T R IY0 - EY2 - T IH0 D
+REPATRIATING  R IY0 - P EY1 - T R IY0 - EY2 - T IH0 NG
+REPATRIATION  R IY0 - P EY2 - T R IY0 - EY1 - SH AH0 N
+REPAY  R IY0 - P EY1
+REPAYABLE  R IY0 - P EY1 - AH0 - B AH0 L
+REPAYING  R IY0 - P EY1 - IH0 NG
+REPAYMENT  R IY0 - P EY1 - M AH0 N T
+REPAYMENTS  R IY0 - P EY1 - M AH0 N T S
+REPAYS  R IY2 - P EY1 Z
+REPEAL  R IH0 - P IY1 L
+REPEAL(2)  R IY0 - P IY1 L
+REPEALED  R IH0 - P IY1 L D
+REPEALED(2)  R IY0 - P IY1 L D
+REPEALING  R IY0 - P IY1 - L IH0 NG
+REPEALS  R IY0 - P IY1 L Z
+REPEAT  R IH0 - P IY1 T
+REPEAT(2)  R IY0 - P IY1 T
+REPEATABLE  R IH0 - P IY1 - T AH0 - B AH0 L
+REPEATED  R IH0 - P IY1 - T IH0 D
+REPEATED(2)  R IY0 - P IY1 - T AH0 D
+REPEATED(3)  R IY0 - P IY1 - T IH0 D
+REPEATEDLY  R IH0 - P IY1 - T IH0 D - L IY0
+REPEATER  R IH0 - P IY1 - T ER0
+REPEATERS  R IH0 - P IY1 - T ER0 Z
+REPEATING  R IH0 - P IY1 - T IH0 NG
+REPEATING(2)  R IY0 - P IY1 - T IH0 NG
+REPEATS  R IH0 - P IY1 T S
+REPEATS(2)  R IY0 - P IY1 T S
+REPEL  R IH0 - P EH1 L
+REPELLED  R AH0 - P EH1 L D
+REPELLED(2)  R IH0 - P EH1 L D
+REPELLENT  R IH0 - P EH1 - L AH0 N T
+REPELLING  R AH0 - P EH1 - L IH0 NG
+REPELS  R IH0 - P EH1 L Z
+REPENT  R IH0 - P EH1 N T
+REPENTANCE  R IH0 - P EH1 N - T AH0 N S
+REPENTANT  R IH0 - P EH1 N - T AH0 N T
+REPENTED  R IH0 - P EH1 N - T IH0 D
+REPERCUSSION  R IY2 - P ER0 - K AH1 - SH AH0 N
+REPERCUSSION(2)  R IY2 - P R AH0 - K AH1 - SH AH0 N
+REPERCUSSIONS  R IY2 - P ER0 - K AH1 - SH AH0 N Z
+REPERCUSSIONS(2)  R IY2 - P R AH0 - K AH1 - SH AH0 N Z
+REPERFUSION  R EH2 - P ER0 - F Y UW1 - ZH AH0 N
+REPERTOIRE  R EH1 - P ER0 - T W AA2 R
+REPERTORY  R EH1 - P ER0 - T AO2 - R IY0
+REPETITION  R EH2 - P AH0 - T IH1 - SH AH0 N
+REPETITIONS  R EH2 - P AH0 - T IH1 - SH AH0 N Z
+REPETITIOUS  R EH2 - P AH0 - T IH1 - SH AH0 S
+REPETITIVE  R IH0 - P EH1 - T IH0 - T IH0 V
+REPETTI  R EH0 - P EH1 - T IY0
+REPETTO  R EH0 - P EH1 - T OW0
+REPH  R EH1 F
+REPHRASE  R IY0 - F R EY1 Z
+REPINSKI  R IH0 - P IH1 N - S K IY0
+REPKA  R EH1 P - K AH0
+REPKO  R EH1 P - K OW0
+REPLACE  R IY2 - P L EY1 S
+REPLACE(2)  ER0 - P L EY1 S
+REPLACEABLE  R IY2 - P L EY1 - S AH0 - B AH0 L
+REPLACED  R IY2 - P L EY1 S T
+REPLACEMENT  R IH0 - P L EY1 S - M AH0 N T
+REPLACEMENTS  R IY0 - P L EY1 S - M AH0 N T S
+REPLACES  R IH0 - P L EY1 - S IH0 Z
+REPLACING  R IH0 - P L EY1 - S IH0 NG
+REPLANT  R IY0 - P L AE1 N T
+REPLANTED  R IY0 - P L AE1 N - T IH0 D
+REPLANTED(2)  R IY0 - P L AE1 - N IH0 D
+REPLANTING  R IY0 - P L AE1 N - T IH0 NG
+REPLAY  R IY0 - P L EY1
+REPLAYED  R IY0 - P L EY1 D
+REPLAYING  R IY0 - P L EY1 - IH0 NG
+REPLAYS  R IY0 - P L EY1 Z
+REPLENISH  R IY0 - P L EH1 - N IH0 SH
+REPLENISHED  R IY0 - P L EH1 - N IH0 SH T
+REPLENISHING  R IY0 - P L EH1 - N IH0 - SH IH0 NG
+REPLENISHMENT  R IH0 - P L EH1 - N IH0 SH - M AH0 N T
+REPLETE  R IY0 - P L IY1 T
+REPLICA  R EH1 - P L IH0 - K AH0
+REPLICAS  R EH1 - P L AH0 - K AH0 Z
+REPLICASE  R EH1 - P L IH0 - K EY2 Z
+REPLICATE  R EH1 - P L AH0 - K EY2 T
+REPLICATED  R EH1 - P L IH0 - K EY2 - T IH0 D
+REPLICATES  R EH1 - P L IH0 - K EY2 T S
+REPLICATING  R EH1 - P L IH0 - K EY2 - T IH0 NG
+REPLICATION  R EH2 - P L AH0 - K EY1 - SH AH0 N
+REPLIED  R IH0 - P L AY1 D
+REPLIED(2)  R IY0 - P L AY1 D
+REPLIES  R IH0 - P L AY1 Z
+REPLIES(2)  R IY0 - P L AY1 Z
+REPLIGEN  R IH2 - P L AY1 - JH IH0 N
+REPLIGEN'S  R IH2 - P L AY1 - JH IH0 N Z
+REPLOGLE  R EH1 - P L OW0 - G AH0 L
+REPLY  R IH0 - P L AY1
+REPLY(2)  R IY0 - P L AY1
+REPLYING  R IH0 - P L AY1 - IH0 NG
+REPLYING(2)  R IY0 - P L AY1 - IH0 NG
+REPO  R IY1 - P OW0
+REPONSE  R IH0 - P AA1 N S
+REPORT  R IY0 - P AO1 R T
+REPORT'S  R IY0 - P AO1 R T S
+REPORT'S(2)  R IH0 - P AO1 R T S
+REPORT(2)  R IH0 - P AO1 R T
+REPORTABLE  R IH0 - P AO1 R - T AH0 - B AH0 L
+REPORTAGE  R IH0 - P AO1 R - T IH0 JH
+REPORTED  R IY2 - P AO1 R - T AH0 D
+REPORTED(2)  R IH0 - P AO1 R - T AH0 D
+REPORTEDLY  R IH0 - P AO1 R - T AH0 D - L IY0
+REPORTEDLY(2)  R IY0 - P AO1 R - T AH0 D - L IY0
+REPORTER  R IH0 - P AO1 R - T ER0
+REPORTER'S  R IH0 - P AO1 R - T ER0 Z
+REPORTERS  R IH0 - P AO1 R - T ER0 Z
+REPORTERS'  R IH0 - P AO1 R - T ER0 Z
+REPORTING  R IY0 - P AO1 R - T IH0 NG
+REPORTING(2)  R IH0 - P AO1 R - T IH0 NG
+REPORTORIAL  R EH2 - P ER0 - T AO1 - R IY0 - AH0 L
+REPORTS  R IH0 - P AO1 R T S
+REPORTS'  R IH0 - P AO1 R T S
+REPORTS'(2)  R IY0 - P AO1 R T S
+REPORTS(2)  R IY0 - P AO1 R T S
+REPOS  R IY1 - P OW2 Z
+REPOSA  R EH0 - P OW1 - S AH0
+REPOSE  R IY0 - P OW1 Z
+REPOSITION  R IY2 - P AH0 - Z IH1 - SH AH0 N
+REPOSITIONED  R IY2 - P AH0 - Z IH1 - SH AH0 N D
+REPOSITIONING  R IY2 - P AH0 - Z IH1 - SH AH0 N - IH0 NG
+REPOSITORIES  R IY0 - P AA1 - Z AH0 - T AO2 - R IY0 Z
+REPOSITORY  R IY0 - P AA1 - Z AH0 - T AO2 - R IY0
+REPOSSESS  R IY2 - P AH0 - Z EH1 S
+REPOSSESSED  R IY2 - P AH0 - Z EH1 S T
+REPOSSESSION  R IY2 - P AH0 - Z EH1 - SH AH0 N
+REPOSSESSIONS  R IY2 - P AH0 - Z EH1 - SH AH0 N Z
+REPP  R EH1 P
+REPPERT  R EH1 - P ER0 T
+REPPOND  R EH1 - P AH0 N D
+REPPUCCI  R EH0 - P UW1 - CH IY0
+REPR  EH1 P
+REPREHENSIBLE  R EH2 - P R IH0 - HH EH1 N - S AH0 - B AH0 L
+REPRESENT  R EH2 - P R AH0 - Z EH1 N T
+REPRESENT(2)  R EH2 - P R IH0 - Z EH1 N T
+REPRESENTATION  R EH2 - P R AH0 - Z EH0 N - T EY1 - SH AH0 N
+REPRESENTATIONAL  R EH2 - P R AH0 - Z AH0 N - T EY1 - SH AH0 - N AH0 L
+REPRESENTATIONS  R EH2 - P R AH0 - Z AH0 N - T EY1 - SH AH0 N Z
+REPRESENTATIVE  R EH2 - P R AH0 - Z EH1 N - T AH0 - T IH0 V
+REPRESENTATIVE'S  R EH2 - P R IH0 - Z EH1 N - T AH0 - T IH0 V Z
+REPRESENTATIVE'S(2)  R EH2 - P R IH0 - Z EH1 - N AH0 - T IH0 V Z
+REPRESENTATIVE(2)  R EH2 - P R IH0 - Z EH1 N - T AH0 - T IH0 V
+REPRESENTATIVE(3)  R EH2 - P R AH0 - Z EH1 - N AH0 - T IH0 V
+REPRESENTATIVE(4)  R EH2 - P R IH0 - Z EH1 - N AH0 - T IH0 V
+REPRESENTATIVES  R EH2 - P R AH0 - Z EH1 N - T AH0 - T IH0 V Z
+REPRESENTATIVES'  R EH2 - P R AH0 - S EH1 N - T AH0 - T IH0 V Z
+REPRESENTATIVES'(2)  R EH2 - P R AH0 - S EH1 - N AH0 - T IH0 V Z
+REPRESENTATIVES(2)  R EH2 - P R IH0 - Z EH1 N - T AH0 - T IH0 V Z
+REPRESENTATIVES(3)  R EH2 - P R AH0 - Z EH1 - N AH0 - T IH0 V Z
+REPRESENTATIVES(4)  R EH2 - P R IH0 - Z EH1 - N AH0 - T IH0 V Z
+REPRESENTED  R EH2 - P R AH0 - Z EH1 N - T AH0 D
+REPRESENTED(2)  R EH2 - P R IH0 - Z EH1 N - T IH0 D
+REPRESENTED(3)  R EH2 - P R AH0 - Z EH1 - N AH0 D
+REPRESENTED(4)  R EH2 - P R IH0 - Z EH1 - N IH0 D
+REPRESENTING  R EH2 - P R IH0 - Z EH1 N - T IH0 NG
+REPRESENTING(2)  R EH2 - P R IH0 - Z EH1 - N IH0 NG
+REPRESENTS  R EH2 - P R AH0 - Z EH1 N T S
+REPRESENTS(2)  R EH2 - P R IH0 - Z EH1 N T S
+REPRESS  R IY0 - P R EH1 S
+REPRESSED  R IY0 - P R EH1 S T
+REPRESSING  R IY0 - P R EH1 - S IH0 NG
+REPRESSION  R IY0 - P R EH1 - SH AH0 N
+REPRESSIONS  R IY0 - P R EH1 - SH AH0 N Z
+REPRESSIVE  R IY0 - P R EH1 - S IH0 V
+REPRICE  R IY0 - P R AY1 S
+REPRICED  R IY0 - P R AY1 S T
+REPRICING  R IY0 - P R AY1 - S IH0 NG
+REPRIEVE  R IY0 - P R IY1 V
+REPRIMAND  R EH1 - P R AH0 - M AE2 N D
+REPRIMANDED  R EH1 - P R AH0 - M AE2 N - D IH0 D
+REPRIMANDS  R EH1 - P R AH0 - M AE2 N D Z
+REPRINT  R IY0 - P R IH1 N T
+REPRINTED  R IY0 - P R IH1 N - T IH0 D
+REPRINTING  R IY0 - P R IH1 N - T IH0 NG
+REPRINTS  R IY0 - P R IH1 N T S
+REPRISAL  R IY0 - P R AY1 - Z AH0 L
+REPRISALS  R IH0 - P R AY1 - Z AH0 L Z
+REPRISALS(2)  R IY0 - P R AY1 - Z AH0 L Z
+REPRISE  R IH0 - P R AY1 Z
+REPRISE(2)  R IH0 - P R IY1 Z
+REPROACH  R IY0 - P R OW1 CH
+REPROBATE  R EH1 - P R AO0 - B EY0 T
+REPROCESS  R IY0 - P R AO1 - S EH0 S
+REPROCESSED  R IY0 - P R AO1 - S EH0 S T
+REPROCESSING  R IY0 - P R AO1 - S EH0 - S IH0 NG
+REPRODUCE  R IY2 - P R AH0 - D UW1 S
+REPRODUCED  R IY2 - P R AH0 - D UW1 S T
+REPRODUCES  R IY2 - P R AH0 - D UW1 - S IH0 Z
+REPRODUCING  R IY2 - P R AH0 - D UW1 - S IH0 NG
+REPRODUCTION  R IY2 - P R AH0 - D AH1 K - SH AH0 N
+REPRODUCTIONS  R IY2 - P R AH0 - D AH1 K - SH AH0 N Z
+REPRODUCTIVE  R IY2 - P R AH0 - D AH1 K - T IH0 V
+REPROGRAM  R IY0 - P R OW1 - G R AE2 M
+REPROGRAMMED  R IY0 - P R OW1 - G R AE2 M D
+REPROGRAMMING  R IY0 - P R OW1 - G R AE2 - M IH0 NG
+REPROGRAMS  R IY0 - P R OW1 - G R AE2 M Z
+REPROGRAPH  R EH1 - P R OW0 - G R AE2 F
+REPROGRAPHIC  R EH2 - P R OW0 - G R AE1 - F IH0 K
+REPROGRAPHICS  R EH2 - P R OW0 - G R AE1 - F IH0 K S
+REPS  R EH1 P S
+REPSHER  R EH1 P - SH ER0
+REPSOL  R EH1 P - S AA0 L
+REPTILE  R EH1 P - T AY0 L
+REPTILES  R EH1 P - T AY0 L Z
+REPUBLIC  R IY0 - P AH1 - B L AH0 K
+REPUBLIC'S  R IY0 - P AH1 - B L IH0 K S
+REPUBLIC(2)  R IY0 - P AH1 - B L IH0 K
+REPUBLICAN  R IH0 - P AH1 - B L IH0 - K AH0 N
+REPUBLICAN'S  R IY0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICAN(2)  R IY0 - P AH1 - B L AH0 - K AH0 N
+REPUBLICAN(3)  R IY0 - P AH1 - B L IH0 - K AH0 N
+REPUBLICANISM  R IH0 - P AH1 - B L IH0 - K AH0 - N IH2 - Z AH0 M
+REPUBLICANS  R IH0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICANS'  R IH0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICANS'(2)  R IY0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICANS(2)  R IY0 - P AH1 - B L AH0 - K AH0 N Z
+REPUBLICANS(3)  R IY0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICBANK  R IY0 - P AH1 - B L IH0 K - B AE2 NG K
+REPUBLICBANK'S  R IY0 - P AH1 - B L IH0 K - B AE2 NG K S
+REPUBLICS  R IY0 - P AH1 - B L IH0 K S
+REPUDIATE  R IY0 - P Y UW1 - D IY0 - EY2 T
+REPUDIATED  R IY0 - P Y UW1 - D IY0 - EY2 - T AH0 D
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+REPUDIATING  R IY0 - P Y UW1 - D IY0 - EY2 - T IH0 NG
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+REQUEST  R IH0 - K W EH1 S T
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+REQUESTED  R IH0 - K W EH1 - S T IH0 D
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+RESCINDED  R IH0 - S IH1 N - D IH0 D
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+RESEALABLE  R IY0 - S IY1 - L AH0 - B AH0 L
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+RESENDE  R IH0 - S EH1 N - D EY0
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+RESENTED  R IY0 - Z EH1 N - T AH0 D
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+RESET(2)  R IY1 - S EH0 T
+RESETAR  R EH1 - S IH0 - T ER0
+RESETING  R IY0 - S EH1 - T IH0 NG
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+RESETTABLE  R IY0 - S EH1 - T AH0 - B AH0 L
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+RESHAPED  R IY0 - SH EY1 P T
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+RESPONSIBILITIES  R IY0 - S P AA2 N - S AH0 - B IH1 - L AH0 - T IY0 Z
+RESPONSIBILITY  R IY0 - S P AA2 N - S AH0 - B IH1 - L AH0 - T IY0
+RESPONSIBLE  R IY0 - S P AA1 N - S AH0 - B AH0 L
+RESPONSIBLY  R IH0 - S P AA1 N - S AH0 - B L IY0
+RESPONSIVE  R IH0 - S P AA1 N - S IH0 V
+RESPONSIVENESS  R IH0 - S P AA1 N - S IH0 V - N AH0 S
+RESPRESS  R EH1 - S P R IH0 S
+RESS  R EH1 S
+RESSA  R EH1 - S AH0
+RESSA'S  R EH1 - S AH0 Z
+RESSEGUIE  R EH1 - S IH0 - G W IY0
+RESSEL  R EH1 - S AH0 L
+RESSLER  R EH1 S - L ER0
+REST  R EH1 S T
+RESTAGE  R IY0 - S T EY1 JH
+RESTAGED  R IY0 - S T EY1 JH D
+RESTAINO  R EH0 - S T AA0 - IY1 - N OW0
+RESTART  R IY0 - S T AA1 R T
+RESTARTED  R IY0 - S T AA1 R - T IH0 D
+RESTARTING  R IY0 - S T AA1 R - T IH0 NG
+RESTATE  R IY0 - S T EY1 T
+RESTATED  R IY0 - S T EY1 - T IH0 D
+RESTATEMENT  R IY0 - S T EY1 T - M AH0 N T
+RESTATEMENTS  R IY0 - S T EY1 T - M AH0 N T S
+RESTATES  R IY0 - S T EY1 T S
+RESTATING  R IY0 - S T EY1 - T IH0 NG
+RESTAURANT  R EH1 - S T ER0 - AA2 N T
+RESTAURANT'S  R EH1 - S T ER0 - AA2 N T S
+RESTAURANT'S(2)  R EH1 - S T R AA2 N T S
+RESTAURANT(2)  R EH1 - S T R AA2 N T
+RESTAURANTS  R EH1 - S T ER0 - AA2 N T S
+RESTAURANTS'  R EH1 - S T ER0 - AA2 N T S
+RESTAURANTS'(2)  R EH1 - S T R AA2 N T S
+RESTAURANTS(2)  R EH1 - S T R AA2 N T S
+RESTAURATEUR  R EH2 - S T ER0 - AH0 - T ER1
+RESTAURATEUR(2)  R EH2 - S T R AH0 - T ER1
+RESTAURATEURS  R EH2 - S T ER0 - AH0 - T ER1 Z
+RESTAURATEURS(2)  R EH2 - S T R AH0 - T ER1 Z
+RESTED  R EH1 - S T AH0 D
+RESTED(2)  R EH1 - S T IH0 D
+RESTER  R EH1 - S T ER0
+RESTFUL  R EH1 S T - F AH0 L
+RESTING  R EH1 - S T IH0 NG
+RESTITUTE  R EH1 - S T IH0 - T UW2 T
+RESTITUTION  R EH2 - S T IH0 - T UW1 - SH AH0 N
+RESTIVE  R EH1 - S T IH0 V
+RESTIVENESS  R EH1 - S T IH0 V - N AH0 S
+RESTIVO  R EH0 - S T IY1 - V OW0
+RESTLESS  R EH1 S T - L AH0 S
+RESTLESSLY  R EH1 S T - L AH0 S - L IY0
+RESTLESSNESS  R EH1 S T - L AH0 S - N AH0 S
+RESTO  R EH1 - S T OW0
+RESTOCK  R IY0 - S T AA1 K
+RESTOCKED  R IY0 - S T AA1 K T
+RESTOCKING  R IY0 - S T AA1 - K IH0 NG
+RESTON  R EH1 - S T AH0 N
+RESTORATION  R EH2 - S T ER0 - EY1 - SH AH0 N
+RESTORATIONS  R EH2 - S T ER0 - EY1 - SH AH0 N Z
+RESTORATIVE  R AH0 - S T AO1 - R AH0 - T IH0 V
+RESTORE  R IH0 - S T AO1 R
+RESTORED  R IH0 - S T AO1 R D
+RESTORER  R IH0 - S T AO1 - R ER0
+RESTORES  R IH0 - S T AO1 R Z
+RESTORING  R IH0 - S T AO1 - R IH0 NG
+RESTRAIN  R IY0 - S T R EY1 N
+RESTRAINED  R IY0 - S T R EY1 N D
+RESTRAINING  R IY0 - S T R EY1 - N IH0 NG
+RESTRAINS  R IY0 - S T R EY1 N Z
+RESTRAINT  R IH0 - S T R EY1 N T
+RESTRAINT(2)  R IY0 - S T R EY1 N T
+RESTRAINTS  R IH0 - S T R EY1 N T S
+RESTRAINTS(2)  R IY0 - S T R EY1 N T S
+RESTREPO  R EH0 - S T R EH1 - P OW0
+RESTRICT  R IY0 - S T R IH1 K T
+RESTRICTED  R IY0 - S T R IH1 K - T AH0 D
+RESTRICTED(2)  R IY0 - S T R IH1 K - T IH0 D
+RESTRICTING  R IY0 - S T R IH1 K - T IH0 NG
+RESTRICTION  R IY0 S - T R IH1 K - SH AH0 N
+RESTRICTIONS  R IY0 S - T R IH1 K - SH AH0 N Z
+RESTRICTIVE  R IY0 - S T R IH1 K - T IH0 V
+RESTRICTIVENESS  R AH0 - S T R IH1 K - T IH0 V - N AH0 S
+RESTRICTS  R IY0 - S T R IH1 K T S
+RESTROOM  R EH1 S T - R UW2 M
+RESTROOMS  R EH1 S T - R UW2 M Z
+RESTRUCTURE  R IY0 - S T R AH1 K - CH ER0
+RESTRUCTURED  R IY0 - S T R AH1 K - CH ER0 D
+RESTRUCTURES  R IY0 - S T R AH1 K - CH ER0 Z
+RESTRUCTURING  R IY0 - S T R AH1 K - CH ER0 - IH0 NG
+RESTRUCTURINGS  R IY0 - S T R AH1 K - CH ER0 - IH0 NG Z
+RESTS  R EH1 S T S
+RESTYLE  R IY0 - S T AY1 L
+RESTYLED  R IY0 - S T AY1 L D
+RESUBMIT  R IY2 - S AH0 B - M IH1 T
+RESUBMITTED  R IY2 - S AH0 B - M IH1 - T IH0 D
+RESUBMITTING  R IY2 - S AH0 B - M IH1 - T IH0 NG
+RESULT  R IH0 - Z AH1 L T
+RESULT(2)  R IY0 - Z AH1 L T
+RESULTANT  R IY0 - Z AH1 L - T AH0 N T
+RESULTED  R IH0 - Z AH1 L - T IH0 D
+RESULTED(2)  R IY0 - Z AH1 L - T AH0 D
+RESULTED(3)  R IY0 - Z AH1 L - T IH0 D
+RESULTING  R IH0 - Z AH1 L - T IH0 NG
+RESULTING(2)  R IY0 - Z AH1 L - T IH0 NG
+RESULTS  R IH0 - Z AH1 L T S
+RESULTS(2)  R IY0 - Z AH1 L T S
+RESUME  R IH0 - Z UW1 M
+RESUME(2)  R IY0 - Z UW1 M
+RESUME(3)  R EH1 - Z AH0 - M EY2
+RESUMED  R IH0 - Z UW1 M D
+RESUMED(2)  R IY0 - Z UW1 M D
+RESUMES  R IH0 - Z UW1 M Z
+RESUMES(2)  R IY0 - Z UW1 M Z
+RESUMES(3)  R EH1 - Z AH0 - M EY2 Z
+RESUMING  R IH0 - Z UW1 - M IH0 NG
+RESUMING(2)  R IY0 - Z UW1 - M IH0 NG
+RESUMPTION  R IH0 - Z AH1 M P - SH AH0 N
+RESUMPTION(2)  R IY0 - Z AH1 M P - SH AH0 N
+RESUMPTION(3)  R IH0 - Z AH1 M - SH AH0 N
+RESUMPTION(4)  R IY0 - Z AH1 M - SH AH0 N
+RESUPPLY  R IY0 - S AH0 - P L AY1
+RESURFACE  R IY0 - S ER1 - F AH0 S
+RESURFACED  R IY0 - S ER1 - F AH0 S T
+RESURFACING  R IY0 - S ER1 - F AH0 - S IH0 NG
+RESURGENCE  R IY0 - S ER1 - JH AH0 N S
+RESURGENCY  R IH0 - S ER1 - JH AH0 N - S IY0
+RESURGENCY(2)  R IY0 - S ER1 - JH AH0 N - S IY0
+RESURGENT  R IH0 - S ER1 - JH AH0 N T
+RESURGENT(2)  R IY0 - S ER1 - JH AH0 N T
+RESURGING  R IY0 - S ER1 - JH IH0 NG
+RESURRECT  R EH2 - Z ER0 - EH1 K T
+RESURRECTED  R EH2 - Z ER0 - EH1 K - T IH0 D
+RESURRECTING  R EH2 - Z ER0 - EH1 K - T IH0 NG
+RESURRECTION  R EH2 - Z ER0 - EH1 K - SH AH0 N
+RESUSCITATE  R IH0 - S AH1 - S IH0 - T EY2 T
+RESUSCITATE(2)  R IY0 - S AH1 - S IH0 - T EY2 T
+RESUSCITATED  R IH0 - S AH1 - S IH0 - T EY2 - T IH0 D
+RESUSCITATING  R IH0 - S AH1 - S IH0 - T EY2 - T IH0 NG
+RESUSCITATION  R IH0 - S AH2 - S IH0 - T EY1 - SH AH0 N
+RET  R EH1 T
+RETA  R EH1 - T AH0
+RETABLOS  R IY0 - T AE1 - B L OW0 S
+RETAIL  R IY1 - T EY2 L
+RETAILED  R IY1 - T EY2 L D
+RETAILER  R IY1 - T EY2 - L ER0
+RETAILER'S  R IY1 - T EY2 - L ER0 Z
+RETAILERS  R IY1 - T EY2 - L ER0 Z
+RETAILERS'  R IY1 - T EY2 - L ER0 Z
+RETAILING  R IY1 - T EY2 - L IH0 NG
+RETAILING'S  R IY1 - T EY2 - L IH0 NG Z
+RETAILS  R IY1 - T EY2 L Z
+RETAIN  R IH0 - T EY1 N
+RETAIN(2)  R IY0 - T EY1 N
+RETAINED  R IH0 - T EY1 N D
+RETAINED(2)  R IY0 - T EY1 N D
+RETAINER  R IH0 - T EY1 - N ER0
+RETAINER(2)  R IY0 - T EY1 - N ER0
+RETAINERS  R IH0 - T EY1 - N ER0 Z
+RETAINING  R IH0 - T EY1 - N IH0 NG
+RETAINING(2)  R IY0 - T EY1 - N IH0 NG
+RETAINS  R IH0 - T EY1 N Z
+RETAINS(2)  R IY0 - T EY1 N Z
+RETAKE  R IY1 - T EY1 K
+RETAKE(2)  R IY0 - T EY1 K
+RETAKEN  R IY0 - T EY1 - K AH0 N
+RETAKING  R IY0 - T EY1 - K IH0 NG
+RETALIATE  R IH0 - T AE1 - L IY0 - EY2 T
+RETALIATE(2)  R IY0 - T AE1 - L IY0 - EY2 T
+RETALIATED  R IH0 - T AE1 - L IY0 - EY2 - T IH0 D
+RETALIATED(2)  R IY0 - T AE1 - L IY0 - EY2 - T IH0 D
+RETALIATING  R IH0 - T AE1 - L IY0 - EY2 - T IH0 NG
+RETALIATION  R IY0 - T AE2 - L IY0 - EY1 - SH AH0 N
+RETALIATORY  R IY0 - T AE1 - L Y AH0 - T AO2 - R IY0
+RETANA  R EH0 - T AE1 - N AH0
+RETARD  R IH0 - T AA1 R D
+RETARD(2)  R IY0 - T AA1 R D
+RETARDANT  R IY0 - T AA1 R - D AH0 N T
+RETARDATION  R IY0 - T AA0 R - D EY1 - SH AH0 N
+RETARDED  R IH0 - T AA1 R - D IH0 D
+RETARDED(2)  R IY0 - T AA1 R - D AH0 D
+RETARDED(3)  R IY0 - T AA1 R - D IH0 D
+RETARDING  R IH0 - T AA1 R - D IH0 NG
+RETARDING(2)  R IY0 - T AA1 R - D IH0 NG
+RETARDS  R IH0 - T AA1 R D Z
+RETARDS(2)  R IY0 - T AA1 R D Z
+RETELL  R IY0 - T EH1 L
+RETELLING  R IY0 - T EH1 - L IH0 NG
+RETEMEYER  R EH1 - T AH0 - M AY2 R
+RETENTION  R IY0 - T EH1 N - SH AH0 N
+RETEST  R IY1 - T EH1 S T
+RETESTED  R IY0 - T EH1 - S T IH0 D
+RETESTING  R IY0 - T EH1 - S T IH0 NG
+RETESTS  R IY1 - T EH1 S T S
+RETESTS(2)  R IY1 - T EH1 S S
+RETESTS(3)  R IY1 - T EH1 S
+RETHERFORD  R IH0 - TH ER1 - F ER0 D
+RETHINK  R IY0 - TH IH1 NG K
+RETHINKING  R IY0 - TH IH1 NG - K IH0 NG
+RETHOUGHT  R IY0 - TH AO1 T
+RETICENCE  R EH1 - T IH0 - S AH0 N S
+RETICENT  R EH1 - T IH0 - S AH0 N T
+RETIN  R EH1 - T IH0 N
+RETINA  R EH1 - T AH0 - N AH0
+RETINAL  R EH1 - T AH0 - N AH0 L
+RETINOBLASTOMA  R EH2 - T IH0 - N OW2 - B L AE2 - S T OW1 - M AH0
+RETINOID  R EH1 - T IH0 - N OY0 D
+RETINOIDS  R EH1 - T IH0 - N OY0 D Z
+RETINUE  R EH1 - T AH0 - N UW2
+RETINYL  R EH1 - T IH0 - N AH0 L
+RETIRE  R IH0 - T AY1 R
+RETIRE(2)  R IY0 - T AY1 R
+RETIRE(3)  R IY2 - T AY1 - ER0
+RETIRED  R IH0 - T AY1 R D
+RETIRED(2)  R IY0 - T AY1 - ER0 D
+RETIRED(3)  R IY0 - T AY1 R D
+RETIREE  R IY0 - T AY1 - R IY1
+RETIREE'S  R IH0 - T AY2 - R IY1 Z
+RETIREES  R IY0 - T AY1 - R IY1 Z
+RETIREES'  R IH0 - T AY2 - R IY1 Z
+RETIREMENT  R IY0 - T AY1 - ER0 - M AH0 N T
+RETIREMENT(2)  R IH0 - T AY1 - ER0 - M AH0 N T
+RETIREMENTS  R IH0 - T AY1 R - M AH0 N T S
+RETIREMENTS(2)  R IY0 - T AY1 R - M AH0 N T S
+RETIRES  R IH0 - T AY1 R Z
+RETIRES(2)  R IY0 - T AY1 - ER0 Z
+RETIRES(3)  R IY0 - T AY1 R Z
+RETIRING  R IH0 - T AY1 - R IH0 NG
+RETIRING(2)  R IY0 - T AY1 - ER0 - IH0 NG
+RETIRING(3)  R IY0 - T AY1 - R IH0 NG
+RETLIN  R EH1 T - L IH0 N
+RETO  R IY1 - T UW1
+RETOOK  R IY0 - T UH1 K
+RETOOL  R IY0 - T UW1 L
+RETOOLED  R IY0 - T UW1 L D
+RETOOLING  R IY0 - T UW1 - L IH0 NG
+RETORT  R IY1 - T AO2 R T
+RETORTED  R IY0 - T AO1 R - T IH0 D
+RETORTS  R IH0 - T AO1 R T S
+RETORTS(2)  R IY0 - T AO1 R T S
+RETOUCHING  R IY0 - T AH1 - CH IH0 NG
+RETRACE  R IY0 - T R EY1 S
+RETRACED  R IY0 - T R EY1 S T
+RETRACEMENT  R IY0 - T R EY1 S - M AH0 N T
+RETRACING  R IY0 - T R EY1 - S IH0 NG
+RETRACT  R IY0 - T R AE1 K T
+RETRACTABLE  R IY0 - T R AE1 K - T AH0 - B AH0 L
+RETRACTED  R IY0 - T R AE1 K - T AH0 D
+RETRACTING  R IY0 - T R AE1 K - T IH0 NG
+RETRACTION  R IY0 - T R AE1 K - SH AH0 N
+RETRACTS  R IY0 - T R AE1 K T S
+RETRAIN  R IY0 - T R EY1 N
+RETRAINED  R IY0 - T R EY1 N D
+RETRAINING  R IY0 - T R EY1 - N IH0 NG
+RETRANSMISSION  R IY2 - T R AE0 N Z - M IH1 - SH AH0 N
+RETREAD  R IY0 - T R EH1 D
+RETREADING  R IY0 - T R EH1 - D IH0 NG
+RETREADS  R IY0 - T R EH1 D Z
+RETREAT  R IY0 - T R IY1 T
+RETREATED  R IY0 - T R IY1 - T AH0 D
+RETREATED(2)  R IY0 - T R IY1 - T IH0 D
+RETREATING  R IY0 - T R IY1 - T IH0 NG
+RETREATS  R IY0 - T R IY1 T S
+RETRENCH  R IY0 - T R EH1 N CH
+RETRENCHED  R IY0 - T R EH1 N CH T
+RETRENCHING  R IY0 - T R EH1 N - CH IH0 NG
+RETRENCHMENT  R IY0 - T R EH1 N CH - M AH0 N T
+RETRENCHMENTS  R IY0 - T R EH1 N CH - M AH0 N T S
+RETRIAL  R IY0 - T R AY1 - AH0 L
+RETRIBUTION  R EH2 - T R AH0 - B Y UW1 - SH AH0 N
+RETRIED  R IY0 - T R AY1 D
+RETRIEVAL  R IH0 - T R IY1 - V AH0 L
+RETRIEVAL(2)  R IY0 - T R IY1 - V AH0 L
+RETRIEVE  R IH0 - T R IY1 V
+RETRIEVE(2)  R IY0 - T R IY1 V
+RETRIEVED  R IY0 - T R IY1 V D
+RETRIEVER  R IY0 - T R IY1 - V ER0
+RETRIEVERS  R IY0 - T R IY1 - V ER0 Z
+RETRIEVES  R IH0 - T R IY1 V Z
+RETRIEVES(2)  R IY0 - T R IY1 V Z
+RETRIEVING  R IY0 - T R IY1 - V IH0 NG
+RETRO  R EH1 - T R OW0
+RETROACTIVE  R EH2 - T R OW0 - AE1 K - T IH0 V
+RETROACTIVELY  R EH2 - T R OW0 - AE1 K - T IH0 V - L IY0
+RETROACTIVITY  R EH2 - T R OW0 - AE0 K - T IH1 - V IH0 - T IY0
+RETROFIT  R EH1 - T R OW0 - F IH2 T
+RETROFITS  R EH1 - T R OW0 - F IH2 T S
+RETROFITTED  R EH1 - T R OW2 - F IH2 - T IH0 D
+RETROFITTER  R EH1 - T R OW2 - F IH2 - T ER0
+RETROFITTING  R EH1 - T R OW0 - F IH2 - T IH0 NG
+RETROGRADE  R EH1 - T R AH0 - G R EY2 D
+RETROSPECT  R EH1 - T R AH0 - S P EH2 K T
+RETROSPECTIVE  R EH2 - T R AH0 - S P EH1 K - T IH0 V
+RETROSPECTIVELY  R EH2 - T R OW0 - S P EH1 K - T IH0 V - L IY0
+RETROVIR  R EH1 - T R OW0 - V IH2 R
+RETROVIRUS  R EH2 - T R OW0 - V AY1 - R AH0 S
+RETROVIRUSES  R EH2 - T R OW0 - V AY1 - R AH0 - S IH0 Z
+RETRY  R IY0 - T R AY1
+RETRYING  R IY0 - T R AY1 - IH0 NG
+RETTBERG  R EH1 T - B ER0 G
+RETTER  R EH1 - T ER0
+RETTEW  R EH1 - CH UW0
+RETTIG  R EH1 - T IH0 G
+RETTINGER  R EH1 - T IH0 N - JH ER0
+RETTINGER(2)  R EH1 - T IH0 - NG ER0
+RETTKE  R EH1 T - K IY0
+RETTON  R EH1 - T AH0 N
+RETURN  R IH0 - T ER1 N
+RETURN(2)  R IY0 - T ER1 N
+RETURNABLE  R IY0 - T ER1 - N AH0 - B AH0 L
+RETURNED  R IH0 - T ER1 N D
+RETURNED(2)  R IY0 - T ER1 N D
+RETURNEE  R IH0 - T ER0 - N IY1
+RETURNEES  R IH0 - T ER0 - N IY1 Z
+RETURNING  R IH0 - T ER1 - N IH0 NG
+RETURNING(2)  R IY0 - T ER1 - N IH0 NG
+RETURNS  R IH0 - T ER1 N Z
+RETURNS'  R AH0 - T ER1 N Z
+RETURNS'(2)  R IY0 - T ER1 N Z
+RETURNS(2)  R IY0 - T ER1 N Z
+RETZ  R EH1 T S
+RETZER  R EH1 T - Z ER0
+RETZLAFF  R EH1 T Z - L AH0 F
+RETZLOFF  R EH1 T Z - L AO0 F
+REUBEN  R UW1 - B AH0 N
+REUBER  R OY1 - B ER0
+REUL  R UW1 L
+REULAND  R OY1 - L AH0 N D
+REULE  R UW1 L
+REUM  R IY1 - AH0 M
+REUNIFICATION  R IY0 - UW2 - N AH0 - F AH0 - K EY1 - SH AH0 N
+REUNIFIED  R IY0 - UW1 - N AH0 - F AY2 D
+REUNIFY  R IY0 - UW1 - N AH0 - F AY2
+REUNION  R IY0 - UW1 - N Y AH0 N
+REUNIONS  R IY0 - UW1 - N Y AH0 N Z
+REUNITE  R IY2 - UW0 - N AY1 T
+REUNITED  R IY2 - UW0 - N AY1 - T IH0 D
+REUNITES  R IY2 - UW0 - N AY1 T S
+REUNITING  R IY2 - UW0 - N AY1 - T IH0 NG
+REUSABLE  R IY0 - UW1 - Z AH0 - B AH0 L
+REUSCH  R OY1 SH
+REUSE  R IY0 - Y UW1 S
+REUSE(2)  R IY0 - Y UW1 Z
+REUSED  R IY0 - UW1 Z D
+REUSING  R IY0 - Y UW1 - Z IH0 NG
+REUSS  R UW1 S
+REUSSER  R OY1 - S ER0
+REUST  R UW1 S T
+REUTER  R OY1 - T ER0
+REUTER'S  R OY1 - T ER0 Z
+REUTERS  R OY1 - T ER0 Z
+REUTERS'  R OY1 - T ER0 Z
+REUTERS'S  R OY1 - T ER0 - Z IH0 Z
+REUTERS'S(2)  R OY1 - T ER0 Z
+REUTGERS  R OY1 T - G ER0 Z
+REUTHER  R OY1 - DH ER0
+REUTTER  R OY1 - T ER0
+REUTZEL  R OY1 T - Z AH0 L
+REV  R EH1 V
+REVA  R EY1 - V AH0
+REVAK  R EH1 - V AH0 K
+REVALUATION  R IY0 - V AE1 L - Y UW0 - EY1 - SH AH0 N
+REVALUATIONS  R IY0 - IH0 - V AE2 L - Y UW0 - EY1 - SH AH0 N Z
+REVALUE  R IY0 - V AE1 L - Y UW2
+REVALUED  R IY0 - V AE1 L - Y UW0 D
+REVALUING  R IY0 - V AE1 L - Y UW0 - IH0 NG
+REVAMP  R IY0 - V AE1 M P
+REVAMPED  R IY0 - V AE1 M P T
+REVAMPING  R IY0 - V AE1 M - P IH0 NG
+REVAMPS  R IY0 - V AE1 M P S
+REVARD  R IH0 - V AA1 R D
+REVCO  R EH1 V - K OW0
+REVCO'S  R EH1 V - K OW0 Z
+REVEAL  R IH0 - V IY1 L
+REVEAL(2)  R IY0 - V IY1 L
+REVEALED  R IH0 - V IY1 L D
+REVEALED(2)  R IY0 - V IY1 L D
+REVEALING  R IH0 - V IY1 - L IH0 NG
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+SCHOENIG  SH OW1 - N IH0 G
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+SCHWEMM  SH W EH1 M
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+SELECTED  S AH0 - L EH1 K - T AH0 D
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+SELECTING  S AH0 - L EH1 K - T IH0 NG
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+SELECTIONS  S AH0 - L EH1 K - SH AH0 N Z
+SELECTIVE  S AH0 - L EH1 K - T IH0 V
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+SELECTIVITY  S IH0 - L EH2 K - T IH1 - V AH0 - T IY0
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+SELF-AGGRANDIZING  S EH1 L - F AH0 - G R AE1 N - D AY2 - Z IH0 NG
+SELF-CONFIDENCE  S EH1 L F - K AA1 N - F AH0 - D AH0 N S
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+SEMICYLINDRICAL  S EH2 - M IY0 - S AH0 - L IH1 N - D R IH0 - K AH0 L
+SEMICYLINDRICAL(2)  S EH2 - M IH0 - S AH0 - L IH1 N - D R IH0 - K AH0 L
+SEMICYLINDRICAL(3)  S EH2 - M AY0 - S AH0 - L IH1 N - D R IH0 - K AH0 L
+SEMIDRY  S EH2 - M IY0 - D R AY1
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+SEMIFINAL(2)  S EH2 - M IH0 - F AY1 - N AH0 L
+SEMIFINAL(3)  S EH2 - M AY0 - F AY1 - N AH0 L
+SEMIFINALIST  S EH2 - M IY0 - F AY1 - N AH0 L - IH0 S T
+SEMIFINALIST(2)  S EH2 - M IH0 - F AY1 - N AH0 - L IH0 S T
+SEMIFINALIST(3)  S EH2 - M AY0 - F AY1 - N AH0 - L IH0 S T
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+SEMIFINALISTS(2)  S EH2 - M IY0 - F AY1 - N AH0 L - IH0 S S
+SEMIFINALISTS(3)  S EH2 - M IY0 - F AY1 - N AH0 L - IH0 S
+SEMIFINALISTS(4)  S EH2 - M AY0 - F AY1 - N AH0 - L IH0 S T S
+SEMIFINALISTS(5)  S EH2 - M AY0 - F AY1 - N AH0 L - IH0 S S
+SEMIFINALISTS(6)  S EH2 - M AY0 - F AY1 - N AH0 L - IH0 S
+SEMIFINALISTS(7)  S EH2 - M IH0 - F AY1 - N AH0 - L IH0 S T S
+SEMIFINALISTS(8)  S EH2 - M IH0 - F AY1 - N AH0 L - IH0 S S
+SEMIFINALISTS(9)  S EH2 - M IH0 - F AY1 - N AH0 L - IH0 S
+SEMIFINALS  S EH2 - M IY0 - F AY1 - N AH0 L Z
+SEMIFINALS(2)  S EH2 - M IH0 - F AY1 - N AH0 L Z
+SEMIFINALS(3)  S EH2 - M AY0 - F AY1 - N AH0 L Z
+SEMIFINISH  S EH2 - M IY0 - F IH1 - N IH0 SH
+SEMIFINISH(2)  S EH2 - M AY0 - F IH1 - N IH0 SH
+SEMIFINISH(3)  S EH2 - M IH0 - F IH1 - N IH0 SH
+SEMIFINISHED  S EH2 - M IY0 - F IH1 - N IH0 SH T
+SEMIFINISHED(2)  S EH2 - M AY0 - F IH1 - N IH0 SH T
+SEMIFINISHED(3)  S EH2 - M IH0 - F IH1 - N IH0 SH T
+SEMIGLOSS  S EH2 - M IY0 - G L AA1 S
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+SHARM  SH AA1 R M
+SHARMA  SH AA1 R - M AH0
+SHARMA'S  SH AA1 R - M AH0 Z
+SHARMAN  SH AA1 R - M AH0 N
+SHARON  SH AE1 - R AH0 N
+SHARON'S  SH EH1 - R AH0 N Z
+SHARON'S(2)  SH AE1 - R AH0 N Z
+SHARON'S(3)  SH AH0 - R OW1 N Z
+SHARON(2)  SH EH1 - R AH0 N
+SHARON(3)  SH AH0 - R OW1 N
+SHARP  SH AA1 R P
+SHARP'S  SH AA1 R P S
+SHARP-SIGN  SH AA1 R P - S AY1 N
+SHARPE  SH AA1 R P
+SHARPEN  SH AA1 R - P AH0 N
+SHARPENED  SH AA1 R - P AH0 N D
+SHARPENING  SH AA1 R - P AH0 - N IH0 NG
+SHARPENING(2)  SH AA1 R P - N IH0 NG
+SHARPENS  SH AA1 R - P AH0 N Z
+SHARPER  SH AA1 R - P ER0
+SHARPEST  SH AA1 R - P AH0 S T
+SHARPEVILLE  SH AA1 R - P AH0 - V IH2 L
+SHARPIE  SH AA1 R - P IY0
+SHARPLES  SH AA1 R - P AH0 L Z
+SHARPLESS  SH AA1 R P - L AH0 S
+SHARPLEY  SH AA1 R P - L IY0
+SHARPLY  SH AA1 R P - L IY0
+SHARPNACK  SH AA1 R P - N AH0 K
+SHARPNESS  SH AA1 R P - N AH0 S
+SHARPS  SH AA1 R P S
+SHARPSHOOTER  SH AA1 R P - SH UW0 - T ER0
+SHARPSHOOTER  SH AA1 R P - SH UW2 - T ER0
+SHARPSHOOTERS  SH AA1 R P - SH UW0 - T ER0 Z
+SHARPTON  SH AA1 R P - T AH0 N
+SHARPY  SH AA1 R - P IY0
+SHARPY'S  SH AA1 R - P IY0 Z
+SHARRAR  SH ER0 - AA1 R
+SHARRER  SH AA1 - R ER0
+SHARRETT  SH AE1 - R IH0 T
+SHARROCK  SH AE1 - R AH0 K
+SHARRON  SH AE1 - R AH0 N
+SHARROW  SH AE1 - R OW0
+SHARRY  SH AA1 - R IY0
+SHARTZER  SH AA1 R T - Z ER0
+SHARUM  SH ER0 - AH1 M
+SHARYL  SH EH1 - R AH0 L
+SHAS  SH AH1 S
+SHASHLIK  SH AE1 SH - L IH0 K
+SHASHOUA  SH AH0 - SH UW1 - AH0
+SHASTA  SH AE1 - S T AH0
+SHASTA'S  SH AE1 - S T AH0 Z
+SHASTEEN  SH AH0 - S T IY1 N
+SHATKIN  SH AE1 T - K IH0 N
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+SHATNER  SH AE1 T - N ER0
+SHATROV  SH AE1 - T R AA0 V
+SHATT  SH AE1 T
+SHATTER  SH AE1 - T ER0
+SHATTERED  SH AE1 - T ER0 D
+SHATTERING  SH AE1 - T ER0 - IH0 NG
+SHATTERPROOF  SH AE1 - T ER0 - P R UW2 F
+SHATTERS  SH AE1 - T ER0 Z
+SHATTUCK  SH AE1 - T AH0 K
+SHATZ  SH AE1 T S
+SHATZ(2)  SH AA1 T S
+SHATZER  SH EY1 T - Z ER0
+SHAUB  SH AO1 B
+SHAUGER  SH AW1 - G ER0
+SHAUGHNESSY  SH AO1 - N IH0 - S IY0
+SHAUL  SH AO1 L
+SHAULIS  SH AW1 - L IH0 S
+SHAULL  SH AO1 L
+SHAUN  SH AO1 N
+SHAUNA  SH AO1 - N AH0
+SHAVE  SH EY1 V
+SHAVED  SH EY1 V D
+SHAVELSON  SH EY1 - V AH0 L - S IH0 N
+SHAVELSON(2)  SH AE1 - V AH0 L - S IH0 N
+SHAVEN  SH EY1 - V AH0 N
+SHAVER  SH EY1 - V ER0
+SHAVERS  SH EY1 - V ER0 Z
+SHAVES  SH EY1 V Z
+SHAVING  SH EY1 - V IH0 NG
+SHAVINGS  SH EY1 - V IH0 NG Z
+SHAVORD  SH AH0 - V AO1 R D
+SHAW  SH AO1
+SHAW'S  SH AO1 Z
+SHAWCROSS  SH AO1 - K R AO2 S
+SHAWGO  SH AO1 - G OW2
+SHAWHAN  SH AE1 - W AH0 N
+SHAWINIGAN  SH AH0 - W IH1 - N IH0 - G AH0 N
+SHAWL  SH AO1 L
+SHAWLER  SH AO1 - L ER0
+SHAWLEY  SH AO1 - L IY0
+SHAWLS  SH AO1 L Z
+SHAWMUT  SH AO1 - M AH0 T
+SHAWMUT'S  SH AO1 - M AH0 T S
+SHAWN  SH AO1 N
+SHAWN'S  SH AO1 N Z
+SHAWNA  SH AO1 - N AH0
+SHAWNEE  SH AO1 - N IY0
+SHAWNUT  SH AO1 - N AH0 T
+SHAWSHANK  SH AO1 - SH AE2 N K
+SHAWVER  SH AO1 - V ER0
+SHAY  SH EY1
+SHAYKIN  SH EY1 - K IH0 N
+SHAYKIN'S  SH EY1 - K IH0 N Z
+SHAYNE  SH EY1 N
+SHAYS  SH EY1 Z
+SHCHARANSKY  SH ER0 - AE1 N S - K IY0
+SHCHEDRIN  SH EH1 D - R IH0 N
+SHCHERBITSKY  SH ER0 - B IH1 T S - K IY0
+SHE  SH IY1
+SHE'D  SH IY1 D
+SHE'LL  SH IY1 L
+SHE'S  SH IY1 Z
+SHEA  SH EY1
+SHEA'S  SH EY1 Z
+SHEAD  S HH EH1 D
+SHEAF  SH IY1 F
+SHEAFFER  SH IY1 - F ER0
+SHEAHAN  SH IY1 - AH0 N
+SHEALEY  SH IY1 - L IY0
+SHEALY  SH IY1 - L IY0
+SHEAN  SH IY1 N
+SHEAR  SH IH1 R
+SHEAR'S  SH IY1 R Z
+SHEARD  SH IH1 R D
+SHEARED  SH IH1 R D
+SHEARER  SH IH1 - R ER0
+SHEARIN  SH IH1 - R IH0 N
+SHEARING  SH IH1 - R IH0 NG
+SHEARMAN  SH IY1 R - M AH0 N
+SHEARN  SH IH1 R N
+SHEARON  SH IH1 - R AH0 N
+SHEAROUSE  SH IH1 - R AW0 S
+SHEARS  SH IY1 R Z
+SHEARSON  SH IH1 R - S AH0 N
+SHEARSON'S  SH IH1 R - S AH0 N Z
+SHEATH  SH IY1 TH
+SHEATHBILL  SH IY1 TH - B IH0 L
+SHEATHBILLS  SH IY1 TH - B IH0 L Z
+SHEATHE  SH IY1 DH
+SHEATHED  SH IY1 DH D
+SHEATHING  SH IY1 - DH IH0 NG
+SHEATS  SH IY1 T S
+SHEAVES  SH IY1 V Z
+SHEBA  SH IY1 - B AH0
+SHEBOYGAN  SH AH0 - B OY1 - G AH0 N
+SHECK  SH EH1 K
+SHECK'S  SH EH1 K S
+SHECKLER  SH EH1 K - L ER0
+SHED  SH EH1 D
+SHEDD  SH EH1 D
+SHEDDEN  SH EH1 - D AH0 N
+SHEDDING  SH EH1 - D IH0 NG
+SHEDLOCK  SH EH1 D - L AA2 K
+SHEDRICK  SH EH1 D - R IH0 K
+SHEDS  SH EH1 D Z
+SHEEDER  SH IY1 - D ER0
+SHEEDY  SH IY1 - D IY0
+SHEEHAN  SH IY1 - AH0 N
+SHEEHAN'S  SH IY1 - AH0 N Z
+SHEEHAN'S(2)  SH IY1 - HH AH0 N Z
+SHEEHAN(2)  SH IY1 - HH AH0 N
+SHEEHY  SH IY1 - HH IY0
+SHEEHY(2)  SH IY1 - IY0
+SHEEK  SH IY1 K
+SHEEKS  SH IY1 K S
+SHEELA  SH IY1 - L AH0
+SHEELAH  SH IY1 - L AH0
+SHEELEN  SH IY1 - L AH0 N
+SHEELER  SH IY1 - L ER0
+SHEELEY  SH IY1 - L IY0
+SHEELY  SH IY1 - L IY0
+SHEEN  SH IY1 N
+SHEENA  SH IY1 - N AH0
+SHEEP  SH IY1 P
+SHEEP'S  SH IY1 P S
+SHEEPISH  SH IY1 - P IH0 SH
+SHEEPISHLY  SH IY1 - P IH0 SH - L IY0
+SHEEPS  SH IY1 P S
+SHEEPSKIN  SH IY1 P - S K IH2 N
+SHEER  SH IH1 R
+SHEERAN  SH IH1 - R AH0 N
+SHEERER  SH IY1 - R ER0
+SHEERIN  SH IH1 - R IH0 N
+SHEESH  SH IY1 SH
+SHEESLEY  SH IY1 Z - L IY0
+SHEET  SH IY1 T
+SHEETING  SH IY1 - T IH0 NG
+SHEETS  SH IY1 T S
+SHEETZ  SH IY1 T S
+SHEFF  SH EH1 F
+SHEFFER  SH EH1 - F ER0
+SHEFFEY  SH EH1 - F IY0
+SHEFFIELD  SH EH1 - F IY0 L D
+SHEFFLER  SH EH1 F - L ER0
+SHEFTEL  SH EH2 F - T EH1 L
+SHEFTEL'S  SH EH2 F - T EH1 L Z
+SHEGOG  SH EH1 - G AA0 G
+SHEHAN  SH EH1 - HH AH0 N
+SHEHANE  SH EH1 - HH AH0 N
+SHEHORN  SH EH1 - HH ER0 N
+SHEIK  SH IY1 K
+SHEIK'S  SH IY1 K S
+SHEIKDOM  SH IY1 K - D AH0 M
+SHEIKDOMS  SH IY1 K - D AH0 M Z
+SHEIKH  SH IY1 K
+SHEIKS  SH IY1 K S
+SHEIL  SH AY1 L
+SHEILA  SH IY1 - L AH0
+SHEILA'S  SH IY1 - L AH0 Z
+SHEILAH  SH IY1 - L AH0
+SHEILDS  SH AY1 L D Z
+SHEILS  SH AY1 L Z
+SHEIN  SH AY1 N
+SHEINBERG  SH AY1 N - B ER0 G
+SHEK  SH EH1 K
+SHEK'S  SH EH1 K S
+SHEKEL  SH EH1 - K AH0 L
+SHEKELS  SH EH1 - K AH0 L Z
+SHELBURNE  SH EH1 L - B ER0 N
+SHELBY  SH EH1 L - B IY0
+SHELBY'S  SH EH1 L - B IY0 Z
+SHELBYVILLE  SH EH1 L - B IY0 - V IH2 L
+SHELDAHL  SH EH1 L - D AA2 L
+SHELDEN  SH EH1 L - D AH0 N
+SHELDON  SH EH1 L - D AH0 N
+SHELEV  SH EH1 - L IH0 V
+SHELEY  SH IY1 - L IY0
+SHELF  SH EH1 L F
+SHELHAMER  SH EH1 L - HH AH0 - M ER0
+SHELINE  SH EH1 - L AY0 N
+SHELL  SH EH1 L
+SHELL'S  SH EH1 L Z
+SHELLABARGER  SH EH1 - L AH0 - B AA2 R - G ER0
+SHELLED  SH EH1 L D
+SHELLENBARGER  SH EH1 - L IH0 N - B AA0 R - G ER0
+SHELLENBERGER  SH EH1 - L AH0 N - B ER0 - G ER0
+SHELLER  SH EH1 - L ER0
+SHELLEY  SH EH1 - L IY0
+SHELLEY'S  SH EH1 - L IY0 Z
+SHELLFISH  SH EH1 L - F IH2 SH
+SHELLHAMMER  SH EH1 L - HH AE2 - M ER0
+SHELLHORN  SH EH1 L - HH ER0 N
+SHELLING  SH EH1 - L IH0 NG
+SHELLINGS  SH EH1 - L IH0 NG Z
+SHELLITO  SH EY0 - L IY1 - T OW0
+SHELLMAN  SH EH1 L - M AH0 N
+SHELLS  SH EH1 L Z
+SHELLSHOCK  SH EH1 L - SH AA2 K
+SHELLSHOCKED  SH EH1 L - SH AA2 K T
+SHELLY  SH EH1 - L IY0
+SHELMAN  SH EH1 L - M AH0 N
+SHELNUTT  SH EH1 L - N AH0 T
+SHELOR  SH EH1 - L ER0
+SHELP  SH EH1 L P
+SHELSTAD  SH EH1 L - S T AH0 D
+SHELTER  SH EH1 L - T ER0
+SHELTERED  SH EH1 L - T ER0 D
+SHELTERING  SH EH1 L - T ER0 - IH0 NG
+SHELTERS  SH EH1 L - T ER0 Z
+SHELTON  SH EH1 L - T AH0 N
+SHELTON'S  SH EH1 L - T AH0 N Z
+SHELVE  SH EH1 L V
+SHELVED  SH EH1 L V D
+SHELVES  SH EH1 L V Z
+SHELVING  SH EH1 L - V IH0 NG
+SHEMANSKI  SH IH0 - M AE1 N - S K IY0
+SHEMONA  SH IH0 - M OW1 - N AH0
+SHEMWELL  SH EH1 M - W EH2 L
+SHEN  SH EH1 N
+SHENA  SH IY1 - N AH0
+SHENANDOAH  SH EH2 - N AH0 N - D OW1 - AH0
+SHENANIGAN  SH AH0 - N AE1 - N IH0 - G AH0 N
+SHENANIGANS  SH AH0 - N AE1 - N IH0 - G AH0 N Z
+SHENBERGER  SH EH1 N - B ER0 - G ER0
+SHENEFIELD  SH EH1 - N IH0 - F IY2 L D
+SHENEMAN  SH IY1 N - M AH0 N
+SHENG  SH EH1 NG
+SHENG-FEN  SH EH1 NG - F EH1 N
+SHENICE  SH AH0 - N IY1 S
+SHENK  SH EH1 NG K
+SHENKER  SH EH1 NG - K ER0
+SHENKMAN  SH EH1 NG K - M AH0 N
+SHENTON  SH EH1 N - T AH0 N
+SHENYANG  SH EH0 - N Y AE1 NG
+SHENZHEN  SH EH1 N - ZH EH2 N
+SHEP  SH EH1 P
+SHEPARD  SH EH1 - P ER0 D
+SHEPARD'S  SH EH1 - P ER0 D Z
+SHEPARDSON  SH EH1 - P AA0 R D - S AH0 N
+SHEPERD  SH EH1 - P ER0 D
+SHEPHARD  SH EH1 - F ER0 D
+SHEPHEARD  SH EH1 - F ER0 D
+SHEPHERD  SH EH1 - P ER0 D
+SHEPHERD'S  SH EH1 - P ER0 D Z
+SHEPHERDED  SH EH1 - P ER0 - D IH0 D
+SHEPHERDING  SH EH1 - P ER0 - D IH0 NG
+SHEPHERDS  SH EH1 - P ER0 D Z
+SHEPLER  SH EH1 P - L ER0
+SHEPLEY  SH EH1 P - L IY0
+SHEPP  SH EH1 P
+SHEPPARD  SH EH1 - P ER0 D
+SHEPPARDS  SH EH1 - P ER0 D Z
+SHEPPER  SH EH1 - P ER0
+SHEPPERD  SH EH1 - P ER0 D
+SHEPPERSON  SH EH1 - P ER0 - S AH0 N
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+SHER  SH ER1
+SHERAK  SH EH1 - R AE0 K
+SHERARD  SH EH1 - R ER0 D
+SHERATON  SH EH1 - R AH0 - T AH0 N
+SHERATON'S  SH EH1 - R AH0 - T AH0 N Z
+SHERBERT  SH ER1 - B ER0 T
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+SHERBORNE  SH ER1 - B ER0 N
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+SHERBOURNE  SH ER0 - B UH1 R N
+SHERBURN  SH ER1 - B ER0 N
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+SHERE  SH IH1 R
+SHEREE  SH ER0 - IY1
+SHERER  SH IH1 - R ER0
+SHERFEY  SH ER1 - F IY0
+SHERFIELD  SH ER1 - F IY0 L D
+SHERI  SH EH1 - R IY0
+SHERICK  SH EH1 - R IH0 K
+SHERIDAN  SH EH1 - R IH0 - D AH0 N
+SHERIFF  SH EH1 - R AH0 F
+SHERIFF'S  SH EH1 - R AH0 F S
+SHERIFF'S(2)  SH EH1 - R IH0 F S
+SHERIFF(2)  SH EH1 - R IH0 F
+SHERIFFS  SH EH1 - R AH0 F S
+SHERIN  SH EH1 - R IH0 N
+SHERK  SH ER1 K
+SHERLEY  SH ER1 - L IY0
+SHERLIN  SH ER1 - L IH0 N
+SHERLOCK  SH ER1 - L AA2 K
+SHERLOCK'S  SH ER1 - L AA2 K S
+SHERLUND  SH ER1 - L AH0 N D
+SHERMAN  SH ER1 - M AH0 N
+SHERMAN'S  SH ER1 - M AH0 N Z
+SHERMER  SH ER1 - M ER0
+SHERNOFF  SH ER1 - N AO2 F
+SHEROD  SH EH1 - R AH0 D
+SHERR  SH EH1 R
+SHERR'S  SH EH1 R Z
+SHERRARD  SH EH1 - R ER0 D
+SHERRELL  SH EH1 - R AH0 L
+SHERRER  SH EH1 - R ER0
+SHERRGOLD  SH EH1 R - G OW2 L D
+SHERRI  SH EH1 - R IY0
+SHERRI'S  SH EH1 - R IY0 Z
+SHERRICK  SH EH1 - R IH0 K
+SHERRIE  SH EH1 - R IY0
+SHERRIFF  SH EH1 - R IH0 F
+SHERRILL  SH EH1 - R IH0 L
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+SHERRIT  SH EH1 - R IH0 T
+SHERRITT  SH EH1 - R IH0 T
+SHERROD  SH EH1 - R AH0 D
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+SHERROW  SH EH1 - R OW0
+SHERRY  SH EH1 - R IY0
+SHERRY'S  SH EH1 - R IY0 Z
+SHERTZER  SH ER1 T - Z ER0
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+SHERWOOD'S  SH ER1 - W UH2 D Z
+SHERYL  SH EH1 - R AH0 L
+SHESHUNOFF  SH EH1 - SH UW0 - N AO0 F
+SHETH  SH EH1 TH
+SHETLAND  SH EH1 T - L AH0 N D
+SHETLER  SH EH1 T - L ER0
+SHETLEY  SH EH1 T - L IY0
+SHETTER  SH EH1 - T ER0
+SHETTERLY  SH EH1 - T ER0 - L IY0
+SHEVARDNADZE  SH EH2 - V ER0 D - N AA1 D - Z IY0
+SHEVARDNADZE'S  SH EH2 - V ER0 D - N AA1 D - Z IY0 Z
+SHEVLIN  SH EH1 V - L IH0 N
+SHEVTL  SH EH1 - V IH0 L
+SHEVTL(2)  SH IY1 - V IH0 L
+SHEW  SH UW1
+SHEWARD  SH UW1 - ER0 D
+SHEWCHUK  SH UW1 - CH AH0 K
+SHEWELL  SH EH1 - W EH0 L
+SHEWMAKE  SH UW1 - M EY2 K
+SHEWMAKER  SH UW1 - M EY0 - K ER0
+SHH  SH
+SHI  SH IY1
+SHIA  SH IY1 - AH0
+SHIAS  SH IY1 - AH0 Z
+SHIBANNA  SH IH0 - B AE1 - N AH0
+SHIBATA  SH IY0 - B AA1 - T AH0
+SHIBBOLETH  SH IH1 - B AH0 - L EH2 TH
+SHIBLEY  SH IH1 - B L IY0
+SHICK  SH IH1 K
+SHICOFF  SH IH1 K - AO2 F
+SHIDELER  SH IH1 - D AH0 L - ER0
+SHIDLER  SH AY1 - D AH0 - L ER0
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+SHIED  SH AY1 D
+SHIEH  SH IY1
+SHIEL  SH IY1 L
+SHIELA  SH AY1 - L AH0
+SHIELD  SH IY1 L D
+SHIELD'S  SH IY1 L D Z
+SHIELDED  SH IY1 L - D IH0 D
+SHIELDING  SH IY1 L - D IH0 NG
+SHIELDS  SH IY1 L D Z
+SHIELS  SH IY1 L Z
+SHIER  SH AY1 - ER0
+SHIES  SH AY1 Z
+SHIFF  SH IH1 F
+SHIFFER  SH IH1 - F ER0
+SHIFFLER  SH IH1 F - L ER0
+SHIFFLET  SH IH1 F - L IH0 T
+SHIFFLETT  SH IH1 F - L IH0 T
+SHIFFMAN  SH IH1 F - M AH0 N
+SHIFLET  SH IH1 F - L IH0 T
+SHIFLETT  SH IH1 F - L IH0 T
+SHIFRIN  SH IH1 - F R IH0 N
+SHIFT  SH IH1 F T
+SHIFTED  SH IH1 F - T AH0 D
+SHIFTED(2)  SH IH1 F - T IH0 D
+SHIFTER  SH IH1 F - T ER0
+SHIFTING  SH IH1 F - T IH0 NG
+SHIFTLESS  SH IH1 F T - L IH0 S
+SHIFTS  SH IH1 F T S
+SHIFTY  SH IH1 F - T IY0
+SHIGEKI  SH IH0 - G EY1 - K IY0
+SHIGEKUNI  SH IY2 - G IH0 - K UW1 - N IY0
+SHIGEO  SH IH0 - G EY1 - OW0
+SHIGERU  SH IH0 - G EY1 - R UW0
+SHIGLEY  SH IH1 G - L IY0
+SHIH  SH IY1
+SHIHAN  SH IY1 - HH AA2 N
+SHIINA  SH IY1 - N AH0
+SHIITE  SH IY1 - AY2 T
+SHIITES  SH IY1 - AY2 T S
+SHILEY  SH IH1 - L IY0
+SHILL  SH IH1 L
+SHILLER  SH IH1 - L ER0
+SHILLING  SH IH1 - L IH0 NG
+SHILLINGBURG  SH IH1 - L IH0 NG - B ER0 G
+SHILLINGER  SH IH1 - L IH0 N - JH ER0
+SHILLINGLAW  SH IH1 - L IH0 NG - L AO2
+SHILLINGS  SH IH1 - L IH0 NG Z
+SHILLINGTON  SH IH1 - L IH0 NG - T AH0 N
+SHILOH  SH AY1 - L OW0
+SHILTS  SH IH1 L T S
+SHIM  SH IH1 M
+SHIMA  SH IY1 - M AH0
+SHIMABUKURO  SH IY0 - M AA0 - B UW0 - K UH1 - R OW0
+SHIMADA  SH IY0 - M AA1 - D AH0
+SHIMBUN  SH IH1 M - B AH2 N
+SHIMBUN(2)  SH IH1 M - B UW2 N
+SHIMEK  SH IH1 - M IH0 K
+SHIMEL  SH IH1 - M AH0 L
+SHIMER  SH AY1 - M ER0
+SHIMIZU  SH IH0 - M IY1 - Z UW0
+SHIMKO  SH IH1 M - K OW0
+SHIMKUS  SH IH1 M - K AH0 S
+SHIMMEL  SH IH1 - M AH0 L
+SHIMMER  SH IH1 - M ER0
+SHIMMERED  SH IH1 - M ER0 D
+SHIMMERING  SH IH1 - M ER0 - IH0 NG
+SHIMMERLIK  SH IH1 - M ER0 - L IH0 K
+SHIMMERS  SH IH1 - M ER0 Z
+SHIMMIN  SH IH1 - M IH0 N
+SHIMMY  SH IH1 - M IY0
+SHIMODA  SH IH0 - M OW1 - D AH0
+SHIMOGA  SH IH0 - M OW1 - G AH0
+SHIMOKAWA  SH IH2 - M OW0 - K AA1 - W AH0
+SHIMON  SH IY1 - M OW0 N
+SHIMON(2)  SH IY1 - M AH0 N
+SHIMONE  SH IY1 - M OW0 N
+SHIMP  SH IH1 M P
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+SHIRKED  SH ER1 K T
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+SHISLER  SH IH1 - S AH0 - L ER0
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+SHLOBIDAN  SH L OW0 - B IH1 - D AH0 N
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+SHOBANA  SH OW2 - B AA1 - N AH0
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+SHOBANNA  SH OW2 - B AA1 - N AH0
+SHOBANNA'S  SH OW2 - B AA1 - N AH0 Z
+SHOBE  SH OW1 B
+SHOBER  SH OW1 - B ER0
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+SHOCK  SH AA1 K
+SHOCKED  SH AA1 K T
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+SHOCKLEY  SH AA1 K - L IY0
+SHOCKS  SH AA1 K S
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+SHOFF  SH AO1 F
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+SHOOTIN'  SH UW1 - T IH0 N
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+SHOOTOUT  SH UW1 T - AW0 T
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+SHOPKO  SH AA1 P - K OW0
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+SHOPPERS'  SH AA1 - P ER0 Z
+SHOPPES  SH AA1 P S
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+SHOPPING'S  SH AA1 - P IH0 NG Z
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+SHORN  SH AO1 R N
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+SHORTAGE  SH AO1 R - T AH0 JH
+SHORTAGE(2)  SH AO1 R - T IH0 JH
+SHORTAGES  SH AO1 R - T AH0 - JH AH0 Z
+SHORTAGES(2)  SH AO1 R - T IH0 - JH IH0 Z
+SHORTALL  SH AO1 R - T AH0 L
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+SHORTCOMING  SH AO1 R T - K AH2 - M IH0 NG
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+SHORTFALL  SH AO1 R T - F AO2 L
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+SHUCK  SH AH1 K
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+SPRING'S  S P R IH1 NG Z
+SPRING(2)  S P ER0 - IH1 NG
+SPRINGBOARD  S P R IH1 NG - B AO2 R D
+SPRINGBORN  S P R IH1 NG - G B ER0 N
+SPRINGDALE  S P R IH1 NG - D EY2 L
+SPRINGER  S P R IH1 - NG ER0
+SPRINGERVILLE  S P R IH1 - NG ER0 - V IH2 L
+SPRINGFIELD  S P R IH1 NG - F IY2 L D
+SPRINGFIELD'S  S P R IH1 NG - F IY2 L D Z
+SPRINGING  S P R IH1 - NG IH0 NG
+SPRINGMAN  S P R IH1 NG - M AH0 N
+SPRINGS  S P R IH1 NG Z
+SPRINGS(2)  S P ER0 - IH1 NG Z
+SPRINGSTEAD  S P R IH1 NG - S T EH2 D
+SPRINGSTEEN  S P R IH1 NG - S T IY2 N
+SPRINGSTEEN'S  S P R IH1 NG - S T IY2 N Z
+SPRINGSTON  S P R IH1 NG - S T AH0 N
+SPRINGTIME  S P R IH1 NG - T AY2 M
+SPRINKEL  S P R IH1 NG - K AH0 L
+SPRINKLE  S P R IH1 NG - K AH0 L
+SPRINKLED  S P R IH1 NG - K AH0 L D
+SPRINKLER  S P R IH1 NG - K L ER0
+SPRINKLER(2)  S P R IH1 NG - K AH0 - L ER0
+SPRINKLERS  S P R IH1 NG - K L ER0 Z
+SPRINKLERS(2)  S P R IH1 NG - K AH0 - L ER0 Z
+SPRINKLES  S P R IH1 NG - K AH0 L Z
+SPRINKLING  S P R IH1 NG - K L IH0 NG
+SPRINKLING(2)  S P R IH1 NG - K AH0 L - IH0 NG
+SPRINT  S P R IH1 N T
+SPRINT'S  S P R IH1 N T S
+SPRINTED  S P R IH1 N - T IH0 D
+SPRINTER  S P R IH1 N - T ER0
+SPRINTERS  S P R IH1 N - T ER0 Z
+SPRINTING  S P R IH1 N - T IH0 NG
+SPRINTS  S P R IH1 N T S
+SPRITE  S P R AY1 T
+SPRIZZO  S P R IH1 - Z OW0
+SPROAT  S P R OW1 T
+SPROCK  S P R AA1 K
+SPROCKET  S P R AA1 - K AH0 T
+SPROGUS  S P R OW1 - G AH0 S
+SPROGUS'S  S P R OW1 - G AH0 - S IH0 Z
+SPROLES  S P R OW1 L Z
+SPRONG  S P R AO1 NG
+SPROTT  S P R AA1 T
+SPROUL  S P R AW1 L
+SPROULE  S P R UW1 L
+SPROULL  S P R UW1 L
+SPROULS  S P R AW1 L Z
+SPROUSE  S P R AW1 S
+SPROUT  S P R AW1 T
+SPROUTED  S P R AW1 - T AH0 D
+SPROUTED(2)  S P R AW1 - T IH0 D
+SPROUTING  S P R AW1 - T IH0 NG
+SPROUTS  S P R AW1 T S
+SPROW  S P R AW1
+SPROWL  S P R AW1 L
+SPROWLE  S P R AW1 - AH0 L
+SPROWLS  S P R AW1 L Z
+SPRUCE  S P R UW1 S
+SPRUCED  S P R UW1 S T
+SPRUCING  S P R UW1 - S IH0 NG
+SPRUELL  S P R UW1 L
+SPRUIELL  S P R UW1 L
+SPRUILL  S P R UW1 L
+SPRUNG  S P R AH1 NG
+SPRUNGER  S P R AH1 - NG ER0
+SPRY  S P R AY1
+SPUD  S P AH1 D
+SPUD'S  S P AH1 D Z
+SPUDS  S P AH1 D Z
+SPUHLER  S P UW1 - L ER0
+SPUN  S P AH1 N
+SPUNK  S P AH1 NG K
+SPUNKY  S P AH1 NG - K IY0
+SPUR  S P ER1
+SPURDLE  S P ER1 - D AH0 L
+SPURGE  S P ER1 JH
+SPURGEON  S P ER1 - JH IH0 N
+SPURGIN  S P ER1 - JH IH0 N
+SPURIOUS  S P Y UH1 - R IY0 - AH0 S
+SPURIOUS(2)  S P ER1 - IY0 - AH0 S
+SPURLIN  S P ER1 - L IH0 N
+SPURLING  S P ER1 - L IH0 NG
+SPURLOCK  S P ER1 - L AA2 K
+SPURN  S P ER1 N
+SPURNED  S P ER1 N D
+SPURNING  S P ER1 - N IH0 NG
+SPURNS  S P ER1 N Z
+SPURR  S P ER1
+SPURRED  S P ER1 D
+SPURRIER  S P ER1 - IY0 - ER0
+SPURRING  S P ER1 - IH0 NG
+SPURS  S P ER1 Z
+SPURT  S P ER1 T
+SPURTED  S P ER1 - T IH0 D
+SPURTING  S P ER1 - T IH0 NG
+SPURTS  S P ER1 T S
+SPUTNIK  S P AH1 T - N IH0 K
+SPUTNIKS  S P AH1 T - N IH0 K S
+SPUTTER  S P AH1 - T ER0
+SPUTTERED  S P AH1 - T ER0 D
+SPUTTERING  S P AH1 - T ER0 - IH0 NG
+SPUTTERS  S P AH1 - T ER0 Z
+SPY  S P AY1
+SPYCATCHER  S P AY1 - K AE2 - CH ER0
+SPYCHALSKI  S P IH0 - HH AA1 L S - K IY0
+SPYGLASS  S P AY1 - G L AE2 S
+SPYING  S P AY1 - IH0 NG
+SPYKER  S P AY1 - K ER0
+SQUABBLE  S K W AA1 - B AH0 L
+SQUABBLED  S K W AA1 - B AH0 L D
+SQUABBLES  S K W AA1 - B AH0 L Z
+SQUABBLING  S K W AA1 - B AH0 L - IH0 NG
+SQUABBLING(2)  S K W AA1 - B L IH0 NG
+SQUAD  S K W AA1 D
+SQUAD'S  S K W AA1 D Z
+SQUADRON  S K W AA1 - D R AH0 N
+SQUADRONS  S K W AA1 - D R AH0 N Z
+SQUADS  S K W AA1 D Z
+SQUALID  S K W AA1 - L AH0 D
+SQUALL  S K W AO1 L
+SQUALLS  S K W AO1 L Z
+SQUALOR  S K W AA1 - L ER0
+SQUANDER  S K W AA1 N - D ER0
+SQUANDERED  S K W AA1 N - D ER0 D
+SQUANDERING  S K W AA1 N - D ER0 - IH0 NG
+SQUARE  S K W EH1 R
+SQUARED  S K W EH1 R D
+SQUARELY  S K W EH1 R - L IY0
+SQUARES  S K W EH1 R Z
+SQUARING  S K W EH1 - R IH0 NG
+SQUASH  S K W AA1 SH
+SQUASHED  S K W AA1 SH T
+SQUASHING  S K W AA1 - SH IH0 NG
+SQUASHY  S K W AA1 - SH IY0
+SQUAT  S K W AA1 T
+SQUATS  S K W AA1 T S
+SQUATTER  S K W AA1 - T ER0
+SQUATTERS  S K W AA1 - T ER0 Z
+SQUATTING  S K W AA1 - T IH0 NG
+SQUAWK  S K W AO1 K
+SQUAWKING  S K W AO1 - K IH0 NG
+SQUAWKS  S K W AO1 K S
+SQUEAK  S K W IY1 K
+SQUEAKED  S K W IY1 K T
+SQUEAKER  S K W IY1 - K ER0
+SQUEAKERS  S K W IY1 - K ER0 Z
+SQUEAKING  S K W IY1 - K IH0 NG
+SQUEAKS  S K W IY1 K S
+SQUEAKY  S K W IY1 - K IY0
+SQUEAL  S K W IY1 L
+SQUEALING  S K W IY1 - L IH0 NG
+SQUEALS  S K W IY1 L Z
+SQUEAMISH  S K W IY1 - M IH0 SH
+SQUEAMISHNESS  S K W IY1 - M IH0 SH - N AH0 S
+SQUEEGEE  S K W IY1 - JH IY0
+SQUEEZE  S K W IY1 Z
+SQUEEZED  S K W IY1 Z D
+SQUEEZES  S K W IY1 - Z IH0 Z
+SQUEEZING  S K W IY1 - Z IH0 NG
+SQUELCH  S K W EH1 L CH
+SQUELCHED  S K W EH1 L CH T
+SQUELCHING  S K W EH1 L - CH IH0 NG
+SQUIBB  S K W IH1 B
+SQUIBB'S  S K W IH1 B Z
+SQUID  S K W IH1 D
+SQUIER  S K W AY1 - ER0
+SQUIERS  S K W AY1 - ER0 Z
+SQUIGGLE  S K W IH1 - G AH0 L
+SQUIGGLES  S K W IH1 - G AH0 L Z
+SQUILLACE  S K W IH1 - L AH0 S
+SQUILLANTE  S K W IY0 - L AA1 N - T EY0
+SQUINT  S K W IH1 N T
+SQUINTING  S K W IH1 N - T IH0 NG
+SQUIRE  S K W AY1 R
+SQUIRES  S K W AY1 - ER0 Z
+SQUIRM  S K W ER1 M
+SQUIRMING  S K W ER1 - M IH0 NG
+SQUIRMS  S K W ER1 M Z
+SQUIRREL  S K W ER1 - AH0 L
+SQUIRRELED  S K W ER1 - AH0 L D
+SQUIRRELS  S K W ER1 - AH0 L Z
+SQUIRT  S K W ER1 T
+SQUIRTED  S K W ER1 - T AH0 D
+SQUIRTED(2)  S K W ER1 - T IH0 D
+SQUIRTING  S K W ER1 - T IH0 NG
+SQUIRTS  S K W ER1 T S
+SQUISHY  S K W IH1 - SH IY0
+SQUITIERI  S K W IY0 - T IH1 - R IY0
+SQUYRES  S K W AY1 R Z
+SR  S IY1 - N Y ER0
+SR(2)  S IH1 - S T ER0
+SRADER  SH R EY1 - D ER0
+SRAM  SH R AE1 M
+SRAM(2)  EH1 - S R AE1 M
+SRAMEK  SH R AE1 - M IH0 K
+SRDAN  S ER1 - D AE2 N
+SREBRENICA  S R EY2 - B R EH0 - N IY1 T - S AH0
+SREBRENICA'S  S R EY2 - B R EH0 - N IY1 T - S AH0 Z
+SREBRENICA'S(2)  SH R EY2 - B R AH0 - N IY1 T - S AH0 Z
+SREBRENICA(2)  SH R EY2 - B R AH0 - N IY1 T - S AH0
+SREMAC  S R IY1 - M AE2 K
+SRI  SH R IY1
+SRI(2)  EH1 - S AA1 - R AY1
+SRI(3)  S R IY1
+SRI-LANKA  S R IY1 - L AE1 NG - K AH0
+SRI-LANKA(2)  SH R IY1 - L AE1 NG - K AH0
+SRINAGAR  S R IY1 - N AH0 - G AA2 R
+SRINAGAR(2)  SH R IY1 - N AH0 - G AA2 R
+SRINAGARB  S R IY1 - N AH0 - G AA2 R B
+SRINAGARB(2)  SH R IY1 - N AH0 - G AA2 R B
+SRINIVAS  S R IY1 - N IH0 - V AA2 S
+SRINIVASAN  S R IY0 - N IY0 - V AA0 - S AA1 N
+SRINIVASAN(2)  SH R IY0 - N IY0 - V AA0 - S AA1 N
+SRIRAM  S R IY1 - R AA2 M
+SRIRAM(2)  SH R IY1 - R AA2 M
+SRIVASTAVA  S R IY0 - V AA0 - S T AA1 - V AH0
+SRIVASTAVA(2)  SH R IY0 - V AA0 - S T AA1 - V AH0
+SROCK  SH R AA1 K
+SROGE  SH R OW1 G
+SROKA  SH R OW1 - K AH0
+SROUFE  SH R OW1 F
+SSANGYONG  S AE1 NG - Y AO2 NG
+ST  S T R IY1 T
+ST(2)  S EY1 N T
+ST-JAMES  S EY1 N T - JH EY1 M Z
+ST-JOHNS  S EY1 N T - JH AA1 N Z
+STA  S T AH1
+STA(2)  EH1 - S T IY1 - EY1
+STAAB  S T AA1 B
+STAACK  S T AA1 K
+STAAL  S T AA1 L
+STAAR  S T AA1 R
+STAAR'S  S T AA1 R Z
+STAAS  S T AA1 Z
+STAAT  S T AA1 T
+STAATS  S T AA1 T S
+STAB  S T AE1 B
+STABBED  S T AE1 B D
+STABBING  S T AE1 - B IH0 NG
+STABBINGS  S T AE1 - B IH0 NG Z
+STABENOW  S T AE1 - B IH0 - N OW0
+STABILE  S T EY1 - B IH0 L
+STABILITY  S T AH0 - B IH1 - L IH0 - T IY0
+STABILIZATION  S T EY2 - B AH0 - L AH0 - Z EY1 - SH AH0 N
+STABILIZE  S T EY1 - B AH0 - L AY2 Z
+STABILIZED  S T EY1 - B AH0 - L AY2 Z D
+STABILIZER  S T EY1 - B AH0 - L AY2 - Z ER0
+STABILIZERS  S T EY1 - B AH0 - L AY2 - Z ER0 Z
+STABILIZES  S T EY1 - B AH0 - L AY2 - Z AH0 Z
+STABILIZES(2)  S T EY1 - B AH0 - L AY2 - Z IH0 Z
+STABILIZING  S T EY1 - B AH0 - L AY2 - Z IH0 NG
+STABLE  S T EY1 - B AH0 L
+STABLER  S T EY1 - B AH0 L - ER0
+STABLER(2)  S T EY1 - B L ER0
+STABLES  S T EY1 - B AH0 L Z
+STABLEST  S T EY1 - B AH0 - L AH0 S T
+STABLEY  S T AE1 - B L IY0
+STABS  S T AE1 B Z
+STAC  S T AE1 K
+STAC'S  S T AE1 K S
+STACCATO  S T AH0 - K AA1 - T OW2
+STACEY  S T EY1 - S IY0
+STACH  S T AE1 CH
+STACHNIK  S T AE1 K - N IH0 K
+STACHOWIAK  S T AH0 - HH AW1 - IY0 - AE0 K
+STACHOWICZ  S T AA1 - HH AH0 - V IH0 CH
+STACHOWSKI  S T AH0 - HH AO1 F S - K IY0
+STACHURA  S T AA0 - K UH1 - R AH0
+STACHURSKI  S T AH0 - HH ER1 S - K IY0
+STACIA  S T AA1 - CH AH0
+STACIE  S T AE1 - K IY0
+STACK  S T AE1 K
+STACKED  S T AE1 K T
+STACKER  S T AE1 - K ER0
+STACKHOUSE  S T AE1 K - HH AW2 S
+STACKING  S T AE1 - K IH0 NG
+STACKPOLE  S T AE1 K - P OW2 L
+STACKS  S T AE1 K S
+STACY  S T EY1 - S IY0
+STADE  S T EY1 D
+STADEL  S T AE1 - D AH0 L
+STADELMAN  S T AE1 - D AH0 L - M AH0 N
+STADER  S T EY1 - D ER0
+STADIA  S T EY1 - D IY0 - AH0
+STADIUM  S T EY1 - D IY0 - AH0 M
+STADIUM'S  S T EY1 - D IY0 - AH0 M Z
+STADIUMS  S T EY1 - D IY0 - AH0 M Z
+STADLER  S T AE1 D - L ER0
+STADNIK  S T AE1 D - N IH0 K
+STADT  S T AE1 D T
+STADTLANDER  SH T AE1 T - L AE2 N - D ER0
+STADTMILLER  SH T AE1 T - M IH1 - L ER0
+STAEBELL  S T EH1 - B EH0 L
+STAEBLER  S T EY1 - L ER0
+STAEHLE  S T EY1 L
+STAFF  S T AE1 F
+STAFF'S  S T AE1 F S
+STAFFA  S T AA1 - F AH0
+STAFFED  S T AE1 F T
+STAFFER  S T AE1 - F ER0
+STAFFERS  S T AE1 - F ER0 Z
+STAFFIERI  S T AA0 - F IH1 - R IY0
+STAFFING  S T AE1 - F IH0 NG
+STAFFORD  S T AE1 - F ER0 D
+STAFFORD'S  S T AE1 - F ER0 D Z
+STAFFORDSHIRE  S T AE1 - F ER0 D - SH ER0
+STAFFORDSHIRE'S  S T AE1 - F ER0 D - SH ER0 Z
+STAFFORDSHIRE'S(2)  S T AE1 - F ER0 D - SH AY2 - ER0 Z
+STAFFORDSHIRE(2)  S T AE1 - F ER0 D - SH AY2 - ER0
+STAFFS  S T AE1 F S
+STAG  S T AE1 G
+STAGE  S T EY1 JH
+STAGECOACH  S T EY1 JH - K OW2 CH
+STAGECRAFT  S T EY1 JH - K R AE2 F T
+STAGED  S T EY1 JH D
+STAGEHAND  S T EY1 JH - HH AE2 N D
+STAGEHANDS  S T EY1 JH - HH AE2 N D Z
+STAGER  S T EY1 - JH ER0
+STAGES  S T EY1 - JH AH0 Z
+STAGES(2)  S T EY1 - JH IH0 Z
+STAGFLATE  S T AE2 G - F L EY1 T
+STAGFLATION  S T AE0 G - F L EY1 - SH AH0 N
+STAGG  S T AE1 G
+STAGGER  S T AE1 - G ER0
+STAGGERED  S T AE1 - G ER0 D
+STAGGERING  S T AE1 - G ER0 - IH0 NG
+STAGGERS  S T AE1 - G ER0 Z
+STAGGS  S T AE1 G Z
+STAGING  S T EY1 - JH IH0 NG
+STAGLIANO  S T AA0 G - L IY0 - AA1 - N OW0
+STAGNANT  S T AE1 G - N AH0 N T
+STAGNARO  S T AA0 G - N AA1 - R OW0
+STAGNATE  S T AE1 G - N EY2 T
+STAGNATED  S T AE1 G - N EY2 - T IH0 D
+STAGNATING  S T AE1 G - N EY2 - T IH0 NG
+STAGNATION  S T AE0 G - N EY1 - SH AH0 N
+STAGNER  S T AE1 G - N ER0
+STAHL  S T AA1 L
+STAHLBERG  S T AA1 L - B ER0 G
+STAHLE  S T EY1 - HH AH0 L
+STAHLECKER  S T AA1 - L IH0 - K ER0
+STAHLER  S T AA1 - L ER0
+STAHLEY  S T AE1 - L IY0
+STAHLHUT  S T AA1 L - HH AH0 T
+STAHLMAN  S T AA1 L - M AH0 N
+STAHLY  S T AE1 - L IY0
+STAHMER  S T AA1 - M ER0
+STAHNKE  S T AE1 NG K
+STAHR  S T AE1 R
+STAI  S T AA1 - IY0
+STAIB  S T EY1 B
+STAID  S T EY1 D
+STAIGER  S T AY1 - G ER0
+STAILEY  S T EY1 - L IY0
+STAIN  S T EY1 N
+STAINBACK  S T EY1 N - B AE2 K
+STAINBROOK  S T EY1 N - B R UH2 K
+STAINED  S T EY1 N D
+STAINES  S T EY1 N Z
+STAINING  S T EY1 - N IH0 NG
+STAINLESS  S T EY1 N - L AH0 S
+STAINMASTER  S T EY1 N - M AE2 - S T ER0
+STAINS  S T EY1 N Z
+STAIR  S T EH1 R
+STAIRCASE  S T EH1 R - K EY2 S
+STAIRCASES  S T EH1 R - K EY2 - S IH0 Z
+STAIRS  S T EH1 R Z
+STAIRWAY  S T EH1 R - W EY2
+STAIRWAYS  S T EH1 R - W EY2 Z
+STAIRWELL  S T EH1 R - W EH2 L
+STAIRWELLS  S T EH1 R - W EH2 L Z
+STAKE  S T EY1 K
+STAKE'S  S T EY1 K S
+STAKE-OUT  S T EY1 K - AW1 T
+STAKED  S T EY1 K T
+STAKEHOLDER  S T EY1 K - HH OW2 L - D ER0
+STAKEHOLDERS  S T EY1 K - HH OW2 L - D ER0 Z
+STAKEOUT  S T EY1 K - AW2 T
+STAKEOUTS  S T EY1 K - AW2 T S
+STAKER  S T EY1 - K ER0
+STAKES  S T EY1 K S
+STAKING  S T EY1 - K IH0 NG
+STALCUP  S T AO1 L K - AH2 P
+STALDER  S T AO1 L - D ER0
+STALE  S T EY1 L
+STALEMATE  S T EY1 L - M EY2 T
+STALEMATED  S T EY1 L - M EY2 - T IH0 D
+STALEY  S T EY1 - L IY0
+STALEY'S  S T EY1 - L IY0 Z
+STALIN  S T AA1 - L AH0 N
+STALIN'S  S T AA1 - L IH0 N Z
+STALINGRAD  S T AE1 - L IH0 N - G R AE2 D
+STALINISM  S T AE1 - L IH0 - N IH2 - Z AH0 M
+STALINIST  S T AA1 - L IH0 - N IH0 S T
+STALINISTIC  S T AA2 - L IH0 - N IH1 - S T IH0 K
+STALINISTS  S T AE1 - L IH0 - N IH0 S T S
+STALINISTS(2)  S T AE1 - L IH0 - N IH0 S S
+STALINISTS(3)  S T AE1 - L IH0 - N IH0 S
+STALINIZATION  S T AE2 - L IH0 - N IH0 - Z EY1 - SH AH0 N
+STALINIZE  S T AE1 - L IH0 - N AY2 Z
+STALK  S T AO1 K
+STALKED  S T AO1 K T
+STALKER  S T AO1 - K ER0
+STALKERS  S T AO1 - K ER0 Z
+STALKING  S T AO1 - K IH0 NG
+STALKS  S T AO1 K S
+STALKY  S T AO1 - K IY0
+STALL  S T AO1 L
+STALLARD  S T AE1 - L ER0 D
+STALLCUP  S T AO1 L K - AH2 P
+STALLED  S T AO1 L D
+STALLER  S T AO1 - L ER0
+STALLIBRASS  S T AE1 - L IH0 - B R AE0 S
+STALLING  S T AO1 - L IH0 NG
+STALLINGS  S T AO1 - L IH0 NG Z
+STALLION  S T AE1 - L Y AH0 N
+STALLIONS  S T AE1 - L Y AH0 N Z
+STALLKAMP  S T AO1 L - K AE2 M P
+STALLMAN  S T AO1 L - M AH0 N
+STALLONE  S T AH0 - L OW1 N
+STALLONE'S  S T AH0 - L OW1 N Z
+STALLS  S T AO1 L Z
+STALLSMITH  S T AO1 L - S M IH2 TH
+STALLWORTH  S T AO1 L - W ER2 TH
+STALNAKER  S T AE1 L - N AH0 - K ER0
+STALOFF  S T AE1 - L AO0 F
+STALON  S T EY1 - L AH0 N
+STALOWA  S T AH0 - L OW1 - AH0
+STALTER  S T AO1 L - T ER0
+STALVEY  S T AE1 L - V IY0
+STALWART  S T AO1 L - W ER0 T
+STALWARTS  S T AO1 L - W ER0 T S
+STALZER  S T EY1 L - Z ER0
+STAM  S T AE1 M
+STAMAND  S T AE1 - M AH0 N D
+STAMANT  S T AE1 - M AH0 N T
+STAMAS  S T AA1 - M AH0 Z
+STAMATY  S T AE1 - M AH0 - T IY0
+STAMBAUGH  S T AE1 M - B AO0
+STAMBERG  S T AE1 M - B ER0 G
+STAMENSON  S T EY1 - M AH0 N - S AH0 N
+STAMER  S T EY1 - M ER0
+STAMEY  S T EY1 - M IY0
+STAMFORD  S T AE1 M - F ER0 D
+STAMINA  S T AE1 - M AH0 - N AH0
+STAMLER  S T AE1 M - L ER0
+STAMM  S T AE1 M
+STAMMEN  S T AE1 - M AH0 N
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+STAMOS  S T EY1 - M OW0 Z
+STAMOUR  S T AH0 - M UH1 R
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+STAMPED  S T AE1 M P T
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+STAMPFLI  S T AE1 M P F - L IY0
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+STAMPS  S T AE1 M P S
+STAN  S T AE1 N
+STAN'S  S T AE1 N Z
+STANADYNE  S T AE1 - N AH0 - D AY2 N
+STANALAND  S T AE1 - N AH0 - L AH0 N D
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+STANAWAY  S T AE1 N - AH0 - W EY0
+STANBACK  S T AE1 N - B AE2 K
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+STANDARD-BEARERS  S T AE1 N - D ER0 D - B EH1 - R ER0 Z
+STANDARDIZATION  S T AE0 N - D ER0 - D IH0 - Z EY1 - SH AH0 N
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+STANDOFFS  S T AE1 N - D AO2 F S
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+STANGER  S T AE1 - NG ER0
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+STANISLAW  S T AE1 - N IH0 S - L AO2
+STANISLAWSKI  S T AH0 - N IH0 S - L AA1 F S - K IY0
+STANISZEWSKI  S T AH0 - N IH0 - SH EH1 F S - K IY0
+STANK  S T AE1 NG K
+STANKE  S T AE1 NG K
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+STANKEY  S T AE1 NG - K IY0
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+STAPEL  S T AE1 - P AH0 L
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+STAPLERS  S T EY1 - P AH0 - L ER0 Z
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+STAPP  S T AE1 P
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+STEARN  S T ER1 N
+STEARNE  S T ER1 N
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+STECHSCHULTE  S T EH1 K - SH AH0 L T
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+STECKLER  S T EH1 K - L ER0
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+STECKMAN  S T EH1 K - M AH0 N
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+STEELMAKERS  S T IY1 L - M EY2 - K ER0 Z
+STEELMAKERS'  S T IY1 L - M AH0 - K ER0 Z
+STEELMAKING  S T IY1 L - M EY2 - K IH0 NG
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+STEELWORKERS  S T IY1 L - W ER2 - K ER0 Z
+STEELWORKERS'  S T IY1 L - W ER0 - K ER0 Z
+STEELWORKS  S T IY1 L - W ER2 K S
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+STEEN  S T IY1 N
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+STEFFENSON  S T EH1 - F IH0 N - S AH0 N
+STEFFES  S T EH1 F S
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+STOOKSBURY  S T UW1 K S - B EH0 - R IY0
+STOOL  S T UW1 L
+STOOLS  S T UW1 L Z
+STOOP  S T UW1 P
+STOOPED  S T UW1 P T
+STOOPING  S T UW1 - P IH0 NG
+STOOPS  S T UW1 P S
+STOOTS  S T UW1 T S
+STOP  S T AA1 P
+STOPA  S T OW1 - P AH0
+STOPGAP  S T AA1 P - G AE2 P
+STOPHER  S T AA1 - F ER0
+STOPKA  S T OW1 P - K AH0
+STOPLIGHT  S T AA1 P - L AY2 T
+STOPLIGHTS  S T AA1 P - L AY2 T S
+STOPOVER  S T AA1 P - OW2 - V ER0
+STOPOVERS  S T AA1 P - OW2 - V ER0 Z
+STOPPAGE  S T AA1 - P IH0 JH
+STOPPAGES  S T AA1 - P IH0 - JH IH0 Z
+STOPPED  S T AA1 P T
+STOPPEL  S T AA1 - P AH0 L
+STOPPER  S T AA1 - P ER0
+STOPPERS  S T AA1 - P ER0 Z
+STOPPING  S T AA1 - P IH0 NG
+STOPS  S T AA1 P S
+STOPWATCH  S T AA1 P - W AA2 CH
+STOPWATCHES  S T AA1 P - W AA2 - CH IH0 Z
+STORA  S T AO1 - R AH0
+STORA'S  S T AO1 - R AH0 Z
+STORAGE  S T AO1 - R AH0 JH
+STORAGE(2)  S T AO1 - R IH0 JH
+STORAGETEK  S T AO2 - R AH0 JH - T EH2 K
+STORASKA  S T AO2 - R AE1 - S AH0
+STORBECK  S T AO1 R - B EH0 K
+STORCH  S T AO1 R K
+STORCK  S T AO1 R K
+STORDAHL  S T AO1 R - D AA0 L
+STORE  S T AO1 R
+STORE'S  S T AO1 R Z
+STORED  S T AO1 R D
+STOREFRONT  S T AO1 R - F R AA2 N T
+STOREFRONTS  S T AO1 R - F R AA2 N T S
+STOREHOUSE  S T AO1 R - HH AW2 S
+STOREHOUSE'S  S T AO1 R - HH AW2 - S IH0 Z
+STOREHOUSES  S T AO1 R - HH AW2 - Z AH0 Z
+STOREKEEPER  S T AO1 R - K IY2 - P ER0
+STOREKEEPERS  S T AO1 R - K IY2 - P ER0 Z
+STORER  S T AO1 - R ER0
+STORER'S  S T AO1 - R ER0 Z
+STOREROOM  S T AO1 - R R UW2 M
+STORES  S T AO1 R Z
+STORES'  S T AO1 R Z
+STOREY  S T AO1 - R IY0
+STORFER  S T AO1 R - F ER0
+STORIE  S T AO1 - R IY0
+STORIED  S T AO1 - R IY0 D
+STORIES  S T AO1 - R IY0 Z
+STORING  S T AO1 - R IH0 NG
+STORK  S T AO1 R K
+STORLIE  S T AO1 R - L IY0
+STORM  S T AO1 R M
+STORM'S  S T AO1 R M Z
+STORMED  S T AO1 R M D
+STORMENT  S T AO1 R - M AH0 N T
+STORMER  S T AO1 R - M ER0
+STORMES  S T AO1 R M Z
+STORMIEST  S T AO1 R - M IY0 - IH0 S T
+STORMING  S T AO1 R - M IH0 NG
+STORMONT  S T AO1 R - M OW0 N T
+STORMS  S T AO1 R M Z
+STORMY  S T AO1 R - M IY0
+STORR  S T AO1 R
+STORROW  S T AA1 - R OW0
+STORRS  S T AO1 R Z
+STORTI  S T AO1 R - T IY0
+STORTING  S T AO1 R - T IH0 NG
+STORTS  S T AO1 R T S
+STORTZ  S T AO1 R T S
+STORY  S T AO1 - R IY0
+STORY'S  S T AO1 - R IY0 Z
+STORYBOARD  S T AO1 - R IY0 - B AO2 R D
+STORYBOOK  S T AO1 - R IY0 - B UH2 K
+STORYBOOKS  S T AO1 - R IY0 - B UH2 K S
+STORYLINE  S T AO1 - R IY0 - L AY2 N
+STORYLINES  S T AO1 - R IY0 - L AY2 N Z
+STORYTELLER  S T AO1 - R IY0 - T EH2 - L ER0
+STORYTELLERS  S T AO1 - R IY0 - T EH2 - L ER0 Z
+STORYTELLING  S T AO1 - R IY0 - T EH2 - L IH0 NG
+STORZ  S T AO1 R Z
+STOSSEL  S T AA1 - S AH0 L
+STOSSEL'S  S T AA1 - S AH0 L Z
+STOTLER  S T AA1 T - L ER0
+STOTT  S T AA1 T
+STOTTLEMYER  S T AA1 - T AH0 L - M AY0 - ER0
+STOTTS  S T AA1 T S
+STOTZ  S T AA1 T S
+STOUDEMIRE  S T UW1 - D AH0 - M AY2 R
+STOUDENMIRE  S T UW1 - D AH0 N - M AY2 R
+STOUDER  S T AH1 - D ER0
+STOUDT  S T AH1 D T
+STOUFFER  S T AH1 - F ER0
+STOUFFER(2)  S T OW1 - F ER0
+STOUFFS  S T AH1 F S
+STOUFFS(2)  S T OW1 F S
+STOUGH  S T AH1 F
+STOUGHTON  S T OW1 - T AH0 N
+STOUP  S T UW1 P
+STOUT  S T AW1 T
+STOUTE  S T AW1 T
+STOUTENBURG  S T AW1 - T AH0 N - B ER0 G
+STOUTLY  S T AW1 T - L IY0
+STOUTNESS  S T AW1 T - N AH0 S
+STOVALL  S T OW1 - V AA0 L
+STOVE  S T OW1 V
+STOVER  S T OW1 - V ER0
+STOVES  S T OW1 V Z
+STOW  S T OW1
+STOWE  S T OW1
+STOWED  S T OW1 D
+STOWELL  S T AA1 - W EH0 L
+STOWER  S T OW1 - ER0
+STOWERS  S T OW1 - ER0 Z
+STOY  S T OY1
+STOYER  S T OY1 - ER0
+STRACENER  S T R AE1 - S IY0 - N ER0
+STRACHAN  S T R AE1 - CH AH0 N
+STRACK  S T R AE1 K
+STRACKE  S T R AE1 K
+STRADA  S T R AA1 - D AH0
+STRADDLE  S T R AE1 - D AH0 L
+STRADDLED  S T R AE1 - D AH0 L D
+STRADDLES  S T R AE1 - D AH0 L Z
+STRADDLING  S T R AE1 D - L IH0 NG
+STRADER  S T R EY1 - D ER0
+STRADFORD  S T R AE1 D - F ER0 D
+STRADIVARIUS  S T R AE2 - D IH0 - V EH1 - R IY0 - AH0 S
+STRADLEY  S T R AE1 D - L IY0
+STRADLING  S T R AE1 D - L IH0 NG
+STRAFE  S T R EY1 F
+STRAFING  S T R EY1 - F IH0 NG
+STRAGGLE  S T R AE1 - G AH0 L
+STRAGGLED  S T R AE1 - G AH0 L D
+STRAGGLER  S T R AE1 - G L ER0
+STRAGGLERS  S T R AE1 - G L ER0 Z
+STRAHAN  S T R AE1 - HH AH0 N
+STRAHL  S T R AA1 L
+STRAHLE  S T R EY1 - HH AH0 L
+STRAHLER  S T R AA1 - L ER0
+STRAHM  S T R AA1 M
+STRAIGHT  S T R EY1 T
+STRAIGHTAWAY  S T R EY1 T - AH0 - W EY2
+STRAIGHTEDGE  S T R EY1 - T EH2 JH
+STRAIGHTEN  S T R EY1 - T AH0 N
+STRAIGHTENED  S T R EY1 - T AH0 N D
+STRAIGHTENING  S T R EY1 - T AH0 N - IH0 NG
+STRAIGHTENING(2)  S T R EY1 T - N IH0 NG
+STRAIGHTENS  S T R EY1 - T AH0 N Z
+STRAIGHTER  S T R EY1 - T ER0
+STRAIGHTFORWARD  S T R EY1 T - F AO1 R - W ER0 D
+STRAIGHTFORWARDLY  S T R EY2 T - F AO1 R - W ER0 D - L IY0
+STRAIGHTS  S T R EY1 T S
+STRAIN  S T R EY1 N
+STRAINED  S T R EY1 N D
+STRAINING  S T R EY1 - N IH0 NG
+STRAINS  S T R EY1 N Z
+STRAIT  S T R EY1 T
+STRAITJACKET  S T R EY1 T - JH AE2 - K AH0 T
+STRAITS  S T R EY1 T S
+STRAKA  S T R AA1 - K AH0
+STRAKER  S T R EY1 - K ER0
+STRALEY  S T R AE1 - L IY0
+STRAM  S T R AE1 M
+STRANAHAN  S T R AE1 - N AH0 - HH AE0 N
+STRAND  S T R AE1 N D
+STRANDBERG  S T R AE1 N D - B ER0 G
+STRANDE  S T R AE1 N D
+STRANDED  S T R AE1 N - D AH0 D
+STRANDED(2)  S T R AE1 N - D IH0 D
+STRANDING  S T R AE1 N - D IH0 NG
+STRANDLINE  S T R AE1 N D - L AY2 N
+STRANDLINE(2)  S T R AE1 N - L AY2 N
+STRANDLINES  S T R AE1 N D - L AY2 N Z
+STRANDLINES(2)  S T R AE1 N - L AY2 N Z
+STRANDS  S T R AE1 N D Z
+STRANG  S T R AE1 NG
+STRANGE  S T R EY1 N JH
+STRANGELOVE  S T R EY1 N - JH L AH2 V
+STRANGELY  S T R EY1 N JH - L IY0
+STRANGENESS  S T R EY1 N JH - N AH0 S
+STRANGER  S T R EY1 N - JH ER0
+STRANGER'S  S T R EY1 N - JH ER0 Z
+STRANGERS  S T R EY1 N - JH ER0 Z
+STRANGEST  S T R EY1 N - JH IH0 S T
+STRANGIS  S T R AE1 N - JH IH0 S
+STRANGLE  S T R AE1 NG - G AH0 L
+STRANGLED  S T R AE1 NG - G AH0 L D
+STRANGLEHOLD  S T R AE1 NG - G AH0 L - HH OW2 L D
+STRANGLER  S T R AE1 NG - G L ER0
+STRANGLING  S T R AE1 NG - G AH0 - L IH0 NG
+STRANGLING(2)  S T R AE1 NG - G L IH0 NG
+STRANGULATE  S T R AE1 NG - G Y AH0 - L EY2 T
+STRANGULATION  S T R AE2 NG - G Y AH0 - L EY1 - SH AH0 N
+STRANGULATIONS  S T R AE2 NG - G Y AH0 - L EY1 - SH AH0 N Z
+STRANGWAYES  S T R AE1 NG - W EY2 Z
+STRANO  S T R AA1 - N OW0
+STRANSKY  S T R AE1 N S - K IY0
+STRAP  S T R AE1 P
+STRAPPED  S T R AE1 P T
+STRAPPING  S T R AE1 - P IH0 NG
+STRAPS  S T R AE1 P S
+STRASBERG  S T R AE1 S - B ER0 G
+STRASBOURG  S T R AE1 S - B AO2 R G
+STRASBURG  S T R AE1 S - B ER0 G
+STRASBURGER  S T R AE1 S - B ER0 - G ER0
+STRASSBURG  S T R AE1 S - B ER0 G
+STRASSBURGER  S T R AE1 S - B ER0 - G ER0
+STRASSER  S T R AE1 - S ER0
+STRASSMAN  S T R AE1 S - M AH0 N
+STRASSNER  S T R AE1 S - N ER0
+STRASZHEIM  S T R AE1 S - HH AY2 M
+STRATA  S T R AE1 - T AH0
+STRATACOM  S T R AE1 - T AH0 - K AA0 M
+STRATAGEMS  S T R AE1 - T AH0 - JH AH0 M Z
+STRATAS  S T R AE1 - T AH0 Z
+STRATE  S T R EY1 T
+STRATEGEM  S T R AE1 - T IH0 - JH EH0 M
+STRATEGIC  S T R AH0 - T IY1 - JH IH0 K
+STRATEGICAL  S T R AH0 - T IY1 - JH IH0 - K AH0 L
+STRATEGICALLY  S T R AH0 - T IY1 - JH IH0 K - L IY0
+STRATEGIES  S T R AE1 - T AH0 - JH IY0 Z
+STRATEGIST  S T R AE1 - T IH0 - JH IH0 S T
+STRATEGISTS  S T R AE1 - T IH0 - JH IH0 S T S
+STRATEGISTS(2)  S T R AE1 - T IH0 - JH IH0 S S
+STRATEGISTS(3)  S T R AE1 - T IH0 - JH IH0 S
+STRATEGIZE  S T R AE1 - T AH0 - JH AY0 Z
+STRATEGIZING  S T R AE1 - T AH0 - JH AY0 - Z IH0 NG
+STRATEGY  S T R AE1 - T AH0 - JH IY0
+STRATEGY'S  S T R AE1 - T AH0 - JH IY0 Z
+STRATER  S T R EY1 - T ER0
+STRATFORD  S T R AE1 T - F ER0 D
+STRATHMAN  S T R AE1 TH - M AH0 N
+STRATIFIED  S T R AE1 - T AH0 - F AY2 D
+STRATIFY  S T R AE1 - T AH0 - F AY2
+STRATIGRAPHIC  S T R AE2 - T AH0 - G R AE1 - F IH0 K
+STRATMAN  S T R AE1 T - M AH0 N
+STRATMANN  S T R AE1 T - M AH0 N
+STRATOCASTER  S T R AE1 - T OW0 - K AE2 - S T ER0
+STRATOFLEX  S T R AE1 - T OW0 - F L EH2 K S
+STRATOSPHERE  S T R AE1 - T AH0 - S F IH2 R
+STRATOSPHERIC  S T R AE2 - T AH0 - S F IH1 - R IH0 K
+STRATTON  S T R AE1 - T AH0 N
+STRATUM  S T R AE1 - T AH0 M
+STRATUS  S T R AE1 - T AH0 S
+STRATUS'S  S T R AE1 - T AH0 - S IH0 Z
+STRAUB  S T R AW1 B
+STRAUBE  S T R AW1 B
+STRAUCH  S T R AW1 K
+STRAUGHAN  S T R AO1 - AH0 N
+STRAUGHN  S T R AO1 N
+STRAUGHTER  S T R AO1 - T ER0
+STRAUM  S T R AW1 M
+STRAUM(2)  S T R AA1 M
+STRAUS  S T R AW1 S
+STRAUSBAUGH  S T R AW1 S - B AW0
+STRAUSE  S T R AW1 S
+STRAUSER  S T R AW1 - S ER0
+STRAUSS  S T R AW1 S
+STRAUSS'S  S T R AW1 - S IH0 Z
+STRAUSSER  S T R AW1 - S ER0
+STRAVINSKY  S T R AH0 - V IH1 N - S K IY0
+STRAVINSKY'S  S T R AH0 - V IH1 N - S K IY0 Z
+STRAW  S T R AO1
+STRAWBERRIES  S T R AO1 - B EH2 - R IY0 Z
+STRAWBERRY  S T R AO1 - B EH2 - R IY0
+STRAWBRIDGE  S T R AO1 - B R IH2 JH
+STRAWDER  S T R AO1 - D ER0
+STRAWDERMAN  S T R AO1 - D ER0 - M AH0 N
+STRAWN  S T R AO1 N
+STRAWS  S T R AO1 Z
+STRAWSER  S T R AO1 - Z ER0
+STRAY  S T R EY1
+STRAYED  S T R EY1 D
+STRAYER  S T R EY1 - ER0
+STRAYHORN  S T R EY1 - HH ER0 N
+STRAYING  S T R EY1 - IH0 NG
+STRAYS  S T R EY1 Z
+STREAK  S T R IY1 K
+STREAKED  S T R IY1 K T
+STREAKER  S T R IY1 - K ER0
+STREAKING  S T R IY1 - K IH0 NG
+STREAKS  S T R IY1 K S
+STREAM  S T R IY1 M
+STREAMED  S T R IY1 M D
+STREAMER  S T R IY1 - M ER0
+STREAMERS  S T R IY1 - M ER0 Z
+STREAMING  S T R IY1 - M IH0 NG
+STREAMLINE  S T R IY1 M - L AY2 N
+STREAMLINED  S T R IY1 M - L AY2 N D
+STREAMLINING  S T R IY1 M - L AY2 - N IH0 NG
+STREAMS  S T R IY1 M Z
+STREATER  S T R IY1 - T ER0
+STREB  S T R EH1 B
+STREBE  S T R IY1 B
+STREBECK  S T R IY1 - B EH0 K
+STREBEL  S T R EH1 - B AH0 L
+STRECK  S T R EH1 K
+STRECKER  S T R EH1 - K ER0
+STREED  S T R IY1 D
+STREEP  S T R IY1 P
+STREEPER  S T R IY1 - P ER0
+STREET  S T R IY1 T
+STREET'S  S T R IY1 T S
+STREETCAR  S T R IY1 T - K AA2 R
+STREETER  S T R IY1 - T ER0
+STREETERS  S T R IY1 - T ER0 Z
+STREETMAN  S T R IY1 T - M AH0 N
+STREETS  S T R IY1 T S
+STREETT  S T R IY1 T
+STREETWISE  S T R IY1 T - W AY2 Z
+STREETY  S T R IY1 - T IY0
+STREFF  S T R EH1 F
+STREGE  S T R IY1 JH
+STREHL  S T R EH1 L
+STREHLE  S T R EH1 L
+STREHLOW  S T R EH1 - L OW0
+STREIB  S T R AY1 B
+STREIBER  S T R AY1 - B ER0
+STREICH  S T R AY1 K
+STREICHER  S T R AY1 - K ER0
+STREIFF  S T R AY1 F
+STREIGHT  S T R EY1 T
+STREIKER  S T R AY1 - K ER0
+STREISAND  S T R AY1 - Z AH0 N D
+STREISAND'S  S T R AY1 - Z AH0 N D Z
+STREISAND'S(2)  S T R AY1 - S AE2 N D Z
+STREISAND(2)  S T R AY1 - S AE2 N D
+STREIT  S T R AY1 T
+STREITMATTER  S T R AY1 T - M AH0 - T ER0
+STRELOW  S T R EH1 - L OW0
+STRENG  S T R EH1 NG
+STRENGER  S T R EH1 NG - G ER0
+STRENGTH  S T R EH1 NG K TH
+STRENGTH(2)  S T R EH1 NG TH
+STRENGTHEN  S T R EH1 NG - TH AH0 N
+STRENGTHENED  S T R EH1 NG - TH AH0 N D
+STRENGTHENING  S T R EH1 NG - TH AH0 N - IH0 NG
+STRENGTHENS  S T R EH1 NG - TH AH0 N Z
+STRENGTHS  S T R EH1 NG K TH S
+STRENGTHS(2)  S T R EH1 NG TH S
+STRENIO  S T R IY1 - N IY0 - OW0
+STRENUOUS  S T R EH1 - N Y UW0 - AH0 S
+STRENUOUSLY  S T R EH1 - N Y UW0 - AH0 S - L IY0
+STREP  S T R EH1 P
+STREPS  S T R EH1 P S
+STREPTOCOCCUS  S T R EH2 P - T AH0 - K AO1 - K AH0 S
+STREPTOKINASE  S T R EH2 P - T AH0 - K AY1 - N EY2 S
+STREPTOKINASE(2)  S T R EH2 P - T OW0 - K AY1 - N EY2 Z
+STRESS  S T R EH1 S
+STRESSED  S T R EH1 S T
+STRESSES  S T R EH1 - S AH0 Z
+STRESSES(2)  S T R EH1 - S IH0 Z
+STRESSFUL  S T R EH1 S - F AH0 L
+STRESSING  S T R EH1 - S IH0 NG
+STRESSOR  S T R EH1 - S ER0
+STRESSORS  S T R EH1 - S ER0 Z
+STRETCH  S T R EH1 CH
+STRETCHED  S T R EH1 CH T
+STRETCHER  S T R EH1 - CH ER0
+STRETCHER-BEARER  S T R EH1 - CH ER0 - B EH1 - R ER0
+STRETCHER-BEARERS  S T R EH1 - CH ER0 - B EH1 - R ER0 Z
+STRETCHERS  S T R EH1 - CH ER0 Z
+STRETCHES  S T R EH1 - CH AH0 Z
+STRETCHES(2)  S T R EH1 - CH IH0 Z
+STRETCHING  S T R EH1 - CH IH0 NG
+STRETTO  S T R EH1 - T OW2
+STREVIG  S T R EH1 - V IH0 G
+STREW  S T R UW1
+STREWN  S T R UW1 N
+STREY  S T R EY1
+STRIAR  S T R AY1 R
+STRIBLING  S T ER1 - AH0 - B AH0 L - IH0 NG
+STRIBLING(2)  S T R IH1 - B L IH0 NG
+STRICK  S T R IH1 K
+STRICKEN  S T R IH1 - K AH0 N
+STRICKER  S T R IH1 - K ER0
+STRICKLAND  S T R IH1 - K L AH0 N D
+STRICKLEN  S T R IH1 - K AH0 - L AH0 N
+STRICKLER  S T R IH1 - K L ER0
+STRICKLIN  S T R IH1 - K L IH0 N
+STRICKLING  S T R IH1 - K L IH0 NG
+STRICT  S T R IH1 K T
+STRICTER  S T R IH1 K - T ER0
+STRICTEST  S T R IH1 K - T AH0 S T
+STRICTLY  S T R IH1 K T - L IY0
+STRICTURE  S T R IH1 K - CH ER0
+STRICTURES  S T R IH1 K - CH ER0 Z
+STRIDE  S T R AY1 D
+STRIDENCY  S T R AY1 - D AH0 N - S IY0
+STRIDENT  S T R AY1 - D AH0 N T
+STRIDENTLY  S T R AY1 - D AH0 N T - L IY0
+STRIDER  S T R AY1 - D ER0
+STRIDES  S T R AY1 D Z
+STRIDING  S T R AY1 - D IH0 NG
+STRIEBER  S T R AY1 - B ER0
+STRIEGEL  S T R IY1 - G AH0 L
+STRIEKER  S T R IY1 - K ER0
+STRIEKER'S  S T R IY1 - K ER0 Z
+STRIETER  S T R IY1 - T ER0
+STRIFE  S T R AY1 F
+STRIFES  S T R AY1 F S
+STRIKE  S T R AY1 K
+STRIKE'S  S T R AY1 K S
+STRIKEBREAKER  S T R AY1 K - B R EY2 - K ER0
+STRIKEBREAKERS  S T R AY1 K - B R EY2 - K ER0 Z
+STRIKEOUT  S T R AY1 K - AW2 T
+STRIKEOUTS  S T R AY1 K - AW2 T S
+STRIKER  S T R AY1 - K ER0
+STRIKERS  S T R AY1 - K ER0 Z
+STRIKERS'  S T R AY1 - K ER0 Z
+STRIKES  S T R AY1 K S
+STRIKES'  S T R AY1 K S
+STRIKING  S T R AY1 - K IH0 NG
+STRIKINGLY  S T R AY1 - K IH0 NG - L IY0
+STRIMPLE  S T R IH1 M - P AH0 L
+STRINDEN  S T R IH1 N - D AH0 N
+STRINE  S T R AY1 N
+STRING  S T R IH1 NG
+STRINGED  S T R IH1 NG D
+STRINGENCY  S T R IH1 N - JH AH0 N - S IY0
+STRINGENT  S T R IH1 N - JH AH0 N T
+STRINGENTLY  S T R IH1 N - JH AH0 N T - L IY0
+STRINGER  S T R IH1 - NG ER0
+STRINGERS  S T R IH1 - NG ER0 Z
+STRINGFELLOW  S T R IH1 NG - F EH2 - L OW0
+STRINGFIELD  S T R IH1 NG - F IY2 L D
+STRINGHAM  S T R IH1 NG - HH AE2 M
+STRINGING  S T R IH1 - NG IH0 NG
+STRINGS  S T R IH1 NG Z
+STRINGY  S T R IH1 - NG IY0
+STRIP  S T R IH1 P
+STRIP'S  S T R IH1 P S
+STRIPE  S T R AY1 P
+STRIPED  S T R AY1 P T
+STRIPER  S T R AY1 - P ER0
+STRIPERS  S T R AY1 - P ER0 Z
+STRIPES  S T R AY1 P S
+STRIPLIN  S T R IH1 - P L IH0 N
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+STRIPS  S T R IH1 P S
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+STUDENTS'  S T UW1 - D AH0 N T S
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+STYLISTS  S T AY1 - L IH0 S T S
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+SUBCONTRACTED  S AH0 B - K AA1 N - T R AE0 K - T IH0 D
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+SUBIA  S UW0 - B IY1 - AH0
+SUBIC  S UW1 - B IH0 K
+SUBJECT  S AH0 B - JH EH1 K T
+SUBJECT'S  S AH1 B - JH IH0 K T S
+SUBJECT(2)  S AH1 B - JH IH0 K T
+SUBJECTED  S AH0 B - JH EH1 K - T IH0 D
+SUBJECTING  S AH0 B - JH EH1 K - T IH0 NG
+SUBJECTIVE  S AH0 B - JH EH1 K - T IH0 V
+SUBJECTIVITY  S AH0 B - JH EH0 K - T IH1 - V IH0 - T IY0
+SUBJECTS  S AH1 B - JH IH0 K T S
+SUBJECTS'  S AH1 B - JH EH0 K T S
+SUBJECTS'(2)  S AH1 B - JH EH0 K S
+SUBJECTS(2)  S AH0 B - JH EH1 K T S
+SUBJECTS(3)  S AH0 B - JH EH1 K S
+SUBJUGATE  S AH1 B - JH AH0 - G EY2 T
+SUBJUGATED  S AH1 B - JH AH0 - G EY2 - T IH0 D
+SUBKINGDOM  S AH0 B - K IH1 NG - D AH0 M
+SUBLEASE  S AH1 B - L IY2 S
+SUBLEASING  S AH0 B - L IY1 - S IH0 NG
+SUBLET  S AH1 - B L EH2 T
+SUBLETT  S UW1 - B L IH0 T
+SUBLIME  S AH0 - B L AY1 M
+SUBLIMINAL  S AH0 B - L IH1 - M IH0 - N AH0 L
+SUBLIMINALLY  S AH0 B - L IH1 - M IH0 - N AH0 - L IY0
+SUBLUXATION  S AH0 - B L AH0 K - S EY1 - SH AH0 N
+SUBLUXATIONS  S AH0 - B L AH0 K - S EY1 - SH AH0 N Z
+SUBMACHINE  S AH2 B - M AH0 - SH IY1 N
+SUBMARINE  S AH1 B - M ER0 - IY2 N
+SUBMARINE'S  S AH1 B - M ER0 - IY2 N Z
+SUBMARINE'S(2)  S AH0 B - M ER0 - IY1 N Z
+SUBMARINE(2)  S AH2 B - M ER0 - IY1 N
+SUBMARINES  S AH1 B - M ER0 - IY2 N Z
+SUBMARINES'S  S AH1 B - M ER0 - IY2 N - Z IH0 Z
+SUBMARINES(2)  S AH0 B - M ER0 - IY1 N Z
+SUBMERGE  S AH0 B - M ER1 JH
+SUBMERGED  S AH0 B - M ER1 JH D
+SUBMERGENCE  S AH0 B - M ER1 - JH AH0 N S
+SUBMERSE  S AH0 B - M ER1 S
+SUBMERSED  S AH0 B - M ER1 S T
+SUBMERSIBLE  S AH0 B - M ER1 - S IH0 - B AH0 L
+SUBMERSION  S AH0 B - M ER1 - ZH AH0 N
+SUBMINIMUM  S AH0 B - M IH1 - N IH0 - M AH0 M
+SUBMISSION  S AH0 B - M IH1 - SH AH0 N
+SUBMISSIONS  S AH0 B - M IH1 - SH AH0 N Z
+SUBMISSIVE  S AH0 B - M IH1 - S IH0 V
+SUBMIT  S AH0 B - M IH1 T
+SUBMITS  S AH2 B - M IH1 T S
+SUBMITTED  S AH0 B - M IH1 - T AH0 D
+SUBMITTING  S AH0 B - M IH1 - T IH0 NG
+SUBNOTEBOOK  S AH1 B - N OW1 T - B UH2 K
+SUBORDINATE  S AH0 - B AO1 R - D AH0 - N EY2 T
+SUBORDINATE(2)  S AH0 - B AO1 R - D AH0 N - AH0 T
+SUBORDINATED  S AH0 - B AO1 R - D AH0 - N EY2 - T IH0 D
+SUBORDINATES  S AH0 - B AO1 R - D AH0 - N EY2 T S
+SUBORDINATES(2)  S AH0 - B AO1 R - D AH0 N - AH0 T S
+SUBORDINATING  S AH0 - B AO1 R - D AH0 - N EY2 - T IH0 NG
+SUBORDINATION  S AH0 - B AO2 R - D AH0 - N EY1 - SH AH0 N
+SUBOTNICK  S AH0 - B AA1 T - N IH0 K
+SUBPAR  S AH0 B - P AA1 R
+SUBPLOT  S AH1 B - P L AA0 T
+SUBPLOTS  S AH1 B - P L AA0 T S
+SUBPOENA  S AH0 - P IY1 - N AH0
+SUBPOENAED  S AH0 - P IY1 - N AH0 D
+SUBPOENAING  S AH0 - P IY1 - N AH0 - IH0 NG
+SUBPOENAS  S AH0 - P IY1 - N AH0 Z
+SUBPRINCIPAL  S AH0 B - P R IH1 N - S AH0 - P AH0 L
+SUBPRINCIPALS  S AH0 B - P R IH1 N - S AH0 - P AH0 L Z
+SUBRAMANIAN  S UW2 - B R AH0 - M AA1 - N IY0 - AH0 N
+SUBROTO  S UW0 - B R OW1 - T OW0
+SUBS  S AH1 B Z
+SUBS'S  S AH1 B - Z IH0 Z
+SUBSAHARAN  S AH2 B - S AH0 - HH EH1 - R AH0 N
+SUBSCRIBE  S AH0 B - S K R AY1 B
+SUBSCRIBED  S AH0 B - S K R AY1 B D
+SUBSCRIBER  S AH0 B - S K R AY1 - B ER0
+SUBSCRIBER'S  S AH0 B - S K R AY1 - B ER0 Z
+SUBSCRIBERS  S AH0 B - S K R AY1 - B ER0 Z
+SUBSCRIBERS'  S AH0 B - S K R AY1 - B ER0 Z
+SUBSCRIBES  S AH0 B - S K R AY1 B Z
+SUBSCRIBING  S AH0 B - S K R AY1 - B IH0 NG
+SUBSCRIPTION  S AH0 B - S K R IH1 P - SH AH0 N
+SUBSCRIPTIONS  S AH0 B - S K R IH1 P - SH AH0 N Z
+SUBSECTION  S AH1 B - S EH0 K - SH AH0 N
+SUBSEQUENT  S AH1 B - S AH0 - K W AH0 N T
+SUBSEQUENTLY  S AH1 B - S AH0 - K W AH0 N T - L IY0
+SUBSERVIENCE  S AH0 B - S ER1 - V IY0 - AH0 N S
+SUBSERVIENT  S AH0 B - S ER1 - V IY0 - AH0 N T
+SUBSET  S AH1 B - S EH2 T
+SUBSIDE  S AH0 B - S AY1 D
+SUBSIDED  S AH0 B - S AY1 - D IH0 D
+SUBSIDENCE  S AH0 B - S AY1 - D AH0 N S
+SUBSIDES  S AH0 B - S AY1 D Z
+SUBSIDIARIES  S AH0 B - S IH1 - D IY0 - EH2 - R IY0 Z
+SUBSIDIARIES'  S AH0 B - S IH1 - D IY0 - EH2 - R IY0 Z
+SUBSIDIARY  S AH0 B - S IH1 - D IY0 - EH2 - R IY0
+SUBSIDIARY'S  S AH0 B - S IH1 - D IY0 - EH2 - R IY0 Z
+SUBSIDIES  S AH1 B - S AH0 - D IY0 Z
+SUBSIDIES(2)  S AH1 B - S IH0 - D IY0 Z
+SUBSIDING  S AH0 B - S AY1 - D IH0 NG
+SUBSIDIZATION  S AH2 B - S IH0 - D IH0 - Z EY1 - SH AH0 N
+SUBSIDIZE  S AH1 B - S IH0 - D AY2 Z
+SUBSIDIZED  S AH1 B - S IH0 - D AY2 Z D
+SUBSIDIZES  S AH1 B - S IH0 - D AY2 - Z IH0 Z
+SUBSIDIZING  S AH1 B - S IH0 - D AY2 - Z IH0 NG
+SUBSIDY  S AH1 B - S IH0 - D IY0
+SUBSIST  S AH0 B - S IH1 S T
+SUBSISTENCE  S AH0 B - S IH1 - S T AH0 N S
+SUBSOIL  S AH1 B - S OY2 L
+SUBSTANCE  S AH1 B - S T AH0 N S
+SUBSTANCES  S AH1 B - S T AH0 N - S AH0 Z
+SUBSTANCES(2)  S AH1 B - S T AH0 N - S IH0 Z
+SUBSTANDARD  S AH0 B - S T AE1 N - D ER0 D
+SUBSTANTIAL  S AH0 B - S T AE1 N - CH AH0 L
+SUBSTANTIAL(2)  S AH0 B - S T AE1 N - SH AH0 L
+SUBSTANTIALLY  S AH0 B - S T AE1 N - SH AH0 - L IY0
+SUBSTANTIALLY(2)  S AH0 B - S T AE1 N - CH AH0 - L IY0
+SUBSTANTIATE  S AH0 B - S T AE1 N - CH IY0 - EY2 T
+SUBSTANTIATE(2)  S AH0 B - S T AE1 N - SH IY0 - EY2 T
+SUBSTANTIATED  S AH0 B - S T AE1 N - SH IY0 - EY2 - T IH0 D
+SUBSTANTIATED(2)  S AH0 B - S T AE1 N - CH IY0 - EY2 - T IH0 D
+SUBSTANTIATES  S AH0 B - S T AE1 N - CH IY0 - EY2 T S
+SUBSTANTIATES(2)  S AH0 B - S T AE1 N - SH IY0 - EY2 T S
+SUBSTANTIATION  S AH0 B - S T AE2 N - CH IY0 - EY1 - SH AH0 N
+SUBSTANTIATION(2)  S AH0 B - S T AE2 N - SH IY0 - EY1 - SH AH0 N
+SUBSTANTIVE  S AH1 B - S T AH0 N - T IH0 V
+SUBSTANTIVELY  S AH1 B - S T AH0 N - T IH0 V - L IY0
+SUBSTATION  S AH1 B - S T EY2 - SH AH0 N
+SUBSTITUTE  S AH1 B - S T AH0 - T UW2 T
+SUBSTITUTED  S AH1 B - S T AH0 - T UW2 - T AH0 D
+SUBSTITUTES  S AH1 B - S T AH0 - T UW2 T S
+SUBSTITUTING  S AH1 B - S T IH0 - T UW2 - T IH0 NG
+SUBSTITUTION  S AH2 B - S T IH0 - T UW1 - SH AH0 N
+SUBSTITUTIONS  S AH2 B - S T IH0 - T Y UW1 - SH AH0 N Z
+SUBSTRATE  S AH1 B - S T R EY2 T
+SUBSTRATES  S AH1 B - S T R EY2 T S
+SUBSURFACE  S AH1 B - S ER2 - F AH0 S
+SUBSYSTEM  S AH1 B - S IH2 - S T AH0 M
+SUBSYSTEMS  S AH1 B - S IH2 - S T AH0 M Z
+SUBTERFUGE  S AH1 B - T ER0 - F Y UW2 JH
+SUBTERRANEAN  S AH0 B - T ER0 - EY1 - N IY0 - AH0 N
+SUBTEXT  S AH1 B - T EH2 K S T
+SUBTITLE  S AH1 B - T AY2 - T AH0 L
+SUBTITLED  S AH1 B - T AY2 - T AH0 L D
+SUBTITLES  S AH1 B - T AY2 - T AH0 L Z
+SUBTLE  S AH1 - T AH0 L
+SUBTLER  S AH1 - T AH0 L - ER0
+SUBTLER(2)  S AH1 T - L ER0
+SUBTLETIES  S AH1 - T AH0 L - T IY0 Z
+SUBTLETY  S AH1 - T AH0 L - T IY0
+SUBTLY  S AH1 - T AH0 - L IY0
+SUBTRACT  S AH0 B - T R AE1 K T
+SUBTRACTED  S AH0 B - T R AE1 K - T IH0 D
+SUBTRACTING  S AH0 B - T R AE1 K - T IH0 NG
+SUBTRACTION  S AH0 B - T R AE1 K - SH AH0 N
+SUBTYPE  S AH1 B - T AY2 P
+SUBTYPING  S AH1 B - T AY2 - P IH0 NG
+SUBURB  S AH1 - B ER0 B
+SUBURB'S  S AH1 - B ER0 B Z
+SUBURBAN  S AH0 - B ER1 - B AH0 N
+SUBURBANITE  S AH0 - B ER1 - B AH0 - N AY2 T
+SUBURBANITES  S AH0 - B ER1 - B AH0 - N AY2 T S
+SUBURBANIZATION  S AH0 - B ER2 - B AH0 - N IH0 - Z EY1 - SH AH0 N
+SUBURBANIZE  S AH0 - B ER1 - B AH0 - N AY2 Z
+SUBURBANS  S AH0 - B ER1 - B AH0 N Z
+SUBURBIA  S AH0 - B ER1 - B IY0 - AH0
+SUBURBS  S AH1 - B ER0 B Z
+SUBVERSION  S AH0 B - V ER1 - ZH AH0 N
+SUBVERSIVE  S AH0 B - V ER1 - S IH0 V
+SUBVERSIVES  S AH0 B - V ER1 - S IH0 V Z
+SUBVERT  S AH0 B - V ER1 T
+SUBVERTED  S AH0 B - V ER1 - T IH0 D
+SUBVERTING  S AH0 B - V ER1 - T IH0 NG
+SUBVERTS  S AH0 B - V ER1 T S
+SUBVOLCANIC  S AH2 B - V AA0 L - K AE1 - N IH0 K
+SUBWAY  S AH1 B - W EY2
+SUBWAYS  S AH1 B - W EY2 Z
+SUBZERO  S AH2 B - Z IH1 - R OW0
+SUBZERO(2)  S AH2 B - Z IY1 - R OW0
+SUCCEED  S AH0 K - S IY1 D
+SUCCEEDED  S AH0 K - S IY1 - D AH0 D
+SUCCEEDED(2)  S AH0 K - S IY1 - D IH0 D
+SUCCEEDING  S AH0 K - S IY1 - D IH0 NG
+SUCCEEDS  S AH0 K - S IY1 D Z
+SUCCESS  S AH0 K - S EH1 S
+SUCCESSES  S AH0 K - S EH1 - S AH0 Z
+SUCCESSES(2)  S AH0 K - S EH1 - S IH0 Z
+SUCCESSFUL  S AH0 K - S EH1 S - F AH0 L
+SUCCESSFULLY  S AH0 K - S EH1 S - F AH0 - L IY0
+SUCCESSION  S AH0 K - S EH1 - SH AH0 N
+SUCCESSIVE  S AH0 K - S EH1 - S IH0 V
+SUCCESSIVELY  S AH0 K - S EH1 - S IH0 V - L IY0
+SUCCESSOR  S AH0 K - S EH1 - S ER0
+SUCCESSORS  S AH0 K - S EH1 - S ER0 Z
+SUCCINCT  S AH0 K - S IH1 NG K T
+SUCCINCTLY  S AH0 K - S IH1 NG K T - L IY0
+SUCCOR  S AH1 - K ER0
+SUCCULENT  S AH1 - K Y AH0 - L IH0 N T
+SUCCULENTS  S AH1 - K Y AH0 - L AH0 N T S
+SUCCUMB  S AH0 - K AH1 M
+SUCCUMBED  S AH0 - K AH1 M D
+SUCCUMBING  S AH0 - K AH1 - M IH0 NG
+SUCCUMBS  S AH0 - K AH1 M Z
+SUCH  S AH1 CH
+SUCHAN  S AH1 - CH AH0 N
+SUCHANEK  S AH1 - K AH0 - N IH0 K
+SUCHARD  S UW0 - SH AA1 R D
+SUCHARSKI  S AH0 - K AA1 R S - K IY0
+SUCHECKI  S AH0 - K EH1 - K IY0
+SUCHER  S AH1 - CH ER0
+SUCHINDA  S UW2 - CH IH1 N - D AH0
+SUCHOCKI  S AH0 - K AA1 - K IY0
+SUCHOMEL  S AH1 - K OW0 - M EH2 L
+SUCHY  S AH1 - CH IY0
+SUCK  S AH1 K
+SUCKED  S AH1 K T
+SUCKER  S AH1 - K ER0
+SUCKERED  S AH1 - K ER0 D
+SUCKERS  S AH1 - K ER0 Z
+SUCKING  S AH1 - K IH0 NG
+SUCKLE  S AH1 - K AH0 L
+SUCKLING  S AH1 - K L IH0 NG
+SUCKOW  S AH1 - K AW0
+SUCKROW  S AH1 - K R OW0
+SUCKS  S AH1 K S
+SUCRALOSE  S UW1 - K R AH0 - L OW2 S
+SUCRE  S UW1 - K ER0
+SUCROSE  S UW1 - K R OW0 S
+SUCTION  S AH1 K - SH AH0 N
+SUDA  S UW1 - D AH0
+SUDAFED  S UW1 - D AH0 - F EH2 D
+SUDAN  S UW0 - D AE1 N
+SUDAN'S  S UW0 - D AE1 N Z
+SUDANESE  S UW2 - D AH0 - N IY1 Z
+SUDANO  S UW0 - D AA1 - N OW0
+SUDBECK  S AH1 D - B EH2 K
+SUDBERRY  S AH1 D - B EH2 - R IY0
+SUDBURY  S AH1 D - B EH2 - R IY0
+SUDBURY'S  S AH1 D - B EH2 - R IY0 Z
+SUDD  S AH1 D
+SUDDAM  S AH2 - D AA1 M
+SUDDAM'S  S AH2 - D AA1 M Z
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+SUDDATH  S AH1 - D AH0 TH
+SUDDEN  S AH1 - D AH0 N
+SUDDENLY  S AH1 - D AH0 N - L IY0
+SUDDENNESS  S AH1 - D AH0 N - N AH0 S
+SUDDERTH  S AH1 - D ER0 TH
+SUDDETH  S AH1 - D IH0 TH
+SUDDRETH  S AH1 - D R IH0 TH
+SUDDUTH  S AH1 - D AH0 TH
+SUDER  S UW1 - D ER0
+SUDERMAN  S UW1 - D ER0 - M AH0 N
+SUDLER  S UW1 - D AH0 - L ER0
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+SUDS  S AH1 D Z
+SUE  S UW1
+SUED  S UW1 D
+SUEDE  S W EY1 D
+SUEDES  S W EY1 D Z
+SUEKER  S UW1 - K ER0
+SUEN  S UW1 N
+SUES  S UW1 Z
+SUESS  S W IH1 S
+SUEY  S UW1 - IY0
+SUEZ  S UW1 - EH0 Z
+SUEZ'S  S UW1 - EH0 - Z IH0 Z
+SUFFER  S AH1 - F ER0
+SUFFERED  S AH1 - F ER0 D
+SUFFERER  S AH1 - F ER0 - ER0
+SUFFERERS  S AH1 - F ER0 - ER0 Z
+SUFFERING  S AH1 - F ER0 - IH0 NG
+SUFFERING(2)  S AH1 - F R IH0 NG
+SUFFERINGS  S AH1 - F ER0 - IH0 NG Z
+SUFFERINGS(2)  S AH1 - F R IH0 NG Z
+SUFFERN  S AH1 - F ER0 N
+SUFFERS  S AH1 - F ER0 Z
+SUFFICE  S AH0 - F AY1 S
+SUFFICED  S AH0 - F AY1 S T
+SUFFICES  S AH0 - F AY1 - S IH0 Z
+SUFFICIENCY  S AH0 - F IH1 - SH AH0 N - S IY0
+SUFFICIENT  S AH0 - F IH1 - SH AH0 N T
+SUFFICIENTLY  S AH0 - F IH1 - SH AH0 N T - L IY0
+SUFFIELD  S AH1 - F IY0 L D
+SUFFIELD'S  S AH1 - F IY0 L D Z
+SUFFIX  S AH1 - F IH0 K S
+SUFFOCATE  S AH1 - F AH0 - K EY2 T
+SUFFOCATED  S AH1 - F AH0 - K EY2 - T IH0 D
+SUFFOCATING  S AH1 - F AH0 - K EY2 - T IH0 NG
+SUFFOCATION  S AH2 - F AH0 - K EY1 - SH AH0 N
+SUFFOLK  S AH1 - F AH0 K
+SUFFRAGE  S AH1 - F R IH0 JH
+SUFFRAGETTE  S AH2 - F R AH0 - JH EH1 T
+SUFFRAGETTES  S AH2 - F R AH0 - JH EH1 T S
+SUFFRAGIST  S AH1 - F R AH0 - JH IH0 S T
+SUFFRAGISTS  S AH1 - F R AH0 - JH IH0 S T S
+SUFFRAGISTS(2)  S AH1 - F R AH0 - JH IH0 S S
+SUFFRAGISTS(3)  S AH1 - F R AH0 - JH IH0 S
+SUFFUSE  S AH0 - F Y UW1 Z
+SUFFUSED  S AH0 - F Y UW1 Z D
+SUGAR  SH UH1 - G ER0
+SUGAR'S  SH UH1 - G ER0 Z
+SUGARED  SH UH1 - G ER0 D
+SUGARMAN  SH UH1 - G ER0 - M AH0 N
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+SUGARY  SH UH1 - G ER0 - IY0
+SUGDEN  S AH1 G - D AH0 N
+SUGERMAN  S UW1 - G ER0 - M AH0 N
+SUGG  S AH1 G
+SUGGEST  S AH0 G - JH EH1 S T
+SUGGESTED  S AH0 G - JH EH1 - S T AH0 D
+SUGGESTED(2)  S AH0 G - JH EH1 - S T IH0 D
+SUGGESTIBLE  S AH0 G - JH EH1 - S T AH0 - B AH0 L
+SUGGESTING  S AH0 G - JH EH1 - S T IH0 NG
+SUGGESTION  S AH0 G - JH EH1 S - CH AH0 N
+SUGGESTIONS  S AH0 G - JH EH1 S - CH AH0 N Z
+SUGGESTIVE  S AH0 G - JH EH1 - S T IH0 V
+SUGGESTIVENESS  S AH0 G - JH EH1 - S T IH0 V - N AH0 S
+SUGGESTS  S AH0 G - JH EH1 S T S
+SUGGESTS(2)  S AH0 G - JH EH1 S S
+SUGGESTS(3)  S AH0 G - JH EH1 S
+SUGGS  S AH1 G Z
+SUGIHARA  S UW0 - G IY0 - HH AA1 - R AH0
+SUGIMOTO  S UW0 - G IY0 - M OW1 - T OW0
+SUGIYAMA  S UW0 - G IY0 - Y AA1 - M AH0
+SUGRUE  S AH1 - G R UW0
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+SUHARTO'S  S UW0 - HH AA1 R - T OW0 Z
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+SUHM  S UW1 M
+SUHR  S UH1 R
+SUHRE  S UH1 R
+SUHUA  S UW1 HH - W AA1
+SUHUD  S UW0 - HH UH1 D
+SUI  S UW1 - IY0
+SUI(2)  S W IY0
+SUICIDAL  S UW2 - AH0 - S AY1 - D AH0 L
+SUICIDE  S UW1 - AH0 - S AY2 D
+SUICIDE(2)  S UW1 - IH0 - S AY2 D
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+SUING  S UW1 - IH0 NG
+SUIRE  S UH1 R
+SUISSE  S W IH1 S
+SUISSE'S  S W IH1 - S IH0 Z
+SUISSE'S(2)  S W IY1 - S IH0 Z
+SUISSE(2)  S W IY1 S
+SUIT  S UW1 T
+SUIT'S  S UW1 T S
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+SUITABLE  S UW1 - T AH0 - B AH0 L
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+SUITE  S W IY1 T
+SUITED  S UW1 - T AH0 D
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+SUITER  S UW1 - T ER0
+SUITES  S W IY1 T S
+SUITOR  S UW1 - T ER0
+SUITOR'S  S UW1 - T ER0 Z
+SUITORS  S UW1 - T ER0 Z
+SUITS  S UW1 T S
+SUITT  S UW1 T
+SUK  S AH1 K
+SUKARNO  S UW0 - K AA1 R - N OW0
+SUKARNO'S  S UW0 - K AA1 R - N OW0 Z
+SUKEY  S UW1 - K IY0
+SUKHAREV  S AH1 K - HH ER0 - AH0 V
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+SUKI  S UW1 - K IY0
+SUKIYAKI  S UW0 - K IY0 - AA1 - K IY0
+SUKRU  S UH1 - K R UW0
+SUKRU(2)  S UW1 - K R UW0
+SUKTHANKAR  S UW0 K - T AA1 NG - K AA2 R
+SUKUP  S UW1 K - AH0 P
+SUL  S AH1 L
+SULAK  S UW1 - L AH0 K
+SULAWESI  S UW2 - L AH0 - W EH1 - S IY0
+SULCER  S AH1 L - S ER0
+SULEK  S UW1 - L IH0 K
+SULESKI  S Y UW0 - L EH1 S - K IY0
+SULEWSKI  S Y UW0 - L EH1 F S - K IY0
+SULEYMAN  S UW1 - L IY0 - M AH0 N
+SULFA  S AH1 L - F AH0
+SULFATE  S AH1 L - F EY2 T
+SULFIDE  S AH1 L - F AY2 D
+SULFITE  S AH1 L - F AY2 T
+SULFITES  S AH1 L - F AY2 T S
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+TEMPERAMENTALLY(3)  T EH2 M - P R AH0 - M EH1 - N AH0 - L IY0
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+TEMPERATE(2)  T EH1 M - P ER0 - AH0 T
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+TRANSCENDENT  T R AE0 N - S EH1 N - D AH0 N T
+TRANSCENDENTAL  T R AE2 N - S AH0 N - D EH1 N - T AH0 L
+TRANSCENDENTAL(2)  T R AE2 N - S AH0 N - D EH1 - N AH0 L
+TRANSCENDING  T R AE0 N - S EH1 N - D IH0 NG
+TRANSCENDS  T R AE0 N - S EH1 N D Z
+TRANSCHANNEL  T R AH1 N S - CH AE1 - N AH0 L
+TRANSCHANNEL'S  T R AH1 N S - CH AE1 - N AH0 L Z
+TRANSCISCO  T R AE2 N - S IH1 - S K OW0
+TRANSCO  T R AE1 N - S K OW0
+TRANSCON  T R AE1 N Z - K AA0 N
+TRANSCONTINENTAL  T R AE2 N Z - K AA2 N - T IH0 - N EH1 N - T AH0 L
+TRANSCONTINENTAL'S  T R AE2 N Z - K AA2 N - T IH0 - N EH1 N - T AH0 L Z
+TRANSCONTINENTAL'S(2)  T R AE2 N Z - K AA2 - N IH0 - N EH1 N - T AH0 L Z
+TRANSCONTINENTAL'S(3)  T R AE2 N Z - K AA2 N - T IH0 - N EH1 - N AH0 L Z
+TRANSCONTINENTAL'S(4)  T R AE2 N Z - K AA2 - N IH0 - N EH1 - N AH0 L Z
+TRANSCONTINENTAL(2)  T R AE2 N Z - K AA2 - N IH0 - N EH1 N - T AH0 L
+TRANSCONTINENTAL(3)  T R AE2 N Z - K AA2 N - T IH0 - N EH1 - N AH0 L
+TRANSCONTINENTAL(4)  T R AE2 N Z - K AA2 - N IH0 - N EH1 - N AH0 L
+TRANSCRIBE  T R AE0 N - S K R AY1 B
+TRANSCRIBED  T R AE0 N - S K R AY1 B D
+TRANSCRIBER  T R AE0 N - S K R AY1 - B ER0
+TRANSCRIBERS  T R AE0 N - S K R AY1 - B ER0 Z
+TRANSCRIBES  T R AE0 N - S K R AY1 B Z
+TRANSCRIBING  T R AE0 N - S K R AY1 - B IH0 NG
+TRANSCRIPT  T R AE1 N - S K R IH2 P T
+TRANSCRIPTION  T R AE2 N - S K R IH1 P - SH AH0 N
+TRANSCRIPTIONS  T R AE2 N - S K R IH1 P - SH AH0 N Z
+TRANSCRIPTS  T R AE1 N - S K R IH2 P T S
+TRANSDUCER  T R AE0 N S - D UW1 - S ER0
+TRANSDUCERS  T R AE0 N S - D UW1 - S ER0 Z
+TRANSECT  T R AE1 N - S EH2 K T
+TRANSECTED  T R AE1 N - S EH2 K - T IH0 D
+TRANSECTION  T R AE1 N - S EH2 K - SH AH0 N
+TRANSFER  T R AE0 N S - F ER1
+TRANSFER(2)  T R AE1 N S - F ER0
+TRANSFERABILITY  T R AE2 N S - F ER0 - AH0 - B IH1 - L IH0 - T IY0
+TRANSFERABLE  T R AE0 N S - F ER1 - AH0 - B AH0 L
+TRANSFERED  T R AE0 N S - F ER1 D
+TRANSFERENCE  T R AE0 N S - F ER1 - AH0 N S
+TRANSFERING  T R AE0 N S - F ER1 - IH0 NG
+TRANSFERRABLE  T R AE2 N S - F ER1 - AH0 - B AH0 L
+TRANSFERRED  T R AE0 N S - F ER1 D
+TRANSFERRED(2)  T R AE1 N S - F ER0 D
+TRANSFERRING  T R AE0 N S - F ER1 - IH0 NG
+TRANSFERS  T R AE0 N S - F ER1 Z
+TRANSFERS(2)  T R AE1 N S - F ER0 Z
+TRANSFIX  T R AE0 N S - F IH1 K S
+TRANSFIXED  T R AE0 N S - F IH1 K S T
+TRANSFORM  T R AE0 N S - F AO1 R M
+TRANSFORM(2)  T R AE1 N S - F AO0 R M
+TRANSFORMATION  T R AE2 N S - F ER0 - M EY1 - SH AH0 N
+TRANSFORMATIONAL  T R AE2 N S - F ER0 - M EY1 - SH AH0 - N AH0 L
+TRANSFORMATIONS  T R AE2 N S - F ER0 - M EY1 - SH AH0 N Z
+TRANSFORMED  T R AE0 N S - F AO1 R M D
+TRANSFORMER  T R AE0 N S - F AO1 R - M ER0
+TRANSFORMERS  T R AE0 N S - F AO1 R - M ER0 Z
+TRANSFORMING  T R AE0 N S - F AO1 R - M IH0 NG
+TRANSFORMS  T R AE0 N S - F AO1 R M Z
+TRANSFUSE  T R AE0 N S - F Y UW1 Z
+TRANSFUSED  T R AE0 N S - F Y UW1 Z D
+TRANSFUSION  T R AE0 N S - F Y UW1 - ZH AH0 N
+TRANSFUSIONS  T R AE0 N S - F Y UW1 - ZH AH0 N Z
+TRANSGENIC  T R AE2 N Z - JH EH1 - N IH0 K
+TRANSGRESS  T R AE0 N Z - G R EH1 S
+TRANSGRESSED  T R AE0 N Z - G R EH1 S T
+TRANSGRESSION  T R AE0 N Z - G R EH1 - SH AH0 N
+TRANSGRESSIONS  T R AE0 N Z - G R EH1 - SH AH0 N Z
+TRANSGRESSOR  T R AE0 N Z - G R EH1 - S ER0
+TRANSIENCE  T R AE1 N - Z IY0 - AH0 N S
+TRANSIENT  T R AE1 N - ZH AH0 N T
+TRANSIENTS  T R AE1 N - Z IY0 - AH0 N T S
+TRANSILLUMINATION  T R AE2 N - Z AH0 - L UW2 - M AH0 - N EY1 - SH AH0 N
+TRANSIMAGE  T R AE2 N - Z IH1 - M IH0 JH
+TRANSISTOR  T R AE0 N - Z IH1 - S T ER0
+TRANSISTORS  T R AE0 N - Z IH1 - S T ER0 Z
+TRANSIT  T R AE1 N - Z AH0 T
+TRANSITION  T R AE0 N - Z IH1 - SH AH0 N
+TRANSITIONAL  T R AE0 N - S IH1 - SH AH0 - N AH0 L
+TRANSITIONAL(2)  T R AE0 N - Z IH1 - SH AH0 - N AH0 L
+TRANSITIONING  T R AE0 N - Z IH1 - SH AH0 N - IH0 NG
+TRANSITIONS  T R AE0 N - Z IH1 - SH AH0 N Z
+TRANSITORY  T R AE1 N - Z AH0 - T AO2 - R IY0
+TRANSKEI  T R AE1 N Z - K EY2
+TRANSLATE  T R AE0 N Z - L EY1 T
+TRANSLATE(2)  T R AE0 N S - L EY1 T
+TRANSLATED  T R AE0 N Z - L EY1 - T AH0 D
+TRANSLATED(2)  T R AE0 N S - L EY1 - T IH0 D
+TRANSLATES  T R AE0 N Z - L EY1 T S
+TRANSLATES(2)  T R AE1 N S - L EY2 T S
+TRANSLATING  T R AE0 N Z - L EY1 - T IH0 NG
+TRANSLATING(2)  T R AE1 N S - L EY2 - T IH0 NG
+TRANSLATION  T R AE0 N Z - L EY1 - SH AH0 N
+TRANSLATION(2)  T R AE0 N S - L EY1 - SH AH0 N
+TRANSLATIONS  T R AE0 N Z - L EY1 - SH AH0 N Z
+TRANSLATIONS(2)  T R AE0 N S - L EY1 - SH AH0 N Z
+TRANSLATOR  T R AE0 N S - L EY1 - T ER0
+TRANSLATOR(2)  T R AE0 N Z - L EY1 - T ER0
+TRANSLATORS  T R AE0 N S - L EY1 - T ER0 Z
+TRANSLATORS(2)  T R AE0 N Z - L EY1 - T ER0 Z
+TRANSLOGIC  T R AE2 N Z - L AA1 - JH IH0 K
+TRANSLUCENT  T R AE0 N S - L UW1 - S AH0 N T
+TRANSMARK  T R AE1 N Z - M AA2 R K
+TRANSMEDIA  T R AE2 N Z - M IY1 - D IY0 - AH0
+TRANSMISSION  T R AE0 N S - M IH1 - SH AH0 N
+TRANSMISSION(2)  T R AE0 N Z - M IH1 - SH AH0 N
+TRANSMISSIONS  T R AE0 N Z - M IH1 - SH AH0 N Z
+TRANSMIT  T R AE0 N Z - M IH1 T
+TRANSMITS  T R AE0 N Z - M IH1 T S
+TRANSMITTABLE  T R AE0 N Z - M IH1 - T AH0 - B AH0 L
+TRANSMITTAL  T R AE0 N S - M IH1 - T AH0 L
+TRANSMITTED  T R AE0 N S - M IH1 - T IH0 D
+TRANSMITTED(2)  T R AE0 N Z - M IH1 - T AH0 D
+TRANSMITTER  T R AE0 N S - M IH1 - T ER0
+TRANSMITTERS  T R AE0 N S - M IH1 - T ER0 Z
+TRANSMITTING  T R AE0 N S - M IH1 - T IH0 NG
+TRANSNATIONAL  T R AE0 N S - N AE1 - SH AH0 - N AH0 L
+TRANSOCEANIC  T R AE2 N Z - OW0 - SH IY0 - AE1 - N IH0 K
+TRANSOHIO  T R AE2 N Z - OW0 - HH AY1 - OW0
+TRANSOM  T R AE1 N - S AH0 M
+TRANSOMS  T R AE1 N - S AH0 M Z
+TRANSOU  T R AE1 N - Z UW2
+TRANSPAC  T R AE1 N Z - P AE2 K
+TRANSPACIFIC  T R AE2 N S - P AH0 - S IH1 - F IH0 K
+TRANSPARENCIES  T R AE0 N - S P EH1 - R AH0 N - S IY0 Z
+TRANSPARENCY  T R AE0 N - S P EH1 - R AH0 N - S IY0
+TRANSPARENT  T R AE0 N - S P EH1 - R AH0 N T
+TRANSPARENTLY  T R AE0 N - S P EH1 - R AH0 N T - L IY0
+TRANSPARK  T R AE1 N S - P AA1 R K
+TRANSPIRE  T R AE0 N - S P AY1 - ER0
+TRANSPIRED  T R AE0 N - S P AY1 - ER0 D
+TRANSPIRES  T R AE0 N - S P AY1 - ER0 Z
+TRANSPIRING  T R AE0 N - S P AY1 - ER0 - IH0 NG
+TRANSPLANT  T R AE0 N S - P L AE1 N T
+TRANSPLANTATION  T R AE2 N Z - P L AE0 N - T EY1 - SH AH0 N
+TRANSPLANTED  T R AE0 N S - P L AE1 N - T IH0 D
+TRANSPLANTING  T R AE0 N S - P L AE1 N - T IH0 NG
+TRANSPLANTS  T R AE0 N S - P L AE1 N T S
+TRANSPONDER  T R AE0 N S - P AA1 N - D ER0
+TRANSPONDERS  T R AE0 N S - P AA1 N - D ER0 Z
+TRANSPORT  T R AE0 N S - P AO1 R T
+TRANSPORT(2)  T R AE1 N S - P AO0 R T
+TRANSPORTABLE  T R AE0 N S - P AO1 R - T AH0 - B AH0 L
+TRANSPORTATION  T R AE2 N S - P ER0 - T EY1 - SH AH0 N
+TRANSPORTATION'S  T R AE2 N S - P ER0 - T EY1 - SH AH0 N Z
+TRANSPORTED  T R AE0 N S - P AO1 R - T AH0 D
+TRANSPORTER  T R AE0 N S - P AO1 R - T ER0
+TRANSPORTERS  T R AE0 N S - P AO1 R - T ER0 Z
+TRANSPORTING  T R AE0 N S - P AO1 R - T IH0 NG
+TRANSPORTS  T R AE0 N S - P AO1 R T S
+TRANSPORTS(2)  T R AE1 N S - P AO0 R T S
+TRANSPOSE  T R AE0 N S - P OW1 Z
+TRANSPOSED  T R AE0 N S - P OW1 Z D
+TRANSRACIAL  T R AE2 N Z - R EY1 - SH AH0 L
+TRANSRAPID  T R AE1 N Z - R AE1 - P IH0 D
+TRANSSEXUAL  T R AE0 N - S EH1 K - SH Y UW0 - AH0 L
+TRANSSEXUALS  T R AE0 N - S EH1 K - SH Y UW0 - AH0 L Z
+TRANSTAR  T R AE0 N S - T AA1 R
+TRANSTECHNOLOGY  T R AE2 N Z - T AH0 K - N AA1 - L AH0 - JH IY0
+TRANSTECTOR  T R AE2 N Z - T EH1 K - T ER0
+TRANSUE  T R AE1 N - Z UW0
+TRANSVAAL  T R AE0 N Z - V AA1 L
+TRANSVERSE  T R AE0 N Z - V ER1 S
+TRANSVESTITE  T R AE0 N Z - V EH1 - S T AY0 T
+TRANSVESTITES  T R AE0 N Z - V EH1 - S T AY0 T S
+TRANSWAY  T R AE1 N Z - W EY2
+TRANSWESTERN  T R AE2 N Z - W EH1 - S T ER0 N
+TRANSWORLD  T R AE0 N S - W ER1 L D
+TRANSYLVANIA  T R AE2 N - Z IY0 L - V EY1 - N IY0 - AH0
+TRANSYLVANIA(2)  T R AE2 N - S IH0 L - V EY1 - N Y AH0
+TRANT  T R AE1 N T
+TRANTER  T R AE1 N - T ER0
+TRANTHAM  T R AE1 N - TH AH0 M
+TRANUM  T R AE1 - N AH0 M
+TRANZONIC  T R AE0 N - Z AA1 - N IH0 K
+TRAP  T R AE1 P
+TRAPANI  T R AA0 - P AA1 - N IY0
+TRAPASSO  T R AA0 - P AA1 - S OW0
+TRAPELO  T R AH0 - P EH1 - L OW0
+TRAPEZE  T R AH0 - P IY1 Z
+TRAPHAGEN  T R AE1 - F AH0 - G AH0 N
+TRAPNELL  T R AE1 P - N AH0 L
+TRAPP  T R AE1 P
+TRAPPE  T R AE1 P
+TRAPPED  T R AE1 P T
+TRAPPERS  T R AE1 - P ER0 Z
+TRAPPING  T R AE1 - P IH0 NG
+TRAPPINGS  T R AE1 - P IH0 NG Z
+TRAPPIST  T R AE1 - P IH0 S T
+TRAPS  T R AE1 P S
+TRASH  T R AE1 SH
+TRASHED  T R AE1 SH T
+TRASHES  T R AE1 - SH IH0 Z
+TRASHING  T R AE1 - SH IH0 NG
+TRASHY  T R AE1 - SH IY0
+TRASK  T R AE1 S K
+TRAUB  T R AO1 B
+TRAUDT  T R AO1 D T
+TRAUGER  T R AW1 - G ER0
+TRAUGH  T R AO1
+TRAUGHBER  T R AO1 - B ER0
+TRAUGOTT  T R AW1 - G AH0 T
+TRAUM  T R AO1 M
+TRAUMA  T R AO1 - M AH0
+TRAUMAS  T R AO1 - M AH0 Z
+TRAUMATIC  T R AO0 - M AE1 - T IH0 K
+TRAUMATIZE  T R AO1 - M AH0 - T AY2 Z
+TRAUMATIZED  T R AO1 - M AH0 - T AY2 Z D
+TRAUSCH  T R AW1 SH
+TRAUT  T R AO1 T
+TRAUTH  T R AO1 TH
+TRAUTMAN  T R AW1 T - M AH0 N
+TRAUTMANN  T R AW1 T - M AH0 N
+TRAUTNER  T R AW1 T - N ER0
+TRAUTWEIN  T R AW1 T - W AY2 N
+TRAVAGLINI  T R AA0 - V AA0 G - L IY1 - N IY0
+TRAVAIL  T R AH0 - V EY1 L
+TRAVAILS  T R AH0 - V EY1 L Z
+TRAVEL  T R AE1 - V AH0 L
+TRAVELDAY  T R AE1 - V AH0 L - D EY2
+TRAVELDAYS  T R AE1 - V AH0 L - D EY2 Z
+TRAVELED  T R AE1 - V AH0 L D
+TRAVELER  T R AE1 - V AH0 - L ER0
+TRAVELER'S  T R AE1 - V AH0 - L ER0 Z
+TRAVELER(2)  T R AE1 V - L ER0
+TRAVELERS  T R AE1 - V AH0 - L ER0 Z
+TRAVELERS'  T R AE1 - V AH0 - L ER0 Z
+TRAVELERS(2)  T R AE1 V - L ER0 Z
+TRAVELGATE  T R AE1 - V AH0 L - G EY2 T
+TRAVELING  T R AE1 - V AH0 L - IH0 NG
+TRAVELING(2)  T R AE1 - V L IH0 NG
+TRAVELLED  T R AE1 - V AH0 L D
+TRAVELLER  T R AE1 - V AH0 - L ER0
+TRAVELLERS  T R AE1 - V AH0 - L ER0 Z
+TRAVELLING  T R AE1 - V AH0 L - IH0 NG
+TRAVELLING(2)  T R AE1 - V L IH0 NG
+TRAVELODGE  T R AE1 - V AH0 - L AA1 JH
+TRAVELOGUE  T R AE1 - V AH0 - L AO2 G
+TRAVELS  T R AE1 - V AH0 L Z
+TRAVELSTEAD  T R AE1 - V AH0 L - S T EH2 D
+TRAVENOL  T R AE1 - V AH0 - N AH0 L
+TRAVER  T R EY1 - V ER0
+TRAVERS  T R AE1 - V ER0 Z
+TRAVERSE  T R AE1 - V ER0 S
+TRAVERSE(2)  T R AH0 - V ER1 S
+TRAVERSED  T R AE1 - V ER0 S T
+TRAVERSED(2)  T R AH0 - V ER1 S T
+TRAVERSING  T R AH0 - V ER1 - S IH0 NG
+TRAVERSO  T R AA0 - V EH1 R - S OW0
+TRAVESTIES  T R AE1 - V AH0 - S T IY0 Z
+TRAVESTY  T R AE1 - V AH0 - S T IY0
+TRAVIATA  T R AA0 - V IY0 - AA1 - T AH0
+TRAVIESO  T R AA0 - V IY1 - S OW0
+TRAVIS  T R AE1 - V IH0 S
+TRAVISANO  T R AE2 - V IH0 - S AA1 - N OW0
+TRAVNIK  T R AE1 V - N IH0 K
+TRAVOLTA  T R AH0 - V OW1 L - T AH0
+TRAVOLTA'S  T R AH0 - V OW1 L - T AH0 Z
+TRAVOLTAS  T R AH0 - V OW1 L - T AH0 Z
+TRAWEEK  T R AO1 - IY2 K
+TRAWICK  T R AO1 - IH0 K
+TRAWLER  T R AO1 - L ER0
+TRAWLERS  T R AO1 - L ER0 Z
+TRAX  T R AE1 K S
+TRAXLER  T R AE1 K S - L ER0
+TRAY  T R EY1
+TRAYER  T R EY1 - ER0
+TRAYLOR  T R EY1 - L ER0
+TRAYNHAM  T R EY1 N - HH AH0 M
+TRAYNOR  T R EY1 - N ER0
+TRAYS  T R EY1 Z
+TRAYWICK  T R EY1 - W IH2 K
+TRBOVICH  T ER0 - B AA1 - V IH0 CH
+TREACHEROUS  T R EH1 - CH ER0 - AH0 S
+TREACHERY  T R EH1 - CH ER0 - IY0
+TREACY  T R EY1 - S IY0
+TREAD  T R EH1 D
+TREADAWAY  T R EH1 D - AH0 - W EY2
+TREADING  T R EH1 - D IH0 NG
+TREADMILL  T R EH1 D - M IH2 L
+TREADMILLS  T R EH1 D - M IH2 L Z
+TREADS  T R EH1 D Z
+TREADWAY  T R EH1 D - W EY2
+TREADWELL  T R EH1 D - W EH2 L
+TREADWHEEL  T R EH1 D - W IY2 L
+TREANOR  T R IY1 - N ER0
+TREASE  T R IY1 Z
+TREASON  T R IY1 - Z AH0 N
+TREASTER  T R IY1 - S T ER0
+TREASURE  T R EH1 - ZH ER0
+TREASURED  T R EH1 - ZH ER0 D
+TREASURER  T R EH1 - ZH ER0 - ER0
+TREASURER'S  T R EH1 - ZH ER0 - ER0 Z
+TREASURERS  T R EH1 - ZH ER0 - ER0 Z
+TREASURES  T R EH1 - ZH ER0 Z
+TREASURIES  T R EH1 - ZH ER0 - IY0 Z
+TREASURY  T R EH1 - ZH ER0 - IY0
+TREASURY'S  T R EH1 - ZH ER0 - IY0 Z
+TREASURYS  T R EH1 - ZH ER0 - IY0 Z
+TREAT  T R IY1 T
+TREATABLE  T R IY1 - T AH0 - B AH0 L
+TREATED  T R IY1 - T AH0 D
+TREATED(2)  T R IY1 - T IH0 D
+TREATER  T R IY1 - T ER0
+TREATERS  T R IY1 - T ER0 Z
+TREATIES  T R IY1 - T IY0 Z
+TREATING  T R IY1 - T IH0 NG
+TREATISE  T R IY1 - T AH0 S
+TREATISES  T R IY1 - T AH0 - S AH0 Z
+TREATMENT  T R IY1 T - M AH0 N T
+TREATMENTS  T R IY1 T - M AH0 N T S
+TREATS  T R IY1 T S
+TREATY  T R IY1 - T IY0
+TREATY'S  T R IY1 - T IY0 Z
+TREBILCOCK  T R IH0 - B IH1 L - K AH0 K
+TREBLE  T R EH1 - B AH0 L
+TREBLED  T R EH1 - B AH0 L D
+TREBLINKA  T R EH0 B - L IH1 NG - K ER0
+TREBLINKA(2)  T R EH0 B - L IH1 NG - K AH0
+TRECKER  T R EH1 - K ER0
+TREDER  T R IY1 - D ER0
+TREDWAY  T R EH1 D - W EY2
+TREE  T R IY1
+TREECE  T R IY1 S
+TREELESS  T R IY1 - L AH0 S
+TREEN  T R IY1 N
+TREES  T R IY1 Z
+TREESE  T R IY1 Z
+TREESH  T R IY1 SH
+TREESWEET  T R IY1 S - W IY2 T
+TREETOP  T R IY1 - T AO2 P
+TREETOPS  T R IY1 - T AO2 P S
+TREFETHEN  T R EH1 - F IH0 - TH AH0 N
+TREFGARNE  T R EH1 F - G AA0 R N
+TREFRY  T R EH1 - F R IY0
+TREFZ  T R EH1 F Z
+TREGLIA  T R EH1 G - L IY0 - AH0
+TREGO  T R EH1 - G OW0
+TREGONING  T R EH1 - G AH0 - N IH0 NG
+TREGRE  T R EH1 - G ER0
+TREGURTHA  T R EH0 - G ER1 - TH AH0
+TREHARNE  T R EH1 - HH AA0 R N
+TREIBER  T R AY1 - B ER0
+TREICHEL  T R AY1 - K AH0
+TREICHLER  T R AY1 - K AH0 - L ER0
+TREICHLER(2)  T R AY1 - K L ER0
+TREINEN  T R AY1 - N AH0 N
+TREJO  T R EY1 - Y OW0
+TREK  T R EH1 K
+TREKKED  T R EH1 K T
+TREKKING  T R EH1 - K IH0 NG
+TREKS  T R EH1 K S
+TRELA  T R EH1 - L AH0
+TRELLA  T R EH1 - L AH0
+TRELLEBORG  T R EH1 - L AH0 - B AO0 R G
+TRELLEBORG'S  T R EH1 - L AH0 - B AO0 R G Z
+TRELLIS  T R EH1 - L AH0 S
+TRELOAR  T R EH1 - L AO0 R
+TREMAIN  T R EH1 - M AY0 N
+TREMAINE  T R IH0 - M EY1 N
+TREMAYNE  T R EH1 - M EY0 N
+TREMBATH  T R EH1 M - B AH0 TH
+TREMBLAY  T R EH1 M - B L EY0
+TREMBLE  T R EH1 M - B AH0 L
+TREMBLED  T R EH1 M - B AH0 L D
+TREMBLEY  T R EH1 M - B L IY0
+TREMBLING  T R EH1 M - B AH0 L - IH0 NG
+TREMBLING(2)  T R EH1 M - B L IH0 NG
+TREMBLY  T R EH1 M - B L IY0
+TREMEL  T R EH1 - M AH0 L
+TREMENDOUS  T R AH0 - M EH1 N - D AH0 S
+TREMENDOUS(2)  T R IH0 - M EH1 N - D AH0 S
+TREMENDOUSLY  T R AH0 - M EH1 N - D AH0 S - L IY0
+TREMENDOUSLY(2)  T R IH0 - M EH1 N - D AH0 S - L IY0
+TREMENS  T R EH1 - M AH0 N Z
+TREML  T R EH1 - M AH0 L
+TREMMEL  T R EH1 - M AH0 L
+TREMONT  T R EH1 - M AH0 N T
+TREMOR  T R EH1 - M ER0
+TREMORS  T R EH1 - M ER0 Z
+TREMPER  T R EH1 M - P ER0
+TREMULOUS  T R EH1 - M Y AH0 - L AH0 S
+TREMULOUSLY  T R EH1 - M Y AH0 - L AH0 S - L IY0
+TRENARY  T R EH1 - N EH0 - R IY0
+TRENCH  T R EH1 N CH
+TRENCHANT  T R EH1 N - CH AH0 N T
+TRENCHARD  T R EH1 NG - K ER0 D
+TRENCHER  T R EH1 N - CH ER0
+TRENCHES  T R EH1 N - CH IH0 Z
+TREND  T R EH1 N D
+TRENDED  T R EH1 N - D IH0 D
+TRENDIER  T R EH1 N - D Y ER0
+TRENDIER(2)  T R EH1 N - D IY0 - ER0
+TRENDIEST  T R EH0 N - D IY1 S T
+TRENDIEST(2)  T R EH1 N - D IY0 - AH0 S T
+TRENDING  T R EH1 N - D IH0 NG
+TRENDLESS  T R EH1 N D - L AH0 S
+TRENDLINE  T R EH1 N D - L AY2 N
+TRENDS  T R EH1 N D Z
+TRENDS(2)  T R EH1 N Z
+TRENDSETTER  T R EH1 N D - S EH2 - T ER0
+TRENDY  T R EH1 N - D IY0
+TRENHOLM  T R EH1 N - HH OW2 L M
+TRENKAMP  T R EH1 N - K AE2 M P
+TRENKLE  T R EH1 NG - K AH0 L
+TRENT  T R EH1 N T
+TRENT'S  T R EH1 N T S
+TRENTE-ET-QUARANTE  T R EY1 N - T EY0 - K W AA2 - R EH1 N - T EY0
+TRENTHAM  T R EH1 N - TH AH0 M
+TRENTMAN  T R EH1 N T - M AH0 N
+TRENTON  T R EH1 N - T AH0 N
+TRENTON'S  T R EH1 N - T AH0 N Z
+TREON  T R IY1 - AH0 N
+TREPAGNIER  T R EH1 - P AH0 G - N IY0 - ER0
+TREPANIER  T R EH1 - P AH0 - N IY0 - ER0
+TREPIDATION  T R EH2 - P IH0 - D EY1 - SH AH0 N
+TREPPEL  T R EH1 - P AH0 L
+TREPTOW  T R EH1 P - T OW0
+TRESCH  T R EH1 SH
+TRESCOTT  T R EH1 S - K AH0 T
+TRESPASS  T R EH1 S - P AE2 S
+TRESPASS(2)  T R EH1 S - P AH0 S
+TRESPASSING  T R EH1 S - P AE2 - S IH0 NG
+TRESPASSING(2)  T R EH1 S - P AH0 - S IH0 NG
+TRESS  T R EH1 S
+TRESSEL  T R EH1 - S AH0 L
+TRESSES  T R EH1 - S IH0 Z
+TRESSLER  T R EH1 S - L ER0
+TREST  T R EH1 S T
+TRESTER  T R EH1 - S T ER0
+TRESTLE  T R EH1 - S AH0 L
+TRETHEWEY  T R EH1 - TH Y UW0 - IY0
+TRETINOIN  T R EH1 - T IH0 - N OY2 N
+TRETTEL  T R EH1 - T AH0 L
+TRETTER  T R EH1 - T ER0
+TRETTIN  T R EH1 - T IH0 N
+TREU  T R UW1
+TREUHAND  T R UW1 - HH AE2 N D
+TREUHANDANSTALT  T R UW2 - HH AE1 N - D AH0 N - S T AA2 L T
+TREURNICHT  T R UW1 R - N IH0 K T
+TREVATHAN  T R EH1 - V AH0 - TH AH0 N
+TREVELYAN  T R AH0 - V EH1 L - Y AH0 N
+TREVINO  T R AH0 - V IY1 - N OW0
+TREVISO  T R EH0 - V IY1 - S OW0
+TREVIZO  T R EH0 - V IY1 - Z OW0
+TREVOR  T R EH1 - V ER0
+TREW  T R UW1
+TREXLER  T R EH1 K S - L ER0
+TREY  T R EY1
+TREYBIG  T R EY1 - B IH0 G
+TREZISE  T R EH1 - Z AY0 Z
+TREZZA  T R EH1 - Z AH0
+TRI  T R AY1
+TRI-STATE  T R AY1 - S T EY1 T
+TRIAD  T R AY1 - AE2 D
+TRIAD'S  T R AY1 - AE2 D Z
+TRIADS  T R AY1 - AE2 D Z
+TRIAGE  T R AY1 - IH0 JH
+TRIAL  T R AY1 - AH0 L
+TRIAL'S  T R AY1 - AH0 L Z
+TRIAL(2)  T R AY1 L
+TRIALS  T R AY1 - AH0 L Z
+TRIALS(2)  T R AY1 L Z
+TRIANA  T R IY0 - AE1 - N AH0
+TRIANGLE  T R AY1 - AE2 NG - G AH0 L
+TRIANGLE'S  T R AY1 - AE2 NG - G AH0 L Z
+TRIANGLES  T R AY1 - AE2 NG - G AH0 L Z
+TRIANGULAR  T R AY0 - AE1 NG - G Y AH0 - L ER0
+TRIANGULATION  T R AY0 - AE2 NG - G Y UW0 - L EY1 - SH AH0 N
+TRIANO  T R IY0 - AA1 - N OW0
+TRIARC  T R AY1 - AA2 R K
+TRIATHLON  T R AY2 - AE1 TH - L AH0 N
+TRIB  T R IH1 B
+TRIBAL  T R AY1 - B AH0 L
+TRIBALISM  T R AY1 - B AH0 - L IH0 Z M
+TRIBALISM(2)  T R AY1 - B AH0 - L IH0 - Z AH0 M
+TRIBASA  T R AY1 - B AE1 - S AH0
+TRIBBETT  T R IH1 - B IH0 T
+TRIBBEY  T R IH1 - B IY0
+TRIBBLE  T R IH1 - B AH0 L
+TRIBBLES  T R IH1 - B AH0 L Z
+TRIBBY  T R IH1 - B IY0
+TRIBE  T R AY1 B
+TRIBE'S  T R AY1 B Z
+TRIBECA  T R IH0 - B EH1 - K AH0
+TRIBES  T R AY1 B Z
+TRIBESMAN  T R AY1 B Z - M AE0 N
+TRIBESMEN  T R AY1 B Z - M IH0 N
+TRIBLE  T R IH1 - B AH0 L
+TRIBOROUGH  T R AY1 - B ER0 - OW0
+TRIBULATION  T R IH2 - B Y AH0 - L EY1 - SH AH0 N
+TRIBULATIONS  T R IH2 - B Y AH0 - L EY1 - SH AH0 N Z
+TRIBULL  T R IH1 - B AH0 L
+TRIBUNAL  T R AH0 - B Y UW1 - N AH0 L
+TRIBUNALS  T R AY2 - B Y UW1 - N AH0 L Z
+TRIBUNE  T R IH1 - B Y UW0 N
+TRIBUNE'S  T R IH1 - B Y UW0 N Z
+TRIBUTARIES  T R IH1 - B Y AH0 - T EH2 - R IY0 Z
+TRIBUTARY  T R IH1 - B Y AH0 - T EH2 - R IY0
+TRIBUTE  T R IH1 - B Y UW0 T
+TRIBUTES  T R IH1 - B Y UW0 T S
+TRICARICO  T R IY0 - K AA0 - R IY1 - K OW0
+TRICE  T R AY1 S
+TRICENTROL  T R AY2 - S EH1 N - T R AA0 L
+TRICENTROL'S  T R AY2 - S EH1 N - T R AA0 L Z
+TRICEPS  T R AY1 - S EH2 P S
+TRICERATOPS  T R AY2 - S EH1 - R AH0 - T AO2 P S
+TRICHE  T R IH1 CH
+TRICHET  T R IH1 - CH IH0 T
+TRICIA  T R IH1 - SH AH0
+TRICIA'S  T R IH1 - SH AH0 Z
+TRICIL  T R IH1 - S IH0 L
+TRICK  T R IH1 K
+TRICKED  T R IH1 K T
+TRICKEL  T R IH1 - K AH0 L
+TRICKERY  T R IH1 - K ER0 - IY0
+TRICKETT  T R IH1 - K IH0 T
+TRICKEY  T R IH1 - K IY0
+TRICKIER  T R IH1 - K IY0 - ER0
+TRICKIEST  T R IH1 - K IY0 - AH0 S T
+TRICKING  T R IH1 - K IH0 NG
+TRICKLE  T R IH1 - K AH0 L
+TRICKLED  T R IH1 - K AH0 L D
+TRICKLES  T R IH1 - K AH0 L Z
+TRICKLING  T R IH1 - K L IH0 NG
+TRICKS  T R IH1 K S
+TRICKSTER  T R IH1 K - S T ER0
+TRICKSTERS  T R IH1 K - S T ER0 Z
+TRICKY  T R IH1 - K IY0
+TRICO  T R IY1 - K OW0
+TRICUSPID  T R AY0 - K AH1 - S P AH0 D
+TRICYCLE  T R IH1 - S IH0 - K AH0 L
+TRIDENT  T R AY1 - D AH0 N T
+TRIDEX  T R IH1 - D EH2 K S
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+TRIENNIAL  T R AY0 - EH1 - N IY0 - AH0 L
+TRIER  T R AY1 - ER0
+TRIERWEILER  T R IH1 R - W AY0 - L ER0
+TRIES  T R AY1 Z
+TRIESTE  T R IY1 S T
+TRIEU  T R UW1
+TRIFARI  T R IH0 - F AA1 - R IY0
+TRIFLE  T R AY1 - F AH0 L
+TRIFLES  T R AY1 - F AH0 L Z
+TRIFLING  T R AY1 - F L IH0 NG
+TRIG  T R IH1 G
+TRIGG  T R IH1 G
+TRIGGER  T R IH1 - G ER0
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+TRIGGERING  T R IH1 - G ER0 - IH0 NG
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+TRIGLYCERIDE  T R AY0 - G L IH1 - S ER0 - AY2 D
+TRIGLYCERIDES  T R AY0 - G L IH1 - S ER0 - AY2 D Z
+TRIGO  T R IY1 - G OW0
+TRILATERAL  T R AY0 - L AE1 - T ER0 - AH0 L
+TRILBY  T R IH1 L - B IY0
+TRILL  T R IH1 L
+TRILLIN  T R IH1 - L IH0 N
+TRILLING  T R IH1 - L IH0 NG
+TRILLION  T R IH1 - L Y AH0 N
+TRILLIONS  T R IH1 - L Y AH0 N Z
+TRILLO  T R IH1 - L OW0
+TRILOGY  T R IH1 - L AH0 - JH IY0
+TRILON  T R IH1 - L AH0 N
+TRIM  T R IH1 M
+TRIMAC  T R IH1 - M AE0 K
+TRIMARCHI  T R IY0 - M AA1 R - K IY0
+TRIMARCO  T R IH0 - M AA1 R - K OW0
+TRIMAS  T R IY1 - M AH0 S
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+TRIMBOLI  T R IY0 M - B OW1 - L IY0
+TRIMEDYNE  T R AY1 M - D AY2 N
+TRIMESTER  T R AY0 - M EH1 - S T ER0
+TRIMETREXATE  T R IH0 - M EH1 - T R AH0 K - S EY2 T
+TRIMM  T R IH1 M
+TRIMMED  T R IH1 M D
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+TRIMMERS  T R IH1 - M ER0 Z
+TRIMMING  T R IH1 - M IH0 NG
+TRIMMINGS  T R IH1 - M IH0 NG Z
+TRIMPE  T R IH1 M P
+TRIMS  T R IH1 M Z
+TRINCOMALEE  T R IH0 NG - K OW1 - M AH0 - L IY0
+TRINE  T R AY1 N
+TRINGALI  T R IH0 NG - G AA1 - L IY0
+TRINH  T R IH1 N
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+TRINITY  T R IH1 - N AH0 - T IY0
+TRINITY'S  T R IH1 - N IH0 - T IY0 Z
+TRINITY(2)  T R IH1 - N IH0 - T IY0
+TRINKA  T R IH1 NG - K AH0
+TRINKET  T R IH1 NG - K AH0 T
+TRINKETS  T R IH1 NG - K AH0 T S
+TRINKLE  T R IH1 NG - K AH0 L
+TRINOVA  T R AY2 - N OW1 - V AH0
+TRINTEX  T R IH1 N - T EH2 K S
+TRIO  T R IY1 - OW2
+TRIO'S  T R IY1 - OW2 Z
+TRIOLA  T R IY0 - OW1 - L AH0
+TRIOLO  T R IY0 - OW1 - L OW0
+TRIOMPHE  T R IY0 - OW1 M F
+TRIP  T R IH1 P
+TRIP'S  T R IH1 P S
+TRIPARTITE  T R AY0 - P AA1 R - T AY2 T
+TRIPE  T R AY1 P
+TRIPI  T R IY1 - P IY0
+TRIPLE  T R IH1 - P AH0 L
+TRIPLECAST  T R IH1 - P AH0 L - K AE2 S T
+TRIPLED  T R IH1 - P AH0 L D
+TRIPLES  T R IH1 - P AH0 L Z
+TRIPLET  T R IH1 - P L AH0 T
+TRIPLETS  T R IH1 - P L AH0 T S
+TRIPLETT  T R IH1 - P L IH0 T
+TRIPLICATE  T R IH1 - P L IH0 - K AH0 T
+TRIPLING  T R IH1 - P AH0 L - IH0 NG
+TRIPLING(2)  T R IH1 - P L IH0 NG
+TRIPOD  T R AY1 - P AA2 D
+TRIPODI  T R IY0 - P OW1 - D IY0
+TRIPODS  T R AY1 - P AA2 D Z
+TRIPOLI  T R IH1 - P AH0 - L IY0
+TRIPOLI'S  T R IH1 - P AH0 - L IY0 Z
+TRIPP  T R IH1 P
+TRIPPE  T R IH1 P
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+TRIPPLE  T R IH1 - P AH0 L
+TRIPS  T R IH1 P S
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+TRIREMES  T R AY1 - R IY2 M Z
+TRISH  T R IH1 SH
+TRISHA  T R IH1 - SH AH0
+TRISHA'S  T R IH1 - SH AH0 Z
+TRISKA  T R IH1 - S K AH0
+TRISLER  T R IH1 - S AH0 - L ER0
+TRISLER(2)  T R IH1 S - L ER0
+TRISM  T R IH1 - Z AH0 M
+TRISTA  T R IH1 - S T AH0
+TRISTAN  T R IH1 - S T AE2 N
+TRISTAR  T R AY1 - S T AA2 R
+TRISTATE  T R AY1 - S T EY2 T
+TRISTRAM  T R IH1 S - T R AH0 M
+TRITCH  T R IH1 CH
+TRITE  T R AY1 T
+TRITES  T R AY1 T S
+TRITIUM  T R IH1 - T IY0 - AH0 M
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+TRITON'S  T R AY1 - T AH0 N Z
+TRITSCH  T R IH1 CH
+TRITSCHLER  T R IH1 CH - L ER0
+TRITT  T R IH1 T
+TRITZ  T R IH1 T S
+TRIUMPH  T R AY1 - AH0 M F
+TRIUMPHAL  T R AY0 - AH1 M - F AH0 L
+TRIUMPHANT  T R AY0 - AH1 M - F AH0 N T
+TRIUMPHANTLY  T R AY0 - AH1 M - F AH0 N T - L IY0
+TRIUMPHED  T R AY1 - AH0 M F T
+TRIUMPHS  T R AY1 - AH0 M F S
+TRIUMVIRATE  T R AY0 - AH1 M - V ER0 - AH0 T
+TRIVEDI  T R IY0 - V EH1 - D IY0
+TRIVEST  T R IH1 - V AH0 S T
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+TRIVETT  T R IH1 - V IH0 T
+TRIVETTE  T R IH0 - V EH1 T
+TRIVIA  T R IH1 - V IY0 - AH0
+TRIVIAL  T R IH1 - V IY0 - AH0 L
+TRIVIALITY  T R IH2 - V IY0 - AE1 - L AH0 - T IY0
+TRIVIALIZE  T R IH1 - V IY0 - AH0 - L AY2 Z
+TRIVIALIZE(2)  T R IH1 - V Y AH0 - L AY2 Z
+TRIVIALIZED  T R IH1 - V IY0 - AH0 - L AY2 Z D
+TRIVIALIZED(2)  T R IH1 - V Y AH0 - L AY2 Z D
+TRIVIALIZES  T R IH1 - V IY0 - AH0 - L AY2 - Z IH0 Z
+TRIVIALIZES(2)  T R IH1 - V Y AH0 - L AY2 - Z IH0 Z
+TRIVIALIZING  T R IH1 - V IY0 - AH0 - L AY2 - Z IH0 NG
+TRIVIALIZING(2)  T R IH1 - V Y AH0 - L AY2 - Z IH0 NG
+TRIXIE  T R IH1 K - S IY0
+TRIXY  T R IH1 K - S IY0
+TRIZEC  T R IH1 - Z AH0 K
+TRIZEC'S  T R IH1 - Z EH0 K S
+TRNKA  T R IH1 NG - K AH0
+TRNKA  T R NG K AA1
+TRNOPOLJE  T R AH0 - N AO1 - P AO0 - L IY0
+TRNOPOLJE(2)  T R AH0 - N AO1 - P AO0 L - Y IY0
+TROBAUGH  T R AA1 - B AO0
+TROCHE  T R AA1 CH
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+TROCKI  T R AA1 - K IY0
+TROD  T R AA1 D
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+TROIANO  T R OW0 - IY0 - AA1 - N OW0
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+TROIS  T W AA1
+TROISE  T R OY1 Z
+TROISI  T R OY1 - S IY0
+TROJAN  T R OW1 - JH AH0 N
+TROJANOWSKI  T R AH0 - Y AH0 - N AO1 F S - K IY0
+TROJANS  T R OW1 - JH AH0 N Z
+TROKEL  T R OW1 - K AH0 L
+TROLINGER  T R OW1 - L IH0 - NG ER0
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+TROLLING  T R OW1 - L IH0 NG
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+TROMBINO  T R OW0 M - B IY1 - N OW0
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+TROMBLY  T R AA1 M - B L IY0
+TROMBONE  T R AA0 M - B OW1 N
+TROMBONES  T R AA0 M - B OW1 N Z
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+TROMP  T R AA1 M P
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+TROPEANO  T R OW2 - P IY1 - N OW0
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+TROUBLESOME  T R AH1 - B AH0 L - S AH0 M
+TROUBLING  T R AH1 - B AH0 L - IH0 NG
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+WHALERS(2)  HH W EY1 - L ER0 Z
+WHALES  W EY1 L Z
+WHALES(2)  HH W EY1 L Z
+WHALEY  W EY1 - L IY0
+WHALEY(2)  HH W EY1 - L IY0
+WHALIN  W AE1 - L IH0 N
+WHALING  W EY1 - L IH0 NG
+WHALING(2)  HH W EY1 - L IH0 NG
+WHALLEY  W AE1 - L IY0
+WHAM  W AE1 M
+WHAM(2)  HH W AE1 M
+WHAMMY  W AE1 - M IY0
+WHAMMY(2)  HH W AE1 - M IY0
+WHAMPOA  W AE0 M - P OW1 - AH0
+WHAN  W AE1 N
+WHAN(2)  HH W AE1 N
+WHANG  W AE1 NG
+WHANG(2)  HH W AE1 NG
+WHARF  W AO1 R F
+WHARF'S  W AO1 R F S
+WHARF'S(2)  HH W AO1 R F S
+WHARF(2)  HH W AO1 R F
+WHARFF  W AA1 R F
+WHARFF(2)  HH W AA1 R F
+WHARRY  W AE1 - R IY0
+WHARRY(2)  HH W AE1 - R IY0
+WHARTON  W AO1 R - T AH0 N
+WHARTON'S  W AO1 R - T AH0 N Z
+WHAT  W AH1 T
+WHAT'D  W AH1 - T IH0 D
+WHAT'D(2)  HH W AH1 - T IH0 D
+WHAT'LL  W AH1 - T AH0 L
+WHAT'LL(2)  HH W AH1 - T AH0 L
+WHAT'RE  W AH1 - T ER0
+WHAT'RE(2)  HH W AH1 - T ER0
+WHAT'S  W AH1 T S
+WHAT'S(2)  HH W AH1 T S
+WHAT(2)  HH W AH1 T
+WHATEVER  W AH2 T - EH1 - V ER0
+WHATEVER'S  W AH2 T - EH1 - V ER0 Z
+WHATEVER'S(2)  HH W AH2 - T EH1 - V ER0 Z
+WHATEVER(2)  HH W AH2 T - EH1 - V ER0
+WHATLEY  W AH1 T - L IY0
+WHATLEY(2)  HH W AH1 T - L IY0
+WHATNOT  W AH1 T - N AA2 T
+WHATNOT(2)  HH W AH1 T - N AA2 T
+WHATS  W AH0 T S
+WHATS(2)  HH W AH0 T S
+WHATSOEVER  W AH2 T - S OW0 - EH1 - V ER0
+WHATSOEVER(2)  HH W AH2 T - S OW0 - EH1 - V ER0
+WHAY-YU  W EY1 - Y UW1
+WHAY-YU(2)  HH W EY1 - Y UW1
+WHEAT  W IY1 T
+WHEAT(2)  HH W IY1 T
+WHEATEN  W IY1 - T AH0 N
+WHEATEN(2)  HH W IY1 - T AH0 N
+WHEATIE  W IY1 - T IY0
+WHEATIE(2)  HH W IY1 - T IY0
+WHEATIES  W IY1 - T IY0 Z
+WHEATIES(2)  HH W IY1 - T IY0 Z
+WHEATLEY  W IY1 T - L IY0
+WHEATLEY(2)  HH W IY1 T - L IY0
+WHEATLY  W IY1 T - L IY0
+WHEATLY(2)  HH W IY1 T - L IY0
+WHEATON  W IY1 - T AH0 N
+WHEATON(2)  HH W IY1 - T AH0 N
+WHEDBEE  W EH1 D - B IY2
+WHEDBEE(2)  HH W EH1 D - B IY2
+WHEDON  W EH1 - D AH0 N
+WHEDON(2)  HH W EH1 - D AH0 N
+WHEEL  W IY1 L
+WHEEL(2)  HH W IY1 L
+WHEELABRATOR  W IY2 - L AH0 - B R EY1 - T ER0
+WHEELABRATOR(2)  HH W IY2 - L AH0 - B R EY1 - T ER0
+WHEELAN  W IY1 - L AH0 N
+WHEELAN(2)  HH W IY1 - L AH0 N
+WHEELAND  W IY1 - L AH0 N D
+WHEELAND(2)  HH W IY1 - L AH0 N D
+WHEELBARROW  W IY1 L - B EH2 - R OW0
+WHEELBARROW(2)  HH W IY1 L - B EH2 - R OW0
+WHEELBARROWS  W IY1 L - B EH2 - R OW0 Z
+WHEELBARROWS(2)  HH W IY1 L - B EH2 - R OW0 Z
+WHEELBASE  W IY1 L - B EY2 S
+WHEELBASE(2)  HH W IY1 L - B EY2 S
+WHEELCHAIR  W IY1 L - CH EH2 R
+WHEELCHAIR(2)  HH W IY1 L - CH EH2 R
+WHEELCHAIRS  W IY1 L - CH EH2 R Z
+WHEELCHAIRS(2)  HH W IY1 L - CH EH2 R Z
+WHEELDON  W IY1 L - D AH0 N
+WHEELDON(2)  HH W IY1 L - D AH0 N
+WHEELED  W IY1 L D
+WHEELED(2)  HH W IY1 L D
+WHEELER  W IY1 - L ER0
+WHEELER'S  W IY1 - L ER0 Z
+WHEELER'S(2)  HH W IY1 - L ER0 Z
+WHEELER(2)  HH W IY1 - L ER0
+WHEELERS  W IY1 - L ER0 Z
+WHEELERS(2)  HH W IY1 - L ER0 Z
+WHEELESS  W IY1 - L AH0 S
+WHEELESS(2)  HH W IY1 - L AH0 S
+WHEELING  W IY1 - L IH0 NG
+WHEELING'S  W IY1 - L IH0 NG Z
+WHEELING'S(2)  HH W IY1 - L IH0 NG Z
+WHEELING(2)  HH W IY1 - L IH0 NG
+WHEELIS  W IY1 - L IH0 S
+WHEELIS(2)  HH W IY1 - L IH0 S
+WHEELOCK  W IY1 - L AA2 K
+WHEELOCK(2)  HH W IY1 - L AA2 K
+WHEELON  W IY1 - L AH0 N
+WHEELON(2)  HH W IY1 - L AH0 N
+WHEELS  W IY1 L Z
+WHEELS(2)  HH W IY1 L Z
+WHEELUS  W IY1 - L AH0 S
+WHEELUS(2)  HH W IY1 - L AH0 S
+WHEELWRIGHT  W IY1 L - R AY2 T
+WHEELWRIGHT(2)  HH W IY1 L - R AY2 T
+WHEELWRITER  W IY1 L - R AY2 - T ER0
+WHEELWRITER(2)  HH W IY1 L - R AY2 - T ER0
+WHEEZE  W IY1 Z
+WHEEZE(2)  HH W IY1 Z
+WHEEZES  W IY1 - Z AH0 Z
+WHEEZES(2)  HH W IY1 - Z AH0 Z
+WHEEZES(3)  W IY1 - Z IH0 Z
+WHEEZING  W IY1 - Z IH0 NG
+WHEEZING(2)  HH W IY1 - Z IH0 NG
+WHELAN  W EH1 - L AH0 N
+WHELAN(2)  HH W EH1 - L AH0 N
+WHELAN(3)  HH W IY1 - L AH0 N
+WHELAN(4)  W IY1 - L AH0 N
+WHELCHEL  W EH1 L - CH AH0 L
+WHELCHEL(2)  HH W EH1 L - CH AH0 L
+WHELESS  W IY1 - L IH0 S
+WHELESS(2)  HH W IY1 - L IH0 S
+WHELPLEY  W EH1 L P - L IY0
+WHELPLEY(2)  HH W EH1 L P - L IY0
+WHELTON  W EH1 L - T AH0 N
+WHELTON(2)  HH W EH1 L - T AH0 N
+WHEN  W EH1 N
+WHEN'LL  W EH1 - N AH0 L
+WHEN'LL(2)  HH W EH1 - N AH0 L
+WHEN'S  W EH1 N Z
+WHEN'S(2)  HH W EH1 N Z
+WHEN(2)  HH W EH1 N
+WHEN(3)  W IH1 N
+WHEN(4)  HH W IH1 N
+WHENCE  W EH1 N S
+WHENCE(2)  HH W EH1 N S
+WHENEVER  W EH0 N - EH1 - V ER0
+WHENEVER(2)  HH W EH0 - N EH1 - V ER0
+WHERE  W EH1 R
+WHERE'D  W EH1 R D
+WHERE'D(2)  HH W EH1 R D
+WHERE'S  W EH1 R Z
+WHERE'S(2)  HH W EH1 R Z
+WHERE(2)  HH W EH1 R
+WHEREABOUTS  W EH1 - R AH0 - B AW2 T S
+WHEREABOUTS(2)  HH W EH1 - R AH0 - B AW2 T S
+WHEREAS  W EH0 - R AE1 Z
+WHEREAS(2)  HH W EH0 - R AE1 Z
+WHEREBY  W EH0 R - B AY1
+WHEREBY(2)  HH W EH0 R - B AY1
+WHEREIN  W EH0 - R IH1 N
+WHEREIN(2)  HH W EH0 - R IH1 N
+WHEREUPON  W EH1 - R AH0 - P AA1 N
+WHEREUPON(2)  HH W EH1 - R AH0 - P AA1 N
+WHEREVER  W EH0 - R EH1 - V ER0
+WHEREVER(2)  HH W EH0 - R EH1 - V ER0
+WHEREWITHAL  W EH1 R - W IH0 - DH AO2 L
+WHEREWITHAL(2)  HH W EH1 R - W IH0 - DH AO2 L
+WHERLEY  W ER1 - L IY0
+WHERLEY(2)  HH W ER1 - L IY0
+WHERRY  W EH1 - R IY0
+WHERRY(2)  HH W EH1 - R IY0
+WHET  W EH1 T
+WHET(2)  HH W EH1 T
+WHETHER  W EH1 - DH ER0
+WHETHER(2)  HH W EH1 - DH ER0
+WHETSEL  W EH1 T - S AH0 L
+WHETSEL(2)  HH W EH1 T - S AH0 L
+WHETSELL  W EH1 T - S AH0 L
+WHETSELL(2)  HH W EH1 T - S AH0 L
+WHETSTINE  W EH1 T - S T IY0 N
+WHETSTINE(2)  HH W EH1 T - S T IY0 N
+WHETSTONE  W EH1 T - S T OW2 N
+WHETSTONE(2)  HH W EH1 T - S T OW2 N
+WHETTED  W EH1 - T IH0 D
+WHETTED(2)  HH W EH1 - T IH0 D
+WHETZEL  W EH1 T - Z AH0 L
+WHETZEL(2)  HH W EH1 T - Z AH0 L
+WHEW  W UW1
+WHEW(2)  HH W UW1
+WHEW(3)  HH Y UW1
+WHEY  W EY1
+WHEY(2)  HH W EY1
+WHICH  W IH1 CH
+WHICH'RE  W IH1 - CH ER0
+WHICH'RE(2)  HH W IH1 - CH ER0
+WHICH'S  W IH1 - CH IH0 Z
+WHICH'S(2)  HH W IH1 - CH IH0 Z
+WHICH(2)  HH W IH1 CH
+WHICHARD  W IH1 - CH ER0 D
+WHICHARD(2)  HH W IH1 - CH ER0 D
+WHICHEVER  W IH0 CH - EH1 - V ER0
+WHICHEVER(2)  HH W IH0 CH - EH1 - V ER0
+WHICKER  W IH1 - K ER0
+WHICKER(2)  HH W IH1 - K ER0
+WHIDBY  W IH1 D - B IY0
+WHIDBY(2)  HH W IH1 D - B IY0
+WHIDDEN  W IH1 - D AH0 N
+WHIDDEN(2)  HH W IH1 - D AH0 N
+WHIDDON  W IH1 - D AH0 N
+WHIDDON(2)  HH W IH1 - D AH0 N
+WHIFF  W IH1 F
+WHIFF(2)  HH W IH1 F
+WHIG  W IH1 G
+WHIG(2)  HH W IH1 G
+WHIGHAM  W IH1 - G AH0 M
+WHIGHAM(2)  HH W IH1 - G AH0 M
+WHIGS  W IH1 G Z
+WHIGS(2)  HH W IH1 G Z
+WHILDEN  W AY1 L - D AH0 N
+WHILDEN(2)  HH W AY1 L - D AH0 N
+WHILE  W AY1 L
+WHILE(2)  HH W AY1 L
+WHILES  W AY1 L Z
+WHILES(2)  HH W AY1 L Z
+WHILST  W AY1 L S T
+WHIM  W IH1 M
+WHIM(2)  HH W IH1 M
+WHIMPER  W IH1 M - P ER0
+WHIMPER(2)  HH W IH1 M - P ER0
+WHIMPERING  W IH1 M - P ER0 - IH0 NG
+WHIMPERING(2)  HH W IH1 M - P ER0 - IH0 NG
+WHIMS  W IH1 M Z
+WHIMS(2)  HH W IH1 M Z
+WHIMSICAL  W IH1 M - Z IH0 - K AH0 L
+WHIMSICAL(2)  HH W IH1 M - Z IH0 - K AH0 L
+WHIMSY  W IH1 M - S IY0
+WHIMSY(2)  HH W IH1 M - S IY0
+WHINE  W AY1 N
+WHINE(2)  HH W AY1 N
+WHINER  W AY1 - N ER0
+WHINER(2)  HH W AY1 - N ER0
+WHINERS  W AY1 - N ER0 Z
+WHINERS(2)  HH W AY1 - N ER0 Z
+WHINERY  W AY1 - N ER0 - IY0
+WHINERY(2)  HH W AY1 - N ER0 - IY0
+WHINES  W AY1 N Z
+WHINES(2)  HH W AY1 N Z
+WHINING  W AY1 - N IH0 NG
+WHINING(2)  HH W AY1 - N IH0 NG
+WHINNERY  W IH1 - N ER0 - IY0
+WHINNERY(2)  HH W IH1 - N ER0 - IY0
+WHINNEY  W IH1 - N IY0
+WHINNEY(2)  HH W IH1 - N IY0
+WHINY  W AY1 - N IY0
+WHINY(2)  HH W AY1 - N IY0
+WHIP  W IH1 P
+WHIP(2)  HH W IH1 P
+WHIPKEY  W IH1 P - K IY2
+WHIPKEY(2)  HH W IH1 P - K IY2
+WHIPLASH  W IH1 P - L AE2 SH
+WHIPLASH(2)  HH W IH1 P - L AE2 SH
+WHIPLASHES  W IH1 P - L AE2 - SH AH0 Z
+WHIPLASHES(2)  HH W IH1 P - L AE2 - SH AH0 Z
+WHIPP  W IH1 P
+WHIPP(2)  HH W IH1 P
+WHIPPANY  W IH1 - P AH0 - N IY0
+WHIPPANY(2)  HH W IH1 - P AH0 - N IY0
+WHIPPED  W IH1 P T
+WHIPPED(2)  HH W IH1 P T
+WHIPPING  W IH1 - P IH0 NG
+WHIPPING(2)  HH W IH1 - P IH0 NG
+WHIPPLE  W IH1 - P AH0 L
+WHIPPLE'S  W IH1 - P AH0 L Z
+WHIPPLE'S(2)  HH W IH1 - P AH0 L Z
+WHIPPLE(2)  HH W IH1 - P AH0 L
+WHIPPOORWILLS  W IH1 P - ER0 - W IH2 L Z
+WHIPPOORWILLS(2)  HH W IH1 P - ER0 - W IH2 L Z
+WHIPPS  W IH1 P S
+WHIPPS(2)  HH W IH1 P S
+WHIPS  W IH1 P S
+WHIPS(2)  HH W IH1 P S
+WHIPSAW  W IH1 P - S AO2
+WHIPSAW(2)  HH W IH1 P - S AO2
+WHIPSAWED  W IH1 P - S AO2 D
+WHIPSAWED(2)  HH W IH1 P - S AO2 D
+WHIPSAWING  W IH1 P - S AO2 - IH0 NG
+WHIPSAWING(2)  HH W IH1 P - S AO2 - IH0 NG
+WHIRL  W ER1 L
+WHIRL(2)  HH W ER1 L
+WHIRLED  W ER1 L D
+WHIRLED(2)  HH W ER1 L D
+WHIRLEY  W ER1 - L IY0
+WHIRLEY(2)  HH W ER1 - L IY0
+WHIRLING  W ER1 - L IH0 NG
+WHIRLING(2)  HH W ER1 - L IH0 NG
+WHIRLPOOL  W ER1 L - P UW2 L
+WHIRLPOOL'S  W ER1 L - P UW2 L Z
+WHIRLPOOL'S(2)  HH W ER1 L - P UW2 L Z
+WHIRLPOOL(2)  HH W ER1 L - P UW2 L
+WHIRLPOOLS  W ER1 L - P UW2 L Z
+WHIRLPOOLS(2)  HH W ER1 L - P UW2 L Z
+WHIRLWIND  W ER1 L - W IH2 N D
+WHIRLWIND(2)  HH W ER1 L - W IH2 N D
+WHIRRING  W ER1 - IH0 NG
+WHIRRING(2)  HH W ER1 - IH0 NG
+WHISENAND  W IH1 - S IH0 - N AE0 N D
+WHISENAND(2)  HH W IH1 - S IH0 - N AE0 N D
+WHISENANT  W IH1 - S IH0 - N AH0 N T
+WHISENANT(2)  HH W IH1 - S IH0 - N AH0 N T
+WHISENHUNT  W AY1 - Z AH0 N - HH AH2 N T
+WHISENHUNT(2)  HH W AY1 - Z AH0 N - HH AH2 N T
+WHISK  W IH1 S K
+WHISK(2)  HH W IH1 S K
+WHISKED  W IH1 S K T
+WHISKED(2)  HH W IH1 S K T
+WHISKER  W IH1 - S K ER0
+WHISKER(2)  HH W IH1 - S K ER0
+WHISKERS  W IH1 - S K ER0 Z
+WHISKERS(2)  HH W IH1 - S K ER0 Z
+WHISKEY  W IH1 S - K IY0
+WHISKEY(2)  HH W IH1 S - K IY0
+WHISKEYS  W IH1 - S K IY0 Z
+WHISKEYS(2)  HH W IH1 - S K IY0 Z
+WHISKS  W IH1 S K S
+WHISKS(2)  HH W IH1 S K S
+WHISKY  W IH1 S - K IY0
+WHISKY(2)  HH W IH1 S - K IY0
+WHISLER  W IH1 S - L ER0
+WHISLER(2)  HH W IH1 S - L ER0
+WHISMAN  W IH1 S - M AH0 N
+WHISMAN(2)  HH W IH1 S - M AH0 N
+WHISNANT  W IH1 S - N AH0 N T
+WHISNANT(2)  HH W IH1 S - N AH0 N T
+WHISNER  W IH1 S - N ER0
+WHISNER(2)  HH W IH1 S - N ER0
+WHISONANT  W IH1 - S AH0 - N AH0 N T
+WHISPER  W IH1 - S P ER0
+WHISPER(2)  HH W IH1 - S P ER0
+WHISPERED  W IH1 - S P ER0 D
+WHISPERED(2)  HH W IH1 - S P ER0 D
+WHISPERING  W IH1 - S P ER0 - IH0 NG
+WHISPERING(2)  HH W IH1 - S P ER0 - IH0 NG
+WHISPERS  W IH1 - S P ER0 Z
+WHISPERS(2)  HH W IH1 - S P ER0 Z
+WHISTLE  W IH1 - S AH0 L
+WHISTLE(2)  HH W IH1 - S AH0 L
+WHISTLEBLOWER  W IH1 - S AH0 L - B L OW2 - ER0
+WHISTLEBLOWER(2)  HH W IH1 - S AH0 L - B L OW2 - ER0
+WHISTLEBLOWERS  W IH1 - S AH0 L - B L OW2 - ER0 Z
+WHISTLEBLOWERS(2)  HH W IH1 - S AH0 L - B L OW2 - ER0 Z
+WHISTLED  W IH1 - S AH0 L D
+WHISTLED(2)  HH W IH1 - S AH0 L D
+WHISTLER  W IH1 S - L ER0
+WHISTLER(2)  HH W IH1 S - L ER0
+WHISTLERS  W IH1 S - L ER0 Z
+WHISTLERS(2)  HH W IH1 S - L ER0 Z
+WHISTLES  W IH1 - S AH0 L Z
+WHISTLES(2)  HH W IH1 - S AH0 L Z
+WHISTLING  W IH1 - S L IH0 NG
+WHISTLING(2)  HH W IH1 - S L IH0 NG
+WHISTON  W IH1 - S T AH0 N
+WHISTON(2)  HH W IH1 - S T AH0 N
+WHIT  W IH1 T
+WHIT(2)  HH W IH1 T
+WHITACRE  W IH1 - T AH0 - K ER0
+WHITACRE(2)  HH W IH1 - T AH0 - K ER0
+WHITAKER  W IH1 - T AH0 - K ER0
+WHITAKER(2)  HH W IH1 - T AH0 - K ER0
+WHITBECK  W IH1 T - B EH2 K
+WHITBECK(2)  HH W IH1 T - B EH2 K
+WHITBREAD  W IH1 T - B R EH2 D
+WHITBREAD(2)  HH W IH1 T - B R EH2 D
+WHITBY  W IH1 T - B IY0
+WHITBY(2)  HH W IH1 T - B IY0
+WHITCHER  W IH1 - CH ER0
+WHITCHER(2)  HH W IH1 - CH ER0
+WHITCHURCH  W IH1 T - CH ER2 CH
+WHITCHURCH(2)  HH W IH1 T - CH ER2 CH
+WHITCOMB  W IH1 T - K AH0 M
+WHITCOMB(2)  HH W IH1 T - K AH0 M
+WHITCRAFT  W IH1 T - K R AE2 F T
+WHITCRAFT(2)  HH W IH1 T - K R AE2 F T
+WHITE  W AY1 T
+WHITE'S  W AY1 T S
+WHITE'S(2)  HH W AY1 T S
+WHITE(2)  HH W AY1 T
+WHITEAKER  W IH1 - T AH0 - K ER0
+WHITEAKER(2)  HH W IH1 - T AH0 - K ER0
+WHITEBREAD  W AY1 T - B R EH2 D
+WHITEBREAD(2)  HH W AY1 T - B R EH2 D
+WHITECOTTON  W AY1 T - K AA2 - T AH0 N
+WHITECOTTON(2)  HH W AY1 T - K AA2 - T AH0 N
+WHITED  W AY1 - T IH0 D
+WHITED(2)  HH W AY1 - T IH0 D
+WHITEFIELD  W AY1 T - F IY2 L D
+WHITEFIELD(2)  HH W AY1 T - F IY2 L D
+WHITEFISH  W AY1 T - F IH2 SH
+WHITEFISH(2)  HH W AY1 T - F IH2 SH
+WHITEFORD  W AY1 T - F AO0 R D
+WHITEFORD(2)  HH W AY1 T - F AO0 R D
+WHITEHAIR  W AY1 T - HH EH1 R
+WHITEHAIR(2)  HH W AY1 T - HH EH1 R
+WHITEHALL  W AY1 T - HH AO2 L
+WHITEHALL'S  W AY1 T - HH AO2 L Z
+WHITEHALL'S(2)  HH W AY1 T - HH AO2 L Z
+WHITEHALL(2)  HH W AY1 T - HH AO2 L
+WHITEHEAD  W AY1 T - HH EH2 D
+WHITEHEAD'S  W AY1 T - HH EH2 D Z
+WHITEHEAD'S(2)  HH W AY1 T - HH EH2 D Z
+WHITEHEAD(2)  HH W AY1 T - HH EH2 D
+WHITEHILL  W AY1 T - HH IH2 L
+WHITEHILL(2)  HH W AY1 T - HH IH2 L
+WHITEHORN  W AY1 T - HH AO2 R N
+WHITEHORN(2)  HH W AY1 T - HH AO2 R N
+WHITEHORSE  W AY1 T - HH AO2 R S
+WHITEHORSE(2)  HH W AY1 T - HH AO2 R S
+WHITEHOUSE  W AY1 T - HH AW2 S
+WHITEHOUSE(2)  HH W AY1 T - HH AW2 S
+WHITEHURST  W AY1 T - HH ER2 S T
+WHITEHURST'S  W AY1 T - HH ER2 S T
+WHITEHURST(2)  HH W AY1 T - HH ER2 S T
+WHITELAW  W AY1 T - L AO2
+WHITELAW(2)  HH W AY1 T - L AO2
+WHITELEY  W AY1 T - L IY0
+WHITELEY(2)  HH W AY1 T - L IY0
+WHITELOCK  W AY1 T - L AA2 K
+WHITELOCK(2)  HH W AY1 T - L AA2 K
+WHITELY  W AY1 T - L IY0
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+WHITEN  W AY1 - T AH0 N
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+WHITENEIR  W AY1 T - N IH2 R
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+WHITENER  W AY1 T - N ER0
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+WHITENESS  W AY1 T - N AH0 S
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+WHITENIGHT  W AY1 T - N AY2 T
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+WHITENING  W AY1 - T IH0 - N IH0 NG
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+WHITESEL  W AY1 T - S EH2 L
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+WHITESELL  W AY1 T - S EH2 L
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+WHITESIDE  W AY1 T - S AY2 D
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+WHITESIDES  W AY1 T - S AY2 D Z
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+WHITING  W AY1 - T IH0 NG
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+WHITLEY  W IH1 T - L IY0
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+WHITMARSH  W IH1 T - M AA2 R SH
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+WHITNEY  W IH1 T - N IY0
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+ZYUGANOV(3)  Z UW1 - G AA0 - N AA0 V
+ZYUGANOV(4)  Z UW1 - G AA0 - N AA0 F
+ZYWICKI  Z IH0 - W IH1 - K IY0
+ZZZZ  Z IY0 Z
+ZZZZ(2)  Z Z
+{BRACE  B R EY1 S
+{LEFT-BRACE  L EH1 F T - B R EY1 S
+}CLOSE-BRACE  K L OW1 Z - B R EY1 S
+}RIGHT-BRACE  R AY1 T - B R EY1 S
diff --git a/text/cmudict_cache.pickle b/text/cmudict_cache.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..cab7f7d732affe0bd8d860a81e770b9bf1f72064
--- /dev/null
+++ b/text/cmudict_cache.pickle
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9b21b20325471934ba92f2e4a5976989e7d920caa32e7a286eacb027d197949
+size 6212655
diff --git a/text/english.py b/text/english.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a2af9523f2f96b7b34a0fff7589a82e1122ecae
--- /dev/null
+++ b/text/english.py
@@ -0,0 +1,495 @@
+import pickle
+import os
+import re
+from g2p_en import G2p
+from transformers import DebertaV2Tokenizer
+
+from text import symbols
+from text.symbols import punctuation
+
+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()
+LOCAL_PATH = "./bert/deberta-v3-large"
+tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
+
+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
+
+
+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 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]
+    else:
+        tone = 3
+    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 re
+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)
+    text = replace_punctuation(text)
+    text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
+    return text
+
+
+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 sep_text(text):
+    words = re.split(r"([,;.\?\!\s+])", text)
+    words = [word for word in words if word.strip() != ""]
+    return words
+
+
+def text_to_words(text):
+    tokens = tokenizer.tokenize(text)
+    words = []
+    for idx, t in enumerate(tokens):
+        if t.startswith("▁"):
+            words.append([t[1:]])
+        else:
+            if t in punctuation:
+                if idx == len(tokens) - 1:
+                    words.append([f"{t}"])
+                else:
+                    if (
+                        not tokens[idx + 1].startswith("▁")
+                        and tokens[idx + 1] not in punctuation
+                    ):
+                        if idx == 0:
+                            words.append([])
+                        words[-1].append(f"{t}")
+                    else:
+                        words.append([f"{t}"])
+            else:
+                if idx == 0:
+                    words.append([])
+                words[-1].append(f"{t}")
+    return words
+
+
+def g2p(text):
+    phones = []
+    tones = []
+    phone_len = []
+    # words = sep_text(text)
+    # tokens = [tokenizer.tokenize(i) for i in words]
+    words = text_to_words(text)
+
+    for word in words:
+        temp_phones, temp_tones = [], []
+        if len(word) > 1:
+            if "'" in word:
+                word = ["".join(word)]
+        for w in word:
+            if w in punctuation:
+                temp_phones.append(w)
+                temp_tones.append(0)
+                continue
+            if w.upper() in eng_dict:
+                phns, tns = refine_syllables(eng_dict[w.upper()])
+                temp_phones += [post_replace_ph(i) for i in phns]
+                temp_tones += tns
+                # w2ph.append(len(phns))
+            else:
+                phone_list = list(filter(lambda p: p != " ", _g2p(w)))
+                phns = []
+                tns = []
+                for ph in phone_list:
+                    if ph in arpa:
+                        ph, tn = refine_ph(ph)
+                        phns.append(ph)
+                        tns.append(tn)
+                    else:
+                        phns.append(ph)
+                        tns.append(0)
+                temp_phones += [post_replace_ph(i) for i in phns]
+                temp_tones += tns
+        phones += temp_phones
+        tones += temp_tones
+        phone_len.append(len(temp_phones))
+        # phones = [post_replace_ph(i) for i in phones]
+
+    word2ph = []
+    for token, pl in zip(words, phone_len):
+        word_len = len(token)
+
+        aaa = distribute_phone(pl, word_len)
+        word2ph += aaa
+
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones), text
+    assert len(phones) == sum(word2ph), text
+
+    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/text/english_bert_mock.py b/text/english_bert_mock.py
new file mode 100644
index 0000000000000000000000000000000000000000..782b65d7f1b1228c372178669c6e434e673dfea0
--- /dev/null
+++ b/text/english_bert_mock.py
@@ -0,0 +1,63 @@
+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,
+    assist_text=None,
+    assist_text_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 == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    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()
+        if assist_text:
+            style_inputs = tokenizer(assist_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) == res.shape[0], (text, res.shape[0], len(word2ph))
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if assist_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_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
diff --git a/text/japanese.py b/text/japanese.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b0f24ba05afece5e1dea7a63c76375eef25a236
--- /dev/null
+++ b/text/japanese.py
@@ -0,0 +1,610 @@
+# Convert Japanese text to phonemes which is
+# compatible with Julius https://github.com/julius-speech/segmentation-kit
+import re
+import unicodedata
+
+import pyopenjtalk
+from num2words import num2words
+from transformers import AutoTokenizer
+
+from common.log import logger
+from text import punctuation
+from text.japanese_mora_list import (
+    mora_kata_to_mora_phonemes,
+    mora_phonemes_to_mora_kata,
+)
+
+# 子音の集合
+COSONANTS = set(
+    [
+        cosonant
+        for cosonant, _ in mora_kata_to_mora_phonemes.values()
+        if cosonant is not None
+    ]
+)
+
+# 母音の集合、便宜上「ん」を含める
+VOWELS = {"a", "i", "u", "e", "o", "N"}
+
+
+# 正規化で記号を変換するための辞書
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    # NFKC正規化後のハイフン・ダッシュの変種を全て通常半角ハイフン - \u002d に変換
+    "\u02d7": "\u002d",  # ˗, Modifier Letter Minus Sign
+    "\u2010": "\u002d",  # ‐, Hyphen,
+    # "\u2011": "\u002d",  # ‑, Non-Breaking Hyphen, NFKCにより\u2010に変換される
+    "\u2012": "\u002d",  # ‒, Figure Dash
+    "\u2013": "\u002d",  # –, En Dash
+    "\u2014": "\u002d",  # —, Em Dash
+    "\u2015": "\u002d",  # ―, Horizontal Bar
+    "\u2043": "\u002d",  # ⁃, Hyphen Bullet
+    "\u2212": "\u002d",  # −, Minus Sign
+    "\u23af": "\u002d",  # ⎯, Horizontal Line Extension
+    "\u23e4": "\u002d",  # ⏤, Straightness
+    "\u2500": "\u002d",  # ─, Box Drawings Light Horizontal
+    "\u2501": "\u002d",  # ━, Box Drawings Heavy Horizontal
+    "\u2e3a": "\u002d",  # ⸺, Two-Em Dash
+    "\u2e3b": "\u002d",  # ⸻, Three-Em Dash
+    # "～": "-",  # これは長音記号「ー」として扱うよう変更
+    # "~": "-",  # これも長音記号「ー」として扱うよう変更
+    "「": "'",
+    "」": "'",
+}
+
+
+def text_normalize(text):
+    """
+    日本語のテキストを正規化する。
+    結果は、ちょうど次の文字のみからなる：
+    - ひらがな
+    - カタカナ（全角長音記号「ー」が入る！）
+    - 漢字
+    - 半角アルファベット（大文字と小文字）
+    - ギリシャ文字
+    - `.` （句点`。`や`…`の一部や改行等）
+    - `,` （読点`、`や`:`等）
+    - `?` （疑問符`？`）
+    - `!` （感嘆符`！`）
+    - `'` （`「`や`」`等）
+    - `-` （`―`（ダッシュ、長音記号ではない）や`-`等）
+
+    注意点:
+    - 三点リーダー`…`は`...`に変換される（`なるほど…。` → `なるほど....`）
+    - 数字は漢字に変換される（`1,100円` → `千百円`、`52.34` → `五十二点三四`）
+    - 読点や疑問符等の位置・個数等は保持される（`??あ、、！！！` → `??あ,,!!!`）
+    """
+    res = unicodedata.normalize("NFKC", text)  # ここでアルファベットは半角になる
+    res = japanese_convert_numbers_to_words(res)  # 「100円」→「百円」等
+    # 「～」と「~」も長音記号として扱う
+    res = res.replace("~", "ー")
+    res = res.replace("～", "ー")
+
+    res = replace_punctuation(res)  # 句読点等正規化、読めない文字を削除
+
+    # 結合文字の濁点・半濁点を削除
+    # 通常の「ば」等はそのままのこされる、「あ゛」は上で「あ゙」になりここで「あ」になる
+    res = res.replace("\u3099", "")  # 結合文字の濁点を削除、る゙ → る
+    res = res.replace("\u309A", "")  # 結合文字の半濁点を削除、な゚ → な
+    return res
+
+
+def replace_punctuation(text: str) -> str:
+    """句読点等を「.」「,」「!」「?」「'」「-」に正規化し、OpenJTalkで読みが取得できるもののみ残す：
+    漢字・平仮名・カタカナ、アルファベット、ギリシャ文字
+    """
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    # 句読点を辞書で置換
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        # ↓ ひらがな、カタカナ、漢字
+        r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+        # ↓ 半角アルファベット（大文字と小文字）
+        + r"\u0041-\u005A\u0061-\u007A"
+        # ↓ 全角アルファベット（大文字と小文字）
+        + r"\uFF21-\uFF3A\uFF41-\uFF5A"
+        # ↓ ギリシャ文字
+        + r"\u0370-\u03FF\u1F00-\u1FFF"
+        # ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている
+        + "".join(punctuation) + r"]+",
+        # 上述以外の文字を削除
+        "",
+        replaced_text,
+    )
+
+    return replaced_text
+
+
+_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 g2p(
+    norm_text: str, use_jp_extra: bool = True
+) -> tuple[list[str], list[int], list[int]]:
+    """
+    他で使われるメインの関数。`text_normalize()`で正規化された`norm_text`を受け取り、
+    - phones: 音素のリスト（ただし`!`や`,`や`.`等punctuationが含まれうる）
+    - tones: アクセントのリスト、0（低）と1（高）からなり、phonesと同じ長さ
+    - word2ph: 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+    のタプルを返す。
+    ただし`phones`と`tones`の最初と終わりに`_`が入り、応じて`word2ph`の最初と最後に1が追加される。
+    use_jp_extra: Falseの場合、「ん」の音素を「N」ではなく「n」とする。
+    """
+    # pyopenjtalkのフルコンテキストラベルを使ってアクセントを取り出すと、punctuationの位置が消えてしまい情報が失われてしまう：
+    # 「こんにちは、世界。」と「こんにちは！世界。」と「こんにちは！！！？？？世界……。」は全て同じになる。
+    # よって、まずpunctuation無しの音素とアクセントのリストを作り、
+    # それとは別にpyopenjtalk.run_frontend()で得られる音素リスト（こちらはpunctuationが保持される）を使い、
+    # アクセント割当をしなおすことによってpunctuationを含めた音素とアクセントのリストを作る。
+
+    # punctuationがすべて消えた、音素とアクセントのタプルのリスト（「ん」は「N」）
+    phone_tone_list_wo_punct = g2phone_tone_wo_punct(norm_text)
+
+    # sep_text: 単語単位の単語のリスト
+    # sep_kata: 単語単位の単語のカタカナ読みのリスト
+    sep_text, sep_kata = text2sep_kata(norm_text)
+
+    # sep_phonemes: 各単語ごとの音素のリストのリスト
+    sep_phonemes = handle_long([kata2phoneme_list(i) for i in sep_kata])
+
+    # phone_w_punct: sep_phonemesを結合した、punctuationを元のまま保持した音素列
+    phone_w_punct: list[str] = []
+    for i in sep_phonemes:
+        phone_w_punct += i
+
+    # punctuation無しのアクセント情報を使って、punctuationを含めたアクセント情報を作る
+    phone_tone_list = align_tones(phone_w_punct, phone_tone_list_wo_punct)
+    # logger.debug(f"phone_tone_list:\n{phone_tone_list}")
+    # word2phは厳密な解答は不可能なので（「今日」「眼鏡」等の熟字訓が存在）、
+    # Bert-VITS2では、単語単位の分割を使って、単語の文字ごとにだいたい均等に音素を分配する
+
+    # sep_textから、各単語を1文字1文字分割して、文字のリスト（のリスト）を作る
+    sep_tokenized: list[list[str]] = []
+    for i in sep_text:
+        if i not in punctuation:
+            sep_tokenized.append(
+                tokenizer.tokenize(i)
+            )  # ここでおそらく`i`が文字単位に分割される
+        else:
+            sep_tokenized.append([i])
+
+    # 各単語について、音素の数と文字の数を比較して、均等っぽく分配する
+    word2ph = []
+    for token, phoneme in zip(sep_tokenized, sep_phonemes):
+        phone_len = len(phoneme)
+        word_len = len(token)
+        word2ph += distribute_phone(phone_len, word_len)
+
+    # 最初と最後に`_`記号を追加、アクセントは0（低）、word2phもそれに合わせて追加
+    phone_tone_list = [("_", 0)] + phone_tone_list + [("_", 0)]
+    word2ph = [1] + word2ph + [1]
+
+    phones = [phone for phone, _ in phone_tone_list]
+    tones = [tone for _, tone in phone_tone_list]
+
+    assert len(phones) == sum(word2ph), f"{len(phones)} != {sum(word2ph)}"
+
+    # use_jp_extraでない場合は「N」を「n」に変換
+    if not use_jp_extra:
+        phones = [phone if phone != "N" else "n" for phone in phones]
+
+    return phones, tones, word2ph
+
+
+def g2kata_tone(norm_text: str) -> list[tuple[str, int]]:
+    phones, tones, _ = g2p(norm_text, use_jp_extra=True)
+    return phone_tone2kata_tone(list(zip(phones, tones)))
+
+
+def phone_tone2kata_tone(phone_tone: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """phone_toneをのphone部分をカタカナに変換する。ただし最初と最後の("_", 0)は無視"""
+    phone_tone = phone_tone[1:]  # 最初の("_", 0)を無視
+    phones = [phone for phone, _ in phone_tone]
+    tones = [tone for _, tone in phone_tone]
+    result: list[tuple[str, int]] = []
+    current_mora = ""
+    for phone, next_phone, tone, next_tone in zip(phones, phones[1:], tones, tones[1:]):
+        # zipの関係で最後の("_", 0)は無視されている
+        if phone in punctuation:
+            result.append((phone, tone))
+            continue
+        if phone in COSONANTS:  # n以外の子音の場合
+            assert current_mora == "", f"Unexpected {phone} after {current_mora}"
+            assert tone == next_tone, f"Unexpected {phone} tone {tone} != {next_tone}"
+            current_mora = phone
+        else:
+            # phoneが母音もしくは「N」
+            current_mora += phone
+            result.append((mora_phonemes_to_mora_kata[current_mora], tone))
+            current_mora = ""
+    return result
+
+
+def kata_tone2phone_tone(kata_tone: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """`phone_tone2kata_tone()`の逆。"""
+    result: list[tuple[str, int]] = [("_", 0)]
+    for mora, tone in kata_tone:
+        if mora in punctuation:
+            result.append((mora, tone))
+        else:
+            cosonant, vowel = mora_kata_to_mora_phonemes[mora]
+            if cosonant is None:
+                result.append((vowel, tone))
+            else:
+                result.append((cosonant, tone))
+                result.append((vowel, tone))
+    result.append(("_", 0))
+    return result
+
+
+def g2phone_tone_wo_punct(text: str) -> list[tuple[str, int]]:
+    """
+    テキストに対して、音素とアクセント（0か1）のペアのリストを返す。
+    ただし「!」「.」「?」等の非音素記号(punctuation)は全て消える（ポーズ記号も残さない）。
+    非音素記号を含める処理は`align_tones()`で行われる。
+    また「っ」は「q」に、「ん」は「N」に変換される。
+    例: "こんにちは、世界ー。。元気？！" →
+    [('k', 0), ('o', 0), ('N', 1), ('n', 1), ('i', 1), ('ch', 1), ('i', 1), ('w', 1), ('a', 1), ('s', 1), ('e', 1), ('k', 0), ('a', 0), ('i', 0), ('i', 0), ('g', 1), ('e', 1), ('N', 0), ('k', 0), ('i', 0)]
+    """
+    prosodies = pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True)
+    # logger.debug(f"prosodies: {prosodies}")
+    result: list[tuple[str, int]] = []
+    current_phrase: list[tuple[str, int]] = []
+    current_tone = 0
+    for i, letter in enumerate(prosodies):
+        # 特殊記号の処理
+
+        # 文頭記号、無視する
+        if letter == "^":
+            assert i == 0, "Unexpected ^"
+        # アクセント句の終わりに来る記号
+        elif letter in ("$", "?", "_", "#"):
+            # 保持しているフレーズを、アクセント数値を0-1に修正し結果に追加
+            result.extend(fix_phone_tone(current_phrase))
+            # 末尾に来る終了記号、無視（文中の疑問文は`_`になる）
+            if letter in ("$", "?"):
+                assert i == len(prosodies) - 1, f"Unexpected {letter}"
+            # あとは"_"（ポーズ）と"#"（アクセント句の境界）のみ
+            # これらは残さず、次のアクセント句に備える。
+            current_phrase = []
+            # 0を基準点にしてそこから上昇・下降する（負の場合は上の`fix_phone_tone`で直る）
+            current_tone = 0
+        # アクセント上昇記号
+        elif letter == "[":
+            current_tone = current_tone + 1
+        # アクセント下降記号
+        elif letter == "]":
+            current_tone = current_tone - 1
+        # それ以外は通常の音素
+        else:
+            if letter == "cl":  # 「っ」の処理
+                letter = "q"
+            # elif letter == "N":  # 「ん」の処理
+            #     letter = "n"
+            current_phrase.append((letter, current_tone))
+    return result
+
+
+def text2sep_kata(norm_text: str) -> tuple[list[str], list[str]]:
+    """
+    `text_normalize`で正規化済みの`norm_text`を受け取り、それを単語分割し、
+    分割された単語リストとその読み（カタカナor記号1文字）のリストのタプルを返す。
+    単語分割結果は、`g2p()`の`word2ph`で1文字あたりに割り振る音素記号の数を決めるために使う。
+    例:
+    `私はそう思う!って感じ?` →
+    ["私", "は", "そう", "思う", "!", "って", "感じ", "?"], ["ワタシ", "ワ", "ソー", "オモウ", "!", "ッテ", "カンジ", "?"]
+    """
+    # parsed: OpenJTalkの解析結果
+    parsed = pyopenjtalk.run_frontend(norm_text)
+    sep_text: list[str] = []
+    sep_kata: list[str] = []
+    for parts in parsed:
+        # word: 実際の単語の文字列
+        # yomi: その読み、但し無声化サインの`’`は除去
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        """
+        ここで`yomi`の取りうる値は以下の通りのはず。
+        - `word`が通常単語 → 通常の読み（カタカナ）
+            （カタカナからなり、長音記号も含みうる、`アー` 等）
+        - `word`が`ー` から始まる → `ーラー` や `ーーー` など
+        - `word`が句読点や空白等 → `、`
+        - `word`がpunctuationの繰り返し → 全角にしたもの
+        基本的にpunctuationは1文字ずつ分かれるが、何故かある程度連続すると1つにまとまる。
+        他にも`word`が読めないキリル文字アラビア文字等が来ると`、`になるが、正規化でこの場合は起きないはず。
+        また元のコードでは`yomi`が空白の場合の処理があったが、これは起きないはず。
+        処理すべきは`yomi`が`、`の場合のみのはず。
+        """
+        assert yomi != "", f"Empty yomi: {word}"
+        if yomi == "、":
+            # wordは正規化されているので、`.`, `,`, `!`, `'`, `-`, `--` のいずれか
+            if not set(word).issubset(set(punctuation)):  # 記号繰り返しか判定
+                # ここはpyopenjtalkが読めない文字等のときに起こる
+                raise ValueError(f"Cannot read: {word} in:\n{norm_text}")
+            # yomiは元の記号のままに変更
+            yomi = word
+        elif yomi == "？":
+            assert word == "?", f"yomi `？` comes from: {word}"
+            yomi = "?"
+        sep_text.append(word)
+        sep_kata.append(yomi)
+    return sep_text, sep_kata
+
+
+# ESPnetの実装から引用、変更点無し。「ん」は「N」なことに注意。
+# https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
+def pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) -> list[str]:
+    """Extract phoneme + prosoody symbol sequence from input full-context labels.
+
+    The algorithm is based on `Prosodic features control by symbols as input of
+    sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
+
+    Args:
+        text (str): Input text.
+        drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
+
+    Returns:
+        List[str]: List of phoneme + prosody symbols.
+
+    Examples:
+        >>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
+        >>> pyopenjtalk_g2p_prosody("こんにちは。")
+        ['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
+
+    .. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
+        modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
+
+    """
+    labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
+    N = len(labels)
+
+    phones = []
+    for n in range(N):
+        lab_curr = labels[n]
+
+        # current phoneme
+        p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
+        # deal unvoiced vowels as normal vowels
+        if drop_unvoiced_vowels and p3 in "AEIOU":
+            p3 = p3.lower()
+
+        # deal with sil at the beginning and the end of text
+        if p3 == "sil":
+            assert n == 0 or n == N - 1
+            if n == 0:
+                phones.append("^")
+            elif n == N - 1:
+                # check question form or not
+                e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
+                if e3 == 0:
+                    phones.append("$")
+                elif e3 == 1:
+                    phones.append("?")
+            continue
+        elif p3 == "pau":
+            phones.append("_")
+            continue
+        else:
+            phones.append(p3)
+
+        # accent type and position info (forward or backward)
+        a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
+        a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
+        a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
+
+        # number of mora in accent phrase
+        f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
+
+        a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
+        # accent phrase border
+        if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
+            phones.append("#")
+        # pitch falling
+        elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
+            phones.append("]")
+        # pitch rising
+        elif a2 == 1 and a2_next == 2:
+            phones.append("[")
+
+    return phones
+
+
+def _numeric_feature_by_regex(regex, s):
+    match = re.search(regex, s)
+    if match is None:
+        return -50
+    return int(match.group(1))
+
+
+def fix_phone_tone(phone_tone_list: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """
+    `phone_tone_list`のtone（アクセントの値）を0か1の範囲に修正する。
+    例: [(a, 0), (i, -1), (u, -1)] → [(a, 1), (i, 0), (u, 0)]
+    """
+    tone_values = set(tone for _, tone in phone_tone_list)
+    if len(tone_values) == 1:
+        assert tone_values == {0}, tone_values
+        return phone_tone_list
+    elif len(tone_values) == 2:
+        if tone_values == {0, 1}:
+            return phone_tone_list
+        elif tone_values == {-1, 0}:
+            return [
+                (letter, 0 if tone == -1 else 1) for letter, tone in phone_tone_list
+            ]
+        else:
+            raise ValueError(f"Unexpected tone values: {tone_values}")
+    else:
+        raise ValueError(f"Unexpected tone values: {tone_values}")
+
+
+def distribute_phone(n_phone: int, n_word: int) -> list[int]:
+    """
+    左から右に1ずつ振り分け、次にまた左から右に1ずつ増やし、というふうに、
+    音素の数`n_phone`を単語の数`n_word`に分配する。
+    """
+    phones_per_word = [0] * n_word
+    for _ 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: list[list[str]]) -> list[list[str]]:
+    """
+    フレーズごとに分かれた音素（長音記号がそのまま）のリストのリスト`sep_phonemes`を受け取り、
+    その長音記号を処理して、音素のリストのリストを返す。
+    基本的には直前の音素を伸ばすが、直前の音素が母音でない場合もしくは冒頭の場合は、
+    おそらく長音記号とダッシュを勘違いしていると思われるので、ダッシュに対応する音素`-`に変換する。
+    """
+    for i in range(len(sep_phonemes)):
+        if sep_phonemes[i][0] == "ー":
+            if i != 0:
+                prev_phoneme = sep_phonemes[i - 1][-1]
+                if prev_phoneme in VOWELS:
+                    # 母音と「ん」のあとの伸ばし棒なので、その母音に変換
+                    sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
+                else:
+                    # 「。ーー」等おそらく予期しない長音記号
+                    # ダッシュの勘違いだと思われる
+                    sep_phonemes[i][0] = "-"
+            else:
+                # 冒頭に長音記号が来ていおり、これはダッシュの勘違いと思われる
+                sep_phonemes[i][0] = "-"
+        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-char-wwm")
+
+
+def align_tones(
+    phones_with_punct: list[str], phone_tone_list: list[tuple[str, int]]
+) -> list[tuple[str, int]]:
+    """
+    例:
+    …私は、、そう思う。
+    phones_with_punct:
+    [".", ".", ".", "w", "a", "t", "a", "sh", "i", "w", "a", ",", ",", "s", "o", "o", "o", "m", "o", "u", "."]
+    phone_tone_list:
+    [("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), ("_", 0), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0))]
+    Return:
+    [(".", 0), (".", 0), (".", 0), ("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), (",", 0), (",", 0), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0), (".", 0)]
+    """
+    result: list[tuple[str, int]] = []
+    tone_index = 0
+    for phone in phones_with_punct:
+        if tone_index >= len(phone_tone_list):
+            # 余ったpunctuationがある場合 → (punctuation, 0)を追加
+            result.append((phone, 0))
+        elif phone == phone_tone_list[tone_index][0]:
+            # phone_tone_listの現在の音素と一致する場合 → toneをそこから取得、(phone, tone)を追加
+            result.append((phone, phone_tone_list[tone_index][1]))
+            # 探すindexを1つ進める
+            tone_index += 1
+        elif phone in punctuation:
+            # phoneがpunctuationの場合 → (phone, 0)を追加
+            result.append((phone, 0))
+        else:
+            logger.debug(f"phones: {phones_with_punct}")
+            logger.debug(f"phone_tone_list: {phone_tone_list}")
+            logger.debug(f"result: {result}")
+            logger.debug(f"tone_index: {tone_index}")
+            logger.debug(f"phone: {phone}")
+            raise ValueError(f"Unexpected phone: {phone}")
+    return result
+
+
+def kata2phoneme_list(text: str) -> list[str]:
+    """
+    原則カタカナの`text`を受け取り、それをそのままいじらずに音素記号のリストに変換。
+    注意点：
+    - punctuationかその繰り返しが来た場合、punctuationたちをそのままリストにして返す。
+    - 冒頭に続く「ー」はそのまま「ー」のままにする（`handle_long()`で処理される）
+    - 文中の「ー」は前の音素記号の最後の音素記号に変換される。
+    例：
+    `ーーソーナノカーー` → ["ー", "ー", "s", "o", "o", "n", "a", "n", "o", "k", "a", "a", "a"]
+    `?` → ["?"]
+    `!?!?!?!?!` → ["!", "?", "!", "?", "!", "?", "!", "?", "!"]
+    """
+    if set(text).issubset(set(punctuation)):
+        return list(text)
+    # `text`がカタカナ（`ー`含む）のみからなるかどうかをチェック
+    if re.fullmatch(r"[\u30A0-\u30FF]+", text) is None:
+        raise ValueError(f"Input must be katakana only: {text}")
+    sorted_keys = sorted(mora_kata_to_mora_phonemes.keys(), key=len, reverse=True)
+    pattern = "|".join(map(re.escape, sorted_keys))
+
+    def mora2phonemes(mora: str) -> str:
+        cosonant, vowel = mora_kata_to_mora_phonemes[mora]
+        if cosonant is None:
+            return f" {vowel}"
+        return f" {cosonant} {vowel}"
+
+    spaced_phonemes = re.sub(pattern, lambda m: mora2phonemes(m.group()), text)
+
+    # 長音記号「ー」の処理
+    long_pattern = r"(\w)(ー*)"
+    long_replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2))
+    spaced_phonemes = re.sub(long_pattern, long_replacement, spaced_phonemes)
+    return spaced_phonemes.strip().split(" ")
+
+
+if __name__ == "__main__":
+    tokenizer = AutoTokenizer.from_pretrained(
+        "./bert/deberta-v2-large-japanese-char-wwm"
+    )
+    text = "こんにちは、世界。"
+    from text.japanese_bert import get_bert_feature
+
+    text = text_normalize(text)
+
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)
diff --git a/text/japanese_bert.py b/text/japanese_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..2974c76e63c4987018779ebcebc790f94e845a4e
--- /dev/null
+++ b/text/japanese_bert.py
@@ -0,0 +1,67 @@
+import sys
+
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from config import config
+from text.japanese import text2sep_kata
+
+LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm"
+
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
+
+models = dict()
+
+
+def get_bert_feature(
+    text,
+    word2ph,
+    device=config.bert_gen_config.device,
+    assist_text=None,
+    assist_text_weight=0.7,
+):
+    text = "".join(text2sep_kata(text)[0])
+    if assist_text:
+        assist_text = "".join(text2sep_kata(assist_text)[0])
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    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 assist_text:
+            style_inputs = tokenizer(assist_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, text
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if assist_text:
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_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
diff --git a/text/japanese_mora_list.py b/text/japanese_mora_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..b43e54d8d8297cf1eac0e3e3f0eef6b4f1c24fa3
--- /dev/null
+++ b/text/japanese_mora_list.py
@@ -0,0 +1,232 @@
+"""
+VOICEVOXのソースコードからお借りして最低限に改造したコード。
+https://github.com/VOICEVOX/voicevox_engine/blob/master/voicevox_engine/tts_pipeline/mora_list.py
+"""
+
+"""
+以下のモーラ対応表はOpenJTalkのソースコードから取得し、
+カタカナ表記とモーラが一対一対応するように改造した。
+ライセンス表記：
+-----------------------------------------------------------------
+          The Japanese TTS System "Open JTalk"
+          developed by HTS Working Group
+          http://open-jtalk.sourceforge.net/
+-----------------------------------------------------------------
+
+ Copyright (c) 2008-2014  Nagoya Institute of Technology
+                          Department of Computer Science
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or
+without modification, are permitted provided that the following
+conditions are met:
+
+- Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+- Redistributions in binary form must reproduce the above
+  copyright notice, this list of conditions and the following
+  disclaimer in the documentation and/or other materials provided
+  with the distribution.
+- Neither the name of the HTS working group nor the names of its
+  contributors may be used to endorse or promote products derived
+  from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS
+BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+"""
+from typing import Optional
+
+# (カタカナ, 子音, 母音)の順。子音がない場合はNoneを入れる。
+# 但し「ン」と「ッ」は母音のみという扱いで、「ン」は「N」、「ッ」は「q」とする。
+# （元々「ッ」は「cl」）
+# また「デェ = dy e」はpyopenjtalkの出力（de e）と合わないため削除
+_mora_list_minimum: list[tuple[str, Optional[str], str]] = [
+    ("ヴォ", "v", "o"),
+    ("ヴェ", "v", "e"),
+    ("ヴィ", "v", "i"),
+    ("ヴァ", "v", "a"),
+    ("ヴ", "v", "u"),
+    ("ン", None, "N"),
+    ("ワ", "w", "a"),
+    ("ロ", "r", "o"),
+    ("レ", "r", "e"),
+    ("ル", "r", "u"),
+    ("リョ", "ry", "o"),
+    ("リュ", "ry", "u"),
+    ("リャ", "ry", "a"),
+    ("リェ", "ry", "e"),
+    ("リ", "r", "i"),
+    ("ラ", "r", "a"),
+    ("ヨ", "y", "o"),
+    ("ユ", "y", "u"),
+    ("ヤ", "y", "a"),
+    ("モ", "m", "o"),
+    ("メ", "m", "e"),
+    ("ム", "m", "u"),
+    ("ミョ", "my", "o"),
+    ("ミュ", "my", "u"),
+    ("ミャ", "my", "a"),
+    ("ミェ", "my", "e"),
+    ("ミ", "m", "i"),
+    ("マ", "m", "a"),
+    ("ポ", "p", "o"),
+    ("ボ", "b", "o"),
+    ("ホ", "h", "o"),
+    ("ペ", "p", "e"),
+    ("ベ", "b", "e"),
+    ("ヘ", "h", "e"),
+    ("プ", "p", "u"),
+    ("ブ", "b", "u"),
+    ("フォ", "f", "o"),
+    ("フェ", "f", "e"),
+    ("フィ", "f", "i"),
+    ("ファ", "f", "a"),
+    ("フ", "f", "u"),
+    ("ピョ", "py", "o"),
+    ("ピュ", "py", "u"),
+    ("ピャ", "py", "a"),
+    ("ピェ", "py", "e"),
+    ("ピ", "p", "i"),
+    ("ビョ", "by", "o"),
+    ("ビュ", "by", "u"),
+    ("ビャ", "by", "a"),
+    ("ビェ", "by", "e"),
+    ("ビ", "b", "i"),
+    ("ヒョ", "hy", "o"),
+    ("ヒュ", "hy", "u"),
+    ("ヒャ", "hy", "a"),
+    ("ヒェ", "hy", "e"),
+    ("ヒ", "h", "i"),
+    ("パ", "p", "a"),
+    ("バ", "b", "a"),
+    ("ハ", "h", "a"),
+    ("ノ", "n", "o"),
+    ("ネ", "n", "e"),
+    ("ヌ", "n", "u"),
+    ("ニョ", "ny", "o"),
+    ("ニュ", "ny", "u"),
+    ("ニャ", "ny", "a"),
+    ("ニェ", "ny", "e"),
+    ("ニ", "n", "i"),
+    ("ナ", "n", "a"),
+    ("ドゥ", "d", "u"),
+    ("ド", "d", "o"),
+    ("トゥ", "t", "u"),
+    ("ト", "t", "o"),
+    ("デョ", "dy", "o"),
+    ("デュ", "dy", "u"),
+    ("デャ", "dy", "a"),
+    # ("デェ", "dy", "e"),
+    ("ディ", "d", "i"),
+    ("デ", "d", "e"),
+    ("テョ", "ty", "o"),
+    ("テュ", "ty", "u"),
+    ("テャ", "ty", "a"),
+    ("ティ", "t", "i"),
+    ("テ", "t", "e"),
+    ("ツォ", "ts", "o"),
+    ("ツェ", "ts", "e"),
+    ("ツィ", "ts", "i"),
+    ("ツァ", "ts", "a"),
+    ("ツ", "ts", "u"),
+    ("ッ", None, "q"),  # 「cl」から「q」に変更
+    ("チョ", "ch", "o"),
+    ("チュ", "ch", "u"),
+    ("チャ", "ch", "a"),
+    ("チェ", "ch", "e"),
+    ("チ", "ch", "i"),
+    ("ダ", "d", "a"),
+    ("タ", "t", "a"),
+    ("ゾ", "z", "o"),
+    ("ソ", "s", "o"),
+    ("ゼ", "z", "e"),
+    ("セ", "s", "e"),
+    ("ズィ", "z", "i"),
+    ("ズ", "z", "u"),
+    ("スィ", "s", "i"),
+    ("ス", "s", "u"),
+    ("ジョ", "j", "o"),
+    ("ジュ", "j", "u"),
+    ("ジャ", "j", "a"),
+    ("ジェ", "j", "e"),
+    ("ジ", "j", "i"),
+    ("ショ", "sh", "o"),
+    ("シュ", "sh", "u"),
+    ("シャ", "sh", "a"),
+    ("シェ", "sh", "e"),
+    ("シ", "sh", "i"),
+    ("ザ", "z", "a"),
+    ("サ", "s", "a"),
+    ("ゴ", "g", "o"),
+    ("コ", "k", "o"),
+    ("ゲ", "g", "e"),
+    ("ケ", "k", "e"),
+    ("グヮ", "gw", "a"),
+    ("グ", "g", "u"),
+    ("クヮ", "kw", "a"),
+    ("ク", "k", "u"),
+    ("ギョ", "gy", "o"),
+    ("ギュ", "gy", "u"),
+    ("ギャ", "gy", "a"),
+    ("ギェ", "gy", "e"),
+    ("ギ", "g", "i"),
+    ("キョ", "ky", "o"),
+    ("キュ", "ky", "u"),
+    ("キャ", "ky", "a"),
+    ("キェ", "ky", "e"),
+    ("キ", "k", "i"),
+    ("ガ", "g", "a"),
+    ("カ", "k", "a"),
+    ("オ", None, "o"),
+    ("エ", None, "e"),
+    ("ウォ", "w", "o"),
+    ("ウェ", "w", "e"),
+    ("ウィ", "w", "i"),
+    ("ウ", None, "u"),
+    ("イェ", "y", "e"),
+    ("イ", None, "i"),
+    ("ア", None, "a"),
+]
+_mora_list_additional: list[tuple[str, Optional[str], str]] = [
+    ("ヴョ", "by", "o"),
+    ("ヴュ", "by", "u"),
+    ("ヴャ", "by", "a"),
+    ("ヲ", None, "o"),
+    ("ヱ", None, "e"),
+    ("ヰ", None, "i"),
+    ("ヮ", "w", "a"),
+    ("ョ", "y", "o"),
+    ("ュ", "y", "u"),
+    ("ヅ", "z", "u"),
+    ("ヂ", "j", "i"),
+    ("ヶ", "k", "e"),
+    ("ャ", "y", "a"),
+    ("ォ", None, "o"),
+    ("ェ", None, "e"),
+    ("ゥ", None, "u"),
+    ("ィ", None, "i"),
+    ("ァ", None, "a"),
+]
+
+# 例: "vo" -> "ヴォ", "a" -> "ア"
+mora_phonemes_to_mora_kata: dict[str, str] = {
+    (consonant or "") + vowel: kana for [kana, consonant, vowel] in _mora_list_minimum
+}
+
+# 例: "ヴォ" -> ("v", "o"), "ア" -> (None, "a")
+mora_kata_to_mora_phonemes: dict[str, tuple[Optional[str], str]] = {
+    kana: (consonant, vowel)
+    for [kana, consonant, vowel] in _mora_list_minimum + _mora_list_additional
+}
diff --git a/text/opencpop-strict.txt b/text/opencpop-strict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1bede89a429e34b1a58781ee570fc313b80c0aee
--- /dev/null
+++ b/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/text/symbols.py b/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..846de64584e9ba4b8d96aab36d4efbcefb1a11e7
--- /dev/null
+++ b/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/text/tone_sandhi.py b/text/tone_sandhi.py
new file mode 100644
index 0000000000000000000000000000000000000000..372308604d52cc32f80d0146efa95cfdf7e40b05
--- /dev/null
+++ b/text/tone_sandhi.py
@@ -0,0 +1,773 @@
+# 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 = [] * len(seg)
+        # function 1
+        i = 0
+        while i < len(seg):
+            word, pos = seg[i]
+            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]
+                i += 2
+            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])
+                i += 1
+        seg = [i for i in new_seg if len(i) > 0]
+        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/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/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..9368b5f8e6cd73bda2249953a9a83e1ffb1ce8ab
--- /dev/null
+++ b/tools/translate.py
@@ -0,0 +1,61 @@
+"""
+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/train_ms.py b/train_ms.py
new file mode 100644
index 0000000000000000000000000000000000000000..57eb5f6a14d5ca75a5c852f1b480f9497543f14b
--- /dev/null
+++ b/train_ms.py
@@ -0,0 +1,880 @@
+import argparse
+import datetime
+import gc
+import os
+import platform
+
+import torch
+import torch.distributed as dist
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn import functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+# logging.getLogger("numba").setLevel(logging.WARNING)
+import commons
+import default_style
+import utils
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+from config import config
+from data_utils import (
+    DistributedBucketSampler,
+    TextAudioSpeakerCollate,
+    TextAudioSpeakerLoader,
+)
+from losses import discriminator_loss, feature_loss, generator_loss, kl_loss
+from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from models import DurationDiscriminator, MultiPeriodDiscriminator, SynthesizerTrn
+from text.symbols import symbols
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = (
+    True  # If encontered training problem,please try to disable TF32.
+)
+torch.set_float32_matmul_precision("medium")
+torch.backends.cuda.sdp_kernel("flash")
+torch.backends.cuda.enable_flash_sdp(True)
+torch.backends.cuda.enable_mem_efficient_sdp(
+    True
+)  # Not available if torch version is lower than 2.0
+torch.backends.cuda.enable_math_sdp(True)
+
+
+global_step = 0
+
+
+def run():
+    # Command line configuration is not recommended unless necessary, use config.yml
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        default=config.train_ms_config.config_path,
+        help="JSON file for configuration",
+    )
+    parser.add_argument(
+        "-m",
+        "--model",
+        type=str,
+        help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
+        default=config.dataset_path,
+    )
+    parser.add_argument(
+        "--assets_root",
+        type=str,
+        help="Root directory of model assets needed for inference.",
+        default=config.assets_root,
+    )
+    parser.add_argument(
+        "--skip_default_style",
+        action="store_true",
+        help="Skip saving default style config and mean vector.",
+    )
+    parser.add_argument(
+        "--no_progress_bar",
+        action="store_true",
+        help="Do not show the progress bar while training.",
+    )
+    parser.add_argument(
+        "--speedup",
+        action="store_true",
+        help="Speed up training by disabling logging and evaluation.",
+    )
+    args = parser.parse_args()
+
+    # Set log file
+    model_dir = os.path.join(args.model, config.train_ms_config.model_dir)
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    logger.add(os.path.join(args.model, f"train_{timestamp}.log"))
+
+    # Parsing environment variables
+    envs = config.train_ms_config.env
+    for env_name, env_value in envs.items():
+        if env_name not in os.environ.keys():
+            logger.info("Loading configuration from config {}".format(str(env_value)))
+            os.environ[env_name] = str(env_value)
+    logger.info(
+        "Loading environment variables \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
+            os.environ["MASTER_ADDR"],
+            os.environ["MASTER_PORT"],
+            os.environ["WORLD_SIZE"],
+            os.environ["RANK"],
+            os.environ["LOCAL_RANK"],
+        )
+    )
+
+    backend = "nccl"
+    if platform.system() == "Windows":
+        backend = "gloo"  # If Windows,switch to gloo backend.
+    dist.init_process_group(
+        backend=backend,
+        init_method="env://",
+        timeout=datetime.timedelta(seconds=300),
+    )  # Use torchrun instead of mp.spawn
+    rank = dist.get_rank()
+    local_rank = int(os.environ["LOCAL_RANK"])
+    n_gpus = dist.get_world_size()
+
+    hps = utils.get_hparams_from_file(args.config)
+    # This is needed because we have to pass values to `train_and_evaluate()`
+    hps.model_dir = model_dir
+    hps.speedup = args.speedup
+
+    # 比较路径是否相同
+    if os.path.realpath(args.config) != os.path.realpath(
+        config.train_ms_config.config_path
+    ):
+        with open(args.config, "r", encoding="utf-8") as f:
+            data = f.read()
+        os.makedirs(os.path.dirname(config.train_ms_config.config_path), exist_ok=True)
+        with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
+            f.write(data)
+
+    """
+    Path constants are a bit complicated...
+    TODO: Refactor or rename these?
+    (Both `config.yml` and `config.json` are used, which is confusing I think.)
+
+    args.model: For saving all info needed for training.
+        default: `Data/{model_name}`.
+    hps.model_dir := model_dir: For saving checkpoints (for resuming training).
+        default: `Data/{model_name}/models`.
+        (Use `hps` since we have to pass `model_dir` to `train_and_evaluate()`.
+
+    args.assets_root: The root directory of model assets needed for inference.
+        default: config.assets_root == `model_assets`.
+
+    config.out_dir: The directory for model assets of this model (for inference).
+        default: `model_assets/{model_name}`.
+    """
+    os.makedirs(config.out_dir, exist_ok=True)
+
+    if not args.skip_default_style:
+        # Save default style to out_dir
+        default_style.set_style_config(
+            args.config, os.path.join(config.out_dir, "config.json")
+        )
+        default_style.save_mean_vector(
+            os.path.join(args.model, "wavs"),
+            os.path.join(config.out_dir, "style_vectors.npy"),
+        )
+
+    torch.manual_seed(hps.train.seed)
+    torch.cuda.set_device(local_rank)
+
+    global global_step
+    writer = None
+    writer_eval = None
+    if rank == 0 and not args.speedup:
+        # logger = utils.get_logger(hps.model_dir)
+        # logger.info(hps)
+        utils.check_git_hash(model_dir)
+        writer = SummaryWriter(log_dir=model_dir)
+        writer_eval = SummaryWriter(log_dir=os.path.join(model_dir, "eval"))
+    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
+    train_sampler = DistributedBucketSampler(
+        train_dataset,
+        hps.train.batch_size,
+        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
+        num_replicas=n_gpus,
+        rank=rank,
+        shuffle=True,
+    )
+    collate_fn = TextAudioSpeakerCollate()
+    train_loader = DataLoader(
+        train_dataset,
+        # メモリ消費量を減らそうとnum_workersを1にしてみる
+        # num_workers=min(config.train_ms_config.num_workers, os.cpu_count() // 2),
+        num_workers=1,
+        shuffle=False,
+        pin_memory=True,
+        collate_fn=collate_fn,
+        batch_sampler=train_sampler,
+        persistent_workers=True,
+        # これもメモリ消費量を減らそうとしてコメントアウト
+        # prefetch_factor=4,
+    )  # DataLoader config could be adjusted.
+    eval_dataset = None
+    eval_loader = None
+    if rank == 0 and not args.speedup:
+        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
+        eval_loader = DataLoader(
+            eval_dataset,
+            num_workers=0,
+            shuffle=False,
+            batch_size=1,
+            pin_memory=True,
+            drop_last=False,
+            collate_fn=collate_fn,
+        )
+    if (
+        "use_noise_scaled_mas" in hps.model.keys()
+        and hps.model.use_noise_scaled_mas is True
+    ):
+        logger.info("Using noise scaled MAS for VITS2")
+        mas_noise_scale_initial = 0.01
+        noise_scale_delta = 2e-6
+    else:
+        logger.info("Using normal MAS for VITS1")
+        mas_noise_scale_initial = 0.0
+        noise_scale_delta = 0.0
+    if (
+        "use_duration_discriminator" in hps.model.keys()
+        and hps.model.use_duration_discriminator is True
+    ):
+        logger.info("Using duration discriminator for VITS2")
+        net_dur_disc = DurationDiscriminator(
+            hps.model.hidden_channels,
+            hps.model.hidden_channels,
+            3,
+            0.1,
+            gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
+        ).cuda(local_rank)
+    if (
+        "use_spk_conditioned_encoder" in hps.model.keys()
+        and hps.model.use_spk_conditioned_encoder is True
+    ):
+        if hps.data.n_speakers == 0:
+            raise ValueError(
+                "n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
+            )
+    else:
+        logger.info("Using normal encoder for VITS1")
+
+    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,
+        mas_noise_scale_initial=mas_noise_scale_initial,
+        noise_scale_delta=noise_scale_delta,
+        **hps.model,
+    ).cuda(local_rank)
+
+    if getattr(hps.train, "freeze_ZH_bert", False):
+        logger.info("Freezing ZH bert encoder !!!")
+        for param in net_g.enc_p.bert_proj.parameters():
+            param.requires_grad = False
+
+    if getattr(hps.train, "freeze_EN_bert", False):
+        logger.info("Freezing EN bert encoder !!!")
+        for param in net_g.enc_p.en_bert_proj.parameters():
+            param.requires_grad = False
+
+    if getattr(hps.train, "freeze_JP_bert", False):
+        logger.info("Freezing JP bert encoder !!!")
+        for param in net_g.enc_p.ja_bert_proj.parameters():
+            param.requires_grad = False
+    if getattr(hps.train, "freeze_style", False):
+        logger.info("Freezing style encoder !!!")
+        for param in net_g.enc_p.style_proj.parameters():
+            param.requires_grad = False
+
+    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
+    optim_g = torch.optim.AdamW(
+        filter(lambda p: p.requires_grad, net_g.parameters()),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    optim_d = torch.optim.AdamW(
+        net_d.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    if net_dur_disc is not None:
+        optim_dur_disc = torch.optim.AdamW(
+            net_dur_disc.parameters(),
+            hps.train.learning_rate,
+            betas=hps.train.betas,
+            eps=hps.train.eps,
+        )
+    else:
+        optim_dur_disc = None
+    net_g = DDP(net_g, device_ids=[local_rank])
+    net_d = DDP(net_d, device_ids=[local_rank])
+    dur_resume_lr = None
+    if net_dur_disc is not None:
+        net_dur_disc = DDP(
+            net_dur_disc, device_ids=[local_rank], find_unused_parameters=True
+        )
+
+    if utils.is_resuming(model_dir):
+        if net_dur_disc is not None:
+            _, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(model_dir, "DUR_*.pth"),
+                net_dur_disc,
+                optim_dur_disc,
+                skip_optimizer=(
+                    hps.train.skip_optimizer if "skip_optimizer" in hps.train else True
+                ),
+            )
+            if not optim_dur_disc.param_groups[0].get("initial_lr"):
+                optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
+        _, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path(model_dir, "G_*.pth"),
+            net_g,
+            optim_g,
+            skip_optimizer=(
+                hps.train.skip_optimizer if "skip_optimizer" in hps.train else True
+            ),
+        )
+        _, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path(model_dir, "D_*.pth"),
+            net_d,
+            optim_d,
+            skip_optimizer=(
+                hps.train.skip_optimizer if "skip_optimizer" in hps.train else True
+            ),
+        )
+        if not optim_g.param_groups[0].get("initial_lr"):
+            optim_g.param_groups[0]["initial_lr"] = g_resume_lr
+        if not optim_d.param_groups[0].get("initial_lr"):
+            optim_d.param_groups[0]["initial_lr"] = d_resume_lr
+
+        epoch_str = max(epoch_str, 1)
+        # global_step = (epoch_str - 1) * len(train_loader)
+        global_step = int(
+            utils.get_steps(utils.latest_checkpoint_path(model_dir, "G_*.pth"))
+        )
+        logger.info(
+            f"******************Found the model. Current epoch is {epoch_str}, gloabl step is {global_step}*********************"
+        )
+    else:
+        try:
+            _ = utils.load_safetensors(
+                os.path.join(model_dir, "G_0.safetensors"), net_g
+            )
+            _ = utils.load_safetensors(
+                os.path.join(model_dir, "D_0.safetensors"), net_d
+            )
+            if net_dur_disc is not None:
+                _ = utils.load_safetensors(
+                    os.path.join(model_dir, "DUR_0.safetensors"), net_dur_disc
+                )
+            logger.info("Loaded the pretrained models.")
+        except Exception as e:
+            logger.warning(e)
+            logger.warning(
+                "It seems that you are not using the pretrained models, so we will train from scratch."
+            )
+        finally:
+            epoch_str = 1
+            global_step = 0
+
+    def lr_lambda(epoch):
+        """
+        Learning rate scheduler for warmup and exponential decay.
+        - During the warmup period, the learning rate increases linearly.
+        - After the warmup period, the learning rate decreases exponentially.
+        """
+        if epoch < hps.train.warmup_epochs:
+            return float(epoch) / float(max(1, hps.train.warmup_epochs))
+        else:
+            return hps.train.lr_decay ** (epoch - hps.train.warmup_epochs)
+
+    scheduler_last_epoch = epoch_str - 2
+    scheduler_g = torch.optim.lr_scheduler.LambdaLR(
+        optim_g, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+    )
+    scheduler_d = torch.optim.lr_scheduler.LambdaLR(
+        optim_d, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+    )
+    if net_dur_disc is not None:
+        scheduler_dur_disc = torch.optim.lr_scheduler.LambdaLR(
+            optim_dur_disc, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+        )
+    else:
+        scheduler_dur_disc = None
+    scaler = GradScaler(enabled=hps.train.bf16_run)
+    logger.info("Start training.")
+
+    diff = abs(
+        epoch_str * len(train_loader) - (hps.train.epochs + 1) * len(train_loader)
+    )
+    pbar = None
+    if not args.no_progress_bar:
+        pbar = tqdm(
+            total=global_step + diff,
+            initial=global_step,
+            smoothing=0.05,
+            file=SAFE_STDOUT,
+        )
+    initial_step = global_step
+
+    for epoch in range(epoch_str, hps.train.epochs + 1):
+        if rank == 0:
+            train_and_evaluate(
+                rank,
+                local_rank,
+                epoch,
+                hps,
+                [net_g, net_d, net_dur_disc],
+                [optim_g, optim_d, optim_dur_disc],
+                [scheduler_g, scheduler_d, scheduler_dur_disc],
+                scaler,
+                [train_loader, eval_loader],
+                logger,
+                [writer, writer_eval],
+                pbar,
+                initial_step,
+            )
+        else:
+            train_and_evaluate(
+                rank,
+                local_rank,
+                epoch,
+                hps,
+                [net_g, net_d, net_dur_disc],
+                [optim_g, optim_d, optim_dur_disc],
+                [scheduler_g, scheduler_d, scheduler_dur_disc],
+                scaler,
+                [train_loader, None],
+                None,
+                None,
+                pbar,
+                initial_step,
+            )
+        scheduler_g.step()
+        scheduler_d.step()
+        if net_dur_disc is not None:
+            scheduler_dur_disc.step()
+
+        if epoch == hps.train.epochs:
+            # Save the final models
+            utils.save_checkpoint(
+                net_g,
+                optim_g,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(model_dir, "G_{}.pth".format(global_step)),
+            )
+            utils.save_checkpoint(
+                net_d,
+                optim_d,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(model_dir, "D_{}.pth".format(global_step)),
+            )
+            if net_dur_disc is not None:
+                utils.save_checkpoint(
+                    net_dur_disc,
+                    optim_dur_disc,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(model_dir, "DUR_{}.pth".format(global_step)),
+                )
+            utils.save_safetensors(
+                net_g,
+                epoch,
+                os.path.join(
+                    config.out_dir,
+                    f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
+                ),
+                for_infer=True,
+            )
+
+    if pbar is not None:
+        pbar.close()
+
+
+def train_and_evaluate(
+    rank,
+    local_rank,
+    epoch,
+    hps,
+    nets,
+    optims,
+    schedulers,
+    scaler,
+    loaders,
+    logger,
+    writers,
+    pbar: tqdm,
+    initial_step: int,
+):
+    net_g, net_d, net_dur_disc = nets
+    optim_g, optim_d, optim_dur_disc = optims
+    scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
+    train_loader, eval_loader = loaders
+    if writers is not None:
+        writer, writer_eval = writers
+
+    train_loader.batch_sampler.set_epoch(epoch)
+    global global_step
+
+    net_g.train()
+    net_d.train()
+    if net_dur_disc is not None:
+        net_dur_disc.train()
+    for batch_idx, (
+        x,
+        x_lengths,
+        spec,
+        spec_lengths,
+        y,
+        y_lengths,
+        speakers,
+        tone,
+        language,
+        bert,
+        ja_bert,
+        en_bert,
+        style_vec,
+    ) in enumerate(train_loader):
+        if net_g.module.use_noise_scaled_mas:
+            current_mas_noise_scale = (
+                net_g.module.mas_noise_scale_initial
+                - net_g.module.noise_scale_delta * global_step
+            )
+            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
+        x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
+            local_rank, non_blocking=True
+        )
+        spec, spec_lengths = spec.cuda(
+            local_rank, non_blocking=True
+        ), spec_lengths.cuda(local_rank, non_blocking=True)
+        y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
+            local_rank, non_blocking=True
+        )
+        speakers = speakers.cuda(local_rank, non_blocking=True)
+        tone = tone.cuda(local_rank, non_blocking=True)
+        language = language.cuda(local_rank, non_blocking=True)
+        bert = bert.cuda(local_rank, non_blocking=True)
+        ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
+        en_bert = en_bert.cuda(local_rank, non_blocking=True)
+        style_vec = style_vec.cuda(local_rank, non_blocking=True)
+
+        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+            (
+                y_hat,
+                l_length,
+                attn,
+                ids_slice,
+                x_mask,
+                z_mask,
+                (z, z_p, m_p, logs_p, m_q, logs_q),
+                (hidden_x, logw, logw_),
+            ) = net_g(
+                x,
+                x_lengths,
+                spec,
+                spec_lengths,
+                speakers,
+                tone,
+                language,
+                bert,
+                ja_bert,
+                en_bert,
+                style_vec,
+            )
+            mel = spec_to_mel_torch(
+                spec,
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+            y_mel = commons.slice_segments(
+                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
+            )
+            y_hat_mel = mel_spectrogram_torch(
+                y_hat.squeeze(1).float(),
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+
+            y = commons.slice_segments(
+                y, ids_slice * hps.data.hop_length, hps.train.segment_size
+            )  # slice
+
+            # Discriminator
+            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+            with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
+                    y_d_hat_r, y_d_hat_g
+                )
+                loss_disc_all = loss_disc
+            if net_dur_disc is not None:
+                y_dur_hat_r, y_dur_hat_g = net_dur_disc(
+                    hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
+                )
+                with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                    # TODO: I think need to mean using the mask, but for now, just mean all
+                    (
+                        loss_dur_disc,
+                        losses_dur_disc_r,
+                        losses_dur_disc_g,
+                    ) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
+                    loss_dur_disc_all = loss_dur_disc
+                optim_dur_disc.zero_grad()
+                scaler.scale(loss_dur_disc_all).backward()
+                scaler.unscale_(optim_dur_disc)
+                commons.clip_grad_value_(net_dur_disc.parameters(), None)
+                scaler.step(optim_dur_disc)
+
+        optim_d.zero_grad()
+        scaler.scale(loss_disc_all).backward()
+        scaler.unscale_(optim_d)
+        if getattr(hps.train, "bf16_run", False):
+            torch.nn.utils.clip_grad_norm_(parameters=net_d.parameters(), max_norm=200)
+        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+        scaler.step(optim_d)
+
+        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+            # Generator
+            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+            if net_dur_disc is not None:
+                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
+            with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                loss_dur = torch.sum(l_length.float())
+                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+                loss_fm = feature_loss(fmap_r, fmap_g)
+                loss_gen, losses_gen = generator_loss(y_d_hat_g)
+                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+                if net_dur_disc is not None:
+                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
+                    loss_gen_all += loss_dur_gen
+        optim_g.zero_grad()
+        scaler.scale(loss_gen_all).backward()
+        scaler.unscale_(optim_g)
+        if getattr(hps.train, "bf16_run", False):
+            torch.nn.utils.clip_grad_norm_(parameters=net_g.parameters(), max_norm=500)
+        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+        scaler.step(optim_g)
+        scaler.update()
+
+        if rank == 0:
+            if global_step % hps.train.log_interval == 0 and not hps.speedup:
+                lr = optim_g.param_groups[0]["lr"]
+                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+                # logger.info(
+                #     "Train Epoch: {} [{:.0f}%]".format(
+                #         epoch, 100.0 * batch_idx / len(train_loader)
+                #     )
+                # )
+                # logger.info([x.item() for x in losses] + [global_step, lr])
+
+                scalar_dict = {
+                    "loss/g/total": loss_gen_all,
+                    "loss/d/total": loss_disc_all,
+                    "learning_rate": lr,
+                    "grad_norm_d": grad_norm_d,
+                    "grad_norm_g": grad_norm_g,
+                }
+                scalar_dict.update(
+                    {
+                        "loss/g/fm": loss_fm,
+                        "loss/g/mel": loss_mel,
+                        "loss/g/dur": loss_dur,
+                        "loss/g/kl": loss_kl,
+                    }
+                )
+                scalar_dict.update(
+                    {"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
+                )
+                scalar_dict.update(
+                    {"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}
+                )
+                scalar_dict.update(
+                    {"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}
+                )
+
+                image_dict = {
+                    "slice/mel_org": utils.plot_spectrogram_to_numpy(
+                        y_mel[0].data.cpu().numpy()
+                    ),
+                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(
+                        y_hat_mel[0].data.cpu().numpy()
+                    ),
+                    "all/mel": utils.plot_spectrogram_to_numpy(
+                        mel[0].data.cpu().numpy()
+                    ),
+                    "all/attn": utils.plot_alignment_to_numpy(
+                        attn[0, 0].data.cpu().numpy()
+                    ),
+                }
+                utils.summarize(
+                    writer=writer,
+                    global_step=global_step,
+                    images=image_dict,
+                    scalars=scalar_dict,
+                )
+
+            if (
+                global_step % hps.train.eval_interval == 0
+                and global_step != 0
+                and initial_step != global_step
+            ):
+                if not hps.speedup:
+                    evaluate(hps, net_g, eval_loader, writer_eval)
+                utils.save_checkpoint(
+                    net_g,
+                    optim_g,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
+                )
+                utils.save_checkpoint(
+                    net_d,
+                    optim_d,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
+                )
+                if net_dur_disc is not None:
+                    utils.save_checkpoint(
+                        net_dur_disc,
+                        optim_dur_disc,
+                        hps.train.learning_rate,
+                        epoch,
+                        os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)),
+                    )
+                keep_ckpts = config.train_ms_config.keep_ckpts
+                if keep_ckpts > 0:
+                    utils.clean_checkpoints(
+                        path_to_models=hps.model_dir,
+                        n_ckpts_to_keep=keep_ckpts,
+                        sort_by_time=True,
+                    )
+                # Save safetensors (for inference) to `model_assets/{model_name}`
+                utils.save_safetensors(
+                    net_g,
+                    epoch,
+                    os.path.join(
+                        config.out_dir,
+                        f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
+                    ),
+                    for_infer=True,
+                )
+
+        global_step += 1
+        if pbar is not None:
+            pbar.set_description(
+                "Epoch {}({:.0f}%)/{}".format(
+                    epoch, 100.0 * batch_idx / len(train_loader), hps.train.epochs
+                )
+            )
+            pbar.update()
+    # 本家ではこれをスピードアップのために消すと書かれていたので、一応消してみる
+    # と思ったけどメモリ使用量が減るかもしれないのでつけてみる
+    gc.collect()
+    torch.cuda.empty_cache()
+    if pbar is None and rank == 0:
+        logger.info(f"====> Epoch: {epoch}, step: {global_step}")
+
+
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    image_dict = {}
+    audio_dict = {}
+    logger.info("Evaluating ...")
+    with torch.no_grad():
+        for batch_idx, (
+            x,
+            x_lengths,
+            spec,
+            spec_lengths,
+            y,
+            y_lengths,
+            speakers,
+            tone,
+            language,
+            bert,
+            ja_bert,
+            en_bert,
+            style_vec,
+        ) in enumerate(eval_loader):
+            x, x_lengths = x.cuda(), x_lengths.cuda()
+            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
+            y, y_lengths = y.cuda(), y_lengths.cuda()
+            speakers = speakers.cuda()
+            bert = bert.cuda()
+            ja_bert = ja_bert.cuda()
+            en_bert = en_bert.cuda()
+            tone = tone.cuda()
+            language = language.cuda()
+            style_vec = style_vec.cuda()
+            for use_sdp in [True, False]:
+                y_hat, attn, mask, *_ = generator.module.infer(
+                    x,
+                    x_lengths,
+                    speakers,
+                    tone,
+                    language,
+                    bert,
+                    ja_bert,
+                    en_bert,
+                    style_vec,
+                    y=spec,
+                    max_len=1000,
+                    sdp_ratio=0.0 if not use_sdp else 1.0,
+                )
+                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
+
+                mel = spec_to_mel_torch(
+                    spec,
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                y_hat_mel = mel_spectrogram_torch(
+                    y_hat.squeeze(1).float(),
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.hop_length,
+                    hps.data.win_length,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                image_dict.update(
+                    {
+                        f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            y_hat_mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update(
+                    {
+                        f"gen/audio_{batch_idx}_{use_sdp}": y_hat[
+                            0, :, : y_hat_lengths[0]
+                        ]
+                    }
+                )
+                image_dict.update(
+                    {
+                        f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
+
+    utils.summarize(
+        writer=writer_eval,
+        global_step=global_step,
+        images=image_dict,
+        audios=audio_dict,
+        audio_sampling_rate=hps.data.sampling_rate,
+    )
+    generator.train()
+
+
+if __name__ == "__main__":
+    run()
diff --git a/train_ms_jp_extra.py b/train_ms_jp_extra.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c46eb25f778383377efea4be7e6f3be957c289a
--- /dev/null
+++ b/train_ms_jp_extra.py
@@ -0,0 +1,1030 @@
+import argparse
+import datetime
+import gc
+import os
+import platform
+
+import torch
+import torch.distributed as dist
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn import functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+# logging.getLogger("numba").setLevel(logging.WARNING)
+import commons
+import default_style
+import utils
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+from config import config
+from data_utils import (
+    DistributedBucketSampler,
+    TextAudioSpeakerCollate,
+    TextAudioSpeakerLoader,
+)
+from losses import WavLMLoss, discriminator_loss, feature_loss, generator_loss, kl_loss
+from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from models_jp_extra import (
+    DurationDiscriminator,
+    MultiPeriodDiscriminator,
+    SynthesizerTrn,
+    WavLMDiscriminator,
+)
+from text.symbols import symbols
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = (
+    True  # If encontered training problem,please try to disable TF32.
+)
+torch.set_num_threads(1)
+torch.set_float32_matmul_precision("medium")
+torch.backends.cuda.sdp_kernel("flash")
+torch.backends.cuda.enable_flash_sdp(True)
+torch.backends.cuda.enable_mem_efficient_sdp(
+    True
+)  # Not available if torch version is lower than 2.0
+global_step = 0
+
+
+def run():
+    # Command line configuration is not recommended unless necessary, use config.yml
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        default=config.train_ms_config.config_path,
+        help="JSON file for configuration",
+    )
+    parser.add_argument(
+        "-m",
+        "--model",
+        type=str,
+        help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
+        default=config.dataset_path,
+    )
+    parser.add_argument(
+        "--assets_root",
+        type=str,
+        help="Root directory of model assets needed for inference.",
+        default=config.assets_root,
+    )
+    parser.add_argument(
+        "--skip_default_style",
+        action="store_true",
+        help="Skip saving default style config and mean vector.",
+    )
+    parser.add_argument(
+        "--no_progress_bar",
+        action="store_true",
+        help="Do not show the progress bar while training.",
+    )
+    parser.add_argument(
+        "--speedup",
+        action="store_true",
+        help="Speed up training by disabling logging and evaluation.",
+    )
+    args = parser.parse_args()
+
+    # Set log file
+    model_dir = os.path.join(args.model, config.train_ms_config.model_dir)
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    logger.add(os.path.join(args.model, f"train_{timestamp}.log"))
+
+    # Parsing environment variables
+    envs = config.train_ms_config.env
+    for env_name, env_value in envs.items():
+        if env_name not in os.environ.keys():
+            logger.info("Loading configuration from config {}".format(str(env_value)))
+            os.environ[env_name] = str(env_value)
+    logger.info(
+        "Loading environment variables \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
+            os.environ["MASTER_ADDR"],
+            os.environ["MASTER_PORT"],
+            os.environ["WORLD_SIZE"],
+            os.environ["RANK"],
+            os.environ["LOCAL_RANK"],
+        )
+    )
+
+    backend = "nccl"
+    if platform.system() == "Windows":
+        backend = "gloo"  # If Windows,switch to gloo backend.
+    dist.init_process_group(
+        backend=backend,
+        init_method="env://",
+        timeout=datetime.timedelta(seconds=300),
+    )  # Use torchrun instead of mp.spawn
+    rank = dist.get_rank()
+    local_rank = int(os.environ["LOCAL_RANK"])
+    n_gpus = dist.get_world_size()
+
+    hps = utils.get_hparams_from_file(args.config)
+    # This is needed because we have to pass values to `train_and_evaluate()
+    hps.model_dir = model_dir
+    hps.speedup = args.speedup
+
+    # 比较路径是否相同
+    if os.path.realpath(args.config) != os.path.realpath(
+        config.train_ms_config.config_path
+    ):
+        with open(args.config, "r", encoding="utf-8") as f:
+            data = f.read()
+        os.makedirs(os.path.dirname(config.train_ms_config.config_path), exist_ok=True)
+        with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
+            f.write(data)
+
+    """
+    Path constants are a bit complicated...
+    TODO: Refactor or rename these?
+    (Both `config.yml` and `config.json` are used, which is confusing I think.)
+
+    args.model: For saving all info needed for training.
+        default: `Data/{model_name}`.
+    hps.model_dir := model_dir: For saving checkpoints (for resuming training).
+        default: `Data/{model_name}/models`.
+        (Use `hps` since we have to pass `model_dir` to `train_and_evaluate()`.
+
+    args.assets_root: The root directory of model assets needed for inference.
+        default: config.assets_root == `model_assets`.
+
+    config.out_dir: The directory for model assets of this model (for inference).
+        default: `model_assets/{model_name}`.
+    """
+    os.makedirs(config.out_dir, exist_ok=True)
+
+    if not args.skip_default_style:
+        # Save default style to out_dir
+        default_style.set_style_config(
+            args.config, os.path.join(config.out_dir, "config.json")
+        )
+        default_style.save_mean_vector(
+            os.path.join(args.model, "wavs"),
+            os.path.join(config.out_dir, "style_vectors.npy"),
+        )
+
+    torch.manual_seed(hps.train.seed)
+    torch.cuda.set_device(local_rank)
+
+    global global_step
+    writer = None
+    writer_eval = None
+    if rank == 0 and not args.speedup:
+        # logger = utils.get_logger(hps.model_dir)
+        # logger.info(hps)
+        utils.check_git_hash(model_dir)
+        writer = SummaryWriter(log_dir=model_dir)
+        writer_eval = SummaryWriter(log_dir=os.path.join(model_dir, "eval"))
+    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
+    train_sampler = DistributedBucketSampler(
+        train_dataset,
+        hps.train.batch_size,
+        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
+        num_replicas=n_gpus,
+        rank=rank,
+        shuffle=True,
+    )
+    collate_fn = TextAudioSpeakerCollate(use_jp_extra=True)
+    train_loader = DataLoader(
+        train_dataset,
+        # メモリ消費量を減らそうとnum_workersを1にしてみる
+        # num_workers=min(config.train_ms_config.num_workers, os.cpu_count() // 2),
+        num_workers=1,
+        shuffle=False,
+        pin_memory=True,
+        collate_fn=collate_fn,
+        batch_sampler=train_sampler,
+        persistent_workers=True,
+        # これもメモリ消費量を減らそうとしてコメントアウト
+        # prefetch_factor=6,
+    )  # DataLoader config could be adjusted.
+    eval_dataset = None
+    eval_loader = None
+    if rank == 0 and not args.speedup:
+        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
+        eval_loader = DataLoader(
+            eval_dataset,
+            num_workers=0,
+            shuffle=False,
+            batch_size=1,
+            pin_memory=True,
+            drop_last=False,
+            collate_fn=collate_fn,
+        )
+    if (
+        "use_noise_scaled_mas" in hps.model.keys()
+        and hps.model.use_noise_scaled_mas is True
+    ):
+        logger.info("Using noise scaled MAS for VITS2")
+        mas_noise_scale_initial = 0.01
+        noise_scale_delta = 2e-6
+    else:
+        logger.info("Using normal MAS for VITS1")
+        mas_noise_scale_initial = 0.0
+        noise_scale_delta = 0.0
+    if (
+        "use_duration_discriminator" in hps.model.keys()
+        and hps.model.use_duration_discriminator is True
+    ):
+        logger.info("Using duration discriminator for VITS2")
+        net_dur_disc = DurationDiscriminator(
+            hps.model.hidden_channels,
+            hps.model.hidden_channels,
+            3,
+            0.1,
+            gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
+        ).cuda(local_rank)
+    else:
+        net_dur_disc = None
+    if (
+        "use_wavlm_discriminator" in hps.model.keys()
+        and hps.model.use_wavlm_discriminator is True
+    ):
+        net_wd = WavLMDiscriminator(
+            hps.model.slm.hidden, hps.model.slm.nlayers, hps.model.slm.initial_channel
+        ).cuda(local_rank)
+    else:
+        net_wd = None
+    if (
+        "use_spk_conditioned_encoder" in hps.model.keys()
+        and hps.model.use_spk_conditioned_encoder is True
+    ):
+        if hps.data.n_speakers == 0:
+            raise ValueError(
+                "n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
+            )
+    else:
+        logger.info("Using normal encoder for VITS1")
+
+    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,
+        mas_noise_scale_initial=mas_noise_scale_initial,
+        noise_scale_delta=noise_scale_delta,
+        **hps.model,
+    ).cuda(local_rank)
+    if getattr(hps.train, "freeze_JP_bert", False):
+        logger.info("Freezing (JP) bert encoder !!!")
+        for param in net_g.enc_p.bert_proj.parameters():
+            param.requires_grad = False
+    if getattr(hps.train, "freeze_style", False):
+        logger.info("Freezing style encoder !!!")
+        for param in net_g.enc_p.style_proj.parameters():
+            param.requires_grad = False
+
+    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
+    optim_g = torch.optim.AdamW(
+        filter(lambda p: p.requires_grad, net_g.parameters()),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    optim_d = torch.optim.AdamW(
+        net_d.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    if net_dur_disc is not None:
+        optim_dur_disc = torch.optim.AdamW(
+            net_dur_disc.parameters(),
+            hps.train.learning_rate,
+            betas=hps.train.betas,
+            eps=hps.train.eps,
+        )
+    else:
+        optim_dur_disc = None
+    if net_wd is not None:
+        optim_wd = torch.optim.AdamW(
+            net_wd.parameters(),
+            hps.train.learning_rate,
+            betas=hps.train.betas,
+            eps=hps.train.eps,
+        )
+    else:
+        optim_wd = None
+    net_g = DDP(
+        net_g,
+        device_ids=[local_rank],
+        # bucket_cap_mb=512
+    )
+    net_d = DDP(
+        net_d,
+        device_ids=[local_rank],
+        # bucket_cap_mb=512
+    )
+    if net_dur_disc is not None:
+        net_dur_disc = DDP(
+            net_dur_disc,
+            device_ids=[local_rank],
+            # bucket_cap_mb=512,
+        )
+    if net_wd is not None:
+        net_wd = DDP(
+            net_wd,
+            device_ids=[local_rank],
+            #  bucket_cap_mb=512
+        )
+
+    if utils.is_resuming(model_dir):
+        if net_dur_disc is not None:
+            try:
+                _, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
+                    utils.latest_checkpoint_path(model_dir, "DUR_*.pth"),
+                    net_dur_disc,
+                    optim_dur_disc,
+                    skip_optimizer=(
+                        hps.train.skip_optimizer
+                        if "skip_optimizer" in hps.train
+                        else True
+                    ),
+                )
+                if not optim_dur_disc.param_groups[0].get("initial_lr"):
+                    optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
+            except:
+                if not optim_dur_disc.param_groups[0].get("initial_lr"):
+                    optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
+                print("Initialize dur_disc")
+        if net_wd is not None:
+            try:
+                _, optim_wd, wd_resume_lr, epoch_str = utils.load_checkpoint(
+                    utils.latest_checkpoint_path(model_dir, "WD_*.pth"),
+                    net_wd,
+                    optim_wd,
+                    skip_optimizer=(
+                        hps.train.skip_optimizer
+                        if "skip_optimizer" in hps.train
+                        else True
+                    ),
+                )
+                if not optim_wd.param_groups[0].get("initial_lr"):
+                    optim_wd.param_groups[0]["initial_lr"] = wd_resume_lr
+            except:
+                if not optim_wd.param_groups[0].get("initial_lr"):
+                    optim_wd.param_groups[0]["initial_lr"] = wd_resume_lr
+                logger.info("Initialize wavlm")
+
+        try:
+            _, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(model_dir, "G_*.pth"),
+                net_g,
+                optim_g,
+                skip_optimizer=(
+                    hps.train.skip_optimizer if "skip_optimizer" in hps.train else True
+                ),
+            )
+            _, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(model_dir, "D_*.pth"),
+                net_d,
+                optim_d,
+                skip_optimizer=(
+                    hps.train.skip_optimizer if "skip_optimizer" in hps.train else True
+                ),
+            )
+            if not optim_g.param_groups[0].get("initial_lr"):
+                optim_g.param_groups[0]["initial_lr"] = g_resume_lr
+            if not optim_d.param_groups[0].get("initial_lr"):
+                optim_d.param_groups[0]["initial_lr"] = d_resume_lr
+
+            epoch_str = max(epoch_str, 1)
+            # global_step = (epoch_str - 1) * len(train_loader)
+            global_step = int(
+                utils.get_steps(utils.latest_checkpoint_path(model_dir, "G_*.pth"))
+            )
+            logger.info(
+                f"******************Found the model. Current epoch is {epoch_str}, gloabl step is {global_step}*********************"
+            )
+        except Exception as e:
+            logger.warning(e)
+            logger.warning(
+                "It seems that you are not using the pretrained models, so we will train from scratch."
+            )
+            epoch_str = 1
+            global_step = 0
+    else:
+        try:
+            _ = utils.load_safetensors(
+                os.path.join(model_dir, "G_0.safetensors"), net_g
+            )
+            _ = utils.load_safetensors(
+                os.path.join(model_dir, "D_0.safetensors"), net_d
+            )
+            if net_dur_disc is not None:
+                _ = utils.load_safetensors(
+                    os.path.join(model_dir, "DUR_0.safetensors"), net_dur_disc
+                )
+            if net_wd is not None:
+                _ = utils.load_safetensors(
+                    os.path.join(model_dir, "WD_0.safetensors"), net_wd
+                )
+            logger.info("Loaded the pretrained models.")
+        except Exception as e:
+            logger.warning(e)
+            logger.warning(
+                "It seems that you are not using the pretrained models, so we will train from scratch."
+            )
+        finally:
+            epoch_str = 1
+            global_step = 0
+
+    def lr_lambda(epoch):
+        """
+        Learning rate scheduler for warmup and exponential decay.
+        - During the warmup period, the learning rate increases linearly.
+        - After the warmup period, the learning rate decreases exponentially.
+        """
+        if epoch < hps.train.warmup_epochs:
+            return float(epoch) / float(max(1, hps.train.warmup_epochs))
+        else:
+            return hps.train.lr_decay ** (epoch - hps.train.warmup_epochs)
+
+    scheduler_last_epoch = epoch_str - 2
+    scheduler_g = torch.optim.lr_scheduler.LambdaLR(
+        optim_g, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+    )
+    scheduler_d = torch.optim.lr_scheduler.LambdaLR(
+        optim_d, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+    )
+    if net_dur_disc is not None:
+        scheduler_dur_disc = torch.optim.lr_scheduler.LambdaLR(
+            optim_dur_disc, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+        )
+    else:
+        scheduler_dur_disc = None
+    if net_wd is not None:
+        scheduler_wd = torch.optim.lr_scheduler.LambdaLR(
+            optim_wd, lr_lambda=lr_lambda, last_epoch=scheduler_last_epoch
+        )
+        wl = WavLMLoss(
+            hps.model.slm.model,
+            net_wd,
+            hps.data.sampling_rate,
+            hps.model.slm.sr,
+        ).to(local_rank)
+    else:
+        scheduler_wd = None
+        wl = None
+    scaler = GradScaler(enabled=hps.train.bf16_run)
+    logger.info("Start training.")
+
+    diff = abs(
+        epoch_str * len(train_loader) - (hps.train.epochs + 1) * len(train_loader)
+    )
+    pbar = None
+    if not args.no_progress_bar:
+        pbar = tqdm(
+            total=global_step + diff,
+            initial=global_step,
+            smoothing=0.05,
+            file=SAFE_STDOUT,
+        )
+    initial_step = global_step
+
+    for epoch in range(epoch_str, hps.train.epochs + 1):
+        if rank == 0:
+            train_and_evaluate(
+                rank,
+                local_rank,
+                epoch,
+                hps,
+                [net_g, net_d, net_dur_disc, net_wd, wl],
+                [optim_g, optim_d, optim_dur_disc, optim_wd],
+                [scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
+                scaler,
+                [train_loader, eval_loader],
+                logger,
+                [writer, writer_eval],
+                pbar,
+                initial_step,
+            )
+        else:
+            train_and_evaluate(
+                rank,
+                local_rank,
+                epoch,
+                hps,
+                [net_g, net_d, net_dur_disc, net_wd, wl],
+                [optim_g, optim_d, optim_dur_disc, optim_wd],
+                [scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
+                scaler,
+                [train_loader, None],
+                None,
+                None,
+                pbar,
+                initial_step,
+            )
+        scheduler_g.step()
+        scheduler_d.step()
+        if net_dur_disc is not None:
+            scheduler_dur_disc.step()
+        if net_wd is not None:
+            scheduler_wd.step()
+        if epoch == hps.train.epochs:
+            # Save the final models
+            utils.save_checkpoint(
+                net_g,
+                optim_g,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(model_dir, "G_{}.pth".format(global_step)),
+            )
+            utils.save_checkpoint(
+                net_d,
+                optim_d,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join(model_dir, "D_{}.pth".format(global_step)),
+            )
+            if net_dur_disc is not None:
+                utils.save_checkpoint(
+                    net_dur_disc,
+                    optim_dur_disc,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(model_dir, "DUR_{}.pth".format(global_step)),
+                )
+            if net_wd is not None:
+                utils.save_checkpoint(
+                    net_wd,
+                    optim_wd,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(model_dir, "WD_{}.pth".format(global_step)),
+                )
+            utils.save_safetensors(
+                net_g,
+                epoch,
+                os.path.join(
+                    config.out_dir,
+                    f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
+                ),
+                for_infer=True,
+            )
+
+    if pbar is not None:
+        pbar.close()
+
+
+def train_and_evaluate(
+    rank,
+    local_rank,
+    epoch,
+    hps,
+    nets,
+    optims,
+    schedulers,
+    scaler,
+    loaders,
+    logger,
+    writers,
+    pbar: tqdm,
+    initial_step: int,
+):
+    net_g, net_d, net_dur_disc, net_wd, wl = nets
+    optim_g, optim_d, optim_dur_disc, optim_wd = optims
+    scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd = schedulers
+    train_loader, eval_loader = loaders
+    if writers is not None:
+        writer, writer_eval = writers
+
+    train_loader.batch_sampler.set_epoch(epoch)
+    global global_step
+
+    net_g.train()
+    net_d.train()
+    if net_dur_disc is not None:
+        net_dur_disc.train()
+    if net_wd is not None:
+        net_wd.train()
+    for batch_idx, (
+        x,
+        x_lengths,
+        spec,
+        spec_lengths,
+        y,
+        y_lengths,
+        speakers,
+        tone,
+        language,
+        bert,
+        style_vec,
+    ) in enumerate(train_loader):
+        if net_g.module.use_noise_scaled_mas:
+            current_mas_noise_scale = (
+                net_g.module.mas_noise_scale_initial
+                - net_g.module.noise_scale_delta * global_step
+            )
+            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
+        x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
+            local_rank, non_blocking=True
+        )
+        spec, spec_lengths = spec.cuda(
+            local_rank, non_blocking=True
+        ), spec_lengths.cuda(local_rank, non_blocking=True)
+        y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
+            local_rank, non_blocking=True
+        )
+        speakers = speakers.cuda(local_rank, non_blocking=True)
+        tone = tone.cuda(local_rank, non_blocking=True)
+        language = language.cuda(local_rank, non_blocking=True)
+        bert = bert.cuda(local_rank, non_blocking=True)
+        style_vec = style_vec.cuda(local_rank, non_blocking=True)
+
+        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+            (
+                y_hat,
+                l_length,
+                attn,
+                ids_slice,
+                x_mask,
+                z_mask,
+                (z, z_p, m_p, logs_p, m_q, logs_q),
+                (hidden_x, logw, logw_),  # , logw_sdp),
+                g,
+            ) = net_g(
+                x,
+                x_lengths,
+                spec,
+                spec_lengths,
+                speakers,
+                tone,
+                language,
+                bert,
+                style_vec,
+            )
+            mel = spec_to_mel_torch(
+                spec,
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+            y_mel = commons.slice_segments(
+                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
+            )
+            y_hat_mel = mel_spectrogram_torch(
+                y_hat.squeeze(1).float(),
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+
+            y = commons.slice_segments(
+                y, ids_slice * hps.data.hop_length, hps.train.segment_size
+            )  # slice
+
+            # Discriminator
+            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+            with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
+                    y_d_hat_r, y_d_hat_g
+                )
+                loss_disc_all = loss_disc
+            if net_dur_disc is not None:
+                y_dur_hat_r, y_dur_hat_g = net_dur_disc(
+                    hidden_x.detach(),
+                    x_mask.detach(),
+                    logw_.detach(),
+                    logw.detach(),
+                    g.detach(),
+                )
+                with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                    # TODO: I think need to mean using the mask, but for now, just mean all
+                    (
+                        loss_dur_disc,
+                        losses_dur_disc_r,
+                        losses_dur_disc_g,
+                    ) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
+                    loss_dur_disc_all = loss_dur_disc
+                optim_dur_disc.zero_grad()
+                scaler.scale(loss_dur_disc_all).backward()
+                scaler.unscale_(optim_dur_disc)
+                # torch.nn.utils.clip_grad_norm_(
+                # parameters=net_dur_disc.parameters(), max_norm=5
+                # )
+                grad_norm_dur = commons.clip_grad_value_(
+                    net_dur_disc.parameters(), None
+                )
+                scaler.step(optim_dur_disc)
+            if net_wd is not None:
+                # logger.debug(f"y.shape: {y.shape}, y_hat.shape: {y_hat.shape}")
+                # shape: (batch, 1, time)
+                with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                    loss_slm = wl.discriminator(
+                        y.detach().squeeze(1), y_hat.detach().squeeze(1)
+                    ).mean()
+
+                optim_wd.zero_grad()
+                scaler.scale(loss_slm).backward()
+                scaler.unscale_(optim_wd)
+                # torch.nn.utils.clip_grad_norm_(parameters=net_wd.parameters(), max_norm=200)
+                grad_norm_wd = commons.clip_grad_value_(net_wd.parameters(), None)
+                scaler.step(optim_wd)
+
+        optim_d.zero_grad()
+        scaler.scale(loss_disc_all).backward()
+        scaler.unscale_(optim_d)
+        if getattr(hps.train, "bf16_run", False):
+            torch.nn.utils.clip_grad_norm_(parameters=net_d.parameters(), max_norm=200)
+        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+        scaler.step(optim_d)
+
+        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+            # Generator
+            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+            if net_dur_disc is not None:
+                _, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw_, logw, g)
+            if net_wd is not None:
+                loss_lm = wl(y.detach().squeeze(1), y_hat.squeeze(1)).mean()
+                loss_lm_gen = wl.generator(y_hat.squeeze(1))
+            with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
+                loss_dur = torch.sum(l_length.float())
+                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+                loss_fm = feature_loss(fmap_r, fmap_g)
+                loss_gen, losses_gen = generator_loss(y_d_hat_g)
+                # loss_commit = loss_commit * hps.train.c_commit
+
+                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+                if net_dur_disc is not None:
+                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
+                    if net_wd is not None:
+                        loss_gen_all += loss_dur_gen + loss_lm + loss_lm_gen
+                    else:
+                        loss_gen_all += loss_dur_gen
+        optim_g.zero_grad()
+        scaler.scale(loss_gen_all).backward()
+        scaler.unscale_(optim_g)
+        # if getattr(hps.train, "bf16_run", False):
+        torch.nn.utils.clip_grad_norm_(parameters=net_g.parameters(), max_norm=500)
+        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+        scaler.step(optim_g)
+        scaler.update()
+
+        if rank == 0:
+            if global_step % hps.train.log_interval == 0 and not hps.speedup:
+                lr = optim_g.param_groups[0]["lr"]
+                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+                # logger.info(
+                #     "Train Epoch: {} [{:.0f}%]".format(
+                #         epoch, 100.0 * batch_idx / len(train_loader)
+                #     )
+                # )
+                # logger.info([x.item() for x in losses] + [global_step, lr])
+
+                scalar_dict = {
+                    "loss/g/total": loss_gen_all,
+                    "loss/d/total": loss_disc_all,
+                    "learning_rate": lr,
+                    "grad_norm_d": grad_norm_d,
+                    "grad_norm_g": grad_norm_g,
+                }
+                scalar_dict.update(
+                    {
+                        "loss/g/fm": loss_fm,
+                        "loss/g/mel": loss_mel,
+                        "loss/g/dur": loss_dur,
+                        "loss/g/kl": loss_kl,
+                    }
+                )
+                scalar_dict.update(
+                    {"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
+                )
+                scalar_dict.update(
+                    {"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}
+                )
+                scalar_dict.update(
+                    {"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}
+                )
+
+                if net_dur_disc is not None:
+                    scalar_dict.update({"loss/dur_disc/total": loss_dur_disc_all})
+
+                    scalar_dict.update(
+                        {
+                            "loss/dur_disc_g/{}".format(i): v
+                            for i, v in enumerate(losses_dur_disc_g)
+                        }
+                    )
+                    scalar_dict.update(
+                        {
+                            "loss/dur_disc_r/{}".format(i): v
+                            for i, v in enumerate(losses_dur_disc_r)
+                        }
+                    )
+
+                    scalar_dict.update({"loss/g/dur_gen": loss_dur_gen})
+                    scalar_dict.update(
+                        {
+                            "loss/g/dur_gen_{}".format(i): v
+                            for i, v in enumerate(losses_dur_gen)
+                        }
+                    )
+
+                if net_wd is not None:
+                    scalar_dict.update(
+                        {
+                            "loss/wd/total": loss_slm,
+                            "grad_norm_wd": grad_norm_wd,
+                            "loss/g/lm": loss_lm,
+                            "loss/g/lm_gen": loss_lm_gen,
+                        }
+                    )
+                image_dict = {
+                    "slice/mel_org": utils.plot_spectrogram_to_numpy(
+                        y_mel[0].data.cpu().numpy()
+                    ),
+                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(
+                        y_hat_mel[0].data.cpu().numpy()
+                    ),
+                    "all/mel": utils.plot_spectrogram_to_numpy(
+                        mel[0].data.cpu().numpy()
+                    ),
+                    "all/attn": utils.plot_alignment_to_numpy(
+                        attn[0, 0].data.cpu().numpy()
+                    ),
+                }
+                utils.summarize(
+                    writer=writer,
+                    global_step=global_step,
+                    images=image_dict,
+                    scalars=scalar_dict,
+                )
+
+            if (
+                global_step % hps.train.eval_interval == 0
+                and global_step != 0
+                and initial_step != global_step
+            ):
+                if not hps.speedup:
+                    evaluate(hps, net_g, eval_loader, writer_eval)
+                utils.save_checkpoint(
+                    net_g,
+                    optim_g,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
+                )
+                utils.save_checkpoint(
+                    net_d,
+                    optim_d,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
+                )
+                if net_dur_disc is not None:
+                    utils.save_checkpoint(
+                        net_dur_disc,
+                        optim_dur_disc,
+                        hps.train.learning_rate,
+                        epoch,
+                        os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)),
+                    )
+                if net_wd is not None:
+                    utils.save_checkpoint(
+                        net_wd,
+                        optim_wd,
+                        hps.train.learning_rate,
+                        epoch,
+                        os.path.join(hps.model_dir, "WD_{}.pth".format(global_step)),
+                    )
+                keep_ckpts = config.train_ms_config.keep_ckpts
+                if keep_ckpts > 0:
+                    utils.clean_checkpoints(
+                        path_to_models=hps.model_dir,
+                        n_ckpts_to_keep=keep_ckpts,
+                        sort_by_time=True,
+                    )
+                # Save safetensors (for inference) to `model_assets/{model_name}`
+                utils.save_safetensors(
+                    net_g,
+                    epoch,
+                    os.path.join(
+                        config.out_dir,
+                        f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
+                    ),
+                    for_infer=True,
+                )
+
+        global_step += 1
+        if pbar is not None:
+            pbar.set_description(
+                "Epoch {}({:.0f}%)/{}".format(
+                    epoch, 100.0 * batch_idx / len(train_loader), hps.train.epochs
+                )
+            )
+            pbar.update()
+    # 本家ではこれをスピードアップのために消すと書かれていたので、一応消してみる
+    # と思ったけどメモリ使用量が減るかもしれないのでつけてみる
+    gc.collect()
+    torch.cuda.empty_cache()
+    if pbar is None and rank == 0:
+        logger.info(f"====> Epoch: {epoch}, step: {global_step}")
+
+
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    image_dict = {}
+    audio_dict = {}
+    logger.info("Evaluating ...")
+    with torch.no_grad():
+        for batch_idx, (
+            x,
+            x_lengths,
+            spec,
+            spec_lengths,
+            y,
+            y_lengths,
+            speakers,
+            tone,
+            language,
+            bert,
+            style_vec,
+        ) in enumerate(eval_loader):
+            x, x_lengths = x.cuda(), x_lengths.cuda()
+            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
+            y, y_lengths = y.cuda(), y_lengths.cuda()
+            speakers = speakers.cuda()
+            bert = bert.cuda()
+            tone = tone.cuda()
+            language = language.cuda()
+            style_vec = style_vec.cuda()
+            for use_sdp in [True, False]:
+                y_hat, attn, mask, *_ = generator.module.infer(
+                    x,
+                    x_lengths,
+                    speakers,
+                    tone,
+                    language,
+                    bert,
+                    style_vec,
+                    y=spec,
+                    max_len=1000,
+                    sdp_ratio=0.0 if not use_sdp else 1.0,
+                )
+                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
+
+                mel = spec_to_mel_torch(
+                    spec,
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                y_hat_mel = mel_spectrogram_torch(
+                    y_hat.squeeze(1).float(),
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.hop_length,
+                    hps.data.win_length,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                image_dict.update(
+                    {
+                        f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            y_hat_mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update(
+                    {
+                        f"gen/audio_{batch_idx}_{use_sdp}": y_hat[
+                            0, :, : y_hat_lengths[0]
+                        ]
+                    }
+                )
+                image_dict.update(
+                    {
+                        f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
+
+    utils.summarize(
+        writer=writer_eval,
+        global_step=global_step,
+        images=image_dict,
+        audios=audio_dict,
+        audio_sampling_rate=hps.data.sampling_rate,
+    )
+    generator.train()
+
+
+if __name__ == "__main__":
+    run()
diff --git a/transcribe.py b/transcribe.py
new file mode 100644
index 0000000000000000000000000000000000000000..956848911499e9b8cb920687d773cbb3ad41a37e
--- /dev/null
+++ b/transcribe.py
@@ -0,0 +1,84 @@
+import argparse
+import os
+import sys
+
+from faster_whisper import WhisperModel
+from tqdm import tqdm
+
+from common.constants import Languages
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+
+
+def transcribe(wav_path, initial_prompt=None, language="ja"):
+    segments, _ = model.transcribe(
+        wav_path, beam_size=5, language=language, initial_prompt=initial_prompt
+    )
+    texts = [segment.text for segment in segments]
+    return "".join(texts)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_dir", "-i", type=str, default="raw")
+    parser.add_argument("--output_file", "-o", type=str, default="esd.list")
+    parser.add_argument(
+        "--initial_prompt", type=str, default="こんにちは。元気、ですかー？ふふっ、私は……ちゃんと元気だよ！"
+    )
+    parser.add_argument(
+        "--language", type=str, default="ja", choices=["ja", "en", "zh"]
+    )
+    parser.add_argument("--speaker_name", type=str, required=True)
+    parser.add_argument("--model", type=str, default="large-v3")
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--compute_type", type=str, default="bfloat16")
+
+    args = parser.parse_args()
+
+    speaker_name = args.speaker_name
+
+    input_dir = args.input_dir
+    output_file = args.output_file
+    initial_prompt = args.initial_prompt
+    language = args.language
+    device = args.device
+    compute_type = args.compute_type
+
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+
+    logger.info(
+        f"Loading Whisper model ({args.model}) with compute_type={compute_type}"
+    )
+    try:
+        model = WhisperModel(args.model, device=device, compute_type=compute_type)
+    except ValueError as e:
+        logger.warning(f"Failed to load model, so use `auto` compute_type: {e}")
+        model = WhisperModel(args.model, device=device)
+
+    wav_files = [
+        os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith(".wav")
+    ]
+    if os.path.exists(output_file):
+        logger.warning(f"{output_file} exists, backing up to {output_file}.bak")
+        if os.path.exists(output_file + ".bak"):
+            logger.warning(f"{output_file}.bak exists, deleting...")
+            os.remove(output_file + ".bak")
+        os.rename(output_file, output_file + ".bak")
+
+    if language == "ja":
+        language_id = Languages.JP.value
+    elif language == "en":
+        language_id = Languages.EN.value
+    elif language == "zh":
+        language_id = Languages.ZH.value
+    else:
+        raise ValueError(f"{language} is not supported.")
+    with open(output_file, "w", encoding="utf-8") as f:
+        for wav_file in tqdm(wav_files, file=SAFE_STDOUT):
+            file_name = os.path.basename(wav_file)
+            text = transcribe(
+                wav_file, initial_prompt=initial_prompt, language=language
+            )
+            f.write(f"{file_name}|{speaker_name}|{language_id}|{text}\n")
+            f.flush()
+    sys.exit(0)
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..30fdead70342d5ed3356a9985a8b5aba15683413
--- /dev/null
+++ b/update_status.py
@@ -0,0 +1,89 @@
+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..ca194bfb523a701d1130184cf1df92bc35e62ee4
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,501 @@
+import argparse
+import glob
+import json
+import logging
+import os
+import re
+import subprocess
+
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors import safe_open
+from safetensors.torch import save_file
+from scipy.io.wavfile import read
+
+from common.log import logger
+
+MATPLOTLIB_FLAG = 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
+    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, for_infer=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"]
+    logger.info(
+        f"Loading model and optimizer at iteration {iteration} from {checkpoint_path}"
+    )
+    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.warning(
+                    f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
+                )
+            elif "enc_q" in k and for_infer:
+                continue
+            else:
+                logger.error(f"{k} is not in the checkpoint {checkpoint_path}")
+
+            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 '{}' (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 save_safetensors(model, iteration, checkpoint_path, is_half=False, for_infer=False):
+    """
+    Save model with safetensors.
+    """
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    keys = []
+    for k in state_dict:
+        if "enc_q" in k and for_infer:
+            continue  # noqa: E701
+        keys.append(k)
+
+    new_dict = (
+        {k: state_dict[k].half() for k in keys}
+        if is_half
+        else {k: state_dict[k] for k in keys}
+    )
+    new_dict["iteration"] = torch.LongTensor([iteration])
+    logger.info(f"Saved safetensors to {checkpoint_path}")
+    save_file(new_dict, checkpoint_path)
+
+
+def load_safetensors(checkpoint_path, model, for_infer=False):
+    """
+    Load safetensors model.
+    """
+
+    tensors = {}
+    iteration = None
+    with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
+        for key in f.keys():
+            if key == "iteration":
+                iteration = f.get_tensor(key).item()
+            tensors[key] = f.get_tensor(key)
+    if hasattr(model, "module"):
+        result = model.module.load_state_dict(tensors, strict=False)
+    else:
+        result = model.load_state_dict(tensors, strict=False)
+    for key in result.missing_keys:
+        if key.startswith("enc_q") and for_infer:
+            continue
+        logger.warning(f"Missing key: {key}")
+    for key in result.unexpected_keys:
+        if key == "iteration":
+            continue
+        logger.warning(f"Unexpected key: {key}")
+    if iteration is None:
+        logger.info(f"Loaded '{checkpoint_path}'")
+    else:
+        logger.info(f"Loaded '{checkpoint_path}' (iteration {iteration})")
+    return model, iteration
+
+
+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 is_resuming(dir_path):
+    # JP-ExtraバージョンではDURがなくWDがあったり変わるため、Gのみで判断する
+    g_list = glob.glob(os.path.join(dir_path, "G_*.pth"))
+    # d_list = glob.glob(os.path.join(dir_path, "D_*.pth"))
+    # dur_list = glob.glob(os.path.join(dir_path, "DUR_*.pth"))
+    return len(g_list) > 0
+
+
+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))))
+    try:
+        x = f_list[-1]
+    except IndexError:
+        raise ValueError(f"No checkpoint found in {dir_path} with regex {regex}")
+    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):
+    sampling_rate, data = read(full_path)
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+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]
+            + x_sorted("DUR_")[:-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.warning(
+            "{} 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.warning(
+                "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..9a4e1b19b01ae7aff4801770de9334592a4550b6
--- /dev/null
+++ b/webui.py
@@ -0,0 +1,557 @@
+"""
+Original `webui.py` for Bert-VITS2, not working with Style-Bert-VITS2 yet.
+"""
+# 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,
+                assist_text=style_text,
+                assist_text_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)
+            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, 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。
+                    """,
+                )
+                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=1.0, 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)
diff --git a/webui_dataset.py b/webui_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a82edcd302c167274ea8791763e6571c63fd50c
--- /dev/null
+++ b/webui_dataset.py
@@ -0,0 +1,222 @@
+import argparse
+import os
+
+import gradio as gr
+import yaml
+
+from common.constants import GRADIO_THEME
+from common.log import logger
+from common.subprocess_utils import run_script_with_log
+
+# Get path settings
+with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
+    path_config: dict[str, str] = yaml.safe_load(f.read())
+    dataset_root = path_config["dataset_root"]
+    # assets_root = path_config["assets_root"]
+
+
+def do_slice(
+    model_name: str,
+    min_sec: float,
+    max_sec: float,
+    min_silence_dur_ms: int,
+    input_dir: str,
+):
+    if model_name == "":
+        return "Error: モデル名を入力してください。"
+    logger.info("Start slicing...")
+    output_dir = os.path.join(dataset_root, model_name, "raw")
+    cmd = [
+        "slice.py",
+        "--output_dir",
+        output_dir,
+        "--min_sec",
+        str(min_sec),
+        "--max_sec",
+        str(max_sec),
+        "--min_silence_dur_ms",
+        str(min_silence_dur_ms),
+    ]
+    if input_dir != "":
+        cmd += ["--input_dir", input_dir]
+    # onnxの警告が出るので無視する
+    success, message = run_script_with_log(cmd, ignore_warning=True)
+    if not success:
+        return f"Error: {message}"
+    return "音声のスライスが完了しました。"
+
+
+def do_transcribe(
+    model_name, whisper_model, compute_type, language, initial_prompt, input_dir, device
+):
+    if model_name == "":
+        return "Error: モデル名を入力してください。"
+    if initial_prompt == "":
+        initial_prompt = "こんにちは。元気、ですかー？私は……ふふっ、ちゃんと元気だよ！"
+    # logger.debug(f"initial_prompt: {initial_prompt}")
+    if input_dir == "":
+        input_dir = os.path.join(dataset_root, model_name, "raw")
+    output_file = os.path.join(dataset_root, model_name, "esd.list")
+    success, message = run_script_with_log(
+        [
+            "transcribe.py",
+            "--input_dir",
+            input_dir,
+            "--output_file",
+            output_file,
+            "--speaker_name",
+            model_name,
+            "--model",
+            whisper_model,
+            "--compute_type",
+            compute_type,
+            "--device",
+            device,
+            "--language",
+            language,
+            "--initial_prompt",
+            f'"{initial_prompt}"',
+        ]
+    )
+    if not success:
+        return f"Error: {message}"
+    return "音声の文字起こしが完了しました。"
+
+
+initial_md = """
+# 簡易学習用データセット作成ツール
+
+Style-Bert-VITS2の学習用データセットを作成するためのツールです。以下の2つからなります。
+
+- 与えられた音声からちょうどいい長さの発話区間を切り取りスライス
+- 音声に対して文字起こし
+
+このうち両方を使ってもよいし、スライスする必要がない場合は後者のみを使ってもよいです。
+
+## 必要なもの
+
+学習したい音声が入ったwavファイルいくつか。
+合計時間がある程度はあったほうがいいかも、10分とかでも大丈夫だったとの報告あり。単一ファイルでも良いし複数ファイルでもよい。
+
+## スライス使い方
+1. `inputs`フォルダにwavファイルをすべて入れる
+2. `モデル名`を入力して、設定を必要なら調整して`音声のスライス`ボタンを押す
+3. 出来上がった音声ファイルたちは`Data/{モデル名}/raw`に保存される
+
+## 書き起こし使い方
+
+1. 書き起こしたい音声ファイルのあるフォルダを指定（デフォルトは`Data/{モデル名}/raw`なのでスライス後に行う場合は省略してよい）
+2. 設定を必要なら調整してボタンを押す
+3. 書き起こしファイルは`Data/{モデル名}/esd.list`に保存される
+
+## 注意
+
+- 長すぎる秒数（12-15秒くらいより長い？）のwavファイルは学習に用いられないようです。また短すぎてもあまりよくない可能性もあります。
+- 書き起こしの結果をどれだけ修正すればいいかはデータセットに依存しそうです。
+- 手動で書き起こしをいろいろ修正したり結果を細かく確認したい場合は、[Aivis Dataset](https://github.com/litagin02/Aivis-Dataset)もおすすめします。書き起こし部分もかなり工夫されています。ですがファイル数が多い場合などは、このツールで簡易的に切り出してデータセットを作るだけでも十分という気もしています。
+"""
+
+with gr.Blocks(theme=GRADIO_THEME) as app:
+    gr.Markdown(initial_md)
+    model_name = gr.Textbox(
+        label="モデル名を入力してください（話者名としても使われます）。"
+    )
+    with gr.Accordion("音声のスライス"):
+        with gr.Row():
+            with gr.Column():
+                input_dir = gr.Textbox(
+                    label="入力フォルダ名（デフォルトはinputs）",
+                    placeholder="inputs",
+                    info="下記フォルダにwavファイルを入れておいてください",
+                )
+                min_sec = gr.Slider(
+                    minimum=0,
+                    maximum=10,
+                    value=2,
+                    step=0.5,
+                    label="この秒数未満は切り捨てる",
+                )
+                max_sec = gr.Slider(
+                    minimum=0,
+                    maximum=15,
+                    value=12,
+                    step=0.5,
+                    label="この秒数以上は切り捨てる",
+                )
+                min_silence_dur_ms = gr.Slider(
+                    minimum=0,
+                    maximum=2000,
+                    value=700,
+                    step=100,
+                    label="無音とみなして区切る最小の無音の長さ（ms）",
+                )
+                slice_button = gr.Button("スライスを実行")
+            result1 = gr.Textbox(label="結果")
+    with gr.Row():
+        with gr.Column():
+            raw_dir = gr.Textbox(
+                label="書き起こしたい音声ファイルが入っているフォルダ（スライスした場合など、`Data/{モデル名}/raw`の場合は省略可",
+            )
+            whisper_model = gr.Dropdown(
+                ["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"],
+                label="Whisperモデル",
+                value="large-v3",
+            )
+            compute_type = gr.Dropdown(
+                [
+                    "int8",
+                    "int8_float32",
+                    "int8_float16",
+                    "int8_bfloat16",
+                    "int16",
+                    "float16",
+                    "bfloat16",
+                    "float32",
+                ],
+                label="計算精度",
+                value="bfloat16",
+            )
+            device = gr.Radio(["cuda", "cpu"], label="デバイス", value="cuda")
+            language = gr.Dropdown(["ja", "en", "zh"], value="ja", label="言語")
+            initial_prompt = gr.Textbox(
+                label="初期プロンプト",
+                placeholder="こんにちは。元気、ですかー？ふふっ、私は……ちゃんと元気だよ！",
+                info="このように書き起こしてほしいという例文、日本語なら省略可、英語等なら書いてください",
+            )
+        transcribe_button = gr.Button("音声の文字起こし")
+        result2 = gr.Textbox(label="結果")
+    slice_button.click(
+        do_slice,
+        inputs=[model_name, min_sec, max_sec, min_silence_dur_ms, input_dir],
+        outputs=[result1],
+    )
+    transcribe_button.click(
+        do_transcribe,
+        inputs=[
+            model_name,
+            whisper_model,
+            compute_type,
+            language,
+            initial_prompt,
+            raw_dir,
+            device,
+        ],
+        outputs=[result2],
+    )
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--server-name",
+    type=str,
+    default=None,
+    help="Server name for Gradio app",
+)
+parser.add_argument(
+    "--no-autolaunch",
+    action="store_true",
+    default=False,
+    help="Do not launch app automatically",
+)
+args = parser.parse_args()
+
+app.launch(inbrowser=not args.no_autolaunch, server_name=args.server_name)
diff --git a/webui_merge.py b/webui_merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d1186265fbe84cd1aa80b6234ed20caf5dcbbff
--- /dev/null
+++ b/webui_merge.py
@@ -0,0 +1,471 @@
+import argparse
+import json
+import os
+import sys
+
+import gradio as gr
+import numpy as np
+import torch
+import yaml
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+from common.constants import DEFAULT_STYLE, GRADIO_THEME
+from common.log import logger
+from common.tts_model import Model, ModelHolder
+
+voice_keys = ["dec"]
+voice_pitch_keys = ["flow"]
+speech_style_keys = ["enc_p"]
+tempo_keys = ["sdp", "dp"]
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Get path settings
+with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
+    path_config: dict[str, str] = yaml.safe_load(f.read())
+    # dataset_root = path_config["dataset_root"]
+    assets_root = path_config["assets_root"]
+
+model_holder = ModelHolder(assets_root, device)
+
+
+def merge_style(model_name_a, model_name_b, weight, output_name, style_triple_list):
+    """
+    style_triple_list: list[(model_aでのスタイル名, model_bでのスタイル名, 出力するスタイル名)]
+    """
+    # 新スタイル名リストにNeutralが含まれているか確認し、Neutralを先頭に持ってくる
+    if any(triple[2] == DEFAULT_STYLE for triple in style_triple_list):
+        # 存在する場合、リストをソート
+        sorted_list = sorted(style_triple_list, key=lambda x: x[2] != DEFAULT_STYLE)
+    else:
+        # 存在しない場合、エラーを発生
+        raise ValueError("No element with {DEFAULT_STYLE} output style name found.")
+
+    style_vectors_a = np.load(
+        os.path.join(assets_root, model_name_a, "style_vectors.npy")
+    )  # (style_num_a, 256)
+    style_vectors_b = np.load(
+        os.path.join(assets_root, model_name_b, "style_vectors.npy")
+    )  # (style_num_b, 256)
+    with open(
+        os.path.join(assets_root, model_name_a, "config.json"), encoding="utf-8"
+    ) as f:
+        config_a = json.load(f)
+    with open(
+        os.path.join(assets_root, model_name_b, "config.json"), encoding="utf-8"
+    ) as f:
+        config_b = json.load(f)
+    style2id_a = config_a["data"]["style2id"]
+    style2id_b = config_b["data"]["style2id"]
+    new_style_vecs = []
+    new_style2id = {}
+    for style_a, style_b, style_out in sorted_list:
+        if style_a not in style2id_a:
+            logger.error(f"{style_a} is not in {model_name_a}.")
+            raise ValueError(f"{style_a} は {model_name_a} にありません。")
+        if style_b not in style2id_b:
+            logger.error(f"{style_b} is not in {model_name_b}.")
+            raise ValueError(f"{style_b} は {model_name_b} にありません。")
+        new_style = (
+            style_vectors_a[style2id_a[style_a]] * (1 - weight)
+            + style_vectors_b[style2id_b[style_b]] * weight
+        )
+        new_style_vecs.append(new_style)
+        new_style2id[style_out] = len(new_style_vecs) - 1
+    new_style_vecs = np.array(new_style_vecs)
+
+    output_style_path = os.path.join(assets_root, output_name, "style_vectors.npy")
+    np.save(output_style_path, new_style_vecs)
+
+    new_config = config_a.copy()
+    new_config["data"]["num_styles"] = len(new_style2id)
+    new_config["data"]["style2id"] = new_style2id
+    new_config["model_name"] = output_name
+    with open(
+        os.path.join(assets_root, output_name, "config.json"), "w", encoding="utf-8"
+    ) as f:
+        json.dump(new_config, f, indent=2, ensure_ascii=False)
+
+    # recipe.jsonを読み込んで、style_triple_listを追記
+    info_path = os.path.join(assets_root, output_name, "recipe.json")
+    if os.path.exists(info_path):
+        with open(info_path, encoding="utf-8") as f:
+            info = json.load(f)
+    else:
+        info = {}
+    info["style_triple_list"] = style_triple_list
+    with open(info_path, "w", encoding="utf-8") as f:
+        json.dump(info, f, indent=2, ensure_ascii=False)
+
+    return output_style_path, list(new_style2id.keys())
+
+
+def merge_models(
+    model_path_a,
+    model_path_b,
+    voice_weight,
+    voice_pitch_weight,
+    speech_style_weight,
+    tempo_weight,
+    output_name,
+):
+    """model Aを起点に、model Bの各要素を重み付けしてマージする。
+    safetensors形式を前提とする。"""
+    model_a_weight = {}
+    with safe_open(model_path_a, framework="pt", device="cpu") as f:
+        for k in f.keys():
+            model_a_weight[k] = f.get_tensor(k)
+
+    model_b_weight = {}
+    with safe_open(model_path_b, framework="pt", device="cpu") as f:
+        for k in f.keys():
+            model_b_weight[k] = f.get_tensor(k)
+
+    merged_model_weight = model_a_weight.copy()
+
+    for key in model_a_weight.keys():
+        if any([key.startswith(prefix) for prefix in voice_keys]):
+            weight = voice_weight
+        elif any([key.startswith(prefix) for prefix in voice_pitch_keys]):
+            weight = voice_pitch_weight
+        elif any([key.startswith(prefix) for prefix in speech_style_keys]):
+            weight = speech_style_weight
+        elif any([key.startswith(prefix) for prefix in tempo_keys]):
+            weight = tempo_weight
+        else:
+            continue
+        merged_model_weight[key] = (
+            model_a_weight[key] * (1 - weight) + model_b_weight[key] * weight
+        )
+
+    merged_model_path = os.path.join(
+        assets_root, output_name, f"{output_name}.safetensors"
+    )
+    os.makedirs(os.path.dirname(merged_model_path), exist_ok=True)
+    save_file(merged_model_weight, merged_model_path)
+
+    info = {
+        "model_a": model_path_a,
+        "model_b": model_path_b,
+        "voice_weight": voice_weight,
+        "voice_pitch_weight": voice_pitch_weight,
+        "speech_style_weight": speech_style_weight,
+        "tempo_weight": tempo_weight,
+    }
+    with open(
+        os.path.join(assets_root, output_name, "recipe.json"), "w", encoding="utf-8"
+    ) as f:
+        json.dump(info, f, indent=2, ensure_ascii=False)
+    return merged_model_path
+
+
+def merge_models_gr(
+    model_name_a,
+    model_path_a,
+    model_name_b,
+    model_path_b,
+    output_name,
+    voice_weight,
+    voice_pitch_weight,
+    speech_style_weight,
+    tempo_weight,
+):
+    if output_name == "":
+        return "Error: 新しいモデル名を入力してください。"
+    merged_model_path = merge_models(
+        model_path_a,
+        model_path_b,
+        voice_weight,
+        voice_pitch_weight,
+        speech_style_weight,
+        tempo_weight,
+        output_name,
+    )
+    return f"Success: モデルを{merged_model_path}に保存しました。"
+
+
+def merge_style_gr(
+    model_name_a,
+    model_name_b,
+    weight,
+    output_name,
+    style_triple_list_str: str,
+):
+    if output_name == "":
+        return "Error: 新しいモデル名を入力してください。", None
+    style_triple_list = []
+    for line in style_triple_list_str.split("\n"):
+        if not line:
+            continue
+        style_triple = line.split(",")
+        if len(style_triple) != 3:
+            logger.error(f"Invalid style triple: {line}")
+            return (
+                f"Error: スタイルを3つのカンマ区切りで入力してください:\n{line}",
+                None,
+            )
+        style_a, style_b, style_out = style_triple
+        style_a = style_a.strip()
+        style_b = style_b.strip()
+        style_out = style_out.strip()
+        style_triple_list.append((style_a, style_b, style_out))
+    try:
+        new_style_path, new_styles = merge_style(
+            model_name_a, model_name_b, weight, output_name, style_triple_list
+        )
+    except ValueError as e:
+        return f"Error: {e}"
+    return f"Success: スタイルを{new_style_path}に保存しました。", gr.Dropdown(
+        choices=new_styles, value=new_styles[0]
+    )
+
+
+def simple_tts(model_name, text, style=DEFAULT_STYLE, style_weight=1.0):
+    model_path = os.path.join(assets_root, model_name, f"{model_name}.safetensors")
+    config_path = os.path.join(assets_root, model_name, "config.json")
+    style_vec_path = os.path.join(assets_root, model_name, "style_vectors.npy")
+
+    model = Model(model_path, config_path, style_vec_path, device)
+    return model.infer(text, style=style, style_weight=style_weight)
+
+
+def update_two_model_names_dropdown():
+    new_names, new_files, _ = model_holder.update_model_names_gr()
+    return new_names, new_files, new_names, new_files
+
+
+def load_styles_gr(model_name_a, model_name_b):
+    config_path_a = os.path.join(assets_root, model_name_a, "config.json")
+    with open(config_path_a, encoding="utf-8") as f:
+        config_a = json.load(f)
+    styles_a = list(config_a["data"]["style2id"].keys())
+
+    config_path_b = os.path.join(assets_root, model_name_b, "config.json")
+    with open(config_path_b, encoding="utf-8") as f:
+        config_b = json.load(f)
+    styles_b = list(config_b["data"]["style2id"].keys())
+    return gr.Textbox(value=", ".join(styles_a)), gr.Textbox(value=", ".join(styles_b))
+
+
+initial_md = """
+# Style-Bert-VITS2 モデルマージツール
+
+2つのStyle-Bert-VITS2モデルから、声質・話し方・話す速さを取り替えたり混ぜたりできます。
+
+## 使い方
+
+1. マージしたい2つのモデルを選択してください（`model_assets`フォルダの中から選ばれます）。
+2. マージ後のモデルの名前を入力してください。
+3. マージ後のモデルの声質・話し方・話す速さを調整してください。
+4. 「モデルファイルのマージ」ボタンを押してください（safetensorsファイルがマージされる）。
+5. スタイルベクトルファイルも生成する必要があるので、指示に従ってマージ方法を入力後、「スタイルのマージ」ボタンを押してください。
+
+以上でマージは完了で、`model_assets/マージ後のモデル名`にマージ後のモデルが保存され、音声合成のときに使えます。
+
+また`model_asses/マージ後のモデル名/recipe.json`には、マージの配合レシピが記録されます（推論にはいらないので配合メモ用です）。
+
+一番下にマージしたモデルによる簡易的な音声合成機能もつけています。
+
+## 注意
+1.x系と2.x-JP-Extraのモデルマージは失敗するようです。
+"""
+
+style_merge_md = f"""
+## スタイルベクトルのマージ
+
+1行に「モデルAのスタイル名, モデルBのスタイル名, 左の2つを混ぜて出力するスタイル名」
+という形式で入力してください。例えば、
+```
+{DEFAULT_STYLE}, {DEFAULT_STYLE}, {DEFAULT_STYLE}
+Happy, Surprise, HappySurprise
+```
+と入力すると、マージ後のスタイルベクトルは、
+- `{DEFAULT_STYLE}`: モデルAの`{DEFAULT_STYLE}`とモデルBの`{DEFAULT_STYLE}`を混ぜたもの
+- `HappySurprise`: モデルAの`Happy`とモデルBの`Surprise`を混ぜたもの
+の2つになります。
+
+### 注意
+- 必ず「{DEFAULT_STYLE}」という名前のスタイルを作ってください。これは、マージ後のモデルの平均スタイルになります。
+- 構造上の相性の関係で、スタイルベクトルを混ぜる重みは、上の「話し方」と同じ比率で混ぜられます。例えば「話し方」が0のときはモデルAのみしか使われません。
+"""
+
+model_names = model_holder.model_names
+if len(model_names) == 0:
+    logger.error(
+        f"モデルが見つかりませんでした。{assets_root}にモデルを置いてください。"
+    )
+    sys.exit(1)
+initial_id = 0
+initial_model_files = model_holder.model_files_dict[model_names[initial_id]]
+
+with gr.Blocks(theme=GRADIO_THEME) as app:
+    gr.Markdown(initial_md)
+    with gr.Accordion(label="使い方", open=False):
+        gr.Markdown(initial_md)
+    with gr.Row():
+        with gr.Column(scale=3):
+            model_name_a = gr.Dropdown(
+                label="モデルA",
+                choices=model_names,
+                value=model_names[initial_id],
+            )
+            model_path_a = gr.Dropdown(
+                label="モデルファイル",
+                choices=initial_model_files,
+                value=initial_model_files[0],
+            )
+        with gr.Column(scale=3):
+            model_name_b = gr.Dropdown(
+                label="モデルB",
+                choices=model_names,
+                value=model_names[initial_id],
+            )
+            model_path_b = gr.Dropdown(
+                label="モデルファイル",
+                choices=initial_model_files,
+                value=initial_model_files[0],
+            )
+        refresh_button = gr.Button("更新", scale=1, visible=True)
+    with gr.Column(variant="panel"):
+        new_name = gr.Textbox(label="新しいモデル名", placeholder="new_model")
+        with gr.Row():
+            voice_slider = gr.Slider(
+                label="声質",
+                value=0,
+                minimum=0,
+                maximum=1,
+                step=0.1,
+            )
+            voice_pitch_slider = gr.Slider(
+                label="声の高さ",
+                value=0,
+                minimum=0,
+                maximum=1,
+                step=0.1,
+            )
+            speech_style_slider = gr.Slider(
+                label="話し方（抑揚・感情表現等）",
+                value=0,
+                minimum=0,
+                maximum=1,
+                step=0.1,
+            )
+            tempo_slider = gr.Slider(
+                label="話す速さ・リズム・テンポ",
+                value=0,
+                minimum=0,
+                maximum=1,
+                step=0.1,
+            )
+        with gr.Column(variant="panel"):
+            gr.Markdown("## モデルファイル（safetensors）のマージ")
+            model_merge_button = gr.Button("モデルファイルのマージ", variant="primary")
+            info_model_merge = gr.Textbox(label="情報")
+        with gr.Column(variant="panel"):
+            gr.Markdown(style_merge_md)
+            with gr.Row():
+                load_style_button = gr.Button("スタイル一覧をロード", scale=1)
+                styles_a = gr.Textbox(label="モデルAのスタイル一覧")
+                styles_b = gr.Textbox(label="モデルBのスタイル一覧")
+            style_triple_list = gr.TextArea(
+                label="スタイルのマージリスト",
+                placeholder=f"{DEFAULT_STYLE}, {DEFAULT_STYLE},{DEFAULT_STYLE}\nAngry, Angry, Angry",
+                value=f"{DEFAULT_STYLE}, {DEFAULT_STYLE}, {DEFAULT_STYLE}",
+            )
+            style_merge_button = gr.Button("スタイルのマージ", variant="primary")
+            info_style_merge = gr.Textbox(label="情報")
+
+    text_input = gr.TextArea(
+        label="テキスト", value="これはテストです。聞こえていますか？"
+    )
+    style = gr.Dropdown(
+        label="スタイル",
+        choices=["スタイルをマージしてください"],
+        value="スタイルをマージしてください",
+    )
+    emotion_weight = gr.Slider(
+        minimum=0,
+        maximum=50,
+        value=1,
+        step=0.1,
+        label="スタイルの強さ",
+    )
+    tts_button = gr.Button("音声合成", variant="primary")
+    audio_output = gr.Audio(label="結果")
+
+    model_name_a.change(
+        model_holder.update_model_files_gr,
+        inputs=[model_name_a],
+        outputs=[model_path_a],
+    )
+    model_name_b.change(
+        model_holder.update_model_files_gr,
+        inputs=[model_name_b],
+        outputs=[model_path_b],
+    )
+
+    refresh_button.click(
+        update_two_model_names_dropdown,
+        outputs=[model_name_a, model_path_a, model_name_b, model_path_b],
+    )
+
+    load_style_button.click(
+        load_styles_gr,
+        inputs=[model_name_a, model_name_b],
+        outputs=[styles_a, styles_b],
+    )
+
+    model_merge_button.click(
+        merge_models_gr,
+        inputs=[
+            model_name_a,
+            model_path_a,
+            model_name_b,
+            model_path_b,
+            new_name,
+            voice_slider,
+            voice_pitch_slider,
+            speech_style_slider,
+            tempo_slider,
+        ],
+        outputs=[info_model_merge],
+    )
+
+    style_merge_button.click(
+        merge_style_gr,
+        inputs=[
+            model_name_a,
+            model_name_b,
+            speech_style_slider,
+            new_name,
+            style_triple_list,
+        ],
+        outputs=[info_style_merge, style],
+    )
+
+    tts_button.click(
+        simple_tts,
+        inputs=[new_name, text_input, style, emotion_weight],
+        outputs=[audio_output],
+    )
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--server-name",
+    type=str,
+    default=None,
+    help="Server name for Gradio app",
+)
+parser.add_argument(
+    "--no-autolaunch",
+    action="store_true",
+    default=False,
+    help="Do not launch app automatically",
+)
+parser.add_argument("--share", action="store_true", default=False)
+args = parser.parse_args()
+
+app.launch(
+    inbrowser=not args.no_autolaunch, server_name=args.server_name, share=args.share
+)
diff --git a/webui_style_vectors.py b/webui_style_vectors.py
new file mode 100644
index 0000000000000000000000000000000000000000..b89c9c9f815595015d78fcc7992fcb7abbf4a6c8
--- /dev/null
+++ b/webui_style_vectors.py
@@ -0,0 +1,480 @@
+import argparse
+import json
+import os
+import shutil
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import yaml
+from scipy.spatial.distance import pdist, squareform
+from sklearn.cluster import DBSCAN, AgglomerativeClustering, KMeans
+from sklearn.manifold import TSNE
+from umap import UMAP
+
+from common.constants import DEFAULT_STYLE, GRADIO_THEME
+from common.log import logger
+from config import config
+
+# Get path settings
+with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
+    path_config: dict[str, str] = yaml.safe_load(f.read())
+    dataset_root = path_config["dataset_root"]
+    # assets_root = path_config["assets_root"]
+
+MAX_CLUSTER_NUM = 10
+MAX_AUDIO_NUM = 10
+
+tsne = TSNE(n_components=2, random_state=42, metric="cosine")
+umap = UMAP(n_components=2, random_state=42, metric="cosine", n_jobs=1, min_dist=0.0)
+
+wav_files = []
+x = np.array([])
+x_reduced = None
+y_pred = np.array([])
+mean = np.array([])
+centroids = []
+
+
+def load(model_name, reduction_method):
+    global wav_files, x, x_reduced, mean
+    wavs_dir = os.path.join(dataset_root, model_name, "wavs")
+    style_vector_files = [
+        os.path.join(wavs_dir, f) for f in os.listdir(wavs_dir) if f.endswith(".npy")
+    ]
+    wav_files = [f.replace(".npy", "") for f in style_vector_files]
+    style_vectors = [np.load(f) for f in style_vector_files]
+    x = np.array(style_vectors)
+    mean = np.mean(x, axis=0)
+    if reduction_method == "t-SNE":
+        x_reduced = tsne.fit_transform(x)
+    elif reduction_method == "UMAP":
+        x_reduced = umap.fit_transform(x)
+    else:
+        raise ValueError("Invalid reduction method")
+    x_reduced = np.asarray(x_reduced)
+    plt.figure(figsize=(6, 6))
+    plt.scatter(x_reduced[:, 0], x_reduced[:, 1])
+    return plt
+
+
+def do_clustering(n_clusters=4, method="KMeans"):
+    global centroids, x_reduced, y_pred
+    if method == "KMeans":
+        model = KMeans(n_clusters=n_clusters, random_state=42, n_init="auto")
+        y_pred = model.fit_predict(x)
+    elif method == "Agglomerative":
+        model = AgglomerativeClustering(n_clusters=n_clusters)
+        y_pred = model.fit_predict(x)
+    elif method == "KMeans after reduction":
+        assert x_reduced is not None
+        model = KMeans(n_clusters=n_clusters, random_state=42, n_init="auto")
+        y_pred = model.fit_predict(x_reduced)
+    elif method == "Agglomerative after reduction":
+        assert x_reduced is not None
+        model = AgglomerativeClustering(n_clusters=n_clusters)
+        y_pred = model.fit_predict(x_reduced)
+    else:
+        raise ValueError("Invalid method")
+
+    centroids = []
+    for i in range(n_clusters):
+        centroids.append(np.mean(x[y_pred == i], axis=0))
+
+    return y_pred, centroids
+
+
+def do_dbscan(eps=2.5, min_samples=15):
+    global centroids, x_reduced, y_pred
+    model = DBSCAN(eps=eps, min_samples=min_samples)
+    assert x_reduced is not None
+    y_pred = model.fit_predict(x_reduced)
+    n_clusters = max(y_pred) + 1
+    centroids = []
+    for i in range(n_clusters):
+        centroids.append(np.mean(x[y_pred == i], axis=0))
+    return y_pred, centroids
+
+
+def representative_wav_files(cluster_id, num_files=1):
+    # y_predの中でcluster_indexに関するメドイドを探す
+    cluster_indices = np.where(y_pred == cluster_id)[0]
+    cluster_vectors = x[cluster_indices]
+    # クラスタ内の全ベクトル間の距離を計算
+    distances = pdist(cluster_vectors)
+    distance_matrix = squareform(distances)
+
+    # 各ベクトルと他の全ベクトルとの平均距離を計算
+    mean_distances = distance_matrix.mean(axis=1)
+
+    # 平均距離が最も小さい順にnum_files個のインデックスを取得
+    closest_indices = np.argsort(mean_distances)[:num_files]
+
+    return cluster_indices[closest_indices]
+
+
+def do_dbscan_gradio(eps=2.5, min_samples=15):
+    global x_reduced, centroids
+
+    y_pred, centroids = do_dbscan(eps, min_samples)
+
+    assert x_reduced is not None
+
+    cmap = plt.get_cmap("tab10")
+    plt.figure(figsize=(6, 6))
+    for i in range(max(y_pred) + 1):
+        plt.scatter(
+            x_reduced[y_pred == i, 0],
+            x_reduced[y_pred == i, 1],
+            color=cmap(i),
+            label=f"Style {i + 1}",
+        )
+    # Noise cluster (-1) is black
+    plt.scatter(
+        x_reduced[y_pred == -1, 0],
+        x_reduced[y_pred == -1, 1],
+        color="black",
+        label="Noise",
+    )
+    plt.legend()
+
+    n_clusters = max(y_pred) + 1
+
+    if n_clusters > MAX_CLUSTER_NUM:
+        # raise ValueError(f"The number of clusters is too large: {n_clusters}")
+        return [
+            plt,
+            gr.Slider(maximum=MAX_CLUSTER_NUM),
+            f"クラスタ数が多すぎます、パラメータを変えてみてください。: {n_clusters}",
+        ] + [gr.Audio(visible=False)] * MAX_AUDIO_NUM
+
+    elif n_clusters == 0:
+        return [
+            plt,
+            gr.Slider(maximum=MAX_CLUSTER_NUM),
+            f"クラスタが数が0です。パラメータを変えてみてください。",
+        ] + [gr.Audio(visible=False)] * MAX_AUDIO_NUM
+
+    return [plt, gr.Slider(maximum=n_clusters, value=1), n_clusters] + [
+        gr.Audio(visible=False)
+    ] * MAX_AUDIO_NUM
+
+
+def representative_wav_files_gradio(cluster_id, num_files=1):
+    cluster_id = cluster_id - 1  # UIでは1から始まるので0からにする
+    closest_indices = representative_wav_files(cluster_id, num_files)
+    actual_num_files = len(closest_indices)  # ファイル数が少ないときのため
+    return [
+        gr.Audio(wav_files[i], visible=True, label=wav_files[i])
+        for i in closest_indices
+    ] + [gr.update(visible=False)] * (MAX_AUDIO_NUM - actual_num_files)
+
+
+def do_clustering_gradio(n_clusters=4, method="KMeans"):
+    global x_reduced, centroids
+    y_pred, centroids = do_clustering(n_clusters, method)
+
+    assert x_reduced is not None
+    cmap = plt.get_cmap("tab10")
+    plt.figure(figsize=(6, 6))
+    for i in range(n_clusters):
+        plt.scatter(
+            x_reduced[y_pred == i, 0],
+            x_reduced[y_pred == i, 1],
+            color=cmap(i),
+            label=f"Style {i + 1}",
+        )
+    plt.legend()
+
+    return [plt, gr.Slider(maximum=n_clusters, value=1)] + [
+        gr.Audio(visible=False)
+    ] * MAX_AUDIO_NUM
+
+
+def save_style_vectors_from_clustering(model_name, style_names_str: str):
+    """centerとcentroidsを保存する"""
+    result_dir = os.path.join(config.assets_root, model_name)
+    os.makedirs(result_dir, exist_ok=True)
+    style_vectors = np.stack([mean] + centroids)
+    style_vector_path = os.path.join(result_dir, "style_vectors.npy")
+    if os.path.exists(style_vector_path):
+        logger.info(f"Backup {style_vector_path} to {style_vector_path}.bak")
+        shutil.copy(style_vector_path, f"{style_vector_path}.bak")
+    np.save(style_vector_path, style_vectors)
+
+    # config.jsonの更新
+    config_path = os.path.join(result_dir, "config.json")
+    if not os.path.exists(config_path):
+        return f"{config_path}が存在しません。"
+    style_names = [name.strip() for name in style_names_str.split(",")]
+    style_name_list = [DEFAULT_STYLE] + style_names
+    if len(style_name_list) != len(centroids) + 1:
+        return f"スタイルの数が合いません。`,`で正しく{len(centroids)}個に区切られているか確認してください: {style_names_str}"
+    if len(set(style_names)) != len(style_names):
+        return f"スタイル名が重複しています。"
+
+    logger.info(f"Backup {config_path} to {config_path}.bak")
+    shutil.copy(config_path, f"{config_path}.bak")
+    with open(config_path, "r", encoding="utf-8") as f:
+        json_dict = json.load(f)
+    json_dict["data"]["num_styles"] = len(style_name_list)
+    style_dict = {name: i for i, name in enumerate(style_name_list)}
+    json_dict["data"]["style2id"] = style_dict
+    with open(config_path, "w", encoding="utf-8") as f:
+        json.dump(json_dict, f, indent=2, ensure_ascii=False)
+    return f"成功!\n{style_vector_path}に保存し{config_path}を更新しました。"
+
+
+def save_style_vectors_from_files(
+    model_name, audio_files_str: str, style_names_str: str
+):
+    """音声ファイルからスタイルベクトルを作成して保存する"""
+    global mean
+    if len(x) == 0:
+        return "Error: スタイルベクトルを読み込んでください。"
+    mean = np.mean(x, axis=0)
+
+    result_dir = os.path.join(config.assets_root, model_name)
+    os.makedirs(result_dir, exist_ok=True)
+    audio_files = [name.strip() for name in audio_files_str.split(",")]
+    style_names = [name.strip() for name in style_names_str.split(",")]
+    if len(audio_files) != len(style_names):
+        return f"音声ファイルとスタイル名の数が合いません。`,`で正しく{len(style_names)}個に区切られているか確認してください: {audio_files_str}と{style_names_str}"
+    style_name_list = [DEFAULT_STYLE] + style_names
+    if len(set(style_names)) != len(style_names):
+        return f"スタイル名が重複しています。"
+    style_vectors = [mean]
+
+    wavs_dir = os.path.join(dataset_root, model_name, "wavs")
+    for audio_file in audio_files:
+        path = os.path.join(wavs_dir, audio_file)
+        if not os.path.exists(path):
+            return f"{path}が存在しません。"
+        style_vectors.append(np.load(f"{path}.npy"))
+    style_vectors = np.stack(style_vectors)
+    assert len(style_name_list) == len(style_vectors)
+    style_vector_path = os.path.join(result_dir, "style_vectors.npy")
+    if os.path.exists(style_vector_path):
+        logger.info(f"Backup {style_vector_path} to {style_vector_path}.bak")
+        shutil.copy(style_vector_path, f"{style_vector_path}.bak")
+    np.save(style_vector_path, style_vectors)
+
+    # config.jsonの更新
+    config_path = os.path.join(result_dir, "config.json")
+    if not os.path.exists(config_path):
+        return f"{config_path}が存在しません。"
+    logger.info(f"Backup {config_path} to {config_path}.bak")
+    shutil.copy(config_path, f"{config_path}.bak")
+
+    with open(config_path, "r", encoding="utf-8") as f:
+        json_dict = json.load(f)
+    json_dict["data"]["num_styles"] = len(style_name_list)
+    style_dict = {name: i for i, name in enumerate(style_name_list)}
+    json_dict["data"]["style2id"] = style_dict
+
+    with open(config_path, "w", encoding="utf-8") as f:
+        json.dump(json_dict, f, indent=2, ensure_ascii=False)
+    return f"成功!\n{style_vector_path}に保存し{config_path}を更新しました。"
+
+
+initial_md = f"""
+# Style Bert-VITS2 スタイルベクトルの作成
+
+Style-Bert-VITS2でこまかくスタイルを指定して音声合成するには、モデルごとにスタイルベクトルのファイル`style_vectors.npy`を手動で作成する必要があります。
+
+ただし、学習の過程で自動的に平均スタイル「{DEFAULT_STYLE}」のみは作成されるので、それをそのまま使うこともできます（その場合はこのWebUIは使いません）。
+
+このプロセスは学習とは全く関係がないので、何回でも独立して繰り返して試せます。また学習中にもたぶん軽いので動くはずです。
+
+## 方法
+
+- 方法1: 音声ファイルを自動でスタイル別に分け、その各スタイルの平均を取って保存
+- 方法2: スタイルを代表する音声ファイルを手動で選んで、その音声のスタイルベクトルを保存
+- 方法3: 自分でもっと頑張ってこだわって作る（JVNVコーパスなど、もともとスタイルラベル等が利用可能な場合はこれがよいかも）
+"""
+
+method1 = f"""
+学習の時に取り出したスタイルベクトルを読み込んで、可視化を見ながらスタイルを分けていきます。
+
+手順:
+1. 図を眺める
+2. スタイル数を決める（平均スタイルを除く）
+3. スタイル分けを行って結果を確認
+4. スタイルの名前を決めて保存
+
+
+詳細: スタイルベクトル(256次元)たちを適当なアルゴリズムでクラスタリングして、各クラスタの中心のベクトル（と全体の平均ベクトル）を保存します。
+
+平均スタイル（{DEFAULT_STYLE}）は自動的に保存されます。
+"""
+
+dbscan_md = """
+DBSCANという方法でスタイル分けを行います。
+こちらの方が方法1よりも特徴がはっきり出るもののみを取り出せ、よいスタイルベクトルが作れるかもしれません。
+ただし事前にスタイル数は指定できません。
+
+パラメータ：
+- eps: この値より近い点同士をどんどん繋げて同じスタイル分類とする。小さいほどスタイル数が増え、大きいほどスタイル数が減る傾向。
+- min_samples: ある点をスタイルの核となる点とみなすために必要な近傍の点の数。小さいほどスタイル数が増え、大きいほどスタイル数が減る傾向。
+
+UMAPの場合はepsは0.3くらい、t-SNEの場合は2.5くらいがいいかもしれません。min_samplesはデータ数に依存するのでいろいろ試してみてください。
+
+詳細：
+https://ja.wikipedia.org/wiki/DBSCAN
+"""
+
+with gr.Blocks(theme=GRADIO_THEME) as app:
+    gr.Markdown(initial_md)
+    with gr.Row():
+        model_name = gr.Textbox(placeholder="your_model_name", label="モデル名")
+        reduction_method = gr.Radio(
+            choices=["UMAP", "t-SNE"],
+            label="次元削減方法",
+            info="v 1.3以前はt-SNEでしたがUMAPのほうがよい可能性もあります。",
+            value="UMAP",
+        )
+        load_button = gr.Button("スタイルベクトルを読み込む", variant="primary")
+    output = gr.Plot(label="音声スタイルの可視化")
+    load_button.click(load, inputs=[model_name, reduction_method], outputs=[output])
+    with gr.Tab("方法1: スタイル分けを自動で行う"):
+        with gr.Tab("スタイル分け1"):
+            n_clusters = gr.Slider(
+                minimum=2,
+                maximum=10,
+                step=1,
+                value=4,
+                label="作るスタイルの数（平均スタイルを除く）",
+                info="上の図を見ながらスタイルの数を試行錯誤してください。",
+            )
+            c_method = gr.Radio(
+                choices=[
+                    "Agglomerative after reduction",
+                    "KMeans after reduction",
+                    "Agglomerative",
+                    "KMeans",
+                ],
+                label="アルゴリズム",
+                info="分類する（クラスタリング）アルゴリズムを選択します。いろいろ試してみてください。",
+                value="Agglomerative after reduction",
+            )
+            c_button = gr.Button("スタイル分けを実行")
+        with gr.Tab("スタイル分け2: DBSCAN"):
+            gr.Markdown(dbscan_md)
+            eps = gr.Slider(
+                minimum=0.1,
+                maximum=10,
+                step=0.01,
+                value=0.3,
+                label="eps",
+            )
+            min_samples = gr.Slider(
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=15,
+                label="min_samples",
+            )
+            with gr.Row():
+                dbscan_button = gr.Button("スタイル分けを実行")
+                num_styles_result = gr.Textbox(label="スタイル数")
+        gr.Markdown("スタイル分けの結果")
+        gr.Markdown(
+            "注意: もともと256次元なものをを2次元に落としているので、正確なベクトルの位置関係ではありません。"
+        )
+        with gr.Row():
+            gr_plot = gr.Plot()
+            with gr.Column():
+                with gr.Row():
+                    cluster_index = gr.Slider(
+                        minimum=1,
+                        maximum=MAX_CLUSTER_NUM,
+                        step=1,
+                        value=1,
+                        label="スタイル番号",
+                        info="選択したスタイルの代表音声を表示します。",
+                    )
+                    num_files = gr.Slider(
+                        minimum=1,
+                        maximum=MAX_AUDIO_NUM,
+                        step=1,
+                        value=5,
+                        label="代表音声の数をいくつ表示するか",
+                    )
+                    get_audios_button = gr.Button("代表音声を取得")
+                with gr.Row():
+                    audio_list = []
+                    for i in range(MAX_AUDIO_NUM):
+                        audio_list.append(gr.Audio(visible=False, show_label=True))
+            c_button.click(
+                do_clustering_gradio,
+                inputs=[n_clusters, c_method],
+                outputs=[gr_plot, cluster_index] + audio_list,
+            )
+            dbscan_button.click(
+                do_dbscan_gradio,
+                inputs=[eps, min_samples],
+                outputs=[gr_plot, cluster_index, num_styles_result] + audio_list,
+            )
+            get_audios_button.click(
+                representative_wav_files_gradio,
+                inputs=[cluster_index, num_files],
+                outputs=audio_list,
+            )
+        gr.Markdown("結果が良さそうなら、これを保存します。")
+        style_names = gr.Textbox(
+            "Angry, Sad, Happy",
+            label="スタイルの名前",
+            info=f"スタイルの名前を`,`で区切って入力してください（日本語可）。例: `Angry, Sad, Happy`や`怒り, 悲しみ, 喜び`など。平均音声は{DEFAULT_STYLE}として自動的に保存されます。",
+        )
+        with gr.Row():
+            save_button1 = gr.Button("スタイルベクトルを保存", variant="primary")
+            info2 = gr.Textbox(label="保存結果")
+
+        save_button1.click(
+            save_style_vectors_from_clustering,
+            inputs=[model_name, style_names],
+            outputs=[info2],
+        )
+    with gr.Tab("方法2: 手動でスタイルを選ぶ"):
+        gr.Markdown(
+            "下のテキスト欄に、各スタイルの代表音声のファイル名を`,`区切りで、その横に対応するスタイル名を`,`区切りで入力してください。"
+        )
+        gr.Markdown("例: `angry.wav, sad.wav, happy.wav`と`Angry, Sad, Happy`")
+        gr.Markdown(
+            f"注意: {DEFAULT_STYLE}スタイルは自動的に保存されます、手動では{DEFAULT_STYLE}という名前のスタイルは指定しないでください。"
+        )
+        with gr.Row():
+            audio_files_text = gr.Textbox(
+                label="音声ファイル名", placeholder="angry.wav, sad.wav, happy.wav"
+            )
+            style_names_text = gr.Textbox(
+                label="スタイル名", placeholder="Angry, Sad, Happy"
+            )
+        with gr.Row():
+            save_button2 = gr.Button("スタイルベクトルを保存", variant="primary")
+            info2 = gr.Textbox(label="保存結果")
+            save_button2.click(
+                save_style_vectors_from_files,
+                inputs=[model_name, audio_files_text, style_names_text],
+                outputs=[info2],
+            )
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--server-name",
+    type=str,
+    default=None,
+    help="Server name for Gradio app",
+)
+parser.add_argument(
+    "--no-autolaunch",
+    action="store_true",
+    default=False,
+    help="Do not launch app automatically",
+)
+parser.add_argument("--share", action="store_true", default=False)
+args = parser.parse_args()
+
+app.launch(
+    inbrowser=not args.no_autolaunch, server_name=args.server_name, share=args.share
+)
diff --git a/webui_train.py b/webui_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebd292e7de75dc6daf5c13efd123c0f076768770
--- /dev/null
+++ b/webui_train.py
@@ -0,0 +1,747 @@
+import argparse
+import json
+import os
+import shutil
+import socket
+import subprocess
+import sys
+import time
+import webbrowser
+from datetime import datetime
+from multiprocessing import cpu_count
+
+import gradio as gr
+import yaml
+
+from common.constants import GRADIO_THEME, LATEST_VERSION
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
+from common.subprocess_utils import run_script_with_log, second_elem_of
+
+logger_handler = None
+tensorboard_executed = False
+
+# Get path settings
+with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
+    path_config: dict[str, str] = yaml.safe_load(f.read())
+    dataset_root = path_config["dataset_root"]
+    # assets_root = path_config["assets_root"]
+
+
+def get_path(model_name):
+    assert model_name != "", "モデル名は空にできません"
+    dataset_path = os.path.join(dataset_root, model_name)
+    lbl_path = os.path.join(dataset_path, "esd.list")
+    train_path = os.path.join(dataset_path, "train.list")
+    val_path = os.path.join(dataset_path, "val.list")
+    config_path = os.path.join(dataset_path, "config.json")
+    return dataset_path, lbl_path, train_path, val_path, config_path
+
+
+def initialize(
+    model_name,
+    batch_size,
+    epochs,
+    save_every_steps,
+    freeze_EN_bert,
+    freeze_JP_bert,
+    freeze_ZH_bert,
+    freeze_style,
+    use_jp_extra,
+    log_interval,
+):
+    global logger_handler
+    dataset_path, _, train_path, val_path, config_path = get_path(model_name)
+
+    # 前処理のログをファイルに保存する
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    file_name = f"preprocess_{timestamp}.log"
+    if logger_handler is not None:
+        logger.remove(logger_handler)
+    logger_handler = logger.add(os.path.join(dataset_path, file_name))
+
+    logger.info(
+        f"Step 1: start initialization...\nmodel_name: {model_name}, batch_size: {batch_size}, epochs: {epochs}, save_every_steps: {save_every_steps}, freeze_ZH_bert: {freeze_ZH_bert}, freeze_JP_bert: {freeze_JP_bert}, freeze_EN_bert: {freeze_EN_bert}, freeze_style: {freeze_style}, use_jp_extra: {use_jp_extra}"
+    )
+
+    default_config_path = (
+        "configs/config.json" if not use_jp_extra else "configs/configs_jp_extra.json"
+    )
+
+    with open(default_config_path, "r", encoding="utf-8") as f:
+        config = json.load(f)
+    config["model_name"] = model_name
+    config["data"]["training_files"] = train_path
+    config["data"]["validation_files"] = val_path
+    config["train"]["batch_size"] = batch_size
+    config["train"]["epochs"] = epochs
+    config["train"]["eval_interval"] = save_every_steps
+    config["train"]["log_interval"] = log_interval
+
+    config["train"]["freeze_EN_bert"] = freeze_EN_bert
+    config["train"]["freeze_JP_bert"] = freeze_JP_bert
+    config["train"]["freeze_ZH_bert"] = freeze_ZH_bert
+    config["train"]["freeze_style"] = freeze_style
+
+    config["train"]["bf16_run"] = False  # デフォルトでFalseのはずだが念のため
+
+    model_path = os.path.join(dataset_path, "models")
+    if os.path.exists(model_path):
+        logger.warning(f"Step 1: {model_path} already exists, so copy it to backup.")
+        shutil.copytree(
+            src=model_path,
+            dst=os.path.join(dataset_path, "models_backup"),
+            dirs_exist_ok=True,
+        )
+        shutil.rmtree(model_path)
+    pretrained_dir = "pretrained" if not use_jp_extra else "pretrained_jp_extra"
+    try:
+        shutil.copytree(
+            src=pretrained_dir,
+            dst=model_path,
+        )
+    except FileNotFoundError:
+        logger.error(f"Step 1: {pretrained_dir} folder not found.")
+        return False, f"Step 1, Error: {pretrained_dir}フォルダが見つかりません。"
+
+    with open(config_path, "w", encoding="utf-8") as f:
+        json.dump(config, f, indent=2, ensure_ascii=False)
+    if not os.path.exists("config.yml"):
+        shutil.copy(src="default_config.yml", dst="config.yml")
+    # yml_data = safe_load(open("config.yml", "r", encoding="utf-8"))
+    with open("config.yml", "r", encoding="utf-8") as f:
+        yml_data = yaml.safe_load(f)
+    yml_data["model_name"] = model_name
+    yml_data["dataset_path"] = dataset_path
+    with open("config.yml", "w", encoding="utf-8") as f:
+        yaml.dump(yml_data, f, allow_unicode=True)
+    logger.success("Step 1: initialization finished.")
+    return True, "Step 1, Success: 初期設定が完了しました"
+
+
+def resample(model_name, normalize, trim, num_processes):
+    logger.info("Step 2: start resampling...")
+    dataset_path, _, _, _, _ = get_path(model_name)
+    in_dir = os.path.join(dataset_path, "raw")
+    out_dir = os.path.join(dataset_path, "wavs")
+    cmd = [
+        "resample.py",
+        "--in_dir",
+        in_dir,
+        "--out_dir",
+        out_dir,
+        "--num_processes",
+        str(num_processes),
+        "--sr",
+        "44100",
+    ]
+    if normalize:
+        cmd.append("--normalize")
+    if trim:
+        cmd.append("--trim")
+    success, message = run_script_with_log(cmd)
+    if not success:
+        logger.error(f"Step 2: resampling failed.")
+        return False, f"Step 2, Error: 音声ファイルの前処理に失敗しました:\n{message}"
+    elif message:
+        logger.warning(f"Step 2: resampling finished with stderr.")
+        return True, f"Step 2, Success: 音声ファイルの前処理が完了しました:\n{message}"
+    logger.success("Step 2: resampling finished.")
+    return True, "Step 2, Success: 音声ファイルの前処理が完了しました"
+
+
+def preprocess_text(model_name, use_jp_extra, val_per_lang):
+    logger.info("Step 3: start preprocessing text...")
+    dataset_path, lbl_path, train_path, val_path, config_path = get_path(model_name)
+    try:
+        lines = open(lbl_path, "r", encoding="utf-8").readlines()
+    except FileNotFoundError:
+        logger.error(f"Step 3: {lbl_path} not found.")
+        return False, f"Step 3, Error: 書き起こしファイル {lbl_path} が見つかりません。"
+    with open(lbl_path, "w", encoding="utf-8") as f:
+        for line in lines:
+            path, spk, language, text = line.strip().split("|")
+            path = os.path.join(dataset_path, "wavs", os.path.basename(path)).replace(
+                "\\", "/"
+            )
+            f.writelines(f"{path}|{spk}|{language}|{text}\n")
+    cmd = [
+        "preprocess_text.py",
+        "--config-path",
+        config_path,
+        "--transcription-path",
+        lbl_path,
+        "--train-path",
+        train_path,
+        "--val-path",
+        val_path,
+        "--val-per-lang",
+        str(val_per_lang),
+    ]
+    if use_jp_extra:
+        cmd.append("--use_jp_extra")
+    success, message = run_script_with_log(cmd)
+    if not success:
+        logger.error(f"Step 3: preprocessing text failed.")
+        return (
+            False,
+            f"Step 3, Error: 書き起こしファイルの前処理に失敗しました:\n{message}",
+        )
+    elif message:
+        logger.warning(f"Step 3: preprocessing text finished with stderr.")
+        return (
+            True,
+            f"Step 3, Success: 書き起こしファイルの前処理が完了しました:\n{message}",
+        )
+    logger.success("Step 3: preprocessing text finished.")
+    return True, "Step 3, Success: 書き起こしファイルの前処理が完了しました"
+
+
+def bert_gen(model_name):
+    logger.info("Step 4: start bert_gen...")
+    _, _, _, _, config_path = get_path(model_name)
+    success, message = run_script_with_log(
+        [
+            "bert_gen.py",
+            "--config",
+            config_path,
+            # "--num_processes",  # bert_genは重いのでプロセス数いじらない
+            #  str(num_processes),
+        ]
+    )
+    if not success:
+        logger.error(f"Step 4: bert_gen failed.")
+        return False, f"Step 4, Error: BERT特徴ファイルの生成に失敗しました:\n{message}"
+    elif message:
+        logger.warning(f"Step 4: bert_gen finished with stderr.")
+        return (
+            True,
+            f"Step 4, Success: BERT特徴ファイルの生成が完了しました:\n{message}",
+        )
+    logger.success("Step 4: bert_gen finished.")
+    return True, "Step 4, Success: BERT特徴ファイルの生成が完了しました"
+
+
+def style_gen(model_name, num_processes):
+    logger.info("Step 5: start style_gen...")
+    _, _, _, _, config_path = get_path(model_name)
+    success, message = run_script_with_log(
+        [
+            "style_gen.py",
+            "--config",
+            config_path,
+            "--num_processes",
+            str(num_processes),
+        ]
+    )
+    if not success:
+        logger.error(f"Step 5: style_gen failed.")
+        return (
+            False,
+            f"Step 5, Error: スタイル特徴ファイルの生成に失敗しました:\n{message}",
+        )
+    elif message:
+        logger.warning(f"Step 5: style_gen finished with stderr.")
+        return (
+            True,
+            f"Step 5, Success: スタイル特徴ファイルの生成が完了しました:\n{message}",
+        )
+    logger.success("Step 5: style_gen finished.")
+    return True, "Step 5, Success: スタイル特徴ファイルの生成が完了しました"
+
+
+def preprocess_all(
+    model_name,
+    batch_size,
+    epochs,
+    save_every_steps,
+    num_processes,
+    normalize,
+    trim,
+    freeze_EN_bert,
+    freeze_JP_bert,
+    freeze_ZH_bert,
+    freeze_style,
+    use_jp_extra,
+    val_per_lang,
+    log_interval,
+):
+    if model_name == "":
+        return False, "Error: モデル名を入力してください"
+    success, message = initialize(
+        model_name,
+        batch_size,
+        epochs,
+        save_every_steps,
+        freeze_EN_bert,
+        freeze_JP_bert,
+        freeze_ZH_bert,
+        freeze_style,
+        use_jp_extra,
+        log_interval,
+    )
+    if not success:
+        return False, message
+    success, message = resample(model_name, normalize, trim, num_processes)
+    if not success:
+        return False, message
+    success, message = preprocess_text(model_name, use_jp_extra, val_per_lang)
+    if not success:
+        return False, message
+    success, message = bert_gen(model_name)  # bert_genは重いのでプロセス数いじらない
+    if not success:
+        return False, message
+    success, message = style_gen(model_name, num_processes)
+    if not success:
+        return False, message
+    logger.success("Success: All preprocess finished!")
+    return (
+        True,
+        "Success: 全ての前処理が完了しました。ターミナルを確認しておかしいところがないか確認するのをおすすめします。",
+    )
+
+
+def train(model_name, skip_style=False, use_jp_extra=True, speedup=False):
+    dataset_path, _, _, _, config_path = get_path(model_name)
+    # 学習再開の場合は念のためconfig.ymlの名前等を更新
+    with open("config.yml", "r", encoding="utf-8") as f:
+        yml_data = yaml.safe_load(f)
+    yml_data["model_name"] = model_name
+    yml_data["dataset_path"] = dataset_path
+    with open("config.yml", "w", encoding="utf-8") as f:
+        yaml.dump(yml_data, f, allow_unicode=True)
+
+    train_py = "train_ms.py" if not use_jp_extra else "train_ms_jp_extra.py"
+    cmd = [train_py, "--config", config_path, "--model", dataset_path]
+    if skip_style:
+        cmd.append("--skip_default_style")
+    if speedup:
+        cmd.append("--speedup")
+    success, message = run_script_with_log(cmd, ignore_warning=True)
+    if not success:
+        logger.error(f"Train failed.")
+        return False, f"Error: 学習に失敗しました:\n{message}"
+    elif message:
+        logger.warning(f"Train finished with stderr.")
+        return True, f"Success: 学習が完了しました:\n{message}"
+    logger.success("Train finished.")
+    return True, "Success: 学習が完了しました"
+
+
+def wait_for_tensorboard(port=6006, timeout=10):
+    start_time = time.time()
+    while True:
+        try:
+            with socket.create_connection(("localhost", port), timeout=1):
+                return True  # ポートが開いている場合
+        except OSError:
+            pass  # ポートがまだ開いていない場合
+
+        if time.time() - start_time > timeout:
+            return False  # タイムアウト
+
+        time.sleep(0.1)
+
+
+def run_tensorboard(model_name):
+    global tensorboard_executed
+    if not tensorboard_executed:
+        python = sys.executable
+        tensorboard_cmd = [
+            python,
+            "-m",
+            "tensorboard.main",
+            "--logdir",
+            f"Data/{model_name}/models",
+        ]
+        subprocess.Popen(
+            tensorboard_cmd,
+            stdout=SAFE_STDOUT,  # type: ignore
+            stderr=SAFE_STDOUT,  # type: ignore
+        )
+        yield gr.Button("起動中…")
+        if wait_for_tensorboard():
+            tensorboard_executed = True
+        else:
+            logger.error("Tensorboard did not start in the expected time.")
+    webbrowser.open("http://localhost:6006")
+    yield gr.Button("Tensorboardを開く")
+
+
+initial_md = f"""
+# Style-Bert-VITS2 ver {LATEST_VERSION} 学習用WebUI
+
+## 使い方
+
+- データを準備して、モデル名を入力して、必要なら設定を調整してから、「自動前処理を実行」ボタンを押してください。進捗状況等はターミナルに表示されます。
+
+- 各ステップごとに実行する場合は「手動前処理」を使ってください（基本的には自動でいいはず）。
+
+- 前処理が終わったら、「学習を開始する」ボタンを押すと学習が開始されます。
+
+- 途中から学習を再開する場合は、モデル名を入力してから「学習を開始する」を押せばよいです。
+
+注意: 標準スタイル以外のスタイルを音声合成で使うには、スタイルベクトルファイル`style_vectors.npy`を作る必要があります。これは、`Style.bat`を実行してそこで作成してください。
+動作は軽いはずなので、学習中でも実行でき、何度でも繰り返して試せます。
+
+## JP-Extra版について
+
+元とするモデル構造として [Bert-VITS2 Japanese-Extra](https://github.com/fishaudio/Bert-VITS2/releases/tag/JP-Exta) を使うことができます。
+日本語のアクセントやイントネーションや自然性が上がる傾向にありますが、英語と中国語は話せなくなります。
+"""
+
+prepare_md = """
+まず音声データ（wavファイルで1ファイルが2-12秒程度の、長すぎず短すぎない発話のものをいくつか）と、書き起こしテキストを用意してください。
+
+それを次のように配置します。
+```
+├── Data
+│   ├── {モデルの名前}
+│   │   ├── esd.list
+│   │   ├── raw
+│   │   │   ├── ****.wav
+│   │   │   ├── ****.wav
+│   │   │   ├── ...
+```
+
+wavファイル名やモデルの名前は空白を含まない半角で、wavファイルの拡張子は小文字`.wav`である必要があります。
+`raw` フォルダにはすべてのwavファイルを入れ、`esd.list` ファイルには、以下のフォーマットで各wavファイルの情報を記述してください。
+```
+****.wav|{話者名}|{言語ID、ZHかJPかEN}|{書き起こしテキスト}
+```
+
+例：
+```
+wav_number1.wav|hanako|JP|こんにちは、聞こえて、いますか？
+wav_next.wav|taro|JP|はい、聞こえています……。
+english_teacher.wav|Mary|EN|How are you? I'm fine, thank you, and you?
+...
+```
+日本語話者の単一話者データセットでも構いません。
+"""
+
+if __name__ == "__main__":
+    with gr.Blocks(theme=GRADIO_THEME).queue() as app:
+        gr.Markdown(initial_md)
+        with gr.Accordion(label="データの前準備", open=False):
+            gr.Markdown(prepare_md)
+        model_name = gr.Textbox(label="モデル名")
+        gr.Markdown("### 自動前処理")
+        with gr.Row(variant="panel"):
+            with gr.Column():
+                use_jp_extra = gr.Checkbox(
+                    label="JP-Extra版を使う（日本語の性能が上がるが英語と中国語は話せなくなる）",
+                    value=True,
+                )
+                batch_size = gr.Slider(
+                    label="バッチサイズ",
+                    info="学習速度が遅い場合は小さくして試し、VRAMに余裕があれば大きくしてください。JP-Extra版でのVRAM使用量目安: 1: 6GB, 2: 8GB, 3: 10GB, 4: 12GB",
+                    value=2,
+                    minimum=1,
+                    maximum=64,
+                    step=1,
+                )
+                epochs = gr.Slider(
+                    label="エポック数",
+                    info="100もあれば十分そうだけどもっと回すと質が上がるかもしれない",
+                    value=100,
+                    minimum=10,
+                    maximum=1000,
+                    step=10,
+                )
+                save_every_steps = gr.Slider(
+                    label="何ステップごとに結果を保存するか",
+                    info="エポック数とは違うことに注意",
+                    value=1000,
+                    minimum=100,
+                    maximum=10000,
+                    step=100,
+                )
+                normalize = gr.Checkbox(
+                    label="音声の音量を正規化する(音量の大小が揃っていない場合など)",
+                    value=False,
+                )
+                trim = gr.Checkbox(
+                    label="音声の最初と最後の無音を取り除く",
+                    value=False,
+                )
+                with gr.Accordion("詳細設定", open=False):
+                    num_processes = gr.Slider(
+                        label="プロセス数",
+                        info="前処理時の並列処理プロセス数、前処理でフリーズしたら下げてください",
+                        value=cpu_count() // 2,
+                        minimum=1,
+                        maximum=cpu_count(),
+                        step=1,
+                    )
+                    val_per_lang = gr.Slider(
+                        label="検証データ数",
+                        info="学習には使われず、tensorboardで元音声と合成音声を比較するためのもの",
+                        value=0,
+                        minimum=0,
+                        maximum=100,
+                        step=1,
+                    )
+                    log_interval = gr.Slider(
+                        label="Tensorboardのログ出力間隔",
+                        info="Tensorboardで詳しく見たい人は小さめにしてください",
+                        value=200,
+                        minimum=10,
+                        maximum=1000,
+                        step=10,
+                    )
+                    gr.Markdown("学習時に特定の部分を凍結させるかどうか")
+                    freeze_EN_bert = gr.Checkbox(
+                        label="英語bert部分を凍結",
+                        value=False,
+                    )
+                    freeze_JP_bert = gr.Checkbox(
+                        label="日本語bert部分を凍結",
+                        value=False,
+                    )
+                    freeze_ZH_bert = gr.Checkbox(
+                        label="中国語bert部分を凍結",
+                        value=False,
+                    )
+                    freeze_style = gr.Checkbox(
+                        label="スタイル部分を凍結",
+                        value=False,
+                    )
+
+            with gr.Column():
+                preprocess_button = gr.Button(
+                    value="自動前処理を実行", variant="primary"
+                )
+                info_all = gr.Textbox(label="状況")
+        with gr.Accordion(open=False, label="手動前処理"):
+            with gr.Row(variant="panel"):
+                with gr.Column():
+                    gr.Markdown(value="#### Step 1: 設定ファイルの生成")
+                    use_jp_extra_manual = gr.Checkbox(
+                        label="JP-Extra版を使う",
+                        value=True,
+                    )
+                    batch_size_manual = gr.Slider(
+                        label="バッチサイズ",
+                        value=2,
+                        minimum=1,
+                        maximum=64,
+                        step=1,
+                    )
+                    epochs_manual = gr.Slider(
+                        label="エポック数",
+                        value=100,
+                        minimum=1,
+                        maximum=1000,
+                        step=1,
+                    )
+                    save_every_steps_manual = gr.Slider(
+                        label="何ステップごとに結果を保存するか",
+                        value=1000,
+                        minimum=100,
+                        maximum=10000,
+                        step=100,
+                    )
+                    log_interval_manual = gr.Slider(
+                        label="Tensorboardのログ出力間隔",
+                        value=200,
+                        minimum=10,
+                        maximum=1000,
+                        step=10,
+                    )
+                    freeze_EN_bert_manual = gr.Checkbox(
+                        label="英語bert部分を凍結",
+                        value=False,
+                    )
+                    freeze_JP_bert_manual = gr.Checkbox(
+                        label="日本語bert部分を凍結",
+                        value=False,
+                    )
+                    freeze_ZH_bert_manual = gr.Checkbox(
+                        label="中国語bert部分を凍結",
+                        value=False,
+                    )
+                    freeze_style_manual = gr.Checkbox(
+                        label="スタイル部分を凍結",
+                        value=False,
+                    )
+                with gr.Column():
+                    generate_config_btn = gr.Button(value="実行", variant="primary")
+                    info_init = gr.Textbox(label="状況")
+            with gr.Row(variant="panel"):
+                with gr.Column():
+                    gr.Markdown(value="#### Step 2: 音声ファイルの前処理")
+                    num_processes_resample = gr.Slider(
+                        label="プロセス数",
+                        value=cpu_count() // 2,
+                        minimum=1,
+                        maximum=cpu_count(),
+                        step=1,
+                    )
+                    normalize_resample = gr.Checkbox(
+                        label="音声の音量を正規化する",
+                        value=False,
+                    )
+                    trim_resample = gr.Checkbox(
+                        label="音声の最初と最後の無音を取り除く",
+                        value=False,
+                    )
+                with gr.Column():
+                    resample_btn = gr.Button(value="実行", variant="primary")
+                    info_resample = gr.Textbox(label="状況")
+            with gr.Row(variant="panel"):
+                with gr.Column():
+                    gr.Markdown(value="#### Step 3: 書き起こしファイルの前処理")
+                    val_per_lang_manual = gr.Slider(
+                        label="検証データ数",
+                        value=0,
+                        minimum=0,
+                        maximum=100,
+                        step=1,
+                    )
+                with gr.Column():
+                    preprocess_text_btn = gr.Button(value="実行", variant="primary")
+                    info_preprocess_text = gr.Textbox(label="状況")
+            with gr.Row(variant="panel"):
+                with gr.Column():
+                    gr.Markdown(value="#### Step 4: BERT特徴ファイルの生成")
+                with gr.Column():
+                    bert_gen_btn = gr.Button(value="実行", variant="primary")
+                    info_bert = gr.Textbox(label="状況")
+            with gr.Row(variant="panel"):
+                with gr.Column():
+                    gr.Markdown(value="#### Step 5: スタイル特徴ファイルの生成")
+                    num_processes_style = gr.Slider(
+                        label="プロセス数",
+                        value=cpu_count() // 2,
+                        minimum=1,
+                        maximum=cpu_count(),
+                        step=1,
+                    )
+                with gr.Column():
+                    style_gen_btn = gr.Button(value="実行", variant="primary")
+                    info_style = gr.Textbox(label="状況")
+        gr.Markdown("## 学習")
+        with gr.Row():
+            skip_style = gr.Checkbox(
+                label="スタイルファイルの生成をスキップする",
+                info="学習再開の場合の場合はチェックしてください",
+                value=False,
+            )
+            use_jp_extra_train = gr.Checkbox(
+                label="JP-Extra版を使う",
+                value=True,
+            )
+            speedup = gr.Checkbox(
+                label="ログ等をスキップして学習を高速化する",
+                value=False,
+                visible=False,  # Experimental
+            )
+            train_btn = gr.Button(value="学習を開始する", variant="primary")
+            tensorboard_btn = gr.Button(value="Tensorboardを開く")
+        gr.Markdown(
+            "進捗はターミナルで確認してください。結果は指定したステップごとに随時保存されており、また学習を途中から再開することもできます。学習を終了するには単にターミナルを終了してください。"
+        )
+        info_train = gr.Textbox(label="状況")
+
+        preprocess_button.click(
+            second_elem_of(preprocess_all),
+            inputs=[
+                model_name,
+                batch_size,
+                epochs,
+                save_every_steps,
+                num_processes,
+                normalize,
+                trim,
+                freeze_EN_bert,
+                freeze_JP_bert,
+                freeze_ZH_bert,
+                freeze_style,
+                use_jp_extra,
+                val_per_lang,
+                log_interval,
+            ],
+            outputs=[info_all],
+        )
+
+        # Manual preprocess
+        generate_config_btn.click(
+            second_elem_of(initialize),
+            inputs=[
+                model_name,
+                batch_size_manual,
+                epochs_manual,
+                save_every_steps_manual,
+                freeze_EN_bert_manual,
+                freeze_JP_bert_manual,
+                freeze_ZH_bert_manual,
+                freeze_style_manual,
+                use_jp_extra_manual,
+                log_interval_manual,
+            ],
+            outputs=[info_init],
+        )
+        resample_btn.click(
+            second_elem_of(resample),
+            inputs=[
+                model_name,
+                normalize_resample,
+                trim_resample,
+                num_processes_resample,
+            ],
+            outputs=[info_resample],
+        )
+        preprocess_text_btn.click(
+            second_elem_of(preprocess_text),
+            inputs=[model_name, use_jp_extra_manual, val_per_lang_manual],
+            outputs=[info_preprocess_text],
+        )
+        bert_gen_btn.click(
+            second_elem_of(bert_gen),
+            inputs=[model_name],
+            outputs=[info_bert],
+        )
+        style_gen_btn.click(
+            second_elem_of(style_gen),
+            inputs=[model_name, num_processes_style],
+            outputs=[info_style],
+        )
+
+        # Train
+        train_btn.click(
+            second_elem_of(train),
+            inputs=[model_name, skip_style, use_jp_extra_train, speedup],
+            outputs=[info_train],
+        )
+        tensorboard_btn.click(
+            run_tensorboard, inputs=[model_name], outputs=[tensorboard_btn]
+        )
+
+        use_jp_extra.change(
+            lambda x: gr.Checkbox(value=x),
+            inputs=[use_jp_extra],
+            outputs=[use_jp_extra_train],
+        )
+        use_jp_extra_manual.change(
+            lambda x: gr.Checkbox(value=x),
+            inputs=[use_jp_extra_manual],
+            outputs=[use_jp_extra_train],
+        )
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--server-name",
+        type=str,
+        default=None,
+        help="Server name for Gradio app",
+    )
+    parser.add_argument(
+        "--no-autolaunch",
+        action="store_true",
+        default=False,
+        help="Do not launch app automatically",
+    )
+    args = parser.parse_args()
+
+    app.launch(inbrowser=not args.no_autolaunch, server_name=args.server_name)