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.github/workflows/pull_format.yml

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+name: pull format
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+jobs:
+  pull_format:
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+        os: [ubuntu-latest]
+      fail-fast: false
+
+    continue-on-error: true
+
+    steps:
+      - name: checkout
+        continue-on-error: true
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.head_ref }}
+          fetch-depth: 0
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Black
+        run: pip install "black[jupyter]"
+
+      - name: Run Black
+        # run: black $(git ls-files '*.py')
+        run: black .
+
+      - name: Commit Back
+        uses: stefanzweifel/git-auto-commit-action@v4
+        with:
+          commit_message: Apply Code Formatter Change

.github/workflows/push_format.yml

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+name: push format
+
+on:
+  push:
+    branches:
+      - master
+      - dev
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  push_format:
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+        os: [ubuntu-latest]
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          ref: ${{github.ref_name}}
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Black
+        run: pip install "black[jupyter]"
+
+      - name: Run Black
+        # run: black $(git ls-files '*.py')
+        run: black .
+
+      - name: Commit Back
+        continue-on-error: true
+        id: commitback
+        run: |
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git add --all
+          git commit -m "Format code"
+
+      - name: Create Pull Request
+        if: steps.commitback.outcome == 'success'
+        continue-on-error: true
+        uses: peter-evans/create-pull-request@v5
+        with:
+          delete-branch: true
+          body: Apply Code Formatter Change
+          title: Apply Code Formatter Change
+          commit-message: Automatic code format

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.DS_Store
+/models
+/logs
+
+filelists/*
+!/filelists/esd.list
+data/*
+/*.yml
+!/default_config.yml
+/Web/
+/emotional/*/*.bin
+/bert/*/*.bin
+/bert/*/*.h5
+/bert/*/*.model
+/bert/*/*.safetensors
+/bert/*/*.msgpack
+
+dataset
+/Data
+Model
+raw/
+logs/
+Data/*

.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.6
+    hooks:
+      - id: ruff
+        args: [ --fix ]
+
+  - repo: https://github.com/psf/black
+    rev: 23.11.0
+    hooks:
+      - id: black
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6
+    hooks:
+      - id: codespell
+        files: ^.*\.(py|md|rst|yml)$
+        args: [-L=fro]

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
+
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+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
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+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
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+
+  When we speak of free software, we are referring to freedom, not
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+
+  An older license, called the Affero General Public License and
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+<https://www.gnu.org/licenses/>.

README.md

@@ -1,13 +1,37 @@
----
-title: Kn
-emoji: 🐨
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 4.8.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<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
+## 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)
++ [Bert-VITS2-en](https://github.com/xwan07017/Bert-VITS2-en)
++ [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。)

all_process.py

@@ -0,0 +1,1384 @@
+import json
+import os
+import platform
+import shutil
+import signal
+import subprocess
+import webbrowser
+
+import GPUtil
+import gradio as gr
+import psutil
+import torch
+import yaml
+
+from config import yml_config
+from tools.log import logger
+
+bert_model_paths = [
+    "./bert/chinese-roberta-wwm-ext-large/pytorch_model.bin",
+    "./bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin",
+    "./bert/deberta-v3-large/pytorch_model.bin",
+    "./bert/deberta-v3-large/spm.model",
+]
+
+emo_model_paths = [
+    "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/pytorch_model.bin"
+]
+
+train_base_model_paths = ["D_0.pth", "G_0.pth", "DUR_0.pth"]
+default_yaml_path = "default_config.yml"
+default_config_path = "configs/config.json"
+
+
+def load_yaml_data_in_raw(yml_path=yml_config):
+    with open(yml_path, "r", encoding="utf-8") as file:
+        # data = yaml.safe_load(file)
+        data = file.read()
+    return str(data)
+
+
+def load_json_data_in_raw(json_path):
+    with open(json_path, "r", encoding="utf-8") as file:
+        json_data = json.load(file)
+    formatted_json_data = json.dumps(json_data, ensure_ascii=False, indent=2)
+    return formatted_json_data
+
+
+def load_json_data_in_fact(json_path):
+    with open(json_path, "r", encoding="utf-8") as file:
+        json_data = json.load(file)
+    return json_data
+
+
+def load_yaml_data_in_fact(yml_path=yml_config):
+    with open(yml_path, "r", encoding="utf-8") as file:
+        yml = yaml.safe_load(file)
+        # data = file.read()
+    return yml
+
+
+def fill_openi_token(token: str):
+    yml = load_yaml_data_in_fact()
+    yml["mirror"] = "openi"
+    yml["openi_token"] = token
+    write_yaml_data_in_fact(yml)
+    msg = "openi 令牌已填写完成"
+    logger.info(msg)
+    return gr.Textbox(value=msg), gr.Code(value=load_yaml_data_in_raw())
+
+
+def load_train_param(cfg_path):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    train_json_path = os.path.join(data_path, cfg_path).replace("\\", "/")
+    json_data = load_json_data_in_fact(train_json_path)
+    bs = json_data["train"]["batch_size"]
+    nc = json_data["train"].get("keep_ckpts", 5)
+    li = json_data["train"]["log_interval"]
+    ei = json_data["train"]["eval_interval"]
+    ep = json_data["train"]["epochs"]
+    lr = json_data["train"]["learning_rate"]
+    ver = json_data["version"]
+    msg = f"加载训练配置文件: {train_json_path}"
+    logger.info(msg)
+    return (
+        gr.Textbox(value=msg),
+        gr.Code(label=train_json_path, value=load_yaml_data_in_raw(train_json_path)),
+        gr.Slider(value=bs),
+        gr.Slider(value=nc),
+        gr.Slider(value=li),
+        gr.Slider(value=ei),
+        gr.Slider(value=ep),
+        gr.Slider(value=lr),
+        gr.Dropdown(value=ver),
+    )
+
+
+def write_yaml_data_in_fact(yml, yml_path=yml_config):
+    with open(yml_path, "w", encoding="utf-8") as file:
+        yaml.safe_dump(yml, file, allow_unicode=True)
+        # data = file.read()
+    return yml
+
+
+def write_json_data_in_fact(json_path, json_data):
+    with open(json_path, "w", encoding="utf-8") as file:
+        json.dump(json_data, file, ensure_ascii=False, indent=2)
+
+
+def check_if_exists_model(paths: list[str]):
+    check_results = {
+        path: os.path.exists(path) and os.path.isfile(path) for path in paths
+    }
+    val = [path for path, exists in check_results.items() if exists]
+    return val
+
+
+def check_bert_models():
+    return gr.CheckboxGroup(value=check_if_exists_model(bert_model_paths))
+
+
+def check_emo_models():
+    return gr.CheckboxGroup(value=check_if_exists_model(emo_model_paths))
+
+
+def check_base_models():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    models_dir = yml["train_ms"]["model"]
+    model_paths = [
+        os.path.join(data_path, models_dir, p).replace("\\", "/")
+        for p in train_base_model_paths
+    ]
+    return gr.CheckboxGroup(
+        label="检测底模状态",
+        info="最好去下载底模进行训练",
+        choices=model_paths,
+        value=check_if_exists_model(model_paths),
+        interactive=False,
+    )
+
+
+def modify_data_path(data_path):
+    yml = load_yaml_data_in_fact()
+    yml["dataset_path"] = data_path
+    write_yaml_data_in_fact(yml)
+    txt_box = gr.Textbox(value=data_path)
+    return (
+        gr.Dropdown(value=data_path),
+        txt_box,
+        txt_box,
+        txt_box,
+        gr.Code(value=load_yaml_data_in_raw()),
+        check_base_models(),
+    )
+
+
+def modify_preprocess_param(trans_path, cfg_path, val_per_spk, max_val_total):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    yml["preprocess_text"]["transcription_path"] = trans_path
+    yml["preprocess_text"]["config_path"] = cfg_path
+    yml["preprocess_text"]["val_per_spk"] = val_per_spk
+    yml["preprocess_text"]["max_val_total"] = max_val_total
+    write_yaml_data_in_fact(yml)
+    whole_path = os.path.join(data_path, cfg_path).replace("\\", "/")
+    logger.info("预处理配置: ", whole_path)
+    if not os.path.exists(whole_path):
+        os.makedirs(os.path.dirname(whole_path), exist_ok=True)
+        shutil.copy(default_config_path, os.path.dirname(whole_path))
+    return gr.Dropdown(value=trans_path), gr.Code(value=load_yaml_data_in_raw())
+
+
+def modify_resample_path(in_dir, out_dir, sr):
+    yml = load_yaml_data_in_fact()
+    yml["resample"]["in_dir"] = in_dir
+    yml["resample"]["out_dir"] = out_dir
+    yml["resample"]["sampling_rate"] = int(sr)
+    write_yaml_data_in_fact(yml)
+    msg = f"重采样参数已更改: [{in_dir}, {out_dir}, {sr}]\n"
+    logger.info(msg)
+    return (
+        gr.Textbox(value=in_dir),
+        gr.Textbox(value=out_dir),
+        gr.Textbox(value=msg),
+        gr.Dropdown(value=sr),
+        gr.Code(value=load_yaml_data_in_raw()),
+    )
+
+
+def modify_bert_config(cfg_path, nps, dev, multi):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    yml["bert_gen"]["config_path"] = cfg_path
+    yml["bert_gen"]["num_processes"] = int(nps)
+    yml["bert_gen"]["device"] = dev
+    yml["bert_gen"]["use_multi_device"] = multi
+    write_yaml_data_in_fact(yml)
+    whole_path = os.path.join(data_path, cfg_path).replace("\\", "/")
+    logger.info("bert配置路径: ", whole_path)
+    if not os.path.exists(whole_path):
+        os.makedirs(os.path.dirname(whole_path), exist_ok=True)
+        shutil.copy(default_config_path, os.path.dirname(whole_path))
+    return (
+        gr.Textbox(value=cfg_path),
+        gr.Slider(value=int(nps)),
+        gr.Dropdown(value=dev),
+        gr.Radio(value=multi),
+        gr.Code(value=load_yaml_data_in_raw()),
+    )
+
+
+def modify_train_path(model, cfg_path):
+    yml = load_yaml_data_in_fact()
+    yml["train_ms"]["config_path"] = cfg_path
+    yml["train_ms"]["model"] = model
+    write_yaml_data_in_fact(yml)
+    logger.info(f"训练配置文件路径: {cfg_path}\n")
+    logger.info(f"训练模型文件夹路径: {model}")
+    return (
+        gr.Textbox(value=model),
+        gr.Textbox(value=cfg_path),
+        gr.Code(value=load_yaml_data_in_raw()),
+        check_base_models(),
+    )
+
+
+def modify_train_param(bs, nc, li, ei, ep, lr, ver):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    cfg_path = yml["train_ms"]["config_path"]
+    ok = False
+    whole_path = os.path.join(data_path, cfg_path).replace("\\", "/")
+    logger.info("config_path: ", whole_path)
+    if not os.path.exists(whole_path):
+        os.makedirs(os.path.dirname(whole_path), exist_ok=True)
+        shutil.copy(default_config_path, os.path.dirname(whole_path))
+    if os.path.exists(whole_path) and os.path.isfile(whole_path):
+        ok = True
+        with open(whole_path, "r", encoding="utf-8") as file:
+            json_data = json.load(file)
+        json_data["train"]["batch_size"] = bs
+        json_data["train"]["keep_ckpts"] = nc
+        json_data["train"]["log_interval"] = li
+        json_data["train"]["eval_interval"] = ei
+        json_data["train"]["epochs"] = ep
+        json_data["train"]["learning_rate"] = lr
+        json_data["version"] = ver
+        with open(whole_path, "w", encoding="utf-8") as file:
+            json.dump(json_data, file, ensure_ascii=False, indent=2)
+        msg = f"成功更改训练参数! [{bs},{nc},{li},{ei},{ep},{lr}]"
+        logger.info(msg)
+    else:
+        msg = f"打开训练配置文件时出现错误: {whole_path}\n" f"该文件不存在或损坏，现在打开默认配置文件"
+        logger.error(msg)
+    return gr.Textbox(value=msg), gr.Code(
+        label=whole_path if ok else default_config_path,
+        value=load_json_data_in_raw(whole_path)
+        if ok
+        else load_json_data_in_raw(default_config_path),
+    )
+
+
+def modify_infer_param(model_path, config_path, port, share, debug, ver):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    yml["webui"]["model"] = os.path.relpath(model_path, start=data_path)
+    yml["webui"]["config_path"] = os.path.relpath(config_path, start=data_path)
+    port = int(port)
+    port = port if 0 <= port <= 65535 else 10086
+    yml["webui"]["port"] = port
+    yml["webui"]["share"] = share
+    yml["webui"]["debug"] = debug
+    write_yaml_data_in_fact(yml)
+    json_data = load_json_data_in_fact(config_path)
+    json_data["version"] = ver
+    write_json_data_in_fact(config_path, json_data)
+    msg = f"修改推理配置文件成功: [{model_path}, {config_path}, {port}, {ver}]"
+    logger.info(msg)
+    return (
+        gr.Textbox(value=msg),
+        gr.Code(value=load_yaml_data_in_raw()),
+        gr.Code(
+            label=config_path,
+            value=load_json_data_in_raw(config_path)
+            if os.path.exists(config_path)
+            else load_json_data_in_raw(default_config_path),
+        ),
+    )
+
+
+def get_status():
+    """获取电脑运行状态"""
+    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}")
+    if torch.cuda.device_count() > 0:
+        gpus = GPUtil.getGPUs()
+        for gpu in gpus:
+            gpuInfo.append(
+                {
+                    "GPU编号": gpu.id,
+                    "GPU负载": f"{gpu.load} %",
+                    "专用GPU内存": {
+                        "总内存": f"{gpu.memoryTotal} MB",
+                        "已使用": f"{gpu.memoryUsed} MB",
+                        "空闲": f"{gpu.memoryFree} MB",
+                    },
+                }
+            )
+    status_data = {
+        "devices": devices,
+        "CPU占用率": f"{cpu_percent} %",
+        "总内存": f"{memory_total // (1024 * 1024)} MB",
+        "可用内存": f"{memory_available // (1024 * 1024)} MB",
+        "已使用内存": f"{memory_used // (1024 * 1024)} MB",
+        "百分数": f"{memory_percent} %",
+        "gpu信息": gpuInfo,
+    }
+    formatted_json_data = json.dumps(status_data, ensure_ascii=False, indent=2)
+    logger.info(formatted_json_data)
+    return str(formatted_json_data)
+
+
+def get_gpu_status():
+    return gr.Code(value=get_status())
+
+
+def list_infer_models():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    inf_models, json_files = [], []
+    for root, dirs, files in os.walk(data_path):
+        for file in files:
+            filepath = os.path.join(root, file).replace("\\", "/")
+            if file.startswith("G_") and file.lower().endswith(".pth"):
+                inf_models.append(filepath)
+            elif file.lower().endswith(".json"):
+                json_files.append(filepath)
+    logger.info("找到推理模型文件: " + str(inf_models))
+    logger.info("找到推理配置文件: " + str(json_files))
+    return gr.Dropdown(choices=inf_models), gr.Dropdown(choices=json_files)
+
+
+def do_resample(nps):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    in_dir = yml["resample"]["in_dir"]
+    comp_in_dir = os.path.join(os.path.abspath(data_path), in_dir).replace("\\", "/")
+    logger.info(f"\n重采样路径: {comp_in_dir}")
+    cmd = f"python resample.py --processes {nps}"
+    logger.info(cmd)
+    subprocess.run(cmd, shell=True)
+    return gr.Textbox(value="重采样完成!")
+
+
+def do_transcript(lang, workers):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    in_dir = yml["resample"]["in_dir"]
+    comp_in_dir = os.path.join(os.path.abspath(data_path), in_dir).replace("\\", "/")
+    logger.info(f"\n转写文件夹路径: {comp_in_dir}")
+    cmd = f'python asr_transcript.py -f "{comp_in_dir}" -l {lang} -w {workers}'
+    logger.info(cmd)
+    subprocess.run(cmd, shell=True)
+    return gr.Textbox(value=f"\n转写文件夹路径: {comp_in_dir}\n转写到.lab完成!")
+
+
+def do_extract(raw_path, lang, unclean, char_name):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    lab_path = os.path.join(os.path.abspath(data_path), raw_path).replace("\\", "/")
+    unclean_path = os.path.join(
+        data_path, os.path.splitext(unclean)[0] + ".txt"
+    ).replace("\\", "/")
+    logger.info(f"\n提取转写文本路径: {lab_path}")
+    lab_ok = False
+    for root, _, files in os.walk(lab_path):
+        for f_name in files:
+            if str(f_name).lower().endswith(".lab"):
+                lab_ok = True
+                break
+        if lab_ok:
+            break
+
+    if os.path.exists(lab_path) and os.path.isdir(lab_path):
+        if lab_ok:
+            cmd = f'python extract_list.py -f "{lab_path}" -l {lang} -n "{char_name}" -o "{unclean_path}"'
+            logger.info(cmd)
+            subprocess.run(cmd, shell=True)
+            msg = f"提取完成!生成如下文件: {unclean_path}"
+            logger.info(msg)
+        else:
+            msg = "未找到提取转写文本路径下的.lab文件!"
+            logger.warning(msg)
+    else:
+        msg = "路径未选择正确!"
+        logger.error(msg)
+    return gr.Textbox(value=msg)
+
+
+def do_clean_list(ban_chars, unclean, clean):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    unclean_path = os.path.join(data_path, unclean)
+    clean_path = os.path.join(data_path, clean)
+    if os.path.exists(unclean_path) and os.path.isfile(unclean_path):
+        cmd = f'python clean_list.py -c "{ban_chars}" -i "{unclean_path}" -o "{clean_path}"'
+        logger.info(cmd)
+        subprocess.run(cmd, shell=True)
+        msg = "清洗标注文本完成!"
+        logger.info(msg)
+    else:
+        msg = "未找到可清洗标注文本，请到2.2节重新生成!"
+        logger.warning(msg)
+    return gr.Textbox(value=msg)
+
+
+def do_preprocess_text():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    trans_path = yml["preprocess_text"]["transcription_path"]
+    comp_trans_path = os.path.join(os.path.abspath(data_path), trans_path).replace(
+        "\\", "/"
+    )
+    logger.info(f"\n清洗后标注文本文件路径: {comp_trans_path}")
+    if os.path.exists(comp_trans_path) and os.path.isfile(comp_trans_path):
+        cmd = "python preprocess_text.py"
+        logger.info(cmd)
+        subprocess.run(cmd, shell=True)
+        msg = "文本预处理完成!"
+    else:
+        msg = "\n清洗后标注文本文件不存在或失效！"
+        logger.info(msg)
+    return gr.Textbox(value=msg)
+
+
+def do_bert_gen():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    train_list_path = yml["preprocess_text"]["train_path"]
+    val_list_path = yml["preprocess_text"]["val_path"]
+    comp_t_path = os.path.join(os.path.abspath(data_path), train_list_path).replace(
+        "\\", "/"
+    )
+    comp_v_path = os.path.join(os.path.abspath(data_path), val_list_path).replace(
+        "\\", "/"
+    )
+    if os.path.exists(comp_t_path) and os.path.isfile(comp_t_path):
+        subprocess.run("python bert_gen.py", shell=True)
+        msg = "bert文件生成完成!"
+        logger.info(msg)
+    else:
+        msg = f"未找到训练集和验证集文本!\ntrain: {comp_t_path}\nval:{comp_v_path}"
+        logger.error(msg)
+    return gr.Textbox(value=msg)
+
+
+def modify_emo_gen(emo_cfg, emo_nps, emo_device):
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    yml["emo_gen"]["config_path"] = emo_cfg
+    yml["emo_gen"]["num_processes"] = emo_nps
+    yml["emo_gen"]["device"] = emo_device
+    write_yaml_data_in_fact(yml)
+    comp_emo_cfg = os.path.join(os.path.abspath(data_path), emo_cfg).replace("\\", "/")
+    if not os.path.exists(comp_emo_cfg):
+        os.makedirs(os.path.dirname(comp_emo_cfg), exist_ok=True)
+        shutil.copy(default_config_path, os.path.dirname(comp_emo_cfg))
+    msg = f"修改emo配置参数: [配置路径:{comp_emo_cfg}, 处理数:{emo_nps}, 设备:{emo_device}]"
+    logger.info(msg)
+    return gr.Textbox(value=msg), gr.Code(value=load_yaml_data_in_raw())
+
+
+def do_emo_gen():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    emo_config_path = yml["emo_gen"]["config_path"]
+    comp_emo_path = os.path.join(os.path.abspath(data_path), emo_config_path).replace(
+        "\\", "/"
+    )
+    if os.path.exists(comp_emo_path) and os.path.isfile(comp_emo_path):
+        subprocess.run("python emo_gen.py", shell=True)
+        msg = "emo.npy文件生成完成!"
+        logger.info(msg)
+    else:
+        msg = f"选定路径下未找到配置文件!\n需要的config路径 : {comp_emo_path}"
+        logger.error(msg)
+
+    return gr.Textbox(value=msg)
+
+
+def do_my_train():
+    yml = load_yaml_data_in_fact()
+    n_gpus = torch.cuda.device_count()
+    # subprocess.run(f'python train_ms.py', shell=True)
+    if os.path.exists(r"..\vits\python.exe") and os.path.isfile(r"..\vits\python.exe"):
+        cmd = (
+            r"..\vits\python ..\vits\Scripts\torchrun.exe "
+            f"--nproc_per_node={n_gpus} train_ms.py"
+        )
+    else:
+        cmd = f"torchrun --nproc_per_node={n_gpus} train_ms.py"
+
+    subprocess.Popen(cmd, shell=True)
+    train_port = yml["train_ms"]["env"]["MASTER_PORT"]
+    train_addr = yml["train_ms"]["env"]["MASTER_ADDR"]
+    url = f"env://{train_addr}:{train_port}"
+    msg = f"训练开始!\nMASTER_URL: {url}\n使用gpu数:{n_gpus}\n推荐按下终止训练按钮来结束!"
+    logger.info(msg)
+    return gr.Textbox(value=msg)
+
+
+def do_tensorboard():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    train_model_dir = yml["train_ms"]["model"]
+    whole_dir = os.path.join(data_path, train_model_dir).replace("\\", "/")
+    if os.path.exists(r"..\vits\python.exe") and os.path.isfile(r"..\vits\python.exe"):
+        first_cmd = r"..\vits\python ..\vits\Scripts\tensorboard.exe "
+    else:
+        first_cmd = "tensorboard "
+    tb_cmd = (
+            first_cmd + f"--logdir={whole_dir} "
+                        f"--port={11451} "
+                        f'--window_title="训练情况一览" '
+                        f"--reload_interval={120}"
+    )
+    subprocess.Popen(tb_cmd, shell=True)
+    url = f"http://localhost:{11451}"
+    webbrowser.open(url=url)
+    msg = tb_cmd + "\n" + url
+    logger.info(msg)
+    return gr.Textbox(value=msg)
+
+
+def do_webui_infer():
+    yml = load_yaml_data_in_fact()
+    data_path = yml["dataset_path"]
+    model_path = yml["webui"]["model"]
+    config_path = yml["webui"]["config_path"]
+    comp_m_path = os.path.join(os.path.abspath(data_path), model_path)
+    comp_c_path = os.path.join(os.path.abspath(data_path), config_path)
+    if os.path.exists(comp_c_path) and os.path.exists(comp_m_path):
+        webui_port = yml["webui"]["port"]
+        subprocess.Popen("python webui.py", shell=True)
+        url = f"http://localhost:{webui_port} | http://127.0.0.1:{webui_port}"
+        msg = f"推理端已开启, 到控制台中复制网址打开页面\n{url}\n选择的模型:{model_path}"
+        logger.info(msg)
+    else:
+        msg = f"未找到有效的模型或配置文件!\n模型路径:{comp_m_path}\n配置路径:{comp_c_path}"
+        logger.error(msg)
+    return gr.Textbox(value=msg)
+
+
+def compress_model(cfg_path, in_path, out_path):
+    subprocess.Popen(
+        "python compress_model.py" f" -c {cfg_path}" f" -i {in_path}", shell=True
+    )
+    msg = "到控制台中查看压缩结果"
+    logger.info(msg)
+    return gr.Textbox(value=msg)
+
+
+def kill_specific_process_linux(cmd):
+    try:
+        output = subprocess.check_output(["pgrep", "-f", cmd], text=True)
+        pids = output.strip().split("\n")
+
+        for pid in pids:
+            if pid:
+                logger.critical(f"终止进程: {pid}")
+                os.kill(int(pid), signal.SIGTERM)
+                # os.kill(int(pid), signal.SIGKILL)
+    except subprocess.CalledProcessError:
+        logger.error("没有找到匹配的进程。")
+    except Exception as e:
+        logger.error(f"发生错误: {e}")
+
+
+def kill_specific_process_windows(cmd):
+    try:
+        # 使用tasklist和findstr来找到匹配特定命令行模式的进程
+        output = subprocess.check_output(
+            f'tasklist /FO CSV /V | findstr /C:"{cmd}"', shell=True, text=True
+        )
+        lines = output.strip().split("\n")
+
+        for line in lines:
+            if line:
+                pid = line.split(",")[1].strip('"')
+                logger.critical(f"终止进程: {pid}")
+                subprocess.run(["taskkill", "/PID", pid, "/F"], shell=True)  # 强制终止
+    except subprocess.CalledProcessError:
+        logger.error(f"没有找到匹配的{cmd}进程。")
+    except Exception as e:
+        logger.error(f"发生错误: {e}")
+
+
+def stop_train_ms():
+    yml = load_yaml_data_in_fact()
+    train_port = yml["train_ms"]["env"]["MASTER_PORT"]
+    train_addr = yml["train_ms"]["env"]["MASTER_ADDR"]
+    if platform.system() == "Windows":
+        kill_specific_process_windows("torchrun")
+    else:
+        kill_specific_process_linux("torchrun")
+    url = f"env://{train_addr}:{train_port}"
+    msg = f"训练结束!\nMASTER_URL: {url}"
+    logger.critical(msg)
+    return gr.Textbox(value=msg)
+
+
+def stop_tensorboard():
+    if platform.system() == "Windows":
+        kill_specific_process_windows("tensorboard")
+    else:
+        kill_specific_process_linux("tensorboard")
+    msg = "关闭tensorboard!\n"
+    logger.critical(msg)
+    return gr.Textbox(value=msg)
+
+
+def stop_webui_infer():
+    yml = load_yaml_data_in_fact()
+    webui_port = yml["webui"]["port"]
+    if platform.system() == "Linux":
+        kill_specific_process_linux("python webui.py")
+    else:
+        kill_specific_process_windows("python webui.py")
+    msg = f"尝试终止推理进程，请到控制台查看情况\nport={webui_port}"
+    logger.critical(msg)
+    return gr.Textbox(value=msg)
+
+
+if __name__ == "__main__":
+    init_yml = load_yaml_data_in_fact()
+    with gr.Blocks(
+            title="Bert-VITS-2-v2.0-管理器",
+            theme=gr.themes.Soft(),
+            css=os.path.abspath("./css/custom.css"),
+    ) as app:
+        with gr.Row():
+            with gr.Tabs():
+                with gr.TabItem("首页"):
+                    gr.Markdown(
+                        """
+                        ## Bert-VITS2-v2.0 可视化界面
+                        #### Copyright/Powered by 怕吃辣滴辣子酱
+                        #### 许可： [AGPL 3.0 Licence](https://github.com/AnyaCoder/Bert-VITS2/blob/master/LICENSE)
+                        #### 请订阅我的频道:
+                        1. Bilibili： [spicysama](https://space.bilibili.com/47278440)
+                        2. github： [AnyaCoder](https://github.com/AnyaCoder)
+
+                        ### 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。
+                        ## 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)
+                        ## 感谢所有贡献者作出的努力
+                        <a href="https://github.com/AnyaCoder/Bert-VITS2/graphs/contributors">
+                          <img src="https://contrib.rocks/image?repo=AnyaCoder/Bert-VITS2" />
+                        </a>
+
+                        Made with [contrib.rocks](https://contrib.rocks).
+
+                    """
+                    )
+                with gr.TabItem("填入openi token"):
+                    with gr.Row():
+                        gr.Markdown(
+                            """
+                        ### 为了后续步骤中能够方便地自动下载模型，强烈推荐完成这一步骤!
+                        ### 去openi官网注册并登录后:
+                        ### [点击此处跳转到openi官网](https://openi.pcl.ac.cn/)
+                        ### , 点击右上角`个人头像`-> `设置` -> `应用`, 生成令牌(token)
+                        ### 复制token, 粘贴到下面的框框, 点击确认
+                        """
+                        )
+                    with gr.Row():
+                        openi_token_box = gr.Textbox(
+                            label="填入openi token", value=init_yml["openi_token"]
+                        )
+                    with gr.Row():
+                        openi_token_btn = gr.Button(value="确认填写", variant="primary")
+                    with gr.Row():
+                        openi_token_status = gr.Textbox(label="状态信息")
+
+                with gr.TabItem("模型检测"):
+                    CheckboxGroup_bert_models = gr.CheckboxGroup(
+                        label="检测bert模型状态",
+                        info="对应文件夹下必须有对应的模型文件(填入openi token后，则后续步骤中会自动下载)",
+                        choices=bert_model_paths,
+                        value=check_if_exists_model(bert_model_paths),
+                        interactive=False,
+                    )
+                    check_pth_btn1 = gr.Button(value="检查bert模型状态")
+                    CheckboxGroup_emo_models = gr.CheckboxGroup(
+                        label="检测emo模型状态",
+                        info="对应文件夹下必须有对应的模型文件",
+                        choices=emo_model_paths,
+                        value=check_if_exists_model(emo_model_paths),
+                        interactive=False,
+                    )
+                    check_pth_btn2 = gr.Button(value="检查emo模型状态")
+                with gr.TabItem("数据处理"):
+                    with gr.Row():
+                        dropdown_data_path = gr.Dropdown(
+                            label="选择数据集存放路径 (右侧的dataset_path)",
+                            info="详细说明可见右侧带注释的yaml文件",
+                            interactive=True,
+                            allow_custom_value=True,
+                            choices=[init_yml["dataset_path"]],
+                            value=init_yml["dataset_path"],
+                        )
+                    with gr.Row():
+                        data_path_btn = gr.Button(value="确认更改存放路径", variant="primary")
+                    with gr.Tabs():
+                        with gr.TabItem("1. 音频重采样"):
+                            with gr.Row():
+                                resample_in_box = gr.Textbox(
+                                    label="输入音频文件夹in_dir",
+                                    value=init_yml["resample"]["in_dir"],
+                                    lines=1,
+                                    interactive=True,
+                                )
+                                resample_out_box = gr.Textbox(
+                                    label="输出音频文件夹out_dir",
+                                    lines=1,
+                                    value=init_yml["resample"]["out_dir"],
+                                    interactive=True,
+                                )
+                            with gr.Row():
+                                dropdown_resample_sr = gr.Dropdown(
+                                    label="输出采样率(Hz)",
+                                    choices=["16000", "22050", "44100", "48000"],
+                                    value="44100",
+                                )
+                                slider_resample_nps = gr.Slider(
+                                    label="采样用的CPU核心数",
+                                    minimum=1,
+                                    maximum=64,
+                                    step=1,
+                                    value=2,
+                                )
+                            with gr.Row():
+                                resample_config_btn = gr.Button(
+                                    value="确认重采样配置",
+                                    variant="secondary",
+                                )
+                                resample_btn = gr.Button(
+                                    value="1. 音频重采样",
+                                    variant="primary",
+                                )
+                            with gr.Row():
+                                resample_status = gr.Textbox(
+                                    label="重采样结果",
+                                    placeholder="执行重采样后可查看",
+                                    lines=3,
+                                    interactive=False,
+                                )
+                        with gr.TabItem("2. 转写文本生成"):
+                            with gr.Row():
+                                dropdown_lang = gr.Dropdown(
+                                    label="选择语言",
+                                    info="ZH中文，JP日语，EN英语",
+                                    choices=["ZH", "JP", "EN"],
+                                    value="ZH",
+                                )
+                                slider_transcribe = gr.Slider(
+                                    label="转写进程数",
+                                    info="目的路径与前一节一致\n 重采样的输入路径",
+                                    minimum=1,
+                                    maximum=10,
+                                    step=1,
+                                    value=1,
+                                    interactive=True,
+                                )
+                                clean_txt_box = gr.Textbox(
+                                    label="非法字符集",
+                                    info="在此文本框内出现的字符都会被整行删除",
+                                    lines=1,
+                                    value="{}<>",
+                                    interactive=True,
+                                )
+                            with gr.Row():
+                                unclean_box = gr.Textbox(
+                                    label="未清洗的文本",
+                                    info="仅将.lab提取到这个文件里, 请保持txt格式",
+                                    lines=1,
+                                    value=os.path.splitext(
+                                        init_yml["preprocess_text"][
+                                            "transcription_path"
+                                        ]
+                                    )[0]
+                                          + ".txt",
+                                    interactive=True,
+                                )
+                                clean_box = gr.Textbox(
+                                    label="已清洗的文本",
+                                    info="将未清洗的文本做去除非法字符集处理后的文本",
+                                    lines=1,
+                                    value=init_yml["preprocess_text"][
+                                        "transcription_path"
+                                    ],
+                                    interactive=True,
+                                )
+                                char_name_box = gr.Textbox(
+                                    label="输入角色名",
+                                    info="区分说话人用",
+                                    lines=1,
+                                    placeholder="填入一个名称",
+                                    interactive=True,
+                                )
+                            with gr.Row():
+                                transcribe_btn = gr.Button(
+                                    value="2.1 转写文本", interactive=True
+                                )
+                                extract_list_btn = gr.Button(
+                                    value="2.2 合成filelist",
+                                )
+                                clean_trans_btn = gr.Button(value="2.3 清洗标注")
+                            with gr.Row():
+                                preprocess_status_box = gr.Textbox(label="标注状态")
+                        with gr.TabItem("3. 文本预处理"):
+                            with gr.Row():
+                                slider_val_per_spk = gr.Slider(
+                                    label="每个speaker的验证集条数",
+                                    info="TensorBoard里的eval音频展示条目",
+                                    minimum=1,
+                                    maximum=20,
+                                    step=1,
+                                    value=init_yml["preprocess_text"]["val_per_spk"],
+                                )
+                                slider_max_val_total = gr.Slider(
+                                    label="验证集最大条数",
+                                    info="多于此项的会被截断并放到训练集中",
+                                    minimum=8,
+                                    maximum=160,
+                                    step=8,
+                                    value=init_yml["preprocess_text"]["max_val_total"],
+                                )
+                            with gr.Row():
+                                dropdown_filelist_path = gr.Dropdown(
+                                    interactive=True,
+                                    label="输入filelist路径",
+                                    allow_custom_value=True,
+                                    choices=[
+                                        init_yml["preprocess_text"][
+                                            "transcription_path"
+                                        ]
+                                    ],
+                                    value=init_yml["preprocess_text"][
+                                        "transcription_path"
+                                    ],
+                                )
+                                preprocess_config_box = gr.Textbox(
+                                    label="预处理配置文件路径",
+                                    value=init_yml["preprocess_text"]["config_path"],
+                                )
+                            with gr.Row():
+                                preprocess_config_btn = gr.Button(value="更新预处理配置文件")
+                                preprocess_text_btn = gr.Button(
+                                    value="标注文本预处理", variant="primary"
+                                )
+                            with gr.Row():
+                                label_status = gr.Textbox(label="转写状态")
+                        with gr.TabItem("4. bert_gen"):
+                            with gr.Row():
+                                bert_dataset_box = gr.Textbox(
+                                    label="数据集存放路径",
+                                    text_align="right",
+                                    value=str(init_yml["dataset_path"]).rstrip("/"),
+                                    lines=1,
+                                    interactive=False,
+                                    scale=10,
+                                )
+                                gr.Markdown(
+                                    """
+                                    <br></br>
+                                    ## +
+                                """
+                                )
+                                bert_config_box = gr.Textbox(
+                                    label="bert_gen配置文件路径",
+                                    text_align="left",
+                                    value=init_yml["bert_gen"]["config_path"],
+                                    lines=1,
+                                    interactive=True,
+                                    scale=10,
+                                )
+                            with gr.Row():
+                                slider_bert_nps = gr.Slider(
+                                    label="bert_gen并行处理数",
+                                    minimum=1,
+                                    maximum=12,
+                                    step=1,
+                                    value=init_yml["bert_gen"]["num_processes"],
+                                )
+                                dropdown_bert_dev = gr.Dropdown(
+                                    label="bert_gen处理设备",
+                                    choices=["cuda", "cpu"],
+                                    value=init_yml["bert_gen"]["device"],
+                                )
+                                radio_bert_multi = gr.Radio(
+                                    label="使用多卡推理", choices=[True, False], value=False
+                                )
+                            with gr.Row():
+                                bert_config_btn = gr.Button(value="确认更改bert配置项")
+                                bert_gen_btn = gr.Button(
+                                    value="Go! Bert Gen!", variant="primary"
+                                )
+                            with gr.Row():
+                                bert_status = gr.Textbox(label="状态信息")
+                        with gr.TabItem("5. emo_gen"):
+                            with gr.Row():
+                                emo_config_box = gr.Textbox(
+                                    label="emo_gen配置文件路径",
+                                    info="找一找你的config.json路径,相对于数据集路径",
+                                    value=init_yml["emo_gen"]["config_path"],
+                                    lines=1,
+                                    interactive=True,
+                                    scale=10,
+                                )
+                            with gr.Row():
+                                slider_emo_nps = gr.Slider(
+                                    label="emo_gen并行处理数",
+                                    info="最好预留2个以上的核数空闲，防卡死",
+                                    minimum=1,
+                                    maximum=32,
+                                    step=1,
+                                    value=init_yml["emo_gen"]["num_processes"],
+                                )
+                                dropdown_emo_device = gr.Dropdown(
+                                    label="emo_gen使用设备",
+                                    info="可选cpu或cuda",
+                                    choices=["cpu", "cuda"],
+                                    value="cuda",
+                                )
+                            with gr.Row():
+                                emo_config_btn = gr.Button(value="更新emo配置")
+                                emo_gen_btn = gr.Button(
+                                    value="Emo Gen!", variant="primary"
+                                )
+                            with gr.Row():
+                                emo_status = gr.Textbox(label="状态信息")
+
+                with gr.TabItem("训练界面"):
+                    with gr.Tabs():
+                        with gr.TabItem("训练配置文件路径"):
+                            with gr.Row():
+                                train_dataset_box_1 = gr.Textbox(
+                                    label="数据集存放路径",
+                                    text_align="right",
+                                    value=str(init_yml["dataset_path"]).rstrip("/"),
+                                    lines=1,
+                                    interactive=False,
+                                    scale=20,
+                                )
+                                gr.Markdown(
+                                    """
+                                    <br></br>
+                                    ## +
+                                """
+                                )
+                                train_config_box = gr.Textbox(
+                                    label="train_ms配置文件路径",
+                                    text_align="left",
+                                    value=init_yml["train_ms"]["config_path"],
+                                    lines=1,
+                                    interactive=True,
+                                    scale=20,
+                                )
+                            with gr.Row():
+                                train_dataset_box_2 = gr.Textbox(
+                                    label="数据集存放路径",
+                                    text_align="right",
+                                    value=str(init_yml["dataset_path"]).rstrip("/"),
+                                    lines=1,
+                                    interactive=False,
+                                    scale=20,
+                                )
+                                gr.Markdown(
+                                    """
+                                    <br></br>
+                                    ## +
+                                """
+                                )
+                                train_model_box = gr.Textbox(
+                                    label="train_ms模型文件夹路径",
+                                    value=init_yml["train_ms"]["model"],
+                                    lines=1,
+                                    interactive=True,
+                                    scale=20,
+                                )
+                            with gr.Row():
+                                train_ms_path_btn = gr.Button(value="更改训练路径配置")
+                            CheckboxGroup_train_models = check_base_models()
+                            check_pth_btn3 = gr.Button(value="检查训练底模状态")
+                        with gr.TabItem("训练参数设置"):
+                            with gr.Row():
+                                slider_batch_size = gr.Slider(
+                                    minimum=1,
+                                    maximum=40,
+                                    value=4,
+                                    step=1,
+                                    label="batch_size 批处理大小",
+                                )
+                                slider_keep_ckpts = gr.Slider(
+                                    minimum=1,
+                                    maximum=20,
+                                    value=5,
+                                    step=1,
+                                    label="keep_ckpts 最多保存n个最新模型",
+                                    info="若超过，则删除最早的"
+                                )
+                            with gr.Row():
+                                slider_log_interval = gr.Slider(
+                                    minimum=50,
+                                    maximum=3000,
+                                    value=200,
+                                    step=50,
+                                    label="log_interval 打印日志步数间隔",
+                                )
+                                slider_eval_interval = gr.Slider(
+                                    minimum=100,
+                                    maximum=5000,
+                                    value=1000,
+                                    step=50,
+                                    label="eval_interval 保存模型步数间隔",
+                                )
+                            with gr.Row():
+                                slider_epochs = gr.Slider(
+                                    minimum=50,
+                                    maximum=2000,
+                                    value=100,
+                                    step=50,
+                                    label="epochs 训练轮数",
+                                )
+                                slider_lr = gr.Slider(
+                                    minimum=0.0001,
+                                    maximum=0.0010,
+                                    value=0.0003,
+                                    step=0.0001,
+                                    label="learning_rate 初始学习率",
+                                )
+                            with gr.Row():
+                                dropdown_version = gr.Dropdown(
+                                    label="模型版本选择",
+                                    info="推荐使用最新版底模和版本训练",
+                                    choices=["2.1", "2.0.2", "2.0.1", "2.0", "1.1.1", "1.1.0", "1.0.1"],
+                                    value="2.1",
+                                )
+                            with gr.Row():
+                                train_ms_load_btn = gr.Button(
+                                    value="加载训练参数配置", variant="primary"
+                                )
+                                train_ms_param_btn = gr.Button(
+                                    value="更改训练参数配置", variant="primary"
+                                )
+                            with gr.Row():
+                                train_btn = gr.Button(
+                                    value="3.1 点击开始训练", variant="primary"
+                                )
+                                train_btn_2 = gr.Button(
+                                    value="3.2 继续训练", variant="primary"
+                                )
+                                stop_train_btn = gr.Button(
+                                    value="终止训练", variant="secondary"
+                                )
+                            with gr.Row():
+                                train_output_box = gr.Textbox(
+                                    label="状态信息", lines=1, autoscroll=True
+                                )
+                        with gr.TabItem("TensorBoard"):
+                            with gr.Row():
+                                gr.Markdown(
+                                    """
+                                    ### Tensorboard的logdir 默认为训练的models路径
+                                    ### 请在前一节 `训练配置文件路径` 查看
+                                """
+                                )
+                            with gr.Row():
+                                open_tb_btn = gr.Button("开启Tensorboard")
+                                stop_tb_btn = gr.Button("关闭Tensorboard")
+                            with gr.Row():
+                                tb_output_box = gr.Textbox(
+                                    label="状态信息", lines=1, autoscroll=True
+                                )
+                with gr.TabItem("推理界面"):
+                    with gr.Tabs():
+                        with gr.TabItem("模型选择"):
+                            with gr.Row():
+                                dropdown_infer_model = gr.Dropdown(
+                                    label="选择推理模型",
+                                    info="默认选择预处理阶段配置的文件夹内容; 也可以自己输入路径。",
+                                    interactive=True,
+                                    allow_custom_value=True,
+                                )
+                                dropdown_infer_config = gr.Dropdown(
+                                    label="选择配置文件",
+                                    info="默认选择预处理阶段配置的文件夹内容; 也可以自己输入路径。",
+                                    interactive=True,
+                                    allow_custom_value=True,
+                                )
+                            with gr.Row():
+                                dropdown_model_fresh_btn = gr.Button(value="刷新推理模型列表")
+                            with gr.Row():
+                                webui_port_box = gr.Textbox(
+                                    label="WebUI推理的端口号",
+                                    placeholder="范围:[0, 65535]",
+                                    max_lines=1,
+                                    lines=1,
+                                    value=init_yml["webui"]["port"],
+                                    interactive=True,
+                                )
+                                infer_ver_box = gr.Dropdown(
+                                    label="更改推理版本",
+                                    info="已经实现兼容推理，请选择合适的版本",
+                                    choices=["2.1", "2.0.2", "2.0.1", "2.0", "1.1.1", "1.1.0", "1.0.1"],
+                                    value="2.1",
+                                )
+                            with gr.Row():
+                                radio_webui_share = gr.Radio(
+                                    label="公开",
+                                    info="是否公开部署，对外网开放",
+                                    choices=[True, False],
+                                    value=init_yml["webui"]["share"],
+                                )
+                                radio_webui_debug = gr.Radio(
+                                    label="调试模式",
+                                    info="是否开启debug模式",
+                                    choices=[True, False],
+                                    value=init_yml["webui"]["debug"],
+                                )
+                            with gr.Row():
+                                infer_config_btn = gr.Button(value="更新推理配置文件")
+                                stop_infer_btn = gr.Button(value="结束WebUI推理")
+                            with gr.Row():
+                                infer_webui_btn = gr.Button(
+                                    value="开启WebUI推理", variant="primary"
+                                )
+                            with gr.Row():
+                                infer_webui_box = gr.Textbox(
+                                    label="提示信息", interactive=False
+                                )
+
+                        with gr.TabItem("模型压缩"):
+                            with gr.Row():
+                                compress_config = gr.Textbox(
+                                    label="压缩配置文件", info="模型对应的config.json"
+                                )
+                            with gr.Row():
+                                compress_input_path = gr.Textbox(
+                                    label="待压缩模型路径", info="所谓的模型是：G_{步数}.pth"
+                                )
+                            with gr.Row():
+                                compress_output_path = gr.Textbox(
+                                    label="输出模型路径",
+                                    info="输出为：G_{步数}_release.pth",
+                                    value="在待压缩模型路径的同一文件夹下",
+                                    interactive=False,
+                                )
+                            with gr.Row():
+                                compress_btn = gr.Button(
+                                    value="压缩模型", variant="primary"
+                                )
+                            with gr.Row():
+                                compress_status = gr.Textbox(label="状态信息")
+            with gr.Tabs():
+                with gr.TabItem("yaml配置文件状态"):
+                    code_config_yml = gr.Code(
+                        interactive=False,
+                        label=yml_config,
+                        value=load_yaml_data_in_raw(),
+                        language="yaml",
+                        elem_id="yml_code",
+                    )
+                with gr.TabItem("带注释的yaml配置文件"):
+                    code_default_yml = gr.Code(
+                        interactive=False,
+                        label=default_yaml_path,
+                        value=load_yaml_data_in_raw(default_yaml_path),
+                        language="yaml",
+                        elem_id="yml_code",
+                    )
+                with gr.TabItem("训练的json配置文件"):
+                    code_train_config_json = gr.Code(
+                        interactive=False,
+                        label=default_config_path,
+                        value=load_json_data_in_raw(default_config_path),
+                        language="json",
+                        elem_id="json_code",
+                    )
+                with gr.TabItem("推理的json配置文件"):
+                    code_infer_config_json = gr.Code(
+                        interactive=False,
+                        label=default_config_path,
+                        value=load_json_data_in_raw(default_config_path),
+                        language="json",
+                        elem_id="json_code",
+                    )
+                with gr.TabItem("其他状态"):
+                    code_gpu_json = gr.Code(
+                        label="本机资源使用情况",
+                        interactive=False,
+                        value=get_status(),
+                        language="json",
+                        elem_id="gpu_code",
+                    )
+                    gpu_json_btn = gr.Button(value="刷新本机状态")
+
+        openi_token_btn.click(
+            fn=fill_openi_token,
+            inputs=[openi_token_box],
+            outputs=[openi_token_status, code_config_yml],
+        )
+        check_pth_btn1.click(
+            fn=check_bert_models, inputs=[], outputs=[CheckboxGroup_bert_models]
+        )
+        check_pth_btn2.click(
+            fn=check_emo_models, inputs=[], outputs=[CheckboxGroup_emo_models]
+        )
+        check_pth_btn3.click(
+            fn=check_base_models, inputs=[], outputs=[CheckboxGroup_train_models]
+        )
+        data_path_btn.click(
+            fn=modify_data_path,
+            inputs=[dropdown_data_path],
+            outputs=[
+                dropdown_data_path,
+                bert_dataset_box,
+                train_dataset_box_1,
+                train_dataset_box_2,
+                code_config_yml,
+                CheckboxGroup_train_models,
+            ],
+        )
+        preprocess_config_btn.click(
+            fn=modify_preprocess_param,
+            inputs=[
+                dropdown_filelist_path,
+                preprocess_config_box,
+                slider_val_per_spk,
+                slider_max_val_total,
+            ],
+            outputs=[dropdown_filelist_path, code_config_yml],
+        )
+        preprocess_text_btn.click(
+            fn=do_preprocess_text, inputs=[], outputs=[label_status]
+        )
+        resample_config_btn.click(
+            fn=modify_resample_path,
+            inputs=[resample_in_box, resample_out_box, dropdown_resample_sr],
+            outputs=[
+                resample_in_box,
+                resample_out_box,
+                resample_status,
+                dropdown_resample_sr,
+                code_config_yml,
+            ],
+        )
+        resample_btn.click(
+            fn=do_resample, inputs=[slider_resample_nps], outputs=[resample_status]
+        )
+        transcribe_btn.click(
+            fn=do_transcript,
+            inputs=[dropdown_lang, slider_transcribe],
+            outputs=[preprocess_status_box],
+        )
+        extract_list_btn.click(
+            fn=do_extract,
+            inputs=[resample_in_box, dropdown_lang, unclean_box, char_name_box],
+            outputs=[preprocess_status_box],
+        )
+        clean_trans_btn.click(
+            fn=do_clean_list,
+            inputs=[clean_txt_box, unclean_box, clean_box],
+            outputs=[preprocess_status_box],
+        )
+        bert_config_btn.click(
+            fn=modify_bert_config,
+            inputs=[
+                bert_config_box,
+                slider_bert_nps,
+                dropdown_bert_dev,
+                radio_bert_multi,
+            ],
+            outputs=[
+                bert_config_box,
+                slider_bert_nps,
+                dropdown_bert_dev,
+                radio_bert_multi,
+                code_config_yml,
+            ],
+        )
+        bert_gen_btn.click(fn=do_bert_gen, inputs=[], outputs=[bert_status])
+        emo_config_btn.click(
+            fn=modify_emo_gen,
+            inputs=[emo_config_box, slider_emo_nps, dropdown_emo_device],
+            outputs=[emo_status, code_config_yml],
+        )
+        emo_gen_btn.click(fn=do_emo_gen, inputs=[], outputs=[emo_status])
+        train_ms_load_btn.click(
+            fn=load_train_param,
+            inputs=[train_config_box],
+            outputs=[
+                train_output_box,
+                code_train_config_json,
+                slider_batch_size,
+                slider_keep_ckpts,
+                slider_log_interval,
+                slider_eval_interval,
+                slider_epochs,
+                slider_lr,
+                dropdown_version,
+            ],
+        )
+        train_ms_path_btn.click(
+            fn=modify_train_path,
+            inputs=[train_model_box, train_config_box],
+            outputs=[
+                train_model_box,
+                train_config_box,
+                code_config_yml,
+                CheckboxGroup_train_models,
+            ],
+        )
+        train_ms_param_btn.click(
+            fn=modify_train_param,
+            inputs=[
+                slider_batch_size,
+                slider_keep_ckpts,
+                slider_log_interval,
+                slider_eval_interval,
+                slider_epochs,
+                slider_lr,
+                dropdown_version,
+            ],
+            outputs=[train_output_box, code_train_config_json],
+        )
+        train_btn.click(fn=do_my_train, inputs=[], outputs=[train_output_box])
+        train_btn_2.click(fn=do_my_train, inputs=[], outputs=[train_output_box])
+        stop_train_btn.click(fn=stop_train_ms, inputs=[], outputs=[train_output_box])
+        open_tb_btn.click(fn=do_tensorboard, inputs=[], outputs=[tb_output_box])
+        stop_tb_btn.click(fn=stop_tensorboard, inputs=[], outputs=[tb_output_box])
+        dropdown_model_fresh_btn.click(
+            fn=list_infer_models,
+            inputs=[],
+            outputs=[dropdown_infer_model, dropdown_infer_config],
+        )
+        infer_config_btn.click(
+            fn=modify_infer_param,
+            inputs=[
+                dropdown_infer_model,
+                dropdown_infer_config,
+                webui_port_box,
+                radio_webui_share,
+                radio_webui_debug,
+                infer_ver_box,
+            ],
+            outputs=[infer_webui_box, code_config_yml, code_infer_config_json],
+        )
+        infer_webui_btn.click(fn=do_webui_infer, inputs=[], outputs=[infer_webui_box])
+        compress_btn.click(
+            fn=compress_model,
+            inputs=[compress_config, compress_input_path, compress_output_path],
+            outputs=[compress_status],
+        )
+        stop_infer_btn.click(fn=stop_webui_infer, inputs=[], outputs=[infer_webui_box])
+        gpu_json_btn.click(fn=get_gpu_status, inputs=[], outputs=[code_gpu_json])
+    os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
+    webbrowser.open("http://127.0.0.1:6006")
+    app.launch(share=False, server_port=6006)

asr_transcript.py

@@ -0,0 +1,102 @@
+import argparse
+import concurrent.futures
+import os
+
+from loguru import logger
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from tqdm import tqdm
+
+os.environ["MODELSCOPE_CACHE"] = "./"
+
+
+def transcribe_worker(file_path: str, inference_pipeline, language):
+    """
+    Worker function for transcribing a segment of an audio file.
+    """
+    rec_result = inference_pipeline(audio_in=file_path)
+    text = str(rec_result.get("text", "")).strip()
+    text_without_spaces = text.replace(" ", "")
+    logger.info(file_path)
+    if language != "EN":
+        logger.info("text: " + text_without_spaces)
+        return text_without_spaces
+    else:
+        logger.info("text: " + text)
+        return text
+
+
+def transcribe_folder_parallel(folder_path, language, max_workers=4):
+    """
+    Transcribe all .wav files in the given folder using ThreadPoolExecutor.
+    """
+    logger.critical(f"parallel transcribe: {folder_path}|{language}|{max_workers}")
+    if language == "JP":
+        workers = [
+            pipeline(
+                task=Tasks.auto_speech_recognition,
+                model="damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline",
+            )
+            for _ in range(max_workers)
+        ]
+
+    elif language == "ZH":
+        workers = [
+            pipeline(
+                task=Tasks.auto_speech_recognition,
+                model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+                model_revision="v1.2.4",
+            )
+            for _ in range(max_workers)
+        ]
+    else:
+        workers = [
+            pipeline(
+                task=Tasks.auto_speech_recognition,
+                model="damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline",
+            )
+            for _ in range(max_workers)
+        ]
+
+    file_paths = []
+    langs = []
+    for root, _, files in os.walk(folder_path):
+        for file in files:
+            if file.lower().endswith(".wav"):
+                file_path = os.path.join(root, file)
+                lab_file_path = os.path.splitext(file_path)[0] + ".lab"
+                file_paths.append(file_path)
+                langs.append(language)
+
+    all_workers = (
+            workers * (len(file_paths) // max_workers)
+            + workers[: len(file_paths) % max_workers]
+    )
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        for i in tqdm(range(0, len(file_paths), max_workers), desc="转写进度: "):
+            l, r = i, min(i + max_workers, len(file_paths))
+            transcriptions = list(
+                executor.map(
+                    transcribe_worker, file_paths[l:r], all_workers[l:r], langs[l:r]
+                )
+            )
+            for file_path, transcription in zip(file_paths[l:r], transcriptions):
+                if transcription:
+                    lab_file_path = os.path.splitext(file_path)[0] + ".lab"
+                    with open(lab_file_path, "w", encoding="utf-8") as lab_file:
+                        lab_file.write(transcription)
+    logger.critical("已经将wav文件转写为同名的.lab文件")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-f", "--filepath", default="./raw/lzy_zh", help="path of your model"
+    )
+    parser.add_argument("-l", "--language", default="ZH", help="language")
+    parser.add_argument("-w", "--workers", default="1", help="trans workers")
+    args = parser.parse_args()
+
+    transcribe_folder_parallel(args.filepath, args.language, int(args.workers))
+    print("转写结束！")

attentions.py

@@ -0,0 +1,464 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
+            device=x.device, dtype=x.dtype
+        )
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # pad along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x

attentions_onnx.py

@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                logging.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x

bert/bert-base-japanese-v3/.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/bert-base-japanese-v3/README.md

@@ -0,0 +1,53 @@
+---
+license: apache-2.0
+datasets:
+- cc100
+- wikipedia
+language:
+- ja
+widget:
+- text: 東北大学で[MASK]の研究をしています。
+---
+
+# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
+
+This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
+
+This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
+Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
+
+The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
+
+## Model architecture
+
+The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
+
+## Training Data
+
+The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
+For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
+The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
+
+For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
+
+## Tokenization
+
+The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
+The vocabulary size is 32768.
+
+We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
+
+## Training
+
+We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
+For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
+
+For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
+
+## Licenses
+
+The pretrained models are distributed under the Apache License 2.0.
+
+## Acknowledgments
+
+This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

bert/bert-base-japanese-v3/config.json

@@ -0,0 +1,19 @@
+{
+    "architectures": [
+        "BertForPreTraining"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-12,
+    "max_position_embeddings": 512,
+    "model_type": "bert",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 32768
+}

bert/bert-base-japanese-v3/tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+    "tokenizer_class": "BertJapaneseTokenizer",
+    "model_max_length": 512,
+    "do_lower_case": false,
+    "word_tokenizer_type": "mecab",
+    "subword_tokenizer_type": "wordpiece",
+    "mecab_kwargs": {
+        "mecab_dic": "unidic_lite"
+    }
+}

bert/bert-large-japanese-v2/.gitattributes

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+*.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/bert-large-japanese-v2/README.md

@@ -0,0 +1,53 @@
+---
+license: apache-2.0
+datasets:
+- cc100
+- wikipedia
+language:
+- ja
+widget:
+- text: 東北大学で[MASK]の研究をしています。
+---
+
+# BERT large Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
+
+This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
+
+This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
+Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
+
+The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
+
+## Model architecture
+
+The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.
+
+## Training Data
+
+The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
+For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
+The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
+
+For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
+
+## Tokenization
+
+The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
+The vocabulary size is 32768.
+
+We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
+
+## Training
+
+We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
+For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
+
+For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
+
+## Licenses
+
+The pretrained models are distributed under the Apache License 2.0.
+
+## Acknowledgments
+
+This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

bert/bert-large-japanese-v2/config.json

@@ -0,0 +1,19 @@
+{
+    "architectures": [
+        "BertForPreTraining"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "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,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 32768
+}

bert/bert-large-japanese-v2/tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+    "tokenizer_class": "BertJapaneseTokenizer",
+    "model_max_length": 512,
+    "do_lower_case": false,
+    "word_tokenizer_type": "mecab",
+    "subword_tokenizer_type": "wordpiece",
+    "mecab_kwargs": {
+        "mecab_dic": "unidic_lite"
+    }
+}

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
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+*.tar.gz filter=lfs diff=lfs merge=lfs -text
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+*.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/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

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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/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-v2-large-japanese/.gitattributes

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

@@ -0,0 +1,111 @@
+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+  - deberta
+  - deberta-v2
+  - fill-mask
+datasets:
+  - wikipedia
+  - cc100
+  - oscar
+metrics:
+  - accuracy
+mask_token: "[MASK]"
+widget:
+  - text: "京都 大学 で 自然 言語 処理 を [MASK] する 。"
+---
+
+# Model Card for Japanese 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.
+
+## 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')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese')
+
+sentence = '京都 大学 で 自然 言語 処理 を [MASK] する 。'  # input should be segmented into words by Juman++ in advance
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in
+advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each
+word is tokenized into subwords 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++](https://github.com/ku-nlp/jumanpp).
+Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC))
+and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
+
+We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model
+using [transformers](https://github.com/huggingface/transformers) library.
+The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 18
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 2,304
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup
+- training_steps: 300,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.799.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Fine-tuning on NLU tasks
+
+We fine-tuned the following models and evaluated them on the dev set of JGLUE.
+We tuned learning rate and training epochs for each model and task
+following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
+
+| Model                         | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
+|-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------|
+| Waseda RoBERTa base           | 0.965       | 0.913        | 0.876         | 0.905    | 0.853     | 0.916     | 0.853      |
+| Waseda RoBERTa large (seq512) | 0.969       | 0.925        | 0.890         | 0.928    | 0.910     | 0.955     | 0.900      |
+| LUKE Japanese base*           | 0.965       | 0.916        | 0.877         | 0.912    | -         | -         | 0.842      |
+| LUKE Japanese large*          | 0.965       | 0.932        | 0.902         | 0.927    | -         | -         | 0.893      |
+| DeBERTaV2 base                | 0.970       | 0.922        | 0.886         | 0.922    | 0.899     | 0.951     | 0.873      |
+| DeBERTaV2 large               | 0.968       | 0.925        | 0.892         | 0.924    | 0.912     | 0.959     | 0.890      |
+
+*The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke).
+
+## 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/config.json

@@ -0,0 +1,38 @@
+{
+  "_name_or_path": "configs/deberta_v2_large.json",
+  "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": "float32",
+  "transformers_version": "4.23.1",
+  "type_vocab_size": 0,
+  "vocab_size": 32000
+}

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

@@ -0,0 +1,9 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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

@@ -0,0 +1,15 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "keep_accents": true,
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": null,
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]"
+}

bert/deberta-v3-large/.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

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/tokenizer_config.json

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

bert_gen.py

@@ -0,0 +1,74 @@
+import argparse
+from multiprocessing import Pool, cpu_count
+
+import torch
+import torch.multiprocessing as mp
+from tqdm import tqdm
+
+import commons
+import utils
+from config import config
+from text import cleaned_text_to_sequence, get_bert
+
+
+def process_line(line):
+    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)
+
+    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())
+    if len(lines) != 0:
+        num_processes = min(args.num_processes, cpu_count())
+        with Pool(processes=num_processes) as pool:
+            for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)):
+                pass
+
+    print(f"bert生成完毕!, 共有{len(lines)}个bert.pt生成!")

clean_list.py

@@ -0,0 +1,48 @@
+import argparse
+import shutil
+from tempfile import NamedTemporaryFile
+
+from loguru import logger
+
+
+def remove_chars_from_file(chars_to_remove, input_file, output_file):
+    rm_cnt = 0
+    with open(input_file, "r", encoding="utf-8") as f_in, NamedTemporaryFile(
+            "w", delete=False, encoding="utf-8"
+    ) as f_tmp:
+        for line in f_in:
+            if any(char in line for char in chars_to_remove):
+                logger.info(f"删除了这一行:\n {line.strip()}")
+                rm_cnt += 1
+            else:
+                f_tmp.write(line)
+
+    shutil.move(f_tmp.name, output_file)
+    logger.critical(f"总计移除了: {rm_cnt} 行")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Remove lines from a file containing specified characters."
+    )
+
+    parser.add_argument(
+        "-c",
+        "--chars",
+        type=str,
+        required=True,
+        help="String of characters. If a line contains any of these characters, it will be removed.",
+    )
+    parser.add_argument(
+        "-i", "--input", type=str, required=True, help="Path to the input file."
+    )
+    parser.add_argument(
+        "-o", "--output", type=str, required=True, help="Path to the output file."
+    )
+
+    args = parser.parse_args()
+
+    # Setting up basic logging configuration for loguru
+    logger.add("removed_lines.log", rotation="1 MB")
+
+    remove_chars_from_file(args.chars, args.input, args.output)

commons.py

@@ -0,0 +1,166 @@
+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):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        if idx_str < 0:
+            i1 = x.size(2) + idx_str
+            r1 = x[i, :, i1:]
+            r2 = x[i, :, :idx_end]
+            ret[i] = torch.cat([r1, r2], dim=1)
+        else:
+            ret[i] = x[i, :, idx_str:idx_end]
+    return ret
+
+
+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 = x_lengths - segment_size + 1
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+        num_timescales - 1
+    )
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm