up

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

Data/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

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

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
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+
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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+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/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

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

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��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。)

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

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

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��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���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
+    )

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

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"]
+    }
+}

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

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}
+ }
+```

bert/chinese-roberta-wwm-ext-large/added_tokens.json

@@ -0,0 +1 @@
+{}

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,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

bert/chinese-roberta-wwm-ext-large/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ac62d49144d770c5ca9a5d1d3039c4995665a080febe63198189857c6bd11cd
+size 1306484351

bert/chinese-roberta-wwm-ext-large/special_tokens_map.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

bert/chinese-roberta-wwm-ext-large/tokenizer_config.json

@@ -0,0 +1 @@
+{"init_inputs": []}

bert/deberta-v2-large-japanese-char-wwm/.gitattributes

@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

bert/deberta-v2-large-japanese-char-wwm/README.md

@@ -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.

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
+}

bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin

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+oid sha256:bf0dab8ad87bd7c22e85ec71e04f2240804fda6d33196157d6b5923af6ea1201
+size 1318456639

bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json

@@ -0,0 +1,19 @@
+{
+  "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"
+}

bert/deberta-v3-large/.gitattributes

@@ -0,0 +1,27 @@
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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.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
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+*.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
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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}
+}
+```

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
+}

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
+}

bert/deberta-v3-large/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd5b5d93e2db101aaf281df0ea1216c07ad73620ff59c5b42dccac4bf2eef5b5
+size 873673253

bert/deberta-v3-large/spm.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

bert/deberta-v3-large/tokenizer_config.json

@@ -0,0 +1,4 @@
+{
+  "do_lower_case": false,
+  "vocab_type": "spm"
+}

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.")

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
+}