diff --git a/.env.webui b/.env.webui new file mode 100644 index 0000000000000000000000000000000000000000..eda09b0c62ad22c20b3e967a79dfb87f84b077b2 --- /dev/null +++ b/.env.webui @@ -0,0 +1,17 @@ +# Server configuration +SERVER_NAME= +SERVER_PORT= + +# Gradio interface options +SHARE= +DEBUG= +AUTH= + +# Model inference options +HALF= +OFF_TQDM=True + +# Text-to-Speech (TTS) configuration +TTS_MAX_LEN=1000 +SSML_MAX_LEN=3000 +MAX_BATCH_SIZE=12 diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.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 +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..45c1d22ae3138a566228e9bb4d06f1ab19ebbceb --- /dev/null +++ b/.gitignore @@ -0,0 +1,165 @@ +# 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/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# 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. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c90ae7105d27af76e397e7f451f57cb8ab0ad0dc --- /dev/null +++ b/README.md @@ -0,0 +1,82 @@ +--- +title: ChatTTS Forge +emoji: 💬 +colorFrom: yellow +colorTo: purple +sdk: gradio +app_file: webui.py +pinned: true +license: agpl-3.0 +--- + +# 🗣️ ChatTTS-Forge +![GitHub Repo stars](https://img.shields.io/github/stars/lenML/ChatTTS-Forge) + +这个Space展示了强大的[ChatTTS-Forge](https://github.com/lenML/ChatTTS-Forge)项目，基于 ChatTTS 开源模型，生成丰富的文本转语音内容。 + +欢迎随时体验我们的项目，或者复制以便无需排队运行你自己的实例！完整文档请访问我们的[GitHub仓库](https://github.com/lenML/ChatTTS-Forge)。 + +🔎 想了解更多关于SSML语法的信息，请查看我们的[SSML指南](https://github.com/lenML/ChatTTS-Forge/blob/main/docs/SSML.md)。 + +🔨 有关API部署的信息，请参考GitHub上的[部署部分](https://github.com/lenML/ChatTTS-Forge#本地部署)。 + +🚀 如果您想使用 Colab 进行部署，点击一键部署：[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lenML/ChatTTS-Forge/blob/main/colab.ipynb) + +# en +This Space demonstrates the powerful [ChatTTS-Forge](https://github.com/lenML/ChatTTS-Forge) project, based on the open-source ChatTTS model, which generates rich text-to-speech content. + +Feel free to explore our project, or duplicate it to run your own instances without a queue! For the full documentation, please visit our [GitHub repository](https://github.com/lenML/ChatTTS-Forge). + +🔎 To learn more about SSML syntax, check out our [SSML guide](https://github.com/lenML/ChatTTS-Forge/blob/main/docs/SSML.md). + +🔨 For information on deploying the API, please refer to the [deployment section](https://github.com/lenML/ChatTTS-Forge#本地部署) on GitHub. + +🚀 If you want to deploy using Colab, click for one-click deployment: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lenML/ChatTTS-Forge/blob/main/colab.ipynb) + +# styles (Experimental) + +由于自回归模型的特性，我们可以对生成过程进行干扰和控制，目前，仅为实验性质功能 + +# styles table + +文件 `./data/styles.csv` 中包含所有风格，下面是具体的设定 + +> 风格名带有 `_p` 结尾的是注入上下文的风格，可能导致质量下降但是控制更强一点 + +| 风格 | 说明 | +| ------------------------- | -------------------------------------------------------------------------------------------------- | +| advertisement_upbeat | 用兴奋和精力充沛的语气推广产品或服务。 | +| affectionate | 以较高的音调和音量表达温暖而亲切的语气。说话者处于吸引听众注意力的状态。说话者的个性往往是讨喜的。 | +| angry | 表达生气和厌恶的语气。 | +| assistant | 数字助理用的是热情而轻松的语气。 | +| calm | 以沉着冷静的态度说话。语气、音调和韵律与其他语音类型相比要统一得多。 | +| chat | 表达轻松随意的语气。 | +| cheerful | 表达积极愉快的语气。 | +| customerservice | 以友好热情的语气为客户提供支持。 | +| depressed | 调低音调和音量来表达忧郁、沮丧的语气。 | +| disgruntled | 表达轻蔑和抱怨的语气。这种情绪的语音表现出不悦和蔑视。 | +| documentary-narration | 用一种轻松、感兴趣和信息丰富的风格讲述纪录片，适合配音纪录片、专家评论和类似内容。 | +| embarrassed | 在说话者感到不舒适时表达不确定、犹豫的语气。 | +| empathetic | 表达关心和理解。 | +| envious | 当你渴望别人拥有的东西时，表达一种钦佩的语气。 | +| excited | 表达乐观和充满希望的语气。似乎发生了一些美好的事情，说话人对此满意。 | +| fearful | 以较高的音调、较高的音量和较快的语速来表达恐惧、紧张的语气。说话人处于紧张和不安的状态。 | +| friendly | 表达一种愉快、怡人且温暖的语气。听起来很真诚且满怀关切。 | +| gentle | 以较低的音调和音量表达温和、礼貌和愉快的语气。 | +| hopeful | 表达一种温暖且渴望的语气。听起来像是会有好事发生在说话人身上。 | +| lyrical | 以优美又带感伤的方式表达情感。 | +| narration-professional | 以专业、客观的语气朗读内容。 | +| narration-relaxed | 为内容阅读表达一种舒缓而悦耳的语气。 | +| newscast | 以正式专业的语气叙述新闻。 | +| newscast-casual | 以通用、随意的语气发布一般新闻。 | +| newscast-formal | 以正式、自信和权威的语气发布新闻。 | +| poetry-reading | 在读诗时表达出带情感和节奏的语气。 | +| sad | 表达悲伤语气。 | +| serious | 表达严肃和命令的语气。说话者的声音通常比较僵硬，节奏也不那么轻松。 | +| shouting | 表达一种听起来好像声音在远处或在另一个地方的语气，努力让别人听清楚。 | +| sports_commentary | 表达一种既轻松又感兴趣的语气，用于播报体育赛事。 | +| sports_commentary_excited | 用快速且充满活力的语气播报体育赛事精彩瞬间。 | +| whispering | 表达一种柔和的语气，试图发出安静而柔和的声音。 | +| terrified | 表达一种害怕的语气，语速快且声音颤抖。听起来说话人处于不稳定的疯狂状态。 | +| unfriendly | 表达一种冷淡无情的语气。 | + diff --git a/data/load_json_spk.py b/data/load_json_spk.py new file mode 100644 index 0000000000000000000000000000000000000000..164e7f1e8d823080696917edfecba0fab5442c10 --- /dev/null +++ b/data/load_json_spk.py @@ -0,0 +1,20 @@ +import json +from modules.speaker import speaker_mgr + + +data = json.load(open("./data/slct_voice_240605.json", "r")) + +print("load speakers: ", len(data)) + +for id, spk in data.items(): + print(id, spk["describe"]) + name = f"spk_{id}" + describe = spk["describe"] + gender = spk["gender"] + speaker_mgr.create_speaker_from_tensor( + filename=name, + name=describe, + describe=describe, + gender=gender, + tensor=spk["tensor"], + ) diff --git a/data/slct_voice_240605.json b/data/slct_voice_240605.json new file mode 100644 index 0000000000000000000000000000000000000000..77c0e0c3516364ca625a20cbaa5caf53b6993efb --- /dev/null +++ b/data/slct_voice_240605.json @@ -0,0 +1,17030 @@ +{ + "1": { + "gender": "female", + "describe": "声音有点像陈一发", + "tensor": [ + 4.920174598693848, + -1.869746208190918, + -1.7132017612457275, + -0.9349462985992432, + -0.37758100032806396, + 2.2498812675476074, + 8.598373413085938, + 2.832313060760498, + 7.149417877197266, + -14.823704719543457, + 4.040902137756348, + -2.6333675384521484, + -0.952739953994751, + -0.8755201101303101, + 1.5750482082366943, + 0.09360229969024658, + 0.536379873752594, + -4.0122222900390625, + -4.980226039886475, + -0.050527721643447876, + 1.6680951118469238, + 3.134003162384033, + 2.377664804458618, + -10.111128807067871, + -2.6171741485595703, + -0.3308676481246948, + 8.602490425109863, + -13.125202178955078, + 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diff --git a/data/speakers/spk_8.pt b/data/speakers/spk_8.pt new file mode 100644 index 0000000000000000000000000000000000000000..60cd294845a09393c73d4fbe7c5b44c52223f8c2 --- /dev/null +++ b/data/speakers/spk_8.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14bc55f1168fd9ee833a4301b1329f59d10cdb179a7628b5e7d0109a2be731fa +size 4370 diff --git a/data/speakers/spk_9.pt b/data/speakers/spk_9.pt new file mode 100644 index 0000000000000000000000000000000000000000..15ed4745c239fa8e94014501fa301def359d2621 --- /dev/null +++ b/data/speakers/spk_9.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5aac9315d98b0378f78f3925e2978c7c6843d2bae25b81de761ecf2db6fa4c5 +size 4370 diff --git a/data/styles.csv b/data/styles.csv new file mode 100644 index 0000000000000000000000000000000000000000..1f06fd1ef8df1a43a2cb60bab101397aeea4cd5a --- /dev/null +++ b/data/styles.csv @@ -0,0 +1,69 @@ +id,name,desc,params +0,advertisement_upbeat,builtin style advertisement_upbeat,"{""speed"": 9, ""oral"": 7, ""laugh"": 2, ""break"": 1}" +1,affectionate,builtin style affectionate,"{""speed"": 6, ""oral"": 8, ""laugh"": 1, ""break"": 2}" +2,angry,builtin style angry,"{""speed"": 7, ""oral"": 5, ""break"": 3}" +3,assistant,builtin style assistant,"{""speed"": 5, ""oral"": 7, ""laugh"": 1, ""break"": 2}" +4,calm,builtin style calm,"{""speed"": 0, ""oral"": 4, ""break"": 4}" +5,chat,builtin style chat,"{""speed"": 6, ""oral"": 9, ""laugh"": 1, ""break"": 2}" +6,cheerful,builtin style cheerful,"{""speed"": 7, ""oral"": 8, ""laugh"": 2, ""break"": 2}" +7,customerservice,builtin style customerservice,"{""speed"": 5, ""oral"": 6, ""laugh"": 1, ""break"": 5}" +8,depressed,builtin style depressed,"{""speed"": 2, ""oral"": 3, ""break"": 5}" +9,disgruntled,builtin style disgruntled,"{""speed"": 4, ""oral"": 5, ""break"": 4}" +10,documentary-narration,builtin style documentary-narration,"{""speed"": 4, ""oral"": 4, ""break"": 3}" +11,embarrassed,builtin style embarrassed,"{""speed"": 3, ""oral"": 3, ""break"": 5}" +12,empathetic,builtin style empathetic,"{""speed"": 5, ""oral"": 6, ""laugh"": 1, ""break"": 3}" +13,envious,builtin style envious,"{""speed"": 5, ""oral"": 5, ""laugh"": 1, ""break"": 2}" +14,excited,builtin style excited,"{""speed"": 8, ""oral"": 7, ""laugh"": 2, ""break"": 1}" +15,fearful,builtin style fearful,"{""speed"": 7, ""oral"": 6, ""break"": 2}" +16,friendly,builtin style friendly,"{""speed"": 6, ""oral"": 7, ""laugh"": 1, ""break"": 2}" +17,gentle,builtin style gentle,"{""speed"": 4, ""oral"": 5, ""laugh"": 1, ""break"": 3}" +18,hopeful,builtin style hopeful,"{""speed"": 5, ""oral"": 6, ""laugh"": 1, ""break"": 4}" +19,lyrical,builtin style lyrical,"{""speed"": 3, ""oral"": 4, ""break"": 4}" +20,narration-professional,builtin style narration-professional,"{""speed"": 5, ""oral"": 3, ""break"": 3}" +21,narration-relaxed,builtin style narration-relaxed,"{""speed"": 2, ""oral"": 4, ""break"": 3}" +22,newscast,builtin style newscast,"{""speed"": 2, ""oral"": 4, ""break"": 2}" +23,newscast-casual,builtin style newscast-casual,"{""speed"": 2, ""oral"": 5, ""break"": 2}" +24,newscast-formal,builtin style newscast-formal,"{""speed"": 2, ""oral"": 3, ""break"": 2}" +25,poetry-reading,builtin style poetry-reading,"{""speed"": 4, ""oral"": 5, ""break"": 3}" +26,sad,builtin style sad,"{""speed"": 0, ""oral"": 3, ""break"": 5}" +27,serious,builtin style serious,"{""speed"": 3, ""oral"": 3, ""break"": 4}" +28,shouting,builtin style shouting,"{""speed"": 0, ""oral"": 6, ""break"": 1}" +29,sports_commentary,builtin style sports_commentary,"{""speed"": 6, ""oral"": 7, ""laugh"": 1, ""break"": 2}" +30,sports_commentary_excited,builtin style sports_commentary_excited,"{""speed"": 8, ""oral"": 8, ""laugh"": 2, ""break"": 1}" +31,whispering,builtin style whispering,"{""speed"": 2, ""oral"": 3, ""break"": 4}" +32,terrified,builtin style terrified,"{""speed"": 7, ""oral"": 7, ""break"": 1}" +33,unfriendly,builtin style unfriendly,"{""speed"": 4, ""oral"": 2, ""break"": 3}" +34,advertisement_upbeat_p,builtin style advertisement_upbeat,"{""speed"": 9, ""oral"": 7, ""laugh"": 2, ""break"": 1, ""prompt2"": ""[Ptts][Ptts][Ptts] 用兴奋和精力充沛的语气像在拍广告 [Stts][Ptts][Stts][Ptts][Stts]""}" +35,affectionate_p,builtin style affectionate,"{""speed"": 6, ""oral"": 8, ""laugh"": 1, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用温暖亲切的语气像在和亲密的人说话 [Stts][Ptts][Stts][Ptts][Stts]""}" +36,angry_p,builtin style angry,"{""speed"": 7, ""oral"": 5, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用生气厌恶的语气像在吵架 [Stts][Ptts][Stts][Ptts][Stts]""}" +37,assistant_p,builtin style assistant,"{""speed"": 5, ""oral"": 7, ""laugh"": 1, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用热情轻松的语气像助理 [Stts][Ptts][Stts][Ptts][Stts]""}" +38,calm_p,builtin style calm,"{""speed"": 0, ""oral"": 4, ""break"": 4, ""prompt2"": ""[Ptts][Ptts][Ptts] 用沉着冷静的语气像在阐述事实 [Stts][Ptts][Stts][Ptts][Stts]""}" +39,chat_p,builtin style chat,"{""speed"": 6, ""oral"": 9, ""laugh"": 1, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用轻松随意的语气像朋友聊天 [Stts][Ptts][Stts][Ptts][Stts]""}" +40,cheerful_p,builtin style cheerful,"{""speed"": 7, ""oral"": 8, ""laugh"": 2, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用积极向上的语气表达愉快心情 [Stts][Ptts][Stts][Ptts][Stts]""}" +41,customerservice_p,builtin style customerservice,"{""speed"": 5, ""oral"": 6, ""laugh"": 1, ""break"": 5, ""prompt2"": ""[Ptts][Ptts][Ptts] 用友好热情的语气像客服人员 [Stts][Ptts][Stts][Ptts][Stts]""}" +42,depressed_p,builtin style depressed,"{""speed"": 2, ""oral"": 3, ""break"": 5, ""prompt2"": ""[Ptts][Ptts][Ptts] 用低沉的语气表达忧郁情绪 [Stts][Ptts][Stts][Ptts][Stts]""}" +43,disgruntled_p,builtin style disgruntled,"{""speed"": 4, ""oral"": 5, ""break"": 4, ""prompt2"": ""[Ptts][Ptts][Ptts] 用轻蔑抱怨的语气表达不满 [Stts][Ptts][Stts][Ptts][Stts]""}" +44,documentary-narration_p,builtin style documentary-narration,"{""speed"": 4, ""oral"": 4, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用轻松感兴趣的语气像纪录片解说 [Stts][Ptts][Stts][Ptts][Stts]""}" +45,embarrassed_p,builtin style embarrassed,"{""speed"": 3, ""oral"": 3, ""break"": 5, ""prompt2"": ""[Ptts][Ptts][Ptts] 用犹豫吞吐的语气表达尴尬 [Stts][Ptts][Stts][Ptts][Stts]""}" +46,empathetic_p,builtin style empathetic,"{""speed"": 5, ""oral"": 6, ""laugh"": 1, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用温柔的语气表达关心和理解 [Stts][Ptts][Stts][Ptts][Stts]""}" +47,envious_p,builtin style envious,"{""speed"": 5, ""oral"": 5, ""laugh"": 1, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用羡慕的语气像看到别人拥有自己想要的东西 [Stts][Ptts][Stts][Ptts][Stts]""}" +48,excited_p,builtin style excited,"{""speed"": 8, ""oral"": 7, ""laugh"": 2, ""break"": 1, ""prompt2"": ""[Ptts][Ptts][Ptts] 用欢快语气表达遇到好事 [Stts][Ptts][Stts][Ptts][Stts]""}" +49,fearful_p,builtin style fearful,"{""speed"": 7, ""oral"": 6, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用紧张语气表达恐惧像遇到危险 [Stts][Ptts][Stts][Ptts][Stts]""}" +50,friendly_p,builtin style friendly,"{""speed"": 6, ""oral"": 7, ""laugh"": 1, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用亲切友好的语气像朋友 [Stts][Ptts][Stts][Ptts][Stts]""}" +51,gentle_p,builtin style gentle,"{""speed"": 4, ""oral"": 5, ""laugh"": 1, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用温柔语气像在哄小孩 [Stts][Ptts][Stts][Ptts][Stts]""}" +52,hopeful_p,builtin style hopeful,"{""speed"": 5, ""oral"": 6, ""laugh"": 1, ""break"": 4, ""prompt2"": ""[Ptts][Ptts][Ptts] 用温暖的语气带着期待 [Stts][Ptts][Stts][Ptts][Stts]""}" +53,lyrical_p,builtin style lyrical,"{""speed"": 3, ""oral"": 4, ""break"": 4, ""prompt2"": ""[Ptts][Ptts][Ptts] 用优美语气带着淡淡的忧伤 [Stts][Ptts][Stts][Ptts][Stts]""}" +54,narration-professional_p,builtin style narration-professional,"{""speed"": 5, ""oral"": 3, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用沉稳语气像专业播音员 [Stts][Ptts][Stts][Ptts][Stts]""}" +55,narration-relaxed_p,builtin style narration-relaxed,"{""speed"": 2, ""oral"": 4, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用舒缓语气像在听睡前故事 [Stts][Ptts][Stts][Ptts][Stts]""}" +56,newscast_p,builtin style newscast,"{""speed"": 2, ""oral"": 4, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用正式严肃语气播报新闻 [Stts][Ptts][Stts][Ptts][Stts]""}" +57,newscast-casual_p,builtin style newscast-casual,"{""speed"": 2, ""oral"": 5, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用轻松自然语气分享新闻 [Stts][Ptts][Stts][Ptts][Stts]""}" +58,newscast-formal_p,builtin style newscast-formal,"{""speed"": 2, ""oral"": 3, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用正式自信语气播报重要新闻 [Stts][Ptts][Stts][Ptts][Stts]""}" +59,poetry-reading_p,builtin style poetry-reading,"{""speed"": 4, ""oral"": 5, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用语气调整表达诗歌情感注意节奏韵律 [Stts][Ptts][Stts][Ptts][Stts]""}" +60,sad_p,builtin style sad,"{""speed"": 0, ""oral"": 3, ""break"": 5, ""prompt2"": ""[Ptts][Ptts][Ptts] 用悲伤低沉语气诉说悲伤事情 [Stts][Ptts][Stts][Ptts][Stts]""}" +61,serious_p,builtin style serious,"{""speed"": 3, ""oral"": 3, ""break"": 4, ""prompt2"": ""[Ptts][Ptts][Ptts] 用严肃坚定语气像发布命令 [Stts][Ptts][Stts][Ptts][Stts]""}" +62,shouting_p,builtin style shouting,"{""speed"": 0, ""oral"": 6, ""break"": 1, ""prompt2"": ""[Ptts] 用提高音量的语气像在远处喊叫 [Stts][Ptts][Stts][Ptts][Stts]""}" +63,sports_commentary_p,builtin style sports_commentary,"{""speed"": 6, ""oral"": 7, ""laugh"": 1, ""break"": 2, ""prompt2"": ""[Ptts][Ptts][Ptts] 用轻松激情语气解说体育比赛 [Stts][Ptts][Stts][Ptts][Stts]""}" +64,sports_commentary_excited_p,builtin style sports_commentary_excited,"{""speed"": 8, ""oral"": 8, ""laugh"": 2, ""break"": 1, ""prompt2"": ""[Ptts][Ptts][Ptts] 用激动活力语气解说精彩瞬间 [Stts][Ptts][Stts][Ptts][Stts]""}" +65,whispering_p,builtin style whispering,"{""speed"": 2, ""oral"": 3, ""break"": 4, ""prompt2"": ""[Ptts][Ptts][Ptts] 用气声低音量语气像说悄悄话 [Stts][Ptts][Stts][Ptts][Stts]""}" +66,terrified_p,builtin style terrified,"{""speed"": 7, ""oral"": 7, ""break"": 1, ""prompt2"": ""[Ptts][Ptts][Ptts] 用恐惧颤抖语气像遇到可怕事情 [Stts][Ptts][Stts][Ptts][Stts]""}" +67,unfriendly_p,builtin style unfriendly,"{""speed"": 4, ""oral"": 2, ""break"": 3, ""prompt2"": ""[Ptts][Ptts][Ptts] 用冷淡无感情语气像对待陌生人 [Stts][Ptts][Stts][Ptts][Stts]""}" \ No newline at end of file diff --git a/docs/api.png b/docs/api.png new file mode 100644 index 0000000000000000000000000000000000000000..fc2a7109025eb28852aa10dba1c37e13ec62e5ec Binary files /dev/null and b/docs/api.png differ diff --git a/docs/model_dir.png b/docs/model_dir.png new file mode 100644 index 0000000000000000000000000000000000000000..793861905d9a987548cc34060a49c98ed1b75fbe Binary files /dev/null and b/docs/model_dir.png differ diff --git a/docs/playground.png b/docs/playground.png new file mode 100644 index 0000000000000000000000000000000000000000..d5db03a066354047b7c37a51c5219b168c928d58 Binary files /dev/null and b/docs/playground.png differ diff --git a/docs/webui.png b/docs/webui.png new file mode 100644 index 0000000000000000000000000000000000000000..d4ccf531f84e111c71349c51be4825cf0a031173 Binary files /dev/null and b/docs/webui.png differ diff --git a/launch.py b/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..e0131d20aa904ae86b1db9336395f8d786ae27bc --- /dev/null +++ b/launch.py @@ -0,0 +1,109 @@ +import torch +from modules import config +from modules import generate_audio as generate + +from functools import lru_cache +from typing import Callable + +from modules.api.Api import APIManager + +from modules.api.impl import ( + base_api, + tts_api, + ssml_api, + google_api, + openai_api, + refiner_api, +) + +torch._dynamo.config.cache_size_limit = 64 +torch._dynamo.config.suppress_errors = True +torch.set_float32_matmul_precision("high") + + +def create_api(): + api = APIManager() + + base_api.setup(api) + tts_api.setup(api) + ssml_api.setup(api) + google_api.setup(api) + openai_api.setup(api) + refiner_api.setup(api) + + return api + + +def conditional_cache(condition: Callable): + def decorator(func): + @lru_cache(None) + def cached_func(*args, **kwargs): + return func(*args, **kwargs) + + def wrapper(*args, **kwargs): + if condition(*args, **kwargs): + return cached_func(*args, **kwargs) + else: + return func(*args, **kwargs) + + return wrapper + + return decorator + + +if __name__ == "__main__": + import argparse + import uvicorn + + parser = argparse.ArgumentParser( + description="Start the FastAPI server with command line arguments" + ) + parser.add_argument( + "--host", type=str, default="0.0.0.0", help="Host to run the server on" + ) + parser.add_argument( + "--port", type=int, default=8000, help="Port to run the server on" + ) + parser.add_argument( + "--reload", action="store_true", help="Enable auto-reload for development" + ) + parser.add_argument("--compile", action="store_true", help="Enable model compile") + parser.add_argument( + "--lru_size", + type=int, + default=64, + help="Set the size of the request cache pool, set it to 0 will disable lru_cache", + ) + parser.add_argument( + "--cors_origin", + type=str, + default="*", + help="Allowed CORS origins. Use '*' to allow all origins.", + ) + + args = parser.parse_args() + + config.args = args + + if args.compile: + print("Model compile is enabled") + config.enable_model_compile = True + + def should_cache(*args, **kwargs): + spk_seed = kwargs.get("spk_seed", -1) + infer_seed = kwargs.get("infer_seed", -1) + return spk_seed != -1 and infer_seed != -1 + + if args.lru_size > 0: + config.lru_size = args.lru_size + generate.generate_audio = conditional_cache(should_cache)( + generate.generate_audio + ) + + api = create_api() + config.api = api + + if args.cors_origin: + api.set_cors(allow_origins=[args.cors_origin]) + + uvicorn.run(api.app, host=args.host, port=args.port, reload=args.reload) diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/ChatTTS/asset/DVAE.pt b/models/ChatTTS/asset/DVAE.pt new file mode 100644 index 0000000000000000000000000000000000000000..70d514fc602e8c7b46b9e82f843724c8cd3a5717 --- /dev/null +++ b/models/ChatTTS/asset/DVAE.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:613cb128adf89188c93ea5880ea0b798e66b1fe6186d0c535d99bcd87bfd6976 +size 27749823 diff --git a/models/ChatTTS/asset/Decoder.pt b/models/ChatTTS/asset/Decoder.pt new file mode 100644 index 0000000000000000000000000000000000000000..f15ee0b814281e7378af51d4bf25af57d29ed3b4 --- /dev/null +++ b/models/ChatTTS/asset/Decoder.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9964e36e840f0e3a748c5f716fe6de6490d2135a5f5155f4a642d51860e2ec38 +size 103718156 diff --git a/models/ChatTTS/asset/GPT.pt b/models/ChatTTS/asset/GPT.pt new file mode 100644 index 0000000000000000000000000000000000000000..d4feaa219d849b8fc56a66bc3efc032455b37bc4 --- /dev/null +++ b/models/ChatTTS/asset/GPT.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7d4ee6461ea097a2be23eb40d73fb94ad3b3d39cb64fbb50cb3357fd466cadb +size 900746442 diff --git a/models/ChatTTS/asset/Vocos.pt b/models/ChatTTS/asset/Vocos.pt new file mode 100644 index 0000000000000000000000000000000000000000..e7f521d0407e975101a66dd94bcadbb122a46893 --- /dev/null +++ b/models/ChatTTS/asset/Vocos.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09a670eda1c08b740013679c7a90ebb7f1a97646ea7673069a6838e6b51d6c58 +size 54363119 diff --git a/models/ChatTTS/asset/spk_stat.pt b/models/ChatTTS/asset/spk_stat.pt new file mode 100644 index 0000000000000000000000000000000000000000..c21dae672dd323c62dd9d53f9fcae77071983515 --- /dev/null +++ b/models/ChatTTS/asset/spk_stat.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3228d8a4cbbf349d107a1b76d2f47820865bd3c9928c4bdfe1cefd5c7071105f +size 4257 diff --git a/models/ChatTTS/asset/tokenizer.pt b/models/ChatTTS/asset/tokenizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..23c153375113a2fa7b68ce6c96f27b9e2e966c94 --- /dev/null +++ b/models/ChatTTS/asset/tokenizer.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e911ae7c6a7c27953433f35c44227a67838fe229a1f428503bdb6cd3d1bcc69c +size 336680 diff --git a/models/ChatTTS/config/decoder.yaml b/models/ChatTTS/config/decoder.yaml new file mode 100644 index 0000000000000000000000000000000000000000..46b55a6c9f48d6f5f4d509f37c3a49200435deaf --- /dev/null +++ b/models/ChatTTS/config/decoder.yaml @@ -0,0 +1,10 @@ +dim: 384 + +decoder_config: + idim: ${dim} + odim: ${dim} + hidden: 512 + n_layer: 12 + bn_dim: 128 + +vq_config: null diff --git a/models/ChatTTS/config/dvae.yaml b/models/ChatTTS/config/dvae.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6f1f8a1541dd86b0af9f920e542764c5039c45a1 --- /dev/null +++ b/models/ChatTTS/config/dvae.yaml @@ -0,0 +1,12 @@ +dim: 512 +decoder_config: + idim: ${dim} + odim: ${dim} + n_layer: 12 + bn_dim: 128 + +vq_config: + dim: 1024 + levels: [5, 5, 5, 5] + G: 2 + R: 2 diff --git a/models/ChatTTS/config/gpt.yaml b/models/ChatTTS/config/gpt.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd1fd63f75ec6c331ce65b10a3e3204f7d7246a2 --- /dev/null +++ b/models/ChatTTS/config/gpt.yaml @@ -0,0 +1,17 @@ +num_audio_tokens: 626 +num_text_tokens: 21178 + +gpt_config: + hidden_size: 768 + intermediate_size: 3072 + num_attention_heads: 12 + num_hidden_layers: 20 + use_cache: False + max_position_embeddings: 4096 + # attn_implementation: flash_attention_2 + + spk_emb_dim: 192 + spk_KL: False + num_audio_tokens: 626 + num_text_tokens: null + num_vq: 4 diff --git a/models/ChatTTS/config/path.yaml b/models/ChatTTS/config/path.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d0820402c7e24dbfbab11d3e4eded3ce771f486f --- /dev/null +++ b/models/ChatTTS/config/path.yaml @@ -0,0 +1,9 @@ +vocos_config_path: config/vocos.yaml +vocos_ckpt_path: asset/Vocos.pt +dvae_config_path: config/dvae.yaml +dvae_ckpt_path: asset/DVAE.pt +gpt_config_path: config/gpt.yaml +gpt_ckpt_path: asset/GPT.pt +decoder_config_path: config/decoder.yaml +decoder_ckpt_path: asset/Decoder.pt +tokenizer_path: asset/tokenizer.pt diff --git a/models/ChatTTS/config/vocos.yaml b/models/ChatTTS/config/vocos.yaml new file mode 100644 index 0000000000000000000000000000000000000000..538262138a8b43863802f279909f26ec31c766b3 --- /dev/null +++ b/models/ChatTTS/config/vocos.yaml @@ -0,0 +1,24 @@ +feature_extractor: + class_path: vocos.feature_extractors.MelSpectrogramFeatures + init_args: + sample_rate: 24000 + n_fft: 1024 + hop_length: 256 + n_mels: 100 + padding: center + +backbone: + class_path: vocos.models.VocosBackbone + init_args: + input_channels: 100 + dim: 512 + intermediate_dim: 1536 + num_layers: 8 + +head: + class_path: vocos.heads.ISTFTHead + init_args: + dim: 512 + n_fft: 1024 + hop_length: 256 + padding: center diff --git a/modules/ChatTTS/.gitignore b/modules/ChatTTS/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..1ed4d073766b8943d66d08d5e86b54ca7b1e53f0 --- /dev/null +++ b/modules/ChatTTS/.gitignore @@ -0,0 +1,163 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*.ckpt +# C extensions +*.so +*.pt + +# Distribution / packaging +.Python +outputs/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +asset/* +.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/ diff --git a/modules/ChatTTS/ChatTTS/__init__.py b/modules/ChatTTS/ChatTTS/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0f4c079ba76ed6799552a3db22f2f35d9489edd3 --- /dev/null +++ b/modules/ChatTTS/ChatTTS/__init__.py @@ -0,0 +1 @@ +from .core import Chat \ No newline at end of file diff --git a/modules/ChatTTS/ChatTTS/core.py b/modules/ChatTTS/ChatTTS/core.py new file mode 100644 index 0000000000000000000000000000000000000000..0474acf8b96b4796fdacc45f5d4aba354469ed86 --- /dev/null +++ b/modules/ChatTTS/ChatTTS/core.py @@ -0,0 +1,346 @@ +import os +import logging +from functools import partial +from omegaconf import OmegaConf + +import torch +from vocos import Vocos +from .model.dvae import DVAE +from .model.gpt import GPT_warpper +from .utils.gpu_utils import select_device +from .utils.infer_utils import ( + count_invalid_characters, + detect_language, + apply_character_map, + apply_half2full_map, +) +from .utils.io_utils import get_latest_modified_file +from .infer.api import refine_text, infer_code + +from huggingface_hub import snapshot_download + +logging.basicConfig(level=logging.INFO) + + +class Chat: + def __init__( + self, + ): + self.pretrain_models = {} + self.normalizer = {} + self.logger = logging.getLogger(__name__) + + def check_model(self, level=logging.INFO, use_decoder=False): + not_finish = False + check_list = ["vocos", "gpt", "tokenizer"] + + if use_decoder: + check_list.append("decoder") + else: + check_list.append("dvae") + + for module in check_list: + if module not in self.pretrain_models: + self.logger.log(logging.WARNING, f"{module} not initialized.") + not_finish = True + + if not not_finish: + self.logger.log(level, f"All initialized.") + + return not not_finish + + def load_models( + self, + source="huggingface", + force_redownload=False, + local_path="", + **kwargs, + ): + if source == "huggingface": + hf_home = os.getenv("HF_HOME", os.path.expanduser("~/.cache/huggingface")) + try: + download_path = get_latest_modified_file( + os.path.join(hf_home, "hub/models--2Noise--ChatTTS/snapshots") + ) + except: + download_path = None + if download_path is None or force_redownload: + self.logger.log( + logging.INFO, + f"Download from HF: https://huggingface.co/2Noise/ChatTTS", + ) + download_path = snapshot_download( + repo_id="2Noise/ChatTTS", allow_patterns=["*.pt", "*.yaml"] + ) + else: + self.logger.log(logging.INFO, f"Load from cache: {download_path}") + elif source == "local": + self.logger.log(logging.INFO, f"Load from local: {local_path}") + download_path = local_path + + self._load( + **{ + k: os.path.join(download_path, v) + for k, v in OmegaConf.load( + os.path.join(download_path, "config", "path.yaml") + ).items() + }, + **kwargs, + ) + + def _load( + self, + vocos_config_path: str = None, + vocos_ckpt_path: str = None, + dvae_config_path: str = None, + dvae_ckpt_path: str = None, + gpt_config_path: str = None, + gpt_ckpt_path: str = None, + decoder_config_path: str = None, + decoder_ckpt_path: str = None, + tokenizer_path: str = None, + device: str = None, + compile: bool = True, + ): + if not device: + device = select_device(4096) + self.logger.log(logging.INFO, f"use {device}") + + if vocos_config_path: + vocos = Vocos.from_hparams(vocos_config_path).to(device).eval() + assert vocos_ckpt_path, "vocos_ckpt_path should not be None" + vocos.load_state_dict(torch.load(vocos_ckpt_path)) + self.pretrain_models["vocos"] = vocos + self.logger.log(logging.INFO, "vocos loaded.") + + if dvae_config_path: + cfg = OmegaConf.load(dvae_config_path) + dvae = DVAE(**cfg).to(device).eval() + assert dvae_ckpt_path, "dvae_ckpt_path should not be None" + dvae.load_state_dict(torch.load(dvae_ckpt_path, map_location=device)) + self.pretrain_models["dvae"] = dvae + self.logger.log(logging.INFO, "dvae loaded.") + + if gpt_config_path: + cfg = OmegaConf.load(gpt_config_path) + gpt = GPT_warpper(**cfg).to(device).eval() + assert gpt_ckpt_path, "gpt_ckpt_path should not be None" + gpt.load_state_dict(torch.load(gpt_ckpt_path, map_location=device)) + if compile and "cuda" in str(device): + self.logger.info("compile gpt model") + gpt.gpt.forward = torch.compile( + gpt.gpt.forward, backend="inductor", dynamic=True + ) + self.pretrain_models["gpt"] = gpt + spk_stat_path = os.path.join(os.path.dirname(gpt_ckpt_path), "spk_stat.pt") + assert os.path.exists( + spk_stat_path + ), f"Missing spk_stat.pt: {spk_stat_path}" + self.pretrain_models["spk_stat"] = torch.load(spk_stat_path).to(device) + self.logger.log(logging.INFO, "gpt loaded.") + + if decoder_config_path: + cfg = OmegaConf.load(decoder_config_path) + decoder = DVAE(**cfg).to(device).eval() + assert decoder_ckpt_path, "decoder_ckpt_path should not be None" + decoder.load_state_dict(torch.load(decoder_ckpt_path, map_location=device)) + self.pretrain_models["decoder"] = decoder + self.logger.log(logging.INFO, "decoder loaded.") + + if tokenizer_path: + tokenizer = torch.load(tokenizer_path, map_location=device) + tokenizer.padding_side = "left" + self.pretrain_models["tokenizer"] = tokenizer + self.logger.log(logging.INFO, "tokenizer loaded.") + + self.check_model() + + def infer( + self, + text, + skip_refine_text=False, + refine_text_only=False, + params_refine_text={}, + params_infer_code={"prompt": "[speed_5]"}, + use_decoder=True, + do_text_normalization=True, + lang=None, + ): + + assert self.check_model(use_decoder=use_decoder) + + if not isinstance(text, list): + text = [text] + + if do_text_normalization: + for i, t in enumerate(text): + _lang = detect_language(t) if lang is None else lang + self.init_normalizer(_lang) + text[i] = self.normalizer[_lang](t) + if _lang == "zh": + text[i] = apply_half2full_map(text[i]) + + for i, t in enumerate(text): + reserved_tokens = self.pretrain_models[ + "tokenizer" + ].additional_special_tokens + invalid_characters = count_invalid_characters(t, reserved_tokens) + if len(invalid_characters): + self.logger.log( + logging.WARNING, f"Invalid characters found! : {invalid_characters}" + ) + text[i] = apply_character_map(t) + + if not skip_refine_text: + text_tokens = refine_text(self.pretrain_models, text, **params_refine_text)[ + "ids" + ] + + text_tokens = [ + i[ + i + < self.pretrain_models["tokenizer"].convert_tokens_to_ids( + "[break_0]" + ) + ] + for i in text_tokens + ] + text = self.pretrain_models["tokenizer"].batch_decode(text_tokens) + + if refine_text_only: + return text + + text = [params_infer_code.get("prompt", "") + i for i in text] + params_infer_code.pop("prompt", "") + result = infer_code( + self.pretrain_models, text, **params_infer_code, return_hidden=use_decoder + ) + + if use_decoder: + mel_spec = [ + self.pretrain_models["decoder"](i[None].permute(0, 2, 1)) + for i in result["hiddens"] + ] + else: + mel_spec = [ + self.pretrain_models["dvae"](i[None].permute(0, 2, 1)) + for i in result["ids"] + ] + + wav = [self.pretrain_models["vocos"].decode(i).cpu().numpy() for i in mel_spec] + + return wav + + def refiner_prompt( + self, + text, + params_refine_text={}, + do_text_normalization=True, + lang=None, + ) -> str: + + # assert self.check_model(use_decoder=False) + + if not isinstance(text, list): + text = [text] + + if do_text_normalization: + for i, t in enumerate(text): + _lang = detect_language(t) if lang is None else lang + self.init_normalizer(_lang) + text[i] = self.normalizer[_lang](t) + if _lang == "zh": + text[i] = apply_half2full_map(text[i]) + + for i, t in enumerate(text): + reserved_tokens = self.pretrain_models[ + "tokenizer" + ].additional_special_tokens + invalid_characters = count_invalid_characters(t, reserved_tokens) + if len(invalid_characters): + self.logger.log( + logging.WARNING, f"Invalid characters found! : {invalid_characters}" + ) + text[i] = apply_character_map(t) + + text_tokens = refine_text(self.pretrain_models, text, **params_refine_text)[ + "ids" + ] + text_tokens = [ + i[i < self.pretrain_models["tokenizer"].convert_tokens_to_ids("[break_0]")] + for i in text_tokens + ] + text = self.pretrain_models["tokenizer"].batch_decode(text_tokens) + + return text[0] + + def generate_audio( + self, + prompt, + params_infer_code={"prompt": "[speed_5]"}, + use_decoder=True, + ) -> list: + + # assert self.check_model(use_decoder=use_decoder) + + if not isinstance(prompt, list): + prompt = [prompt] + + prompt = [params_infer_code.get("prompt", "") + i for i in prompt] + params_infer_code.pop("prompt", "") + result = infer_code( + self.pretrain_models, prompt, **params_infer_code, return_hidden=use_decoder + ) + + if use_decoder: + mel_spec = [ + self.pretrain_models["decoder"](i[None].permute(0, 2, 1)) + for i in result["hiddens"] + ] + else: + mel_spec = [ + self.pretrain_models["dvae"](i[None].permute(0, 2, 1)) + for i in result["ids"] + ] + + wav = [self.pretrain_models["vocos"].decode(i).cpu().numpy() for i in mel_spec] + + return wav + + def sample_random_speaker( + self, + ) -> torch.Tensor: + + dim = self.pretrain_models["gpt"].gpt.layers[0].mlp.gate_proj.in_features + std, mean = self.pretrain_models["spk_stat"].chunk(2) + return torch.randn(dim, device=std.device) * std + mean + + def init_normalizer(self, lang): + + if lang not in self.normalizer: + if lang == "zh": + try: + from tn.chinese.normalizer import Normalizer + except: + self.logger.log( + logging.WARNING, + f"Package WeTextProcessing not found! \ + Run: conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing", + ) + self.normalizer[lang] = Normalizer().normalize + else: + try: + from nemo_text_processing.text_normalization.normalize import ( + Normalizer, + ) + except: + self.logger.log( + logging.WARNING, + f"Package nemo_text_processing not found! \ + Run: conda install -c conda-forge pynini=2.1.5 && pip install nemo_text_processing", + ) + self.normalizer[lang] = partial( + Normalizer(input_case="cased", lang=lang).normalize, + verbose=False, + punct_post_process=True, + ) diff --git a/modules/ChatTTS/ChatTTS/experimental/llm.py b/modules/ChatTTS/ChatTTS/experimental/llm.py new file mode 100644 index 0000000000000000000000000000000000000000..a4e5f94d8e08b0d2c2ceb806cf4efc11b891dd1f --- /dev/null +++ b/modules/ChatTTS/ChatTTS/experimental/llm.py @@ -0,0 +1,40 @@ + +from openai import OpenAI + +prompt_dict = { + 'kimi': [ {"role": "system", "content": "你是 Kimi，由 Moonshot AI 提供的人工智能助手，你更擅长中文和英文的对话。"}, + {"role": "user", "content": "你好，请注意你现在生成的文字要按照人日常生活的口吻，你的回复将会后续用TTS模型转为语音，并且请把回答控制在100字以内。并且标点符号仅包含逗号和句号，将数字等转为文字回答。"}, + {"role": "assistant", "content": "好的，我现在生成的文字将按照人日常生活的口吻， 并且我会把回答控制在一百字以内, 标点符号仅包含逗号和句号，将阿拉伯数字等转为中文文字回答。下面请开始对话。"},], + 'deepseek': [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "你好，请注意你现在生成的文字要按照人日常生活的口吻，你的回复将会后续用TTS模型转为语音，并且请把回答控制在100字以内。并且标点符号仅包含逗号和句号，将数字等转为文字回答。"}, + {"role": "assistant", "content": "好的，我现在生成的文字将按照人日常生活的口吻， 并且我会把回答控制在一百字以内, 标点符号仅包含逗号和句号，将阿拉伯数字等转为中文文字回答。下面请开始对话。"},], + 'deepseek_TN': [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "你好，现在我们在处理TTS的文本输入，下面将会给你输入一段文本，请你将其中的阿拉伯数字等等转为文字表达，并且输出的文本里仅包含逗号和句号这两个标点符号"}, + {"role": "assistant", "content": "好的，我现在对TTS的文本输入进行处理。这一般叫做text normalization。下面请输入"}, + {"role": "user", "content": "We paid $123 for this desk."}, + {"role": "assistant", "content": "We paid one hundred and twenty three dollars for this desk."}, + {"role": "user", "content": "详询请拨打010-724654"}, + {"role": "assistant", "content": "详询请拨打零幺零，七二四六五四"}, + {"role": "user", "content": "罗森宣布将于7月24日退市，在华门店超6000家！"}, + {"role": "assistant", "content": "罗森宣布将于七月二十四日退市，在华门店超过六千家。"}, + ], +} + +class llm_api: + def __init__(self, api_key, base_url, model): + self.client = OpenAI( + api_key = api_key, + base_url = base_url, + ) + self.model = model + def call(self, user_question, temperature = 0.3, prompt_version='kimi', **kwargs): + + completion = self.client.chat.completions.create( + model = self.model, + messages = prompt_dict[prompt_version]+[{"role": "user", "content": user_question},], + temperature = temperature, + **kwargs + ) + return completion.choices[0].message.content diff --git a/modules/ChatTTS/ChatTTS/infer/api.py b/modules/ChatTTS/ChatTTS/infer/api.py new file mode 100644 index 0000000000000000000000000000000000000000..7a147709f22a0faa6e5a38ac12dcb4e4db342afc --- /dev/null +++ b/modules/ChatTTS/ChatTTS/infer/api.py @@ -0,0 +1,156 @@ +import torch +import torch.nn.functional as F +from transformers.generation import TopKLogitsWarper, TopPLogitsWarper +from ..utils.infer_utils import CustomRepetitionPenaltyLogitsProcessorRepeat + + +def infer_code( + models, + text, + spk_emb=None, + top_P=0.7, + top_K=20, + temperature=0.3, + repetition_penalty=1.05, + max_new_token=2048, + prompt1="", + prompt2="", + prefix="", + **kwargs, +): + + device = next(models["gpt"].parameters()).device + + if not isinstance(text, list): + text = [text] + + if not isinstance(temperature, list): + temperature = [temperature] * models["gpt"].num_vq + + if spk_emb is not None: + text = [f"{prompt1}[Stts]{prompt2}[spk_emb]{i}[Ptts]{prefix}" for i in text] + else: + text = [f"{prompt1}[Stts]{prompt2}[empty_spk]{i}[Ptts]{prefix}" for i in text] + + text_token = models["tokenizer"]( + text, return_tensors="pt", add_special_tokens=False, padding=True + ).to(device) + input_ids = text_token["input_ids"][..., None].expand(-1, -1, models["gpt"].num_vq) + text_mask = torch.ones(text_token["input_ids"].shape, dtype=bool, device=device) + + inputs = { + "input_ids": input_ids, + "text_mask": text_mask, + "attention_mask": text_token["attention_mask"], + } + + emb = models["gpt"].get_emb(**inputs) + if spk_emb is not None: + emb[ + inputs["input_ids"][..., 0] + == models["tokenizer"].convert_tokens_to_ids("[spk_emb]") + ] = F.normalize( + spk_emb.to(device).to(emb.dtype)[None].expand(len(text), -1), + p=2.0, + dim=1, + eps=1e-12, + ) + + num_code = models["gpt"].emb_code[0].num_embeddings - 1 + + LogitsWarpers = [] + if top_P is not None: + LogitsWarpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3)) + if top_K is not None: + LogitsWarpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3)) + + LogitsProcessors = [] + if repetition_penalty is not None and repetition_penalty != 1: + LogitsProcessors.append( + CustomRepetitionPenaltyLogitsProcessorRepeat( + repetition_penalty, num_code, 16 + ) + ) + + result = models["gpt"].generate( + emb, + inputs["input_ids"], + temperature=torch.tensor(temperature, device=device), + attention_mask=inputs["attention_mask"], + LogitsWarpers=LogitsWarpers, + LogitsProcessors=LogitsProcessors, + eos_token=num_code, + max_new_token=max_new_token, + infer_text=False, + **kwargs, + ) + + return result + + +def refine_text( + models, + text, + top_P=0.7, + top_K=20, + temperature=0.7, + repetition_penalty=1.0, + max_new_token=384, + prompt="", + **kwargs, +): + device = next(models["gpt"].parameters()).device + + if not isinstance(text, list): + text = [text] + + assert len(text), "text should not be empty" + + text = [f"[Sbreak]{i}[Pbreak]{prompt}" for i in text] + text_token = models["tokenizer"]( + text, return_tensors="pt", add_special_tokens=False, padding=True + ).to(device) + text_mask = torch.ones(text_token["input_ids"].shape, dtype=bool, device=device) + + inputs = { + "input_ids": text_token["input_ids"][..., None].expand( + -1, -1, models["gpt"].num_vq + ), + "text_mask": text_mask, + "attention_mask": text_token["attention_mask"], + } + + LogitsWarpers = [] + if top_P is not None: + LogitsWarpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3)) + if top_K is not None: + LogitsWarpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3)) + + LogitsProcessors = [] + if repetition_penalty is not None and repetition_penalty != 1: + LogitsProcessors.append( + CustomRepetitionPenaltyLogitsProcessorRepeat( + repetition_penalty, len(models["tokenizer"]), 16 + ) + ) + + result = models["gpt"].generate( + models["gpt"].get_emb(**inputs), + inputs["input_ids"], + temperature=torch.tensor( + [ + temperature, + ], + device=device, + ), + attention_mask=inputs["attention_mask"], + LogitsWarpers=LogitsWarpers, + LogitsProcessors=LogitsProcessors, + eos_token=torch.tensor( + models["tokenizer"].convert_tokens_to_ids("[Ebreak]"), device=device + )[None], + max_new_token=max_new_token, + infer_text=True, + **kwargs, + ) + return result diff --git a/modules/ChatTTS/ChatTTS/model/dvae.py b/modules/ChatTTS/ChatTTS/model/dvae.py new file mode 100644 index 0000000000000000000000000000000000000000..336e8e16cdea41669a581f326047d945d81f2933 --- /dev/null +++ b/modules/ChatTTS/ChatTTS/model/dvae.py @@ -0,0 +1,155 @@ +import math +from einops import rearrange +from vector_quantize_pytorch import GroupedResidualFSQ + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ConvNeXtBlock(nn.Module): + def __init__( + self, + dim: int, + intermediate_dim: int, + kernel, dilation, + layer_scale_init_value: float = 1e-6, + ): + # ConvNeXt Block copied from Vocos. + super().__init__() + self.dwconv = nn.Conv1d(dim, dim, + kernel_size=kernel, padding=dilation*(kernel//2), + dilation=dilation, groups=dim + ) # depthwise conv + + self.norm = nn.LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = nn.Linear(intermediate_dim, dim) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True) + if layer_scale_init_value > 0 + else None + ) + + def forward(self, x: torch.Tensor, cond = None) -> torch.Tensor: + residual = x + x = self.dwconv(x) + x = x.transpose(1, 2) # (B, C, T) -> (B, T, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.transpose(1, 2) # (B, T, C) -> (B, C, T) + + x = residual + x + return x + + + +class GFSQ(nn.Module): + + def __init__(self, + dim, levels, G, R, eps=1e-5, transpose = True + ): + super(GFSQ, self).__init__() + self.quantizer = GroupedResidualFSQ( + dim=dim, + levels=levels, + num_quantizers=R, + groups=G, + ) + self.n_ind = math.prod(levels) + self.eps = eps + self.transpose = transpose + self.G = G + self.R = R + + def _embed(self, x): + if self.transpose: + x = x.transpose(1,2) + x = rearrange( + x, "b t (g r) -> g b t r", g = self.G, r = self.R, + ) + feat = self.quantizer.get_output_from_indices(x) + return feat.transpose(1,2) if self.transpose else feat + + def forward(self, x,): + if self.transpose: + x = x.transpose(1,2) + feat, ind = self.quantizer(x) + ind = rearrange( + ind, "g b t r ->b t (g r)", + ) + embed_onehot = F.one_hot(ind.long(), self.n_ind).to(x.dtype) + e_mean = torch.mean(embed_onehot, dim=[0,1]) + e_mean = e_mean / (e_mean.sum(dim=1) + self.eps).unsqueeze(1) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + self.eps), dim=1)) + + return ( + torch.zeros(perplexity.shape, dtype=x.dtype, device=x.device), + feat.transpose(1,2) if self.transpose else feat, + perplexity, + None, + ind.transpose(1,2) if self.transpose else ind, + ) + +class DVAEDecoder(nn.Module): + def __init__(self, idim, odim, + n_layer = 12, bn_dim = 64, hidden = 256, + kernel = 7, dilation = 2, up = False + ): + super().__init__() + self.up = up + self.conv_in = nn.Sequential( + nn.Conv1d(idim, bn_dim, 3, 1, 1), nn.GELU(), + nn.Conv1d(bn_dim, hidden, 3, 1, 1) + ) + self.decoder_block = nn.ModuleList([ + ConvNeXtBlock(hidden, hidden* 4, kernel, dilation,) + for _ in range(n_layer)]) + self.conv_out = nn.Conv1d(hidden, odim, kernel_size=1, bias=False) + + def forward(self, input, conditioning=None): + # B, T, C + x = input.transpose(1, 2) + x = self.conv_in(x) + for f in self.decoder_block: + x = f(x, conditioning) + + x = self.conv_out(x) + return x.transpose(1, 2) + + +class DVAE(nn.Module): + def __init__( + self, decoder_config, vq_config, dim=512 + ): + super().__init__() + self.register_buffer('coef', torch.randn(1, 100, 1)) + + self.decoder = DVAEDecoder(**decoder_config) + self.out_conv = nn.Conv1d(dim, 100, 3, 1, 1, bias=False) + if vq_config is not None: + self.vq_layer = GFSQ(**vq_config) + else: + self.vq_layer = None + + def forward(self, inp): + + if self.vq_layer is not None: + vq_feats = self.vq_layer._embed(inp) + else: + vq_feats = inp.detach().clone() + + temp = torch.chunk(vq_feats, 2, dim=1) # flatten trick :) + temp = torch.stack(temp, -1) + vq_feats = temp.reshape(*temp.shape[:2], -1) + + vq_feats = vq_feats.transpose(1, 2) + dec_out = self.decoder(input=vq_feats) + dec_out = self.out_conv(dec_out.transpose(1, 2)) + mel = dec_out * self.coef + + return mel diff --git a/modules/ChatTTS/ChatTTS/model/gpt.py b/modules/ChatTTS/ChatTTS/model/gpt.py new file mode 100644 index 0000000000000000000000000000000000000000..da1967c04fddadc6bf0a5c21dbe049a65b5b3597 --- /dev/null +++ b/modules/ChatTTS/ChatTTS/model/gpt.py @@ -0,0 +1,270 @@ +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +import logging +from tqdm import tqdm +from einops import rearrange +from transformers.cache_utils import Cache + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.utils.parametrize as P +from torch.nn.utils.parametrizations import weight_norm +from transformers import LlamaModel, LlamaConfig + + +class LlamaMLP(nn.Module): + def __init__(self, hidden_size, intermediate_size): + super().__init__() + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = F.silu + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class GPT_warpper(nn.Module): + def __init__( + self, + gpt_config, + num_audio_tokens, + num_text_tokens, + num_vq=4, + **kwargs, + ): + super().__init__() + + self.logger = logging.getLogger(__name__) + self.gpt = self.build_model(gpt_config) + self.model_dim = self.gpt.config.hidden_size + + self.num_vq = num_vq + self.emb_code = nn.ModuleList([nn.Embedding(num_audio_tokens, self.model_dim) for i in range(self.num_vq)]) + self.emb_text = nn.Embedding(num_text_tokens, self.model_dim) + self.head_text = weight_norm(nn.Linear(self.model_dim, num_text_tokens, bias=False), name='weight') + self.head_code = nn.ModuleList([weight_norm(nn.Linear(self.model_dim, num_audio_tokens, bias=False), name='weight') for i in range(self.num_vq)]) + + def build_model(self, config): + + configuration = LlamaConfig(**config) + model = LlamaModel(configuration) + del model.embed_tokens + + return model + + def get_emb(self, input_ids, text_mask, **kwargs): + + emb_text = self.emb_text(input_ids[text_mask][:, 0]) + + emb_code = [self.emb_code[i](input_ids[~text_mask][:, i]) for i in range(self.num_vq)] + emb_code = torch.stack(emb_code, 2).sum(2) + + emb = torch.zeros((input_ids.shape[:-1])+(emb_text.shape[-1],), device=emb_text.device, dtype=emb_text.dtype) + emb[text_mask] = emb_text + emb[~text_mask] = emb_code.to(emb.dtype) + + return emb + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs + ): + # With static cache, the `past_key_values` is None + # TODO joao: standardize interface for the different Cache classes and remove of this if + has_static_cache = False + if past_key_values is None: + past_key_values = getattr(self.gpt.layers[0].self_attn, "past_key_value", None) + has_static_cache = past_key_values is not None + + past_length = 0 + if past_key_values is not None: + if isinstance(past_key_values, Cache): + past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length() + max_cache_length = ( + torch.tensor(past_key_values.get_max_length(), device=input_ids.device) + if past_key_values.get_max_length() is not None + else None + ) + cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length) + # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise + # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114 + # TODO: use `next_tokens` directly instead. + model_inputs = {"input_ids": input_ids.contiguous()} + + input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1] + if cache_position is None: + cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device) + else: + cache_position = cache_position[-input_length:] + + if has_static_cache: + past_key_values = None + + model_inputs.update( + { + "position_ids": position_ids, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + def generate( + self, + emb, + inputs_ids, + temperature, + eos_token, + attention_mask = None, + max_new_token = 2048, + min_new_token = 0, + LogitsWarpers = [], + LogitsProcessors = [], + infer_text=False, + return_attn=False, + return_hidden=False, + disable_tqdm=False + ): + if disable_tqdm: + tqdm = lambda x: x + else: + from tqdm import tqdm + + with torch.no_grad(): + + attentions = [] + hiddens = [] + + start_idx, end_idx = inputs_ids.shape[1], torch.zeros(inputs_ids.shape[0], device=inputs_ids.device, dtype=torch.long) + finish = torch.zeros(inputs_ids.shape[0], device=inputs_ids.device).bool() + + temperature = temperature[None].expand(inputs_ids.shape[0], -1) + temperature = rearrange(temperature, "b n -> (b n) 1") + + attention_mask_cache = torch.ones((inputs_ids.shape[0], inputs_ids.shape[1]+max_new_token,), dtype=torch.bool, device=inputs_ids.device) + if attention_mask is not None: + attention_mask_cache[:, :attention_mask.shape[1]] = attention_mask + + for i in tqdm(range(max_new_token)): + + model_input = self.prepare_inputs_for_generation(inputs_ids, + outputs.past_key_values if i!=0 else None, + attention_mask_cache[:, :inputs_ids.shape[1]], use_cache=True) + + if i == 0: + model_input['inputs_embeds'] = emb + else: + if infer_text: + model_input['inputs_embeds'] = self.emb_text(model_input['input_ids'][:,:,0]) + else: + code_emb = [self.emb_code[i](model_input['input_ids'][:,:,i]) for i in range(self.num_vq)] + model_input['inputs_embeds'] = torch.stack(code_emb, 3).sum(3) + + model_input['input_ids'] = None + outputs = self.gpt.forward(**model_input, output_attentions=return_attn) + attentions.append(outputs.attentions) + hidden_states = outputs[0] # 🐻 + if return_hidden: + hiddens.append(hidden_states[:, -1]) + + with P.cached(): + if infer_text: + logits = self.head_text(hidden_states) + else: + logits = torch.stack([self.head_code[i](hidden_states) for i in range(self.num_vq)], 3) + + logits = logits[:, -1].float() + + if not infer_text: + logits = rearrange(logits, "b c n -> (b n) c") + logits_token = rearrange(inputs_ids[:, start_idx:], "b c n -> (b n) c") + else: + logits_token = inputs_ids[:, start_idx:, 0] + + logits = logits / temperature + + for logitsProcessors in LogitsProcessors: + logits = logitsProcessors(logits_token, logits) + + for logitsWarpers in LogitsWarpers: + logits = logitsWarpers(logits_token, logits) + + if i < min_new_token: + logits[:, eos_token] = -torch.inf + + scores = F.softmax(logits, dim=-1) + + idx_next = torch.multinomial(scores, num_samples=1) + + if not infer_text: + idx_next = rearrange(idx_next, "(b n) 1 -> b n", n=self.num_vq) + finish = finish | (idx_next == eos_token).any(1) + inputs_ids = torch.cat([inputs_ids, idx_next.unsqueeze(1)], 1) + else: + finish = finish | (idx_next == eos_token).any(1) + inputs_ids = torch.cat([inputs_ids, idx_next.unsqueeze(-1).expand(-1, -1, self.num_vq)], 1) + + end_idx = end_idx + (~finish).int() + + if finish.all(): + break + + inputs_ids = [inputs_ids[idx, start_idx: start_idx+i] for idx, i in enumerate(end_idx.int())] + inputs_ids = [i[:, 0] for i in inputs_ids] if infer_text else inputs_ids + + if return_hidden: + hiddens = torch.stack(hiddens, 1) + hiddens = [hiddens[idx, :i] for idx, i in enumerate(end_idx.int())] + + if not finish.all(): + self.logger.warn(f'Incomplete result. hit max_new_token: {max_new_token}') + + return { + 'ids': inputs_ids, + 'attentions': attentions, + 'hiddens':hiddens, + } \ No newline at end of file diff --git a/modules/ChatTTS/ChatTTS/utils/gpu_utils.py b/modules/ChatTTS/ChatTTS/utils/gpu_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..13c7add8f07c600324a76e2fc16e5c4f60ed67fd --- /dev/null +++ b/modules/ChatTTS/ChatTTS/utils/gpu_utils.py @@ -0,0 +1,23 @@ + +import torch +import logging + +def select_device(min_memory = 2048): + logger = logging.getLogger(__name__) + if torch.cuda.is_available(): + available_gpus = [] + for i in range(torch.cuda.device_count()): + props = torch.cuda.get_device_properties(i) + free_memory = props.total_memory - torch.cuda.memory_reserved(i) + available_gpus.append((i, free_memory)) + selected_gpu, max_free_memory = max(available_gpus, key=lambda x: x[1]) + device = torch.device(f'cuda:{selected_gpu}') + free_memory_mb = max_free_memory / (1024 * 1024) + if free_memory_mb < min_memory: + logger.log(logging.WARNING, f'GPU {selected_gpu} has {round(free_memory_mb, 2)} MB memory left.') + device = torch.device('cpu') + else: + logger.log(logging.WARNING, f'No GPU found, use CPU instead') + device = torch.device('cpu') + + return device diff --git a/modules/ChatTTS/ChatTTS/utils/infer_utils.py b/modules/ChatTTS/ChatTTS/utils/infer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e37919e210454ecfa737e72958bb54c5258d7de4 --- /dev/null +++ b/modules/ChatTTS/ChatTTS/utils/infer_utils.py @@ -0,0 +1,154 @@ +import re +import torch +import torch.nn.functional as F + + +class CustomRepetitionPenaltyLogitsProcessorRepeat: + + def __init__(self, penalty: float, max_input_ids, past_window): + if not isinstance(penalty, float) or not (penalty > 0): + raise ValueError( + f"`penalty` has to be a strictly positive float, but is {penalty}" + ) + + self.penalty = penalty + self.max_input_ids = max_input_ids + self.past_window = past_window + + def __call__( + self, input_ids: torch.LongTensor, scores: torch.FloatTensor + ) -> torch.FloatTensor: + + input_ids = input_ids[:, -self.past_window :] + freq = F.one_hot(input_ids, scores.size(1)).sum(1) + freq[self.max_input_ids :] = 0 + alpha = self.penalty**freq + scores = torch.where(scores < 0, scores * alpha, scores / alpha) + + return scores + + +class CustomRepetitionPenaltyLogitsProcessor: + + def __init__(self, penalty: float, max_input_ids, past_window): + if not isinstance(penalty, float) or not (penalty > 0): + raise ValueError( + f"`penalty` has to be a strictly positive float, but is {penalty}" + ) + + self.penalty = penalty + self.max_input_ids = max_input_ids + self.past_window = past_window + + def __call__( + self, input_ids: torch.LongTensor, scores: torch.FloatTensor + ) -> torch.FloatTensor: + + input_ids = input_ids[:, -self.past_window :] + score = torch.gather(scores, 1, input_ids) + _score = score.detach().clone() + score = torch.where(score < 0, score * self.penalty, score / self.penalty) + score[input_ids >= self.max_input_ids] = _score[input_ids >= self.max_input_ids] + scores.scatter_(1, input_ids, score) + + return scores + + +def count_invalid_characters(s, reserved_tokens: list = []): + escaped_tokens = [re.escape(token) for token in reserved_tokens] + reserved_pattern = "|".join(escaped_tokens) + s = re.sub(rf"{reserved_pattern}", "", s) + pattern = re.compile(r"[^\u4e00-\u9fffA-Za-z，。、,\. ]") + non_alphabetic_chinese_chars = pattern.findall(s) + return set(non_alphabetic_chinese_chars) + + +def detect_language(sentence): + + chinese_char_pattern = re.compile(r"[\u4e00-\u9fff]") + english_word_pattern = re.compile(r"\b[A-Za-z]+\b") + + chinese_chars = chinese_char_pattern.findall(sentence) + english_words = english_word_pattern.findall(sentence) + + if len(chinese_chars) > len(english_words): + return "zh" + else: + return "en" + + +character_map = { + "：": "，", + "；": "，", + "！": "。", + "（": "，", + "）": "，", + "【": "，", + "】": "，", + "『": "，", + "』": "，", + "「": "，", + "」": "，", + "《": "，", + "》": "，", + "－": "，", + "‘": "", + "“": "", + "’": "", + "”": "", + ":": ",", + ";": ",", + "!": ".", + "(": ",", + ")": ",", + # '[': ',', + # ']': ',', + ">": ",", + "<": ",", + "-": ",", +} + +halfwidth_2_fullwidth_map = { + "!": "！", + '"': "“", + "'": "‘", + "#": "＃", + "$": "＄", + "%": "％", + "&": "＆", + "(": "（", + ")": "）", + ",": "，", + "-": "－", + "*": "＊", + "+": "＋", + ".": "。", + "/": "／", + ":": "：", + ";": "；", + "<": "＜", + "=": "＝", + ">": "＞", + "?": "？", + "@": "＠", + # '[': '［', + "\\": "＼", + # ']': '］', + "^": "＾", + # '_': '＿', + "`": "｀", + "{": "｛", + "|": "｜", + "}": "｝", + "~": "～", +} + + +def apply_half2full_map(text): + translation_table = str.maketrans(halfwidth_2_fullwidth_map) + return text.translate(translation_table) + + +def apply_character_map(text): + translation_table = str.maketrans(character_map) + return text.translate(translation_table) diff --git a/modules/ChatTTS/ChatTTS/utils/io_utils.py b/modules/ChatTTS/ChatTTS/utils/io_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b3f5eda06504ac69ceff9cb4fe5ecb0fbf112b3f --- /dev/null +++ b/modules/ChatTTS/ChatTTS/utils/io_utils.py @@ -0,0 +1,14 @@ + +import os +import logging + +def get_latest_modified_file(directory): + logger = logging.getLogger(__name__) + + files = [os.path.join(directory, f) for f in os.listdir(directory)] + if not files: + logger.log(logging.WARNING, f'No files found in the directory: {directory}') + return None + latest_file = max(files, key=os.path.getmtime) + + return latest_file \ No newline at end of file diff --git a/modules/ChatTTS/LICENSE b/modules/ChatTTS/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..dc1adf98654156baeb94d2e055c224a847e5820d --- /dev/null +++ b/modules/ChatTTS/LICENSE @@ -0,0 +1,157 @@ +# 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You could join our QQ group: 808364215 for discussion. Adding github issues is always welcomed. + +--- +## Highlights +1. **Conversational TTS**: ChatTTS is optimized for dialogue-based tasks, enabling natural and expressive speech synthesis. It supports multiple speakers, facilitating interactive conversations. +2. **Fine-grained Control**: The model could predict and control fine-grained prosodic features, including laughter, pauses, and interjections. +3. **Better Prosody**: ChatTTS surpasses most of open-source TTS models in terms of prosody. We provide pretrained models to support further research and development. + +For the detailed description of the model, you can refer to **[video on Bilibili](https://www.bilibili.com/video/BV1zn4y1o7iV)** + +--- + +## Disclaimer + +This repo is for academic purposes only. It is intended for educational and research use, and should not be used for any commercial or legal purposes. The authors do not guarantee the accuracy, completeness, or reliability of the information. The information and data used in this repo, are for academic and research purposes only. The data obtained from publicly available sources, and the authors do not claim any ownership or copyright over the data. + +ChatTTS is a powerful text-to-speech system. However, it is very important to utilize this technology responsibly and ethically. To limit the use of ChatTTS, we added a small amount of high-frequency noise during the training of the 40,000-hour model, and compressed the audio quality as much as possible using MP3 format, to prevent malicious actors from potentially using it for criminal purposes. At the same time, we have internally trained a detection model and plan to open-source it in the future. + + +--- +## Usage + +

Basic usage

+ +```python +import ChatTTS +from IPython.display import Audio + +chat = ChatTTS.Chat() +chat.load_models(compile=False) # Set to True for better performance + +texts = ["PUT YOUR TEXT HERE",] + +wavs = chat.infer(texts, ) + +torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000) +``` + +

Advanced usage

+ +```python +################################### +# Sample a speaker from Gaussian. + +rand_spk = chat.sample_random_speaker() + +params_infer_code = { + 'spk_emb': rand_spk, # add sampled speaker + 'temperature': .3, # using custom temperature + 'top_P': 0.7, # top P decode + 'top_K': 20, # top K decode +} + +################################### +# For sentence level manual control. + +# use oral_(0-9), laugh_(0-2), break_(0-7) +# to generate special token in text to synthesize. +params_refine_text = { + 'prompt': '[oral_2][laugh_0][break_6]' +} + +wav = chat.infer(texts, params_refine_text=params_refine_text, params_infer_code=params_infer_code) + +################################### +# For word level manual control. +text = 'What is [uv_break]your favorite english food?[laugh][lbreak]' +wav = chat.infer(text, skip_refine_text=True, params_refine_text=params_refine_text, params_infer_code=params_infer_code) +torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000) +``` + +
+

Example: self introduction

 + +```python +inputs_en = """ +chat T T S is a text to speech model designed for dialogue applications. +[uv_break]it supports mixed language input [uv_break]and offers multi speaker +capabilities with precise control over prosodic elements [laugh]like like +[uv_break]laughter[laugh], [uv_break]pauses, [uv_break]and intonation. +[uv_break]it delivers natural and expressive speech,[uv_break]so please +[uv_break] use the project responsibly at your own risk.[uv_break] +""".replace('\n', '') # English is still experimental. + +params_refine_text = { + 'prompt': '[oral_2][laugh_0][break_4]' +} +# audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text) +audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text) +torchaudio.save("output3.wav", torch.from_numpy(audio_array_en[0]), 24000) +``` +[male speaker](https://github.com/2noise/ChatTTS/assets/130631963/e0f51251-db7f-4d39-a0e9-3e095bb65de1) + +[female speaker](https://github.com/2noise/ChatTTS/assets/130631963/f5dcdd01-1091-47c5-8241-c4f6aaaa8bbd) +
+ +--- +## Roadmap +- [x] Open-source the 40k hour base model and spk_stats file +- [ ] Open-source VQ encoder and Lora training code +- [ ] Streaming audio generation without refining the text* +- [ ] Open-source the 40k hour version with multi-emotion control +- [ ] ChatTTS.cpp maybe? (PR or new repo are welcomed.) + +---- +## FAQ + +##### How much VRAM do I need? How about infer speed? +For a 30-second audio clip, at least 4GB of GPU memory is required. For the 4090 GPU, it can generate audio corresponding to approximately 7 semantic tokens per second. The Real-Time Factor (RTF) is around 0.3. + +##### model stability is not good enough, with issues such as multi speakers or poor audio quality. + +This is a problem that typically occurs with autoregressive models(for bark and valle). It's generally difficult to avoid. One can try multiple samples to find a suitable result. + +##### Besides laughter, can we control anything else? Can we control other emotions? + +In the current released model, the only token-level control units are [laugh], [uv_break], and [lbreak]. In future versions, we may open-source models with additional emotional control capabilities. + +--- +## Acknowledgements +- [bark](https://github.com/suno-ai/bark), [XTTSv2](https://github.com/coqui-ai/TTS) and [valle](https://arxiv.org/abs/2301.02111) demostrate a remarkable TTS result by a autoregressive-style system. +- [fish-speech](https://github.com/fishaudio/fish-speech) reveals capability of GVQ as audio tokenizer for LLM modeling. +- [vocos](https://github.com/gemelo-ai/vocos) which is used as a pretrained vocoder. + +--- +## Special Appreciation +- [wlu-audio lab](https://audio.westlake.edu.cn/) for early algorithm experiments. diff --git a/modules/ChatTTS/README_CN.md b/modules/ChatTTS/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..78cfb54ed0e634ccb5d5312d2dcc7ee57099e696 --- /dev/null +++ b/modules/ChatTTS/README_CN.md @@ -0,0 +1,136 @@ +# ChatTTS +[**English**](./README.md) | [**中文简体**](./README_CN.md) + +ChatTTS是专门为对话场景设计的文本转语音模型，例如LLM助手对话任务。它支持英文和中文两种语言。最大的模型使用了10万小时以上的中英文数据进行训练。在HuggingFace中开源的版本为4万小时训练且未SFT的版本. + +如需就模型进行正式商业咨询，请发送邮件至 **open-source@2noise.com**。对于中文用户，您可以加入我们的QQ群：808364215 进行讨论。同时欢迎在GitHub上提出问题。如果遇到无法使用 **[HuggingFace](https://huggingface.co/2Noise/ChatTTS)** 的情况,可以在 [modelscope](https://www.modelscope.cn/models/pzc163/chatTTS) 上进行下载. + +--- +## 亮点 +1. **对话式 TTS**: ChatTTS针对对话式任务进行了优化，实现了自然流畅的语音合成，同时支持多说话人。 +2. **细粒度控制**: 该模型能够预测和控制细粒度的韵律特征，包括笑声、停顿和插入词等。 +3. **更好的韵律**: ChatTTS在韵律方面超越了大部分开源TTS模型。同时提供预训练模型，支持进一步的研究。 + +对于模型的具体介绍, 可以参考B站的 **[宣传视频](https://www.bilibili.com/video/BV1zn4y1o7iV)** + +--- + +## 免责声明 +本文件中的信息仅供学术交流使用。其目的是用于教育和研究，不得用于任何商业或法律目的。作者不保证信息的准确性、完整性或可靠性。本文件中使用的信息和数据，仅用于学术研究目的。这些数据来自公开可用的来源，作者不对数据的所有权或版权提出任何主张。 + +ChatTTS是一个强大的文本转语音系统。然而，负责任地和符合伦理地利用这项技术是非常重要的。为了限制ChatTTS的使用，我们在4w小时模型的训练过程中添加了少量额外的高频噪音，并用mp3格式尽可能压低了音质，以防不法分子用于潜在的犯罪可能。同时我们在内部训练了检测模型，并计划在未来开放。 + +--- +## 用法 + +

基本用法

+ +```python +import ChatTTS +from IPython.display import Audio + +chat = ChatTTS.Chat() +chat.load_models(compile=False) # 设置为True以获得更快速度 + +texts = ["在这里输入你的文本",] + +wavs = chat.infer(texts, use_decoder=True) + +torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000) +``` + +

进阶用法

+ +```python +################################### +# Sample a speaker from Gaussian. + +rand_spk = chat.sample_random_speaker() + +params_infer_code = { + 'spk_emb': rand_spk, # add sampled speaker + 'temperature': .3, # using custom temperature + 'top_P': 0.7, # top P decode + 'top_K': 20, # top K decode +} + +################################### +# For sentence level manual control. + +# use oral_(0-9), laugh_(0-2), break_(0-7) +# to generate special token in text to synthesize. +params_refine_text = { + 'prompt': '[oral_2][laugh_0][break_6]' +} + +wav = chat.infer(texts, params_refine_text=params_refine_text, params_infer_code=params_infer_code) + +################################### +# For word level manual control. +# use_decoder=False to infer faster with a bit worse quality +text = 'What is [uv_break]your favorite english food?[laugh][lbreak]' +wav = chat.infer(text, skip_refine_text=True, params_infer_code=params_infer_code, use_decoder=False) + +torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000) +``` + +
+

自我介绍样例

 + +```python +inputs_cn = """ +chat T T S 是一款强大的对话式文本转语音模型。它有中英混读和多说话人的能力。 +chat T T S 不仅能够生成自然流畅的语音，还能控制[laugh]笑声啊[laugh]， +停顿啊[uv_break]语气词啊等副语言现象[uv_break]。这个韵律超越了许多开源模型[uv_break]。 +请注意，chat T T S 的使用应遵守法律和伦理准则，避免滥用的安全风险。[uv_break]' +""".replace('\n', '') + +params_refine_text = { + 'prompt': '[oral_2][laugh_0][break_4]' +} +audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text) +# audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text) + +torchaudio.save("output3.wav", torch.from_numpy(audio_array_cn[0]), 24000) +``` +[男说话人](https://github.com/2noise/ChatTTS/assets/130631963/bbfa3b83-2b67-4bb6-9315-64c992b63788) + +[女说话人](https://github.com/2noise/ChatTTS/assets/130631963/e061f230-0e05-45e6-8e4e-0189f2d260c4) +
+ + +--- +## 计划路线 +- [x] 开源4w小时基础模型和spk_stats文件 +- [ ] 开源VQ encoder和Lora 训练代码 +- [ ] 在非refine text情况下, 流式生成音频* +- [ ] 开源多情感可控的4w小时版本 +- [ ] ChatTTS.cpp maybe? (欢迎社区PR或独立的新repo) + +--- +## 常见问题 + +##### 连不上HuggingFace +请使用[modelscope](https://www.modelscope.cn/models/pzc163/chatTTS)的版本. 并设置cache的位置: +```python +chat.load_models(source='local', local_path='你的下载位置') +``` + +##### 我要多少显存? Infer的速度是怎么样的? +对于30s的音频, 至少需要4G的显存. 对于4090, 1s生成约7个字所对应的音频. RTF约0.3. + +##### 模型稳定性似乎不够好, 会出现其他说话人或音质很差的现象. +这是自回归模型通常都会出现的问题. 说话人可能会在中间变化, 可能会采样到音质非常差的结果, 这通常难以避免. 可以多采样几次来找到合适的结果. + +##### 除了笑声还能控制什么吗? 还能控制其他情感吗? +在现在放出的模型版本中, 只有[laugh]和[uv_break], [lbreak]作为字级别的控制单元. 在未来的版本中我们可能会开源其他情感控制的版本. + +--- +## 致谢 +- [bark](https://github.com/suno-ai/bark),[XTTSv2](https://github.com/coqui-ai/TTS)和[valle](https://arxiv.org/abs/2301.02111)展示了自回归任务用于TTS任务的可能性. +- [fish-speech](https://github.com/fishaudio/fish-speech)一个优秀的自回归TTS模型, 揭示了GVQ用于LLM任务的可能性. +- [vocos](https://github.com/gemelo-ai/vocos)作为模型中的vocoder. + +--- +## 特别致谢 +- [wlu-audio lab](https://audio.westlake.edu.cn/)为我们提供了早期算法试验的支持. diff --git a/modules/ChatTTS/example.ipynb b/modules/ChatTTS/example.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b229e702a2cbab53cf03fe2712bc27a4ebad25e3 --- /dev/null +++ b/modules/ChatTTS/example.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "torch._dynamo.config.cache_size_limit = 64\n", + "torch._dynamo.config.suppress_errors = True\n", + "torch.set_float32_matmul_precision('high')\n", + "\n", + "import ChatTTS\n", + "from IPython.display import Audio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Models" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "chat = ChatTTS.Chat()\n", + "chat.load_models()\n", + "\n", + "# Use force_redownload=True if the weights updated.\n", + "# chat.load_models(force_redownload=True)\n", + "\n", + "# If you download the weights manually, set source='locals'.\n", + "# chat.load_models(source='local', local_path='YOUR LOCAL PATH')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Batch infer" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:ChatTTS.core:All initialized.\n", + " 28%|██▊ | 106/384 [00:00<00:01, 140.38it/s]\n", + " 47%|████▋ | 960/2048 [00:07<00:08, 133.25it/s]\n" + ] + } + ], + "source": [ + "texts = [\"So we found being competitive and collaborative was a huge way of staying motivated towards our goals, so one person to call when you fall off, one person who gets you back on then one person to actually do the activity with.\",]*3 \\\n", + " + [\"我觉得像我们这些写程序的人，他，我觉得多多少少可能会对开源有一种情怀在吧我觉得开源是一个很好的形式。现在其实最先进的技术掌握在一些公司的手里的话，就他们并不会轻易的开放给所有的人用。\"]*3 \n", + " \n", + "wavs = chat.infer(texts)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Audio(wavs[0], rate=24_000, autoplay=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Audio(wavs[3], rate=24_000, autoplay=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Custom params" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:ChatTTS.core:All initialized.\n", + " 14%|█▍ | 53/384 [00:00<00:02, 146.65it/s]\n", + " 22%|██▏ | 452/2048 [00:03<00:11, 140.51it/s]\n" + ] + } + ], + "source": [ + "params_infer_code = {'prompt':'[speed_5]', 'temperature':.3}\n", + "params_refine_text = {'prompt':'[oral_2][laugh_0][break_6]'}\n", + "\n", + "wav = chat.infer('四川美食可多了，有麻辣火锅、宫保鸡丁、麻婆豆腐、担担面、回锅肉、夫妻肺片等，每样都让人垂涎三尺。', \\\n", + " params_refine_text=params_refine_text, params_infer_code=params_infer_code)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Audio(wav[0], rate=24_000, autoplay=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### fix random speaker" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "rand_spk = chat.sample_random_speaker()\n", + "params_infer_code = {'spk_emb' : rand_spk, }\n", + "\n", + "wav = chat.infer('四川美食确实以辣闻名，但也有不辣的选择。比如甜水面、赖汤圆、蛋烘糕、叶儿粑等，这些小吃口味温和，甜而不腻，也很受欢迎。', \\\n", + " params_refine_text=params_refine_text, params_infer_code=params_infer_code)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Audio(wav[0], rate=24_000, autoplay=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Two stage control" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:ChatTTS.core:All initialized.\n", + " 23%|██▎ | 87/384 [00:00<00:01, 150.60it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "['so we found being competitive and collaborative [uv_break] was a huge way of staying [uv_break] motivated towards our goals, [uv_break] so [uv_break] one person to call [uv_break] when you fall off, [uv_break] one person who [uv_break] gets you back [uv_break] on then [uv_break] one person [uv_break] to actually do the activity with.']" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = \"So we found being competitive and collaborative was a huge way of staying motivated towards our goals, so one person to call when you fall off, one person who gets you back on then one person to actually do the activity with.\"\n", + "chat.infer(text, refine_text_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:ChatTTS.core:All initialized.\n", + " 49%|████▊ | 995/2048 [00:07<00:07, 141.85it/s]\n" + ] + } + ], + "source": [ + "text = 'so we found being competitive and collaborative [uv_break] was a huge way of staying [uv_break] motivated towards our goals, [uv_break] so [uv_break] one person to call [uv_break] when you fall off, [uv_break] one person who [uv_break] gets you back [uv_break] on then [uv_break] one person [uv_break] to actually do the activity with.'\n", + "wav = chat.infer(text, skip_refine_text=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LLM Call" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from ChatTTS.experimental.llm import llm_api\n", + "\n", + "API_KEY = ''\n", + "client = llm_api(api_key=API_KEY,\n", + " base_url=\"https://api.deepseek.com\",\n", + " model=\"deepseek-chat\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.deepseek.com/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "四川美食可多了, 有麻辣火锅、宫保鸡丁、麻婆豆腐、担担面、回锅肉、夫妻肺片、串串香、龙抄手、宜宾燃面、乐山钵钵鸡等, 每样都让人垂涎三尺。\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.deepseek.com/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "四川美食可多了，有麻辣火锅、宫保鸡丁、麻婆豆腐、担担面、回锅肉、夫妻肺片、串串香、龙抄手、宜宾燃面、乐山钵钵鸡等，每样都让人垂涎三尺。\n" + ] + } + ], + "source": [ + "user_question = '四川有哪些好吃的美食呢?'\n", + "text = client.call(user_question, prompt_version = 'deepseek')\n", + "print(text)\n", + "text = client.call(text, prompt_version = 'deepseek_TN')\n", + "print(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:ChatTTS.core:All initialized.\n", + " 20%|█▉ | 75/384 [00:00<00:02, 144.93it/s]\n", + " 32%|███▏ | 647/2048 [00:04<00:09, 140.27it/s]\n" + ] + } + ], + "source": [ + "params_infer_code = {'spk_emb' : rand_spk, 'temperature':.3}\n", + "\n", + "wav = chat.infer(text, params_infer_code=params_infer_code)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/modules/ChatTTS/requirements.txt b/modules/ChatTTS/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..cab134c66c4fd8f2045a7ea007c24970ac902cc1 --- /dev/null +++ b/modules/ChatTTS/requirements.txt @@ -0,0 +1,8 @@ +omegaconf~=2.3.0 +# torch~=2.1.0 +tqdm +einops +vector_quantize_pytorch +transformers~=4.41.1 +vocos +IPython \ No newline at end of file diff --git a/modules/SentenceSplitter.py b/modules/SentenceSplitter.py new file mode 100644 index 0000000000000000000000000000000000000000..6865ce68d778bba60893981d96f324fe1ee88c17 --- /dev/null +++ b/modules/SentenceSplitter.py @@ -0,0 +1,70 @@ +import re +import zhon + + +def split_zhon_sentence(text): + result = [] + pattern = re.compile(zhon.hanzi.sentence) + start = 0 + for match in pattern.finditer(text): + # 获取匹配的中文句子 + end = match.end() + result.append(text[start:end]) + start = end + + # 最后一个中文句子后面的内容（如果有）也需要添加到结果中 + if start < len(text): + result.append(text[start:]) + + result = [t for t in result if t.strip()] + return result + + +# 解析文本 并根据停止符号分割成句子 +# 可以设置最大阈值，即如果分割片段小于这个阈值会与下一段合并 +class SentenceSplitter: + def __init__(self, threshold=100): + self.sentence_threshold = threshold + + def parse(self, text): + sentences = split_zhon_sentence(text) + + # 合并小于最大阈值的片段 + merged_sentences = [] + temp_sentence = [] + for sentence in sentences: + if len(sentence) < self.sentence_threshold: + temp_sentence.extend(sentence) + if len(temp_sentence) >= self.sentence_threshold: + merged_sentences.append(temp_sentence) + temp_sentence = [] + else: + if temp_sentence: + merged_sentences.append(temp_sentence) + temp_sentence = [] + merged_sentences.append(sentence) + + if temp_sentence: + merged_sentences.append(temp_sentence) + + joind_sentences = [ + "".join(sentence) for sentence in merged_sentences if sentence + ] + return joind_sentences + + +if __name__ == "__main__": + max_threshold = 100 + parser = SentenceSplitter(max_threshold) + text = """ +中华美食，作为世界饮食文化的瑰宝，以其丰富的种类、独特的风味和精湛的烹饪技艺而闻名于世。中国地大物博，各地区的饮食习惯和烹饪方法各具特色，形成了独树一帜的美食体系。从北方的京鲁菜、东北菜，到南方的粤菜、闽菜，无不展现出中华美食的多样性。 + +在中华美食的世界里，五味调和，色香味俱全。无论是辣味浓郁的川菜，还是清淡鲜美的淮扬菜，都能够满足不同人的口味需求。除了味道上的独特，中华美食还注重色彩的搭配和形态的美感，让每一道菜品不仅是味觉的享受，更是一场视觉的盛宴。 + +中华美食不仅仅是食物，更是一种文化的传承。每一道菜背后都有着深厚的历史背景和文化故事。比如，北京的烤鸭，代表着皇家气派；而西安的羊肉泡馍，则体现了浓郁的地方风情。中华美食的精髓在于它追求的“天人合一”，讲究食材的自然性和烹饪过程中的和谐。 + +总之，中华美食博大精深，其丰富的口感和多样的烹饪技艺，构成了一个充满魅力和无限可能的美食世界。无论你来自哪里，都会被这独特的美食文化所吸引和感动。 + """ + result = parser.parse(text) + for idx, sentence in enumerate(result): + print(f"Sentence {idx + 1}: {sentence}") diff --git a/modules/SynthesizeSegments.py b/modules/SynthesizeSegments.py new file mode 100644 index 0000000000000000000000000000000000000000..3fcaa509084acfaced7be32ef1f38341f4fdab8c --- /dev/null +++ b/modules/SynthesizeSegments.py @@ -0,0 +1,284 @@ +import numpy as np +from pydub import AudioSegment +from typing import Any, List, Dict +from scipy.io.wavfile import write +import io +from modules.utils.audio import time_stretch, pitch_shift +from modules import generate_audio +from modules.normalization import text_normalize +import logging +import json +import random + +from modules.speaker import Speaker + +logger = logging.getLogger(__name__) + + +def audio_data_to_segment(audio_data, sr): + byte_io = io.BytesIO() + write(byte_io, rate=sr, data=audio_data) + byte_io.seek(0) + + return AudioSegment.from_file(byte_io, format="wav") + + +def combine_audio_segments(audio_segments: list) -> AudioSegment: + combined_audio = AudioSegment.empty() + for segment in audio_segments: + combined_audio += segment + return combined_audio + + +def apply_prosody( + audio_segment: AudioSegment, rate: float, volume: float, pitch: float +) -> AudioSegment: + if rate != 1: + audio_segment = time_stretch(audio_segment, rate) + + if volume != 0: + audio_segment += volume + + if pitch != 0: + audio_segment = pitch_shift(audio_segment, pitch) + + return audio_segment + + +def to_number(value, t, default=0): + try: + number = t(value) + return number + except (ValueError, TypeError) as e: + return default + + +class SynthesizeSegments: + batch_default_spk_seed = int(np.random.randint(0, 2**32 - 1)) + batch_default_infer_seed = int(np.random.randint(0, 2**32 - 1)) + + def __init__(self, batch_size: int = 8): + self.batch_size = batch_size + + def segment_to_generate_params(self, segment: Dict[str, Any]) -> Dict[str, Any]: + text = segment.get("text", "") + is_end = segment.get("is_end", False) + + text = str(text).strip() + + attrs = segment.get("attrs", {}) + spk = attrs.get("spk", "") + if isinstance(spk, str): + spk = int(spk) + seed = to_number(attrs.get("seed", ""), int, -1) + top_k = to_number(attrs.get("top_k", ""), int, None) + top_p = to_number(attrs.get("top_p", ""), float, None) + temp = to_number(attrs.get("temp", ""), float, None) + + prompt1 = attrs.get("prompt1", "") + prompt2 = attrs.get("prompt2", "") + prefix = attrs.get("prefix", "") + disable_normalize = attrs.get("normalize", "") == "False" + + params = { + "text": text, + "temperature": temp if temp is not None else 0.3, + "top_P": top_p if top_p is not None else 0.5, + "top_K": top_k if top_k is not None else 20, + "spk": spk if spk else -1, + "infer_seed": seed if seed else -1, + "prompt1": prompt1 if prompt1 else "", + "prompt2": prompt2 if prompt2 else "", + "prefix": prefix if prefix else "", + } + + if not disable_normalize: + params["text"] = text_normalize(text, is_end=is_end) + + # Set default values for spk and infer_seed + if params["spk"] == -1: + params["spk"] = self.batch_default_spk_seed + if params["infer_seed"] == -1: + params["infer_seed"] = self.batch_default_infer_seed + + return params + + def bucket_segments( + self, segments: List[Dict[str, Any]] + ) -> List[List[Dict[str, Any]]]: + # Create a dictionary to hold buckets + buckets = {} + for segment in segments: + params = self.segment_to_generate_params(segment) + + key_params = params + if isinstance(key_params.get("spk"), Speaker): + key_params["spk"] = str(key_params["spk"].id) + key = json.dumps( + {k: v for k, v in key_params.items() if k != "text"}, sort_keys=True + ) + if params["spk"] == -1 or params["infer_seed"] == -1: + key = random.random() + buckets[key] = [segment] + else: + if key not in buckets: + buckets[key] = [] + buckets[key].append(segment) + + # Convert dictionary to list of buckets + bucket_list = list(buckets.values()) + return bucket_list + + def synthesize_segments(self, segments: List[Dict[str, Any]]) -> List[AudioSegment]: + audio_segments = [None] * len( + segments + ) # Create a list with the same length as segments + buckets = self.bucket_segments(segments) + logger.debug(f"segments len: {len(segments)}") + logger.debug(f"bucket pool size: {len(buckets)}") + for bucket in buckets: + for i in range(0, len(bucket), self.batch_size): + batch = bucket[i : i + self.batch_size] + param_arr = [ + self.segment_to_generate_params(segment) for segment in batch + ] + texts = [params["text"] for params in param_arr] + + params = param_arr[0] # Use the first segment to get the parameters + audio_datas = generate_audio.generate_audio_batch( + texts=texts, + temperature=params["temperature"], + top_P=params["top_P"], + top_K=params["top_K"], + spk=params["spk"], + infer_seed=params["infer_seed"], + prompt1=params["prompt1"], + prompt2=params["prompt2"], + prefix=params["prefix"], + ) + for idx, segment in enumerate(batch): + (sr, audio_data) = audio_datas[idx] + rate = float(segment.get("rate", "1.0")) + volume = float(segment.get("volume", "0")) + pitch = float(segment.get("pitch", "0")) + + audio_segment = audio_data_to_segment(audio_data, sr) + audio_segment = apply_prosody(audio_segment, rate, volume, pitch) + original_index = segments.index( + segment + ) # Get the original index of the segment + audio_segments[original_index] = ( + audio_segment # Place the audio_segment in the correct position + ) + + return audio_segments + + +def generate_audio_segment( + text: str, + spk: int = -1, + seed: int = -1, + top_p: float = 0.5, + top_k: int = 20, + temp: float = 0.3, + prompt1: str = "", + prompt2: str = "", + prefix: str = "", + enable_normalize=True, + is_end: bool = False, +) -> AudioSegment: + if enable_normalize: + text = text_normalize(text, is_end=is_end) + + logger.debug(f"generate segment: {text}") + + sample_rate, audio_data = generate_audio.generate_audio( + text=text, + temperature=temp if temp is not None else 0.3, + top_P=top_p if top_p is not None else 0.5, + top_K=top_k if top_k is not None else 20, + spk=spk if spk else -1, + infer_seed=seed if seed else -1, + prompt1=prompt1 if prompt1 else "", + prompt2=prompt2 if prompt2 else "", + prefix=prefix if prefix else "", + ) + + byte_io = io.BytesIO() + write(byte_io, sample_rate, audio_data) + byte_io.seek(0) + + return AudioSegment.from_file(byte_io, format="wav") + + +def synthesize_segment(segment: Dict[str, Any]) -> AudioSegment | None: + if "break" in segment: + pause_segment = AudioSegment.silent(duration=segment["break"]) + return pause_segment + + attrs = segment.get("attrs", {}) + text = segment.get("text", "") + is_end = segment.get("is_end", False) + + text = str(text).strip() + + if text == "": + return None + + spk = attrs.get("spk", "") + if isinstance(spk, str): + spk = int(spk) + seed = to_number(attrs.get("seed", ""), int, -1) + top_k = to_number(attrs.get("top_k", ""), int, None) + top_p = to_number(attrs.get("top_p", ""), float, None) + temp = to_number(attrs.get("temp", ""), float, None) + + prompt1 = attrs.get("prompt1", "") + prompt2 = attrs.get("prompt2", "") + prefix = attrs.get("prefix", "") + disable_normalize = attrs.get("normalize", "") == "False" + + audio_segment = generate_audio_segment( + text, + enable_normalize=not disable_normalize, + spk=spk, + seed=seed, + top_k=top_k, + top_p=top_p, + temp=temp, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + is_end=is_end, + ) + + rate = float(attrs.get("rate", "1.0")) + volume = float(attrs.get("volume", "0")) + pitch = float(attrs.get("pitch", "0")) + + audio_segment = apply_prosody(audio_segment, rate, volume, pitch) + + return audio_segment + + +# 示例使用 +if __name__ == "__main__": + ssml_segments = [ + { + "text": "大🍌，一条大🍌，嘿，你的感觉真的很奇妙 [lbreak]", + "attrs": {"spk": 2, "temp": 0.1, "seed": 42}, + }, + { + "text": "大🍉，一个大🍉，嘿，你的感觉真的很奇妙 [lbreak]", + "attrs": {"spk": 2, "temp": 0.1, "seed": 42}, + }, + { + "text": "大🍌，一条大🍌，嘿，你的感觉真的很奇妙 [lbreak]", + "attrs": {"spk": 2, "temp": 0.3, "seed": 42}, + }, + ] + + synthesizer = SynthesizeSegments(batch_size=2) + audio_segments = synthesizer.synthesize_segments(ssml_segments) + combined_audio = combine_audio_segments(audio_segments) + combined_audio.export("output.wav", format="wav") diff --git a/modules/api/Api.py b/modules/api/Api.py new file mode 100644 index 0000000000000000000000000000000000000000..8e51df95f1bf5c5289473a712f2df446847df640 --- /dev/null +++ b/modules/api/Api.py @@ -0,0 +1,91 @@ +from fastapi import FastAPI, APIRouter +from fastapi.middleware.cors import CORSMiddleware + +import logging + +from fastapi.staticfiles import StaticFiles + +import fnmatch + + +def is_excluded(path, exclude_patterns): + """ + 检查路径是否被排除 + + :param path: 需要检查的路径 + :param exclude_patterns: 包含通配符的排除路径列表 + :return: 如果路径被排除，返回 True；否则返回 False + """ + for pattern in exclude_patterns: + if fnmatch.fnmatch(path, pattern): + print(path, pattern) + return True + return False + + +class APIManager: + def __init__(self, no_docs=False, exclude_patterns=[]): + self.app = FastAPI( + title="ChatTTS Forge API", + description="ChatTTS-Forge 是一个功能强大的文本转语音生成工具，支持通过类 SSML 语法生成丰富的音频长文本，并提供全面的 API 服务，适用于各种场景。\n\nChatTTS-Forge is a powerful text-to-speech generation tool that supports generating rich audio long texts through class SSML syntax\n\n https://github.com/lenML/ChatTTS-Forge", + version="0.1.0", + redoc_url=None if no_docs else "/redoc", + docs_url=None if no_docs else "/docs", + ) + self.registered_apis = {} + self.logger = logging.getLogger(__name__) + self.exclude = exclude_patterns + + def is_excluded(self, path): + return is_excluded(path, self.exclude) + + def set_cors( + self, + allow_origins: list = ["*"], + allow_credentials: bool = True, + allow_methods: list = ["*"], + allow_headers: list = ["*"], + ): + self.app.add_middleware( + CORSMiddleware, + allow_origins=allow_origins, + allow_credentials=allow_credentials, + allow_methods=allow_methods, + allow_headers=allow_headers, + ) + + def setup_playground(self): + app = self.app + app.mount( + "/playground", + StaticFiles(directory="playground", html=True), + name="playground", + ) + + def get(self, path: str, **kwargs): + def decorator(func): + if self.is_excluded(path): + return func + + self.app.get(path, **kwargs)(func) + + self.registered_apis[path] = func + self.logger.info(f"Registered API: GET {path}") + + return func + + return decorator + + def post(self, path: str, **kwargs): + def decorator(func): + if self.is_excluded(path): + return func + + self.app.post(path, **kwargs)(func) + + self.registered_apis[path] = func + self.logger.info(f"Registered API: POST {path}") + + return func + + return decorator diff --git a/modules/api/__init__.py b/modules/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/api/impl/__init__.py b/modules/api/impl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/api/impl/base_api.py b/modules/api/impl/base_api.py new file mode 100644 index 0000000000000000000000000000000000000000..d7ba73ba3ad0914cceca42d53c5aed8a325dc716 --- /dev/null +++ b/modules/api/impl/base_api.py @@ -0,0 +1,48 @@ +from pydantic import BaseModel + + +from modules.speaker import speaker_mgr + + +from modules.data import styles_mgr + + +from modules.api import utils as api_utils +from modules.api.Api import APIManager + + +async def list_styles(): + return {"message": "ok", "data": styles_mgr.list_items()} + + +async def list_speakers(): + return { + "message": "ok", + "data": [spk.to_json() for spk in speaker_mgr.list_speakers()], + } + + +class CreateSpeaker(BaseModel): + seed: int + name: str = "" + + +async def create_speaker(request: CreateSpeaker): + speaker = speaker_mgr.create_speaker(request.seed, request.name) + return {"message": "ok", "data": speaker.to_json()} + + +async def refresh_speakers(): + speaker_mgr.refresh_speakers() + return {"message": "ok"} + + +def setup(app: APIManager): + app.get("/v1/styles/list", response_model=api_utils.BaseResponse)(list_styles) + app.get("/v1/speakers/list", response_model=api_utils.BaseResponse)(list_speakers) + app.post("/v1/speaker/create", response_model=api_utils.BaseResponse)( + create_speaker + ) + app.post("/v1/speaker/refresh", response_model=api_utils.BaseResponse)( + refresh_speakers + ) diff --git a/modules/api/impl/google_api.py b/modules/api/impl/google_api.py new file mode 100644 index 0000000000000000000000000000000000000000..d2187570f0b876e248f522c1aebff2ea228bda73 --- /dev/null +++ b/modules/api/impl/google_api.py @@ -0,0 +1,165 @@ +import base64 +from fastapi import HTTPException + +import io +import soundfile as sf +from pydantic import BaseModel + + +from modules.api.Api import APIManager +from modules.utils.audio import apply_prosody_to_audio_data +from modules.normalization import text_normalize + +from modules import generate_audio as generate + + +from modules.ssml import parse_ssml +from modules.SynthesizeSegments import ( + SynthesizeSegments, + combine_audio_segments, + synthesize_segment, +) + +from modules.api import utils as api_utils + + +class SynthesisInput(BaseModel): + text: str = "" + ssml: str = "" + + +class VoiceSelectionParams(BaseModel): + languageCode: str = "ZH-CN" + name: str = "female2" + style: str = "" + temperature: float = 0.3 + topP: float = 0.7 + topK: int = 20 + seed: int = 42 + + +class AudioConfig(BaseModel): + audioEncoding: api_utils.AudioFormat = "mp3" + speakingRate: float = 1 + pitch: float = 0 + volumeGainDb: float = 0 + sampleRateHertz: int + batchSize: int = 1 + spliterThreshold: int = 100 + + +class GoogleTextSynthesizeRequest(BaseModel): + input: SynthesisInput + voice: VoiceSelectionParams + audioConfig: dict + + +class GoogleTextSynthesizeResponse(BaseModel): + audioContent: str + + +async def google_text_synthesize(request: GoogleTextSynthesizeRequest): + input = request.input + voice = request.voice + audioConfig = request.audioConfig + + # 提取参数 + language_code = voice.languageCode + voice_name = voice.name + infer_seed = voice.seed or 42 + audio_format = audioConfig.get("audioEncoding", "mp3") + speaking_rate = audioConfig.get("speakingRate", 1) + pitch = audioConfig.get("pitch", 0) + volume_gain_db = audioConfig.get("volumeGainDb", 0) + + batch_size = audioConfig.get("batchSize", 1) + spliter_threshold = audioConfig.get("spliterThreshold", 100) + + # TODO sample_rate + sample_rate_hertz = audioConfig.get("sampleRateHertz", 24000) + + params = api_utils.calc_spk_style(spk=voice.name, style=voice.style) + + # TODO maybe need to change the sample rate + sample_rate = 24000 + + try: + if input.text: + # 处理文本合成逻辑 + text = text_normalize(input.text, is_end=True) + sample_rate, audio_data = generate.generate_audio( + text, + temperature=( + voice.temperature + if voice.temperature + else params.get("temperature", 0.3) + ), + top_P=voice.topP if voice.topP else params.get("top_p", 0.7), + top_K=voice.topK if voice.topK else params.get("top_k", 20), + spk=params.get("spk", -1), + infer_seed=infer_seed, + prompt1=params.get("prompt1", ""), + prompt2=params.get("prompt2", ""), + prefix=params.get("prefix", ""), + ) + + elif input.ssml: + # 处理SSML合成逻辑 + segments = parse_ssml(input.ssml) + for seg in segments: + seg["text"] = text_normalize(seg["text"], is_end=True) + + if len(segments) == 0: + raise HTTPException( + status_code=400, detail="The SSML text is empty or parsing failed." + ) + + synthesize = SynthesizeSegments(batch_size=batch_size) + audio_segments = synthesize.synthesize_segments(segments) + combined_audio = combine_audio_segments(audio_segments) + + buffer = io.BytesIO() + combined_audio.export(buffer, format="wav") + + buffer.seek(0) + + audio_data = buffer.read() + + else: + raise HTTPException( + status_code=400, detail="Either text or SSML input must be provided." + ) + + audio_data = apply_prosody_to_audio_data( + audio_data, + rate=speaking_rate, + pitch=pitch, + volume=volume_gain_db, + sr=sample_rate, + ) + + buffer = io.BytesIO() + sf.write(buffer, audio_data, sample_rate, format="wav") + buffer.seek(0) + + if audio_format == "mp3": + buffer = api_utils.wav_to_mp3(buffer) + + base64_encoded = base64.b64encode(buffer.read()) + base64_string = base64_encoded.decode("utf-8") + + return { + "audioContent": f"data:audio/{audio_format.lower()};base64,{base64_string}" + } + + except Exception as e: + import logging + + logging.exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + +def setup(app: APIManager): + app.post("/v1/google/text:synthesize", response_model=GoogleTextSynthesizeResponse)( + google_text_synthesize + ) diff --git a/modules/api/impl/openai_api.py b/modules/api/impl/openai_api.py new file mode 100644 index 0000000000000000000000000000000000000000..f0b6ad7d356060994db2d1abb6b1409d83e6e645 --- /dev/null +++ b/modules/api/impl/openai_api.py @@ -0,0 +1,112 @@ +from fastapi import HTTPException, Body +from fastapi.responses import StreamingResponse + +import io +from numpy import clip +import soundfile as sf +from pydantic import BaseModel, Field +from fastapi.responses import FileResponse + + +from modules.synthesize_audio import synthesize_audio +from modules.normalization import text_normalize + +from modules import generate_audio as generate + + +from typing import Literal +import pyrubberband as pyrb + +from modules.api import utils as api_utils +from modules.api.Api import APIManager + +import numpy as np + + +class AudioSpeechRequest(BaseModel): + input: str # 需要合成的文本 + model: str = "chattts-4w" + voice: str = "female2" + response_format: Literal["mp3", "wav"] = "mp3" + speed: int = Field(1, ge=1, le=10, description="Speed of the audio") + style: str = "" + # 是否开启batch合成，小于等于1表示不适用batch + # 开启batch合成会自动分割句子 + batch_size: int = Field(1, ge=1, le=10, description="Batch size") + spliter_threshold: float = Field( + 100, ge=10, le=1024, description="Threshold for sentence spliter" + ) + + +async def openai_speech_api( + request: AudioSpeechRequest = Body( + ..., description="JSON body with model, input text, and voice" + ) +): + try: + model = request.model + input_text = request.input + voice = request.voice + style = request.style + response_format = request.response_format + batch_size = request.batch_size + spliter_threshold = request.spliter_threshold + speed = request.speed + speed = clip(speed, 0.1, 10) + + if not input_text: + raise HTTPException(status_code=400, detail="Input text is required.") + + # Normalize the text + text = text_normalize(input_text, is_end=True) + + # Calculate speaker and style based on input voice + params = api_utils.calc_spk_style(spk=voice, style=style) + + spk = params.get("spk", -1) + seed = params.get("seed", 42) + temperature = params.get("temperature", 0.3) + prompt1 = params.get("prompt1", "") + prompt2 = params.get("prompt2", "") + prefix = params.get("prefix", "") + + # Generate audio + sample_rate, audio_data = synthesize_audio( + text, + temperature=temperature, + top_P=0.7, + top_K=20, + spk=spk, + infer_seed=seed, + batch_size=batch_size, + spliter_threshold=spliter_threshold, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + ) + + if speed != 1: + audio_data = pyrb.time_stretch(audio_data, sample_rate, speed) + + # Convert audio data to wav format + buffer = io.BytesIO() + sf.write(buffer, audio_data, sample_rate, format="wav") + buffer.seek(0) + + if response_format == "mp3": + # Convert wav to mp3 + buffer = api_utils.wav_to_mp3(buffer) + + return StreamingResponse(buffer, media_type="audio/mp3") + + except Exception as e: + import logging + + logging.exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + +def setup(api_manager: APIManager): + api_manager.post("/v1/openai/audio/speech", response_class=FileResponse)( + openai_speech_api + ) diff --git a/modules/api/impl/refiner_api.py b/modules/api/impl/refiner_api.py new file mode 100644 index 0000000000000000000000000000000000000000..411313a8a35ae0dc69874e7ce3fd3d92d825f0d2 --- /dev/null +++ b/modules/api/impl/refiner_api.py @@ -0,0 +1,51 @@ +from fastapi import HTTPException + +from pydantic import BaseModel + + +from modules import refiner + +from modules.api import utils as api_utils +from modules.api.Api import APIManager + + +class RefineTextRequest(BaseModel): + text: str + prompt: str = "[oral_2][laugh_0][break_6]" + seed: int = -1 + top_P: float = 0.7 + top_K: int = 20 + temperature: float = 0.7 + repetition_penalty: float = 1.0 + max_new_token: int = 384 + + +async def refiner_prompt_post(request: RefineTextRequest): + """ + This endpoint receives a prompt and returns the refined result + """ + + try: + refined_text = refiner.refine_text( + text=request.text, + prompt=request.prompt, + seed=request.seed, + top_P=request.top_P, + top_K=request.top_K, + temperature=request.temperature, + repetition_penalty=request.repetition_penalty, + max_new_token=request.max_new_token, + ) + return {"message": "ok", "data": refined_text} + + except Exception as e: + import logging + + logging.exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + +def setup(api_manager: APIManager): + api_manager.post("/v1/prompt/refine", response_model=api_utils.BaseResponse)( + refiner_prompt_post + ) diff --git a/modules/api/impl/speaker_api.py b/modules/api/impl/speaker_api.py new file mode 100644 index 0000000000000000000000000000000000000000..80dfac94ed61b0977b3cd4ce38704f38d9baede3 --- /dev/null +++ b/modules/api/impl/speaker_api.py @@ -0,0 +1,119 @@ +from fastapi import HTTPException +from pydantic import BaseModel +import torch +from modules.speaker import speaker_mgr +from modules.api import utils as api_utils +from modules.api.Api import APIManager + + +class CreateSpeaker(BaseModel): + seed: int + name: str + gender: str + describe: str + tensor: list + + +class UpdateSpeaker(BaseModel): + id: str + name: str + gender: str + describe: str + tensor: list + + +class SpeakerDetail(BaseModel): + id: str + with_emb: bool = False + + +class SpeakersUpdate(BaseModel): + speakers: list + + +def setup(app: APIManager): + + @app.get("/v1/speakers/list", response_model=api_utils.BaseResponse) + async def list_speakers(): + return { + "message": "ok", + "data": [spk.to_json() for spk in speaker_mgr.list_speakers()], + } + + @app.post("/v1/speakers/update", response_model=api_utils.BaseResponse) + async def update_speakers(request: SpeakersUpdate): + for spk in request.speakers: + speaker = speaker_mgr.get_speaker_by_id(spk["id"]) + if speaker is None: + raise HTTPException( + status_code=404, detail=f"Speaker not found: {spk['id']}" + ) + speaker.name = spk.get("name", speaker.name) + speaker.gender = spk.get("gender", speaker.gender) + speaker.describe = spk.get("describe", speaker.describe) + if ( + spk.get("tensor") + and isinstance(spk["tensor"], list) + and len(spk["tensor"]) > 0 + ): + # number array => Tensor + speaker.emb = torch.tensor(spk["tensor"]) + speaker_mgr.save_all() + return {"message": "ok", "data": None} + + @app.post("/v1/speaker/create", response_model=api_utils.BaseResponse) + async def create_speaker(request: CreateSpeaker): + if ( + request.tensor + and isinstance(request.tensor, list) + and len(request.tensor) > 0 + ): + # from tensor + tensor = torch.tensor(request.tensor) + speaker = speaker_mgr.create_speaker_from_tensor( + tensor=tensor, + name=request.name, + gender=request.gender, + describe=request.describe, + ) + else: + # from seed + speaker = speaker_mgr.create_speaker_from_seed( + seed=request.seed, + name=request.name, + gender=request.gender, + describe=request.describe, + ) + return {"message": "ok", "data": speaker.to_json()} + + @app.post("/v1/speaker/refresh", response_model=api_utils.BaseResponse) + async def refresh_speakers(): + speaker_mgr.refresh_speakers() + return {"message": "ok"} + + @app.post("/v1/speaker/update", response_model=api_utils.BaseResponse) + async def update_speaker(request: UpdateSpeaker): + speaker = speaker_mgr.get_speaker_by_id(request.id) + if speaker is None: + raise HTTPException( + status_code=404, detail=f"Speaker not found: {request.id}" + ) + speaker.name = request.name + speaker.gender = request.gender + speaker.describe = request.describe + if ( + request.tensor + and isinstance(request.tensor, list) + and len(request.tensor) > 0 + ): + # number array => Tensor + speaker.emb = torch.tensor(request.tensor) + speaker_mgr.update_speaker(speaker) + return {"message": "ok"} + + @app.post("/v1/speaker/detail", response_model=api_utils.BaseResponse) + async def speaker_detail(request: SpeakerDetail): + speaker = speaker_mgr.get_speaker_by_id(request.id) + if speaker is None: + raise HTTPException(status_code=404, detail="Speaker not found") + return {"message": "ok", "data": speaker.to_json(with_emb=request.with_emb)} diff --git a/modules/api/impl/ssml_api.py b/modules/api/impl/ssml_api.py new file mode 100644 index 0000000000000000000000000000000000000000..87eef77171e39fd1bdc4a36f3733993571a16dbe --- /dev/null +++ b/modules/api/impl/ssml_api.py @@ -0,0 +1,78 @@ +from fastapi import HTTPException, Body +from fastapi.responses import StreamingResponse + +import io +from pydantic import BaseModel +from fastapi.responses import FileResponse + + +from modules.normalization import text_normalize +from modules.ssml import parse_ssml +from modules.SynthesizeSegments import ( + SynthesizeSegments, + synthesize_segment, + combine_audio_segments, +) + + +from modules.api import utils as api_utils + +from modules.api.Api import APIManager + + +class SSMLRequest(BaseModel): + ssml: str + format: str = "mp3" + batch: bool = False + + +async def synthesize_ssml( + request: SSMLRequest = Body( + ..., description="JSON body with SSML string and format" + ) +): + try: + ssml = request.ssml + format = request.format + batch = request.batch + + if not ssml: + raise HTTPException(status_code=400, detail="SSML content is required.") + + segments = parse_ssml(ssml) + for seg in segments: + seg["text"] = text_normalize(seg["text"], is_end=True) + + if batch: + synthesize = SynthesizeSegments(16) + audio_segments = synthesize.synthesize_segments(segments) + combined_audio = combine_audio_segments(audio_segments) + buffer = io.BytesIO() + combined_audio.export(buffer, format="wav") + buffer.seek(0) + if format == "mp3": + buffer = api_utils.wav_to_mp3(buffer) + return StreamingResponse(buffer, media_type=f"audio/{format}") + else: + + def audio_streamer(): + for segment in segments: + audio_segment = synthesize_segment(segment=segment) + buffer = io.BytesIO() + audio_segment.export(buffer, format="wav") + buffer.seek(0) + if format == "mp3": + buffer = api_utils.wav_to_mp3(buffer) + yield buffer.read() + + return StreamingResponse(audio_streamer(), media_type=f"audio/{format}") + + except Exception as e: + import logging + + logging.exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + +def setup(api_manager: APIManager): + api_manager.post("/v1/ssml", response_class=FileResponse)(synthesize_ssml) diff --git a/modules/api/impl/style_api.py b/modules/api/impl/style_api.py new file mode 100644 index 0000000000000000000000000000000000000000..eb07c044352259c7334f3180ea2f63615e53647a --- /dev/null +++ b/modules/api/impl/style_api.py @@ -0,0 +1,16 @@ +from modules.data import styles_mgr +from modules.api import utils as api_utils +from modules.api.Api import APIManager + + +async def list_styles(): + return {"message": "ok", "data": styles_mgr.list_items()} + + +async def create_style(): + # TODO + pass + + +def setup(app: APIManager): + app.get("/v1/styles/list", response_model=api_utils.BaseResponse)(list_styles) diff --git a/modules/api/impl/tts_api.py b/modules/api/impl/tts_api.py new file mode 100644 index 0000000000000000000000000000000000000000..dc114d63e8a4bb8ada44d70c7ed1c99c092ee052 --- /dev/null +++ b/modules/api/impl/tts_api.py @@ -0,0 +1,94 @@ +from fastapi import Depends, HTTPException, Query +from fastapi.responses import StreamingResponse + +import io +from pydantic import BaseModel +import soundfile as sf +from fastapi.responses import FileResponse + + +from modules.normalization import text_normalize + +from modules import generate_audio as generate + +from modules.api import utils as api_utils +from modules.api.Api import APIManager +from modules.synthesize_audio import synthesize_audio + + +class TTSParams(BaseModel): + text: str = Query(..., description="Text to synthesize") + spk: str = Query( + "female2", description="Specific speaker by speaker name or speaker seed" + ) + style: str = Query("chat", description="Specific style by style name") + temperature: float = Query( + 0.3, description="Temperature for sampling (may be overridden by style or spk)" + ) + top_P: float = Query( + 0.5, description="Top P for sampling (may be overridden by style or spk)" + ) + top_K: int = Query( + 20, description="Top K for sampling (may be overridden by style or spk)" + ) + seed: int = Query( + 42, description="Seed for generate (may be overridden by style or spk)" + ) + format: str = Query("mp3", description="Response audio format: [mp3,wav]") + prompt1: str = Query("", description="Text prompt for inference") + prompt2: str = Query("", description="Text prompt for inference") + prefix: str = Query("", description="Text prefix for inference") + bs: str = Query("8", description="Batch size for inference") + thr: str = Query("100", description="Threshold for sentence spliter") + + +async def synthesize_tts(params: TTSParams = Depends()): + try: + text = text_normalize(params.text, is_end=False) + + calc_params = api_utils.calc_spk_style(spk=params.spk, style=params.style) + + spk = calc_params.get("spk", params.spk) + seed = params.seed or calc_params.get("seed", params.seed) + temperature = params.temperature or calc_params.get( + "temperature", params.temperature + ) + prefix = params.prefix or calc_params.get("prefix", params.prefix) + prompt1 = params.prompt1 or calc_params.get("prompt1", params.prompt1) + prompt2 = params.prompt2 or calc_params.get("prompt2", params.prompt2) + + batch_size = int(params.bs) + threshold = int(params.thr) + + sample_rate, audio_data = synthesize_audio( + text, + temperature=temperature, + top_P=params.top_P, + top_K=params.top_K, + spk=spk, + infer_seed=seed, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + batch_size=batch_size, + spliter_threshold=threshold, + ) + + buffer = io.BytesIO() + sf.write(buffer, audio_data, sample_rate, format="wav") + buffer.seek(0) + + if format == "mp3": + buffer = api_utils.wav_to_mp3(buffer) + + return StreamingResponse(buffer, media_type="audio/wav") + + except Exception as e: + import logging + + logging.exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + +def setup(api_manager: APIManager): + api_manager.get("/v1/tts", response_class=FileResponse)(synthesize_tts) diff --git a/modules/api/utils.py b/modules/api/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3bc363ebf86cdcea5850d98e6cd1e54eccac6f39 --- /dev/null +++ b/modules/api/utils.py @@ -0,0 +1,107 @@ +from pydantic import BaseModel +from typing import Any + +import torch + +from modules.speaker import Speaker, speaker_mgr + + +from modules.data import styles_mgr + +from pydub import AudioSegment + +from modules.ssml import merge_prompt + + +from enum import Enum + + +class ParamsTypeError(Exception): + pass + + +class AudioFormat(str, Enum): + mp3 = "mp3" + wav = "wav" + + +class BaseResponse(BaseModel): + message: str + data: Any + + class Config: + json_encoders = { + torch.Tensor: lambda v: v.tolist(), + Speaker: lambda v: v.to_json(), + } + + +def wav_to_mp3(wav_data, bitrate="48k"): + audio = AudioSegment.from_wav( + wav_data, + ) + return audio.export(format="mp3", bitrate=bitrate) + + +def to_number(value, t, default=0): + try: + number = t(value) + return number + except (ValueError, TypeError) as e: + return default + + +def calc_spk_style(spk: str | int, style: str | int): + voice_attrs = { + "spk": None, + "seed": None, + "prompt1": None, + "prompt2": None, + "prefix": None, + "temperature": None, + } + params = {} + + if type(spk) == int: + voice_attrs["spk"] = spk + elif type(spk) == str: + if spk.isdigit(): + voice_attrs["spk"] = int(spk) + else: + spker = speaker_mgr.get_speaker(spk) + if spker: + voice_attrs["spk"] = spker + + if type(style) == int or type(style) == float: + raise ParamsTypeError("The style parameter cannot be a number.") + elif type(style) == str and style != "": + if style.isdigit(): + raise ParamsTypeError("The style parameter cannot be a number.") + else: + style_params = styles_mgr.find_params_by_name(style) + for k, v in style_params.items(): + params[k] = v + + voice_attrs = {k: v for k, v in voice_attrs.items() if v is not None} + + merge_prompt(voice_attrs, params) + + voice_attrs["spk"] = params.get("spk", voice_attrs.get("spk", None)) + voice_attrs["seed"] = params.get("seed", voice_attrs.get("seed", None)) + voice_attrs["temperature"] = params.get( + "temp", voice_attrs.get("temperature", None) + ) + voice_attrs["prefix"] = params.get("prefix", voice_attrs.get("prefix", None)) + voice_attrs["prompt1"] = params.get("prompt1", voice_attrs.get("prompt1", None)) + voice_attrs["prompt2"] = params.get("prompt2", voice_attrs.get("prompt2", None)) + + if voice_attrs.get("temperature", "") == "min": + # ref: https://github.com/2noise/ChatTTS/issues/123#issue-2326908144 + voice_attrs["temperature"] = 0.000000000001 + if voice_attrs.get("temperature", "") == "max": + voice_attrs["temperature"] = 1 + + voice_attrs = {k: v for k, v in voice_attrs.items() if v is not None} + # print(voice_attrs) + + return voice_attrs diff --git a/modules/config.py b/modules/config.py new file mode 100644 index 0000000000000000000000000000000000000000..875ab823d077fa641dc678aa49cd3e7d492b2192 --- /dev/null +++ b/modules/config.py @@ -0,0 +1,11 @@ +enable_model_compile = False + +lru_size = 64 + +args = {} + +api = None + +model_config = {"half": False} + +disable_tqdm = False diff --git a/modules/data.py b/modules/data.py new file mode 100644 index 0000000000000000000000000000000000000000..c5b461b7a1e24438616a9818814f6faa504ac9b3 --- /dev/null +++ b/modules/data.py @@ -0,0 +1,452 @@ +from modules.utils.CsvMgr import BaseManager + + +# speakers_mgr = BaseManager("./data/speakers.csv") +styles_mgr = BaseManager("./data/styles.csv") + +if __name__ == "__main__": + # builtin style + styles = { + "advertisement_upbeat": { + "speed": 9, + "oral": 7, + "laugh": 2, + "break": 1, + "prefix": "兴奋的语气推广产品。", + }, + "affectionate": { + "speed": 6, + "oral": 8, + "laugh": 1, + "break": 2, + "prefix": "温暖亲切的语气。", + }, + "angry": { + "speed": 7, + "oral": 5, + "break": 3, + "prefix": "生气厌恶的语气。", + }, + "assistant": { + "speed": 5, + "oral": 7, + "laugh": 1, + "break": 2, + "prefix": "热情轻松的语气。", + }, + "calm": { + "speed": 0, + "oral": 4, + "break": 4, + "prefix": "沉着冷静的语气。", + }, + "chat": { + "speed": 6, + "oral": 9, + "laugh": 1, + "break": 2, + "prefix": "轻松随意的语气。", + }, + "cheerful": { + "speed": 7, + "oral": 8, + "laugh": 2, + "break": 2, + "prefix": "积极愉快的语气。", + }, + "customerservice": { + "speed": 5, + "oral": 6, + "laugh": 1, + "break": 5, + "prefix": "友好热情的语气。", + }, + "depressed": { + "speed": 2, + "oral": 3, + "break": 5, + "prefix": "忧郁沮丧的语气。", + }, + "disgruntled": { + "speed": 4, + "oral": 5, + "break": 4, + "prefix": "轻蔑抱怨的语气。", + }, + "documentary-narration": { + "speed": 4, + "oral": 4, + "break": 3, + "prefix": "轻松且信息丰富的语气。", + }, + "embarrassed": { + "speed": 3, + "oral": 3, + "break": 5, + "prefix": "不确定犹豫的语气。", + }, + "empathetic": { + "speed": 5, + "oral": 6, + "laugh": 1, + "break": 3, + "prefix": "关心理解的语气。", + }, + "envious": { + "speed": 5, + "oral": 5, + "laugh": 1, + "break": 2, + "prefix": "钦佩的语气。", + }, + "excited": { + "speed": 8, + "oral": 7, + "laugh": 2, + "break": 1, + "prefix": "乐观充满希望的语气。", + }, + "fearful": { + "speed": 9, + "oral": 6, + "break": 2, + "prefix": "恐惧紧张的语气。", + }, + "friendly": { + "speed": 6, + "oral": 7, + "laugh": 1, + "break": 2, + "prefix": "愉快温暖的语气。", + }, + "gentle": { + "speed": 4, + "oral": 5, + "laugh": 1, + "break": 3, + "prefix": "温和礼貌的语气。", + }, + "hopeful": { + "speed": 5, + "oral": 6, + "laugh": 1, + "break": 4, + "prefix": "温暖渴望的语气。", + }, + "lyrical": { + "speed": 3, + "oral": 4, + "break": 4, + "prefix": "优美感伤的语气。", + }, + "narration-professional": { + "speed": 5, + "oral": 3, + "break": 3, + "prefix": "专业客观的语气。", + }, + "narration-relaxed": { + "speed": 2, + "oral": 4, + "break": 3, + "prefix": "舒缓悦耳的语气。", + }, + "newscast": { + "speed": 6, + "oral": 4, + "break": 2, + "prefix": "正式专业的语气。", + }, + "newscast-casual": { + "speed": 5, + "oral": 5, + "break": 2, + "prefix": "随意的新闻语气。", + }, + "newscast-formal": { + "speed": 6, + "oral": 3, + "break": 2, + "prefix": "正式权威的语气。", + }, + "poetry-reading": { + "speed": 4, + "oral": 5, + "break": 3, + "prefix": "带情感和节奏的语气。", + }, + "sad": { + "speed": 0, + "oral": 3, + "break": 5, + "prefix": "悲伤的语气。", + }, + "serious": { + "speed": 5, + "oral": 3, + "break": 4, + "prefix": "严肃命令的语气。", + }, + "shouting": { + "speed": 7, + "oral": 6, + "break": 1, + "prefix": "努力让别人听清楚的语气。", + }, + "sports_commentary": { + "speed": 8, + "oral": 7, + "laugh": 1, + "break": 2, + "prefix": "轻松感兴趣的体育播报语气。", + }, + "sports_commentary_excited": { + "speed": 9, + "oral": 8, + "laugh": 2, + "break": 1, + "prefix": "快速充满活力的体育播报语气。", + }, + "whispering": { + "speed": 2, + "oral": 3, + "break": 4, + "prefix": "柔和的语气。", + }, + "terrified": { + "speed": 9, + "oral": 7, + "break": 1, + "prefix": "害怕的语气。", + }, + "unfriendly": { + "speed": 4, + "oral": 2, + "break": 3, + "prefix": "冷淡无情的语气。", + }, + } + styles_en = { + "advertisement_upbeat": { + "speed": 9, + "oral": 7, + "laugh": 2, + "break": 1, + "prefix": "Promote products or services with an excited and energetic tone.", + }, + "affectionate": { + "speed": 6, + "oral": 8, + "laugh": 1, + "break": 2, + "prefix": "Express a warm and affectionate tone with a higher pitch and volume. The speaker is in a state of capturing the listener's attention and is often likeable.", + }, + "angry": { + "speed": 7, + "oral": 5, + "break": 3, + "prefix": "Express an angry and disgusted tone.", + }, + "assistant": { + "speed": 5, + "oral": 7, + "laugh": 1, + "break": 2, + "prefix": "Digital assistant uses an enthusiastic and relaxed tone.", + }, + "calm": { + "speed": 0, + "oral": 4, + "break": 4, + "prefix": "Speak with a calm and composed attitude. The tone, pitch, and rhythm are much more consistent compared to other speech types.", + }, + "chat": { + "speed": 6, + "oral": 9, + "laugh": 1, + "break": 2, + "prefix": "Express a relaxed and casual tone.", + }, + "cheerful": { + "speed": 7, + "oral": 8, + "laugh": 2, + "break": 2, + "prefix": "Express a positive and cheerful tone.", + }, + "customerservice": { + "speed": 5, + "oral": 6, + "laugh": 1, + "break": 5, + "prefix": "Provide support to customers with a friendly and enthusiastic tone.", + }, + "depressed": { + "speed": 2, + "oral": 3, + "break": 5, + "prefix": "Express a depressed and downcast tone with lower pitch and volume.", + }, + "disgruntled": { + "speed": 4, + "oral": 5, + "break": 4, + "prefix": "Express a tone of contempt and complaint. This emotional voice exhibits displeasure and disdain.", + }, + "documentary-narration": { + "speed": 4, + "oral": 4, + "break": 3, + "prefix": "Narrate documentaries in a relaxed, interested, and informative style, suitable for documentary voiceovers, expert commentary, and similar content.", + }, + "embarrassed": { + "speed": 3, + "oral": 3, + "break": 5, + "prefix": "Express an uncertain, hesitant tone when the speaker feels uncomfortable.", + }, + "empathetic": { + "speed": 5, + "oral": 6, + "laugh": 1, + "break": 3, + "prefix": "Express care and understanding.", + }, + "envious": { + "speed": 5, + "oral": 5, + "laugh": 1, + "break": 2, + "prefix": "Express an admiring tone when you desire something others have.", + }, + "excited": { + "speed": 8, + "oral": 7, + "laugh": 2, + "break": 1, + "prefix": "Express an optimistic and hopeful tone. It seems something wonderful has happened, and the speaker is pleased.", + }, + "fearful": { + "speed": 9, + "oral": 6, + "break": 2, + "prefix": "Express a fearful and anxious tone with a higher pitch, higher volume, and faster speech rate. The speaker is in a state of tension and unease.", + }, + "friendly": { + "speed": 6, + "oral": 7, + "laugh": 1, + "break": 2, + "prefix": "Express a pleasant, delightful, and warm tone. It sounds sincere and full of concern.", + }, + "gentle": { + "speed": 4, + "oral": 5, + "laugh": 1, + "break": 3, + "prefix": "Express a gentle, polite, and pleasant tone with a lower pitch and volume.", + }, + "hopeful": { + "speed": 5, + "oral": 6, + "laugh": 1, + "break": 4, + "prefix": "Express a warm and desirous tone. It sounds like something good is going to happen to the speaker.", + }, + "lyrical": { + "speed": 3, + "oral": 4, + "break": 4, + "prefix": "Express emotions in a beautiful and sentimental way.", + }, + "narration-professional": { + "speed": 5, + "oral": 3, + "break": 3, + "prefix": "Read content in a professional and objective tone.", + }, + "narration-relaxed": { + "speed": 2, + "oral": 4, + "break": 3, + "prefix": "Express a soothing and pleasant tone when reading content.", + }, + "newscast": { + "speed": 6, + "oral": 4, + "break": 2, + "prefix": "Narrate news in a formal and professional tone.", + }, + "newscast-casual": { + "speed": 5, + "oral": 5, + "break": 2, + "prefix": "Deliver general news in a generic and casual tone.", + }, + "newscast-formal": { + "speed": 6, + "oral": 3, + "break": 2, + "prefix": "Deliver news in a formal, confident, and authoritative tone.", + }, + "poetry-reading": { + "speed": 4, + "oral": 5, + "break": 3, + "prefix": "Express an emotional and rhythmic tone when reading poetry.", + }, + "sad": {"speed": 0, "oral": 3, "break": 5, "prefix": "Express a sad tone."}, + "serious": { + "speed": 5, + "oral": 3, + "break": 4, + "prefix": "Express a serious and commanding tone. The speaker's voice is usually stiff, and the pace is not as relaxed.", + }, + "shouting": { + "speed": 7, + "oral": 6, + "break": 1, + "prefix": "Express a tone that sounds like the voice is far away or in another place, trying hard to be heard clearly.", + }, + "sports_commentary": { + "speed": 8, + "oral": 7, + "laugh": 1, + "break": 2, + "prefix": "Express a relaxed yet interested tone, used for sports commentary.", + }, + "sports_commentary_excited": { + "speed": 9, + "oral": 8, + "laugh": 2, + "break": 1, + "prefix": "Commentate on exciting moments in sports with a fast and energetic tone.", + }, + "whispering": { + "speed": 2, + "oral": 3, + "break": 4, + "prefix": "Express a soft tone, trying to make a quiet and gentle sound.", + }, + "terrified": { + "speed": 9, + "oral": 7, + "break": 1, + "prefix": "Express a terrified tone, with fast speech and trembling voice. It sounds like the speaker is in an unstable, frantic state.", + }, + "unfriendly": { + "speed": 4, + "oral": 2, + "break": 3, + "prefix": "Express a cold and unfeeling tone.", + }, + } + id = 0 + for k, v in styles.items(): + v.pop("prefix") + styles_mgr.add_item(id, k, f"builtin style {k}", v) + id += 1 + # for k, v in styles_en.items(): + + # styles_mgr.add_item(id, f"en_{k}", f"builtin style {k}", v) + # id += 1 diff --git a/modules/generate_audio.py b/modules/generate_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..76ee79dfa077528147179a2349e28198a0ab07c7 --- /dev/null +++ b/modules/generate_audio.py @@ -0,0 +1,117 @@ +import numpy as np +import torch + +from modules.speaker import Speaker +from modules.utils.SeedContext import SeedContext + +from modules import models, config + +import logging + +logger = logging.getLogger(__name__) + + +@torch.inference_mode() +def generate_audio( + text: str, + temperature: float = 0.3, + top_P: float = 0.7, + top_K: float = 20, + spk: int | Speaker = -1, + infer_seed: int = -1, + use_decoder: bool = True, + prompt1: str = "", + prompt2: str = "", + prefix: str = "", +): + (sample_rate, wav) = generate_audio_batch( + [text], + temperature=temperature, + top_P=top_P, + top_K=top_K, + spk=spk, + infer_seed=infer_seed, + use_decoder=use_decoder, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + )[0] + + return (sample_rate, wav) + + +@torch.inference_mode() +def generate_audio_batch( + texts: list[str], + temperature: float = 0.3, + top_P: float = 0.7, + top_K: float = 20, + spk: int | Speaker = -1, + infer_seed: int = -1, + use_decoder: bool = True, + prompt1: str = "", + prompt2: str = "", + prefix: str = "", +): + chat_tts = models.load_chat_tts() + params_infer_code = { + "spk_emb": None, + "temperature": temperature, + "top_P": top_P, + "top_K": top_K, + "prompt1": prompt1 or "", + "prompt2": prompt2 or "", + "prefix": prefix or "", + "repetition_penalty": 1.0, + "disable_tqdm": config.disable_tqdm, + } + + if isinstance(spk, int): + with SeedContext(spk): + params_infer_code["spk_emb"] = chat_tts.sample_random_speaker() + logger.info(("spk", spk)) + elif isinstance(spk, Speaker): + params_infer_code["spk_emb"] = spk.emb + logger.info(("spk", spk.name)) + + logger.info( + { + "text": texts, + "infer_seed": infer_seed, + "temperature": temperature, + "top_P": top_P, + "top_K": top_K, + "prompt1": prompt1 or "", + "prompt2": prompt2 or "", + "prefix": prefix or "", + } + ) + + with SeedContext(infer_seed): + wavs = chat_tts.generate_audio( + texts, params_infer_code, use_decoder=use_decoder + ) + + sample_rate = 24000 + + return [(sample_rate, np.array(wav).flatten().astype(np.float32)) for wav in wavs] + + +if __name__ == "__main__": + import soundfile as sf + + # 测试batch生成 + inputs = ["你好[lbreak]", "再见[lbreak]", "长度不同的文本片段[lbreak]"] + outputs = generate_audio_batch(inputs, spk=5, infer_seed=42) + + for i, (sample_rate, wav) in enumerate(outputs): + print(i, sample_rate, wav.shape) + + sf.write(f"batch_{i}.wav", wav, sample_rate, format="wav") + + # 单独生成 + for i, text in enumerate(inputs): + sample_rate, wav = generate_audio(text, spk=5, infer_seed=42) + print(i, sample_rate, wav.shape) + + sf.write(f"one_{i}.wav", wav, sample_rate, format="wav") diff --git a/modules/models.py b/modules/models.py new file mode 100644 index 0000000000000000000000000000000000000000..127e8e1831a010440b6b912da3aefaea61779727 --- /dev/null +++ b/modules/models.py @@ -0,0 +1,41 @@ +from modules.ChatTTS import ChatTTS +import torch + +from modules import config + +import logging + +logger = logging.getLogger(__name__) + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +print(f"device use {device}") + +chat_tts = None + + +def load_chat_tts(): + global chat_tts + if chat_tts: + return chat_tts + chat_tts = ChatTTS.Chat() + chat_tts.load_models( + compile=config.enable_model_compile, + source="local", + local_path="./models/ChatTTS", + device=device, + ) + + if config.model_config.get("half", False): + logging.info("half precision enabled") + for model_name, model in chat_tts.pretrain_models.items(): + if isinstance(model, torch.nn.Module): + model.cpu() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + model.half() + if torch.cuda.is_available(): + model.cuda() + model.eval() + logger.log(logging.INFO, f"{model_name} converted to half precision.") + + return chat_tts diff --git a/modules/normalization.py b/modules/normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..b700ab37a9bbaf8fbe4c3844c2af3a00dc8c8641 --- /dev/null +++ b/modules/normalization.py @@ -0,0 +1,222 @@ +from modules.utils.zh_normalization.text_normlization import * +import emojiswitch +from modules.utils.markdown import markdown_to_text + +post_normalize_pipeline = [] +pre_normalize_pipeline = [] + + +def post_normalize(): + def decorator(func): + post_normalize_pipeline.append(func) + return func + + return decorator + + +def pre_normalize(): + def decorator(func): + pre_normalize_pipeline.append(func) + return func + + return decorator + + +def apply_pre_normalize(text): + for func in pre_normalize_pipeline: + text = func(text) + return text + + +def apply_post_normalize(text): + for func in post_normalize_pipeline: + text = func(text) + return text + + +def is_markdown(text): + markdown_patterns = [ + r"(^|\s)#[^#]", # 标题 + r"\*\*.*?\*\*", # 加粗 + r"\*.*?\*", # 斜体 + r"!\[.*?\]\(.*?\)", # 图片 + r"\[.*?\]\(.*?\)", # 链接 + r"`[^`]+`", # 行内代码 + r"```[\s\S]*?```", # 代码块 + r"(^|\s)\* ", # 无序列表 + r"(^|\s)\d+\. ", # 有序列表 + r"(^|\s)> ", # 引用 + r"(^|\s)---", # 分隔线 + ] + + for pattern in markdown_patterns: + if re.search(pattern, text, re.MULTILINE): + return True + + return False + + +character_map = { + "：": "，", + "；": "，", + "！": "。", + "（": "，", + "）": "，", + "【": "，", + "】": "，", + "『": "，", + "』": "，", + "「": "，", + "」": "，", + "《": "，", + "》": "，", + "－": "，", + "‘": " ", + "“": " ", + "’": " ", + "”": " ", + ":": ",", + ";": ",", + "!": ".", + "(": ",", + ")": ",", + # '[': ',', + # ']': ',', + ">": ",", + "<": ",", + "-": ",", +} + +character_to_word = { + " & ": " and ", +} + + +@post_normalize() +def apply_character_to_word(text): + for k, v in character_to_word.items(): + text = text.replace(k, v) + return text + + +@post_normalize() +def apply_character_map(text): + translation_table = str.maketrans(character_map) + return text.translate(translation_table) + + +@post_normalize() +def apply_emoji_map(text): + return emojiswitch.demojize(text, delimiters=("", ""), lang="zh") + + +@pre_normalize() +def apply_markdown_to_text(text): + if is_markdown(text): + text = markdown_to_text(text) + return text + + +@post_normalize() +def insert_spaces_between_uppercase(s): + # 使用正则表达式在每个相邻的大写字母之间插入空格 + return re.sub( + r"(?<=[A-Z])(?=[A-Z])|(?<=[a-z])(?=[A-Z])|(?<=[\u4e00-\u9fa5])(?=[A-Z])|(?<=[A-Z])(?=[\u4e00-\u9fa5])", + " ", + s, + ) + + +def ensure_suffix(a: str, b: str, c: str): + a = a.strip() + if not a.endswith(b): + a += c + return a + + +email_domain_map = { + "outlook.com": "Out look", + "hotmail.com": "Hot mail", + "yahoo.com": "雅虎", +} + + +# 找到所有 email 并将 name 分割为单个字母，@替换为 at ，. 替换为 dot，常见域名替换为单词 +# +# 例如: +# zhzluke96@outlook.com => z h z l u k e 9 6 at out look dot com +def email_detect(text): + email_pattern = re.compile(r"([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})") + + def replace(match): + email = match.group(1) + name, domain = email.split("@") + name = " ".join(name) + if domain in email_domain_map: + domain = email_domain_map[domain] + domain = domain.replace(".", " dot ") + return f"{name} at {domain}" + + return email_pattern.sub(replace, text) + + +def sentence_normalize(sentence_text: str): + # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization + tx = TextNormalizer() + + # 匹配 \[.+?\] 的部分 + pattern = re.compile(r"(\[.+?\])|([^[]+)") + + def normalize_part(part): + sentences = tx.normalize(part) + dest_text = "" + for sentence in sentences: + dest_text += sentence + return dest_text + + def replace(match): + if match.group(1): + return f" {match.group(1)} " + else: + return normalize_part(match.group(2)) + + result = pattern.sub(replace, sentence_text) + + # NOTE: 加了会有杂音... + # if is_end: + # 加这个是为了防止吞字 + # result = ensure_suffix(result, "[uv_break]", "。。。[uv_break]。。。") + + return result + + +def text_normalize(text, is_end=False): + text = apply_pre_normalize(text) + lines = text.split("\n") + lines = [line.strip() for line in lines] + lines = [line for line in lines if line] + lines = [sentence_normalize(line) for line in lines] + content = "\n".join(lines) + content = apply_post_normalize(content) + return content + + +if __name__ == "__main__": + test_cases = [ + "ChatTTS是专门为对话场景设计的文本转语音模型，例如LLM助手对话任务。它支持英文和中文两种语言。最大的模型使用了10万小时以上的中英文数据进行训练。在HuggingFace中开源的版本为4万小时训练且未SFT的版本.", + " [oral_9] [laugh_0] [break_0] 电 [speed_0] 影 [speed_0] 中 梁朝伟 [speed_9] 扮演的陈永仁的编号27149", + " 明天有62％的概率降雨", + "大🍌，一条大🍌，嘿，你的感觉真的很奇妙 [lbreak]", + """ +# 你好，世界 +```js +console.log('1') +``` +**加粗** + +*一条文本* + """, + ] + + for i, test_case in enumerate(test_cases): + print(f"case {i}:\n", {"x": text_normalize(test_case, is_end=True)}) diff --git a/modules/refiner.py b/modules/refiner.py new file mode 100644 index 0000000000000000000000000000000000000000..7d4d6bf59c5499c934cab21af352d4bdf9f6fba2 --- /dev/null +++ b/modules/refiner.py @@ -0,0 +1,36 @@ +import numpy as np +import torch + +from modules.utils.SeedContext import SeedContext + +from modules import models, config + + +@torch.inference_mode() +def refine_text( + text: str, + prompt="[oral_2][laugh_0][break_6]", + seed=-1, + top_P=0.7, + top_K=20, + temperature=0.7, + repetition_penalty=1.0, + max_new_token=384, +) -> str: + chat_tts = models.load_chat_tts() + + with SeedContext(seed): + refined_text = chat_tts.refiner_prompt( + text, + { + "prompt": prompt, + "top_K": top_K, + "top_P": top_P, + "temperature": temperature, + "repetition_penalty": repetition_penalty, + "max_new_token": max_new_token, + "disable_tqdm": config.disable_tqdm, + }, + do_text_normalization=False, + ) + return refined_text diff --git a/modules/speaker.py b/modules/speaker.py new file mode 100644 index 0000000000000000000000000000000000000000..5919b6fbd5ce49d671b2105f2383a7cb3f01bb00 --- /dev/null +++ b/modules/speaker.py @@ -0,0 +1,150 @@ +import os +import torch + +from modules import models +from modules.utils.SeedContext import SeedContext + +import uuid + + +def create_speaker_from_seed(seed): + chat_tts = models.load_chat_tts() + with SeedContext(seed): + emb = chat_tts.sample_random_speaker() + return emb + + +class Speaker: + def __init__(self, seed, name="", gender="", describe=""): + self.id = uuid.uuid4() + self.seed = seed + self.name = name + self.gender = gender + self.describe = describe + self.emb = None + + def to_json(self, with_emb=False): + return { + "id": str(self.id), + "seed": self.seed, + "name": self.name, + "gender": self.gender, + "describe": self.describe, + "emb": self.emb.tolist() if with_emb else None, + } + + def fix(self): + is_update = False + if "id" not in self.__dict__: + setattr(self, "id", uuid.uuid4()) + is_update = True + if "seed" not in self.__dict__: + setattr(self, "seed", -2) + is_update = True + if "name" not in self.__dict__: + setattr(self, "name", "") + is_update = True + if "gender" not in self.__dict__: + setattr(self, "gender", "*") + is_update = True + if "describe" not in self.__dict__: + setattr(self, "describe", "") + is_update = True + + return is_update + + +# 每个speaker就是一个 emb 文件 .pt +# 管理 speaker 就是管理 ./data/speaker/ 下的所有 speaker +# 可以 用 seed 创建一个 speaker +# 可以 刷新列表 读取所有 speaker +# 可以列出所有 speaker +class SpeakerManager: + def __init__(self): + self.speakers = {} + self.speaker_dir = "./data/speakers/" + self.refresh_speakers() + + def refresh_speakers(self): + self.speakers = {} + for speaker_file in os.listdir(self.speaker_dir): + if speaker_file.endswith(".pt"): + speaker = torch.load( + self.speaker_dir + speaker_file, map_location=torch.device("cpu") + ) + self.speakers[speaker_file] = speaker + + is_update = speaker.fix() + if is_update: + torch.save(speaker, self.speaker_dir + speaker_file) + + def list_speakers(self): + return list(self.speakers.values()) + + def create_speaker_from_seed(self, seed, name="", gender="", describe=""): + if name == "": + name = seed + filename = name + ".pt" + speaker = Speaker(seed, name=name, gender=gender, describe=describe) + speaker.emb = create_speaker_from_seed(seed) + torch.save(speaker, self.speaker_dir + filename) + self.refresh_speakers() + return speaker + + def create_speaker_from_tensor( + self, tensor, filename="", name="", gender="", describe="" + ): + if name == "": + name = filename + speaker = Speaker(seed=-2, name=name, gender=gender, describe=describe) + if isinstance(tensor, torch.Tensor): + speaker.emb = tensor + if isinstance(tensor, list): + speaker.emb = torch.tensor(tensor) + torch.save(speaker, self.speaker_dir + filename + ".pt") + self.refresh_speakers() + return speaker + + def get_speaker(self, name) -> Speaker | None: + filename = name + ".pt" + return self.speakers.get(filename, None) + + def get_speaker_by_id(self, id) -> Speaker | None: + for speaker in self.speakers.values(): + if str(speaker.id) == str(id): + return speaker + return None + + def get_speaker_filename(self, id: str): + filename = None + for fname, spk in self.speakers.items(): + if str(spk.id) == str(id): + filename = fname + break + return filename + + def update_speaker(self, speaker: Speaker): + filename = None + for fname, spk in self.speakers.items(): + if str(spk.id) == str(speaker.id): + filename = fname + break + + if filename: + torch.save(speaker, self.speaker_dir + filename) + self.refresh_speakers() + return speaker + else: + raise ValueError("Speaker not found for update") + + def save_all(self): + for speaker in self.speakers.values(): + filename = self.get_speaker_filename(speaker.id) + torch.save(speaker, self.speaker_dir + filename) + # self.refresh_speakers() + + def __len__(self): + return len(self.speakers) + + +speaker_mgr = SpeakerManager() diff --git a/modules/ssml.py b/modules/ssml.py new file mode 100644 index 0000000000000000000000000000000000000000..ceb5e84c15e10112a687696881e0604458015477 --- /dev/null +++ b/modules/ssml.py @@ -0,0 +1,310 @@ +from lxml import etree + + +from typing import Any, List, Dict +import numpy as np + + +import logging + +from modules.data import styles_mgr +from modules.speaker import speaker_mgr + +import random + + +logger = logging.getLogger(__name__) + + +def expand_spk(attrs: dict): + input_spk = attrs.get("spk", "") + if isinstance(input_spk, int): + return + if isinstance(input_spk, str) and input_spk.isdigit(): + attrs.update({"spk": int(input_spk)}) + return + try: + speaker = speaker_mgr.get_speaker(input_spk) + attrs.update({"spk": speaker}) + except Exception as e: + logger.error(f"apply style failed, {e}") + + +def expand_style(attrs: dict): + if attrs.get("style", "") != "": + try: + params = styles_mgr.find_params_by_name(str(attrs["style"])) + attrs.update(params) + except Exception as e: + logger.error(f"apply style failed, {e}") + + +def merge_prompt(attrs: dict, elem): + + def attr_num(attrs: Dict[str, Any], k: str, min_value: int, max_value: int): + val = elem.get(k, attrs.get(k, "")) + if val == "": + return + if val == "max": + val = max_value + if val == "min": + val = min_value + val = np.clip(int(val), min_value, max_value) + if "prefix" not in attrs or attrs["prefix"] == None: + attrs["prefix"] = "" + attrs["prefix"] += " " + f"[{k}_{val}]" + + attr_num(attrs, "oral", 0, 9) + attr_num(attrs, "speed", 0, 9) + attr_num(attrs, "laugh", 0, 2) + attr_num(attrs, "break", 0, 7) + + +def apply_random_seed(attrs: dict): + seed = attrs.get("seed", "") + if seed == "random" or seed == "rand": + seed = random.randint(0, 2**32 - 1) + attrs["seed"] = seed + logger.info(f"random seed: {seed}") + + +class NotSupportSSML(Exception): + pass + + +def parse_ssml(ssml: str) -> List[Dict[str, Any]]: + root = etree.fromstring(ssml) + + ssml_version = root.get("version", "NONE") + if ssml_version != "0.1": + raise NotSupportSSML("Unsupported ssml version: {ssml_version}") + + segments = [] + + for voice in root.findall(".//voice"): + voice_attrs = { + "spk": voice.get("spk"), + "style": voice.get("style"), + "seed": voice.get("seed"), + "top_p": voice.get("top_p"), + "top_k": voice.get("top_k"), + "temp": voice.get("temp"), + "prompt1": voice.get("prompt1"), + "prompt2": voice.get("prompt2"), + "prefix": voice.get("prefix"), + "normalize": voice.get("normalize"), + } + + voice_attrs = {k: v for k, v in voice_attrs.items() if v is not None} + + expand_spk(voice_attrs) + expand_style(voice_attrs) + + merge_prompt(voice_attrs, voice) + apply_random_seed(voice_attrs) + + voice_segments = [] + + if voice_attrs.get("temp", "") == "min": + # ref: https://github.com/2noise/ChatTTS/issues/123#issue-2326908144 + voice_attrs["temp"] = 0.000000000001 + if voice_attrs.get("temp", "") == "max": + voice_attrs["temp"] = 1 + + # 处理 voice 开头的文本 + if voice.text and voice.text.strip(): + voice_segments.append( + {"text": voice.text.strip(), "attrs": voice_attrs.copy()} + ) + + # 处理 voice 内部的文本和 prosody 元素 + for node in voice.iterchildren(): + if node.tag == "prosody": + prosody_attrs = voice_attrs.copy() + new_attrs = { + "rate": node.get("rate"), + "volume": node.get("volume"), + "pitch": node.get("pitch"), + } + prosody_attrs.update( + {k: v for k, v in new_attrs.items() if v is not None} + ) + expand_style(prosody_attrs) + merge_prompt(prosody_attrs, node) + apply_random_seed(voice_attrs) + + if node.text and node.text.strip(): + voice_segments.append( + {"text": node.text.strip(), "attrs": prosody_attrs} + ) + elif node.tag == "break": + time_ms = int(node.get("time", "0").replace("ms", "")) + segment = {"break": time_ms} + voice_segments.append(segment) + + if node.tail and node.tail.strip(): + voice_segments.append( + {"text": node.tail.strip(), "attrs": voice_attrs.copy()} + ) + + end_segment = voice_segments[-1] + end_segment["is_end"] = True + + segments = segments + voice_segments + + logger.info(f"collect len(segments): {len(segments)}") + # logger.info(f"segments: {json.dumps(segments, ensure_ascii=False)}") + + return segments + + +if __name__ == "__main__": + # 示例 SSML 输入 + ssml1 = """ + + + 电影中梁朝伟扮演的陈永仁的 + + 编号27149 + + + 编号27149 + + + 编号27149 + + + 编号27149 + + + + 编号27149 + + + 电影中梁朝伟扮演的陈永仁的编号27149 + + + """ + + ssml2 = """ + + + 也可以合成多角色多情感的有声 [uv_break] 书 [uv_break] ，例如： + + + 黛玉冷笑道： + + + 我说呢，亏了绊住，不然，早就飞了来了。 + + + 宝玉道： + + + “只许和你玩，替你解闷。不过偶然到他那里，就说这些闲话。” + + + “好没意思的话！去不去，关我什么事儿？又没叫你替我解闷儿，还许你不理我呢” + + + 说着，便赌气回房去了。 + + + """ + ssml22 = """ + + + 下面是一个 ChatTTS 用于合成多角色多情感的有声书示例 + + + 黛玉冷笑道： + + + 我说呢 [uv_break] ，亏了绊住，不然，早就飞起来了。 + + + 宝玉道： + + + “只许和你玩 [uv_break] ，替你解闷。不过偶然到他那里，就说这些闲话。” + + + “好没意思的话！[uv_break] 去不去，关我什么事儿？ 又没叫你替我解闷儿 [uv_break]，还许你不理我呢” + + + 说着，便赌气回房去了。 + + + """ + + ssml3 = """ + + + “你到底在想什么？这已经是第三次了！每次我都告诉你要按时完成任务，可你总是拖延。你知道这对整个团队有多大的影响吗？！” + + + “你到底在想什么？这已经是第三次了！每次我都告诉你要按时完成任务，可你总是拖延。你知道这对整个团队有多大的影响吗？！” + + + “你到底在想什么？这已经是第三次了！每次我都告诉你要按时完成任务，可你总是拖延。你知道这对整个团队有多大的影响吗？！” + + + """ + + ssml4 = """ + + + 使用 prosody 控制生成文本的语速语调和音量，示例如下 + + + 无任何限制将会继承父级voice配置进行生成 + + + 设置 rate 大于1表示加速，小于1为减速 + + + 设置 pitch 调整音调，设置为6表示提高6个半音 + + + 设置 volume 调整音量，设置为2表示提高2个分贝 + + + 在 voice 中无prosody包裹的文本即为默认生成状态下的语音 + + + """ + + ssml5 = """ + + + 使用 break 标签将会简单的 + + + + 插入一段空白到生成结果中 + + + """ + + ssml6 = """ + + + temperature for sampling (may be overridden by style or speaker) + + 温度值用于采样，这个值有可能被 style 或者 speaker 覆盖 + + temperature for sampling ，这个值有可能被 style 或者 speaker 覆盖 + + 温度值用于采样，(may be overridden by style or speaker) + + + """ + + segments = parse_ssml(ssml6) + + print(segments) + + # audio_segments = synthesize_segments(segments) + # combined_audio = combine_audio_segments(audio_segments) + + # combined_audio.export("output.wav", format="wav") diff --git a/modules/synthesize_audio.py b/modules/synthesize_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..6df800296dedbdcfcd6e8f78b3774e53161d399c --- /dev/null +++ b/modules/synthesize_audio.py @@ -0,0 +1,58 @@ +from modules.SentenceSplitter import SentenceSplitter +from modules.normalization import text_normalize + +from modules import generate_audio as generate + + +import numpy as np + +from modules.speaker import Speaker + + +def synthesize_audio( + text: str, + temperature: float = 0.3, + top_P: float = 0.7, + top_K: float = 20, + spk: int | Speaker = -1, + infer_seed: int = -1, + use_decoder: bool = True, + prompt1: str = "", + prompt2: str = "", + prefix: str = "", + batch_size: int = 1, + spliter_threshold: int = 100, +): + if batch_size == 1: + return generate.generate_audio( + text, + temperature=temperature, + top_P=top_P, + top_K=top_K, + spk=spk, + infer_seed=infer_seed, + use_decoder=use_decoder, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + ) + else: + spliter = SentenceSplitter(spliter_threshold) + sentences = spliter.parse(text) + sentences = [text_normalize(s) for s in sentences] + audio_data_batch = generate.generate_audio_batch( + texts=sentences, + temperature=temperature, + top_P=top_P, + top_K=top_K, + spk=spk, + infer_seed=infer_seed, + use_decoder=use_decoder, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + ) + sample_rate = audio_data_batch[0][0] + audio_data = np.concatenate([data for _, data in audio_data_batch]) + + return sample_rate, audio_data diff --git a/modules/utils/CsvMgr.py b/modules/utils/CsvMgr.py new file mode 100644 index 0000000000000000000000000000000000000000..31c0ebc8e2e31666368c7592cd4abc637406be83 --- /dev/null +++ b/modules/utils/CsvMgr.py @@ -0,0 +1,157 @@ +import pandas as pd +import os +import json + +import logging + +logger = logging.getLogger(__name__) + + +class DataExistsError(Exception): + pass + + +class DataNotFoundError(Exception): + pass + + +class BaseManager: + def __init__(self, csv_file): + self.csv_file = csv_file + self.columns = ["id", "name", "desc", "params"] + if not os.path.exists(csv_file): + df = pd.DataFrame(columns=self.columns) + df.to_csv(self.csv_file, index=False) + + def _load_data(self): + return pd.read_csv(self.csv_file) + + def _save_data(self, df): + df.to_csv(self.csv_file, index=False) + + def add_item(self, item_id, name, desc, params): + df = self._load_data() + if item_id in df["id"].values: + raise DataExistsError(f"Item ID {item_id} already exists.") + new_row = pd.DataFrame( + [ + { + "id": item_id, + "name": name, + "desc": desc, + "params": json.dumps(params, ensure_ascii=False), + } + ] + ) + df = pd.concat([df, new_row], ignore_index=True) + self._save_data(df) + + def delete_item(self, item_id): + df = self._load_data() + if item_id not in df["id"].values: + raise DataNotFoundError(f"Item ID {item_id} not found.") + df = df[df["id"] != item_id] + self._save_data(df) + + def update_item(self, item_id, name=None, desc=None, params=None): + df = self._load_data() + if item_id not in df["id"].values: + raise DataNotFoundError(f"Item ID {item_id} not found.") + if name: + df.loc[df["id"] == item_id, "name"] = name + if desc: + df.loc[df["id"] == item_id, "desc"] = desc + if params: + df.loc[df["id"] == item_id, "params"] = params + self._save_data(df) + + def get_item(self, item_id): + df = self._load_data() + if item_id not in df["id"].values: + raise DataNotFoundError(f"Item ID {item_id} not found.") + item = df[df["id"] == item_id].to_dict("records")[0] + item["params"] = json.loads(item["params"]) + return item + + def list_items(self): + df = self._load_data() + items = df.to_dict("records") + for item in items: + item["params"] = json.loads(item["params"]) + return items + + def find_item_by_name(self, name): + df = self._load_data() + if name not in df["name"].values: + raise DataNotFoundError(f"Name {name} not found.") + item = df[df["name"] == name].to_dict("records")[0] + item["params"] = json.loads(item["params"]) + return item + + def find_params_by_name(self, name): + try: + return self.find_item_by_name(name)["params"] + except Exception as e: + logger.error(e) + return {} + + def find_params_by_id(self, id): + try: + return self.get_item(id)["params"] + except Exception as e: + logger.error(e) + return {} + + +# Usage example +if __name__ == "__main__": + + class SpeakerManager(BaseManager): + def __init__(self, csv_file): + super().__init__(csv_file) + + manager = SpeakerManager("speakers.test.csv") + + try: + # Add speaker + manager.add_item( + 1, "Speaker1", "Description for speaker 1", '{"param1": "value1"}' + ) + except DataExistsError as e: + print(e) + + # List all speakers + speakers = manager.list_items() + print(speakers) + + try: + # Get specific speaker + speaker = manager.get_item(1) + print(speaker) + except DataNotFoundError as e: + print(e) + + try: + # Update speaker + manager.update_item( + 1, name="Updated Speaker1", desc="Updated description for speaker 1" + ) + except DataNotFoundError as e: + print(e) + + try: + # Delete speaker + manager.delete_item(1) + except DataNotFoundError as e: + print(e) + + try: + # Find speaker by name + speaker_by_name = manager.find_item_by_name("Updated Speaker1") + print(speaker_by_name) + except DataNotFoundError as e: + print(e) + + # Find speakers by params + speakers_by_params = manager.find_items_by_params('{"param1": "value1"}') + print(speakers_by_params) diff --git a/modules/utils/SeedContext.py b/modules/utils/SeedContext.py new file mode 100644 index 0000000000000000000000000000000000000000..c7190ba2d41ac00b56ddd772d6e595e9a2eecbfd --- /dev/null +++ b/modules/utils/SeedContext.py @@ -0,0 +1,77 @@ +import torch +import random +import numpy as np + + +def deterministic(seed=0): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def is_numeric(obj): + if isinstance(obj, str): + try: + float(obj) + return True + except ValueError: + return False + elif isinstance(obj, (np.integer, np.signedinteger, np.unsignedinteger)): + return True + elif isinstance(obj, np.floating): + return True + elif isinstance(obj, (int, float)): + return True + else: + return False + + +class SeedContext: + def __init__(self, seed): + assert is_numeric(seed), "Seed must be an number." + + try: + self.seed = int(np.clip(int(seed), -1, 2**32 - 1)) + except Exception as e: + raise ValueError("Seed must be an integer.") + + self.seed = seed + self.state = None + + if isinstance(seed, str) and seed.isdigit(): + self.seed = int(seed) + + if isinstance(self.seed, float): + self.seed = int(self.seed) + + if self.seed == -1: + self.seed = random.randint(0, 2**32 - 1) + + def __enter__(self): + self.state = (torch.get_rng_state(), random.getstate(), np.random.get_state()) + + deterministic(self.seed) + + def __exit__(self, exc_type, exc_value, traceback): + torch.set_rng_state(self.state[0]) + random.setstate(self.state[1]) + np.random.set_state(self.state[2]) + + +if __name__ == "__main__": + print(is_numeric("1234")) # True + print(is_numeric("12.34")) # True + print(is_numeric("-1234")) # True + print(is_numeric("abc123")) # False + print(is_numeric(np.int32(10))) # True + print(is_numeric(np.float64(10.5))) # True + print(is_numeric(10)) # True + print(is_numeric(10.5)) # True + print(is_numeric(np.int8(10))) # True + print(is_numeric(np.uint64(10))) # True + print(is_numeric(np.float16(10.5))) # True + print(is_numeric([1, 2, 3])) # False diff --git a/modules/utils/__init__.py b/modules/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/utils/audio.py b/modules/utils/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..b95e61620e9d7eefa46d9ef65f25f7fa10ed5be1 --- /dev/null +++ b/modules/utils/audio.py @@ -0,0 +1,96 @@ +import sys +from pydub import AudioSegment +import soundfile as sf +import pyrubberband as pyrb +import numpy as np +from io import BytesIO + + +def audiosegment_to_librosawav(audiosegment): + channel_sounds = audiosegment.split_to_mono() + samples = [s.get_array_of_samples() for s in channel_sounds] + + fp_arr = np.array(samples).T.astype(np.float32) + fp_arr /= np.iinfo(samples[0].typecode).max + fp_arr = fp_arr.reshape(-1) + + return fp_arr + + +def ndarray_to_segment(ndarray, frame_rate): + buffer = BytesIO() + sf.write(buffer, ndarray, frame_rate, format="wav") + buffer.seek(0) + sound = AudioSegment.from_wav( + buffer, + ) + return sound + + +def time_stretch(input_segment: AudioSegment, time_factor: float) -> AudioSegment: + """ + factor range -> [0.2,10] + """ + time_factor = np.clip(time_factor, 0.2, 10) + sr = input_segment.frame_rate + y = audiosegment_to_librosawav(input_segment) + y_stretch = pyrb.time_stretch(y, sr, time_factor) + + sound = ndarray_to_segment( + y_stretch, + frame_rate=sr, + ) + return sound + + +def pitch_shift( + input_segment: AudioSegment, + pitch_shift_factor: float, +) -> AudioSegment: + """ + factor range -> [-12,12] + """ + pitch_shift_factor = np.clip(pitch_shift_factor, -12, 12) + sr = input_segment.frame_rate + y = audiosegment_to_librosawav(input_segment) + y_shift = pyrb.pitch_shift(y, sr, pitch_shift_factor) + + sound = ndarray_to_segment( + y_shift, + frame_rate=sr, + ) + return sound + + +def apply_prosody_to_audio_data( + audio_data: np.ndarray, rate: float, volume: float, pitch: float, sr: int +) -> np.ndarray: + if rate != 1: + audio_data = pyrb.time_stretch(audio_data, sr=sr, rate=rate) + + if volume != 0: + audio_data = audio_data * volume + + if pitch != 0: + audio_data = pyrb.pitch_shift(audio_data, sr=sr, n_steps=pitch) + + return audio_data + + +if __name__ == "__main__": + input_file = sys.argv[1] + + time_stretch_factors = [0.5, 0.75, 1.5, 1.0] + pitch_shift_factors = [-12, -5, 0, 5, 12] + + input_sound = AudioSegment.from_mp3(input_file) + + for time_factor in time_stretch_factors: + output_wav = f"time_stretched_{int(time_factor * 100)}.wav" + sound = time_stretch(input_sound, time_factor) + sound.export(output_wav, format="wav") + + for pitch_factor in pitch_shift_factors: + output_wav = f"pitch_shifted_{int(pitch_factor * 100)}.wav" + sound = pitch_shift(input_sound, pitch_factor) + sound.export(output_wav, format="wav") diff --git a/modules/utils/env.py b/modules/utils/env.py new file mode 100644 index 0000000000000000000000000000000000000000..7bb8c79314cd31f5fe3e1dceeacfd97427108f38 --- /dev/null +++ b/modules/utils/env.py @@ -0,0 +1,29 @@ +import os + + +def get_env_val(key, val_type): + env_val = os.getenv(key.upper()) + if env_val is not None and env_val != "": + if val_type == bool: + return env_val.lower() in ("true", "1", "t") + return val_type(env_val) + + if env_val == "": + return None + + return env_val + + +def get_env_or_arg(args, arg_name, default, arg_type): + arg_val = getattr(args, arg_name) + env_val = get_env_val(arg_name, arg_type) + + if arg_type == bool and env_val is not None: + return env_val + + if arg_val is not None: + return arg_val + elif env_val is not None: + return env_val + + return default diff --git a/modules/utils/markdown.py b/modules/utils/markdown.py new file mode 100644 index 0000000000000000000000000000000000000000..8ceac27809be3aa6c6758e135d197da0a4e06f41 --- /dev/null +++ b/modules/utils/markdown.py @@ -0,0 +1,77 @@ +import mistune + + +class PlainTextRenderer(mistune.HTMLRenderer): + def text(self, text): + return text + + def link(self, text, url, title=None): + return text + + def image(self, alt, url, title=None): + return alt + + def emphasis(self, text): + return text + + def strong(self, text): + return text + + def block_code(self, code, info=None): + # remove code + return "" + + def block_quote(self, text): + return text + + def heading(self, text, level): + return text + "\n" + + def newline(self): + return "\n" + + def list(self, text: str, ordered: bool, **attrs) -> str: + if ordered: + html = "" + return html + "\n" + text + "\n" + return "\n" + text + "\n" + + def list_item(self, text): + return "" + text + "\n" + + def paragraph(self, text): + return text + "\n" + + def codespan(self, text: str) -> str: + # remove code + return "" + + +def markdown_to_text(markdown_text): + renderer = PlainTextRenderer() + markdown = mistune.create_markdown(renderer=renderer) + text = markdown(markdown_text) + text = text.strip() + return text + + +if __name__ == "__main__": + markdown_text = """ +# 标题 + +这是一个示例文本，其中包含 **加粗**、*斜体*、[链接](http://example.com) 和其他 Markdown 语法。 + +```ts +console.log(1) +``` + +- 列表项 1 +- 列表项 2 +- 列表项 3 + +> 这是一个引用。 + +`代码片段` + """ + plain_text = markdown_to_text(markdown_text) + print(plain_text) diff --git a/modules/utils/normalization.py b/modules/utils/normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..241486b9e131b8226130431a690406a35c3be308 --- /dev/null +++ b/modules/utils/normalization.py @@ -0,0 +1,147 @@ +from modules.utils.zh_normalization.text_normlization import * + +character_map = { + "：": "，", + "；": "，", + "！": "。", + "（": "，", + "）": "，", + "【": "，", + "】": "，", + "『": "，", + "』": "，", + "「": "，", + "」": "，", + "《": "，", + "》": "，", + "－": "，", + "‘": " ", + "“": " ", + "’": " ", + "”": " ", + ":": ",", + ";": ",", + "!": ".", + "(": ",", + ")": ",", + # '[': ',', + # ']': ',', + ">": ",", + "<": ",", + "-": ",", +} + +character_to_word = { + " & ": " and ", +} + + +def apply_character_to_word(text): + for k, v in character_to_word.items(): + text = text.replace(k, v) + return text + + +def apply_character_map(text): + translation_table = str.maketrans(character_map) + return text.translate(translation_table) + + +def insert_spaces_between_uppercase(s): + # 使用正则表达式在每个相邻的大写字母之间插入空格 + return re.sub( + r"(?<=[A-Z])(?=[A-Z])|(?<=[a-z])(?=[A-Z])|(?<=[\u4e00-\u9fa5])(?=[A-Z])|(?<=[A-Z])(?=[\u4e00-\u9fa5])", + " ", + s, + ) + + +def ensure_suffix(a: str, b: str, c: str): + a = a.strip() + if not a.endswith(b): + a += c + return a + + +email_domain_map = { + "outlook.com": "Out look", + "hotmail.com": "Hot mail", + "yahoo.com": "雅虎", +} + + +# 找到所有 email 并将 name 分割为单个字母，@替换为 at ，. 替换为 dot，常见域名替换为单词 +# +# 例如: +# zhzluke96@outlook.com => z h z l u k e 9 6 at out look dot com +def email_detect(text): + email_pattern = re.compile(r"([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})") + + def replace(match): + email = match.group(1) + name, domain = email.split("@") + name = " ".join(name) + if domain in email_domain_map: + domain = email_domain_map[domain] + domain = domain.replace(".", " dot ") + return f"{name} at {domain}" + + return email_pattern.sub(replace, text) + + +def pre_normalize(text): + # NOTE: 效果一般... + # text = email_detect(text) + return text + + +def post_normalize(text): + text = insert_spaces_between_uppercase(text) + text = apply_character_map(text) + text = apply_character_to_word(text) + return text + + +def text_normalize(text, is_end=False): + # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization + tx = TextNormalizer() + + # 匹配 \[.+?\] 的部分 + pattern = re.compile(r"(\[.+?\])|([^[]+)") + + def normalize_part(part): + part = pre_normalize(part) + sentences = tx.normalize(part) + dest_text = "" + for sentence in sentences: + dest_text += post_normalize(sentence) + return dest_text + + def replace(match): + if match.group(1): + return f" {match.group(1)} " + else: + return normalize_part(match.group(2)) + + result = pattern.sub(replace, text) + + # NOTE: 加了会有杂音... + # if is_end: + # 加这个是为了防止吞字 + # result = ensure_suffix(result, "[uv_break]", "。。。[uv_break]。。。") + + return result + + +if __name__ == "__main__": + print( + text_normalize( + "ChatTTS是专门为对话场景设计的文本转语音模型，例如LLM助手对话任务。它支持英文和中文两种语言。最大的模型使用了10万小时以上的中英文数据进行训练。在HuggingFace中开源的版本为4万小时训练且未SFT的版本." + ) + ) + print( + text_normalize( + " [oral_9] [laugh_0] [break_0] 电 [speed_0] 影 [speed_0] 中 梁朝伟 [speed_9] 扮演的陈永仁的编号27149" + ) + ) + print(text_normalize(" 明天有62％的概率降雨")) diff --git a/modules/utils/zh_normalization/README.md b/modules/utils/zh_normalization/README.md new file mode 100644 index 0000000000000000000000000000000000000000..92eea9f54630dfd41dfc3ce53bc511cc7595062c --- /dev/null +++ b/modules/utils/zh_normalization/README.md @@ -0,0 +1,16 @@ +## Supported NSW (Non-Standard-Word) Normalization + +|NSW type|raw|normalized| +|:--|:-|:-| +|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九| +|cardinal|这块黄金重达324.75克
我们班的最高总分为583分|这块黄金重达三百二十四点七五克
我们班的最高总分为五百八十三分| +|numeric range |12\~23
-1.5\~2|十二到二十三
负一点五到二| +|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日| +|time|等会请在12:05请通知我|等会请在十二点零五分请通知我 +|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度 +|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票| +|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨| +|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万| +|telephone|这是固话0421-33441122
这是手机+86 18544139121|这是固话零四二一三三四四一一二二
这是手机八六一八五四四一三九一二一| +## References +[Pull requests #658 of DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech/pull/658/files) diff --git a/modules/utils/zh_normalization/__init__.py b/modules/utils/zh_normalization/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..697be01b5e4f37f7f84bc8cfeb41ed846aab5f18 --- /dev/null +++ b/modules/utils/zh_normalization/__init__.py @@ -0,0 +1,57 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .text_normlization import * + +rep_map = { + "：": ",", + "；": ",", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "·": ",", + "、": ",", + "...": "…", + "$": ".", + "/": ",", + "—": "-", + "~": "…", + "～": "…", +} + + +def replace_punctuation(text): + text = text.replace("嗯", "恩").replace("呣", "母") + pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) + + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + punctuation = ["!", "?", "…", ",", "."] + replaced_text = re.sub( + r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text + ) + print(replaced_text) + + return replaced_text + + +def text_normalize(text): + # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization + tx = TextNormalizer() + sentences = tx.normalize(text) + dest_text = "" + for sentence in sentences: + dest_text += replace_punctuation(sentence) + print(dest_text, sentence) + return dest_text diff --git a/modules/utils/zh_normalization/char_convert.py b/modules/utils/zh_normalization/char_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..3eef97552aa47f8741a58c86ecd1486b24d23dac --- /dev/null +++ b/modules/utils/zh_normalization/char_convert.py @@ -0,0 +1,46 @@ +# coding=utf-8 +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters. +""" +simplified_charcters = 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+ +traditional_characters = 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鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤' + +assert len(simplified_charcters) == len(simplified_charcters) + +s2t_dict = {} +t2s_dict = {} +for i, item in enumerate(simplified_charcters): + s2t_dict[item] = traditional_characters[i] + t2s_dict[traditional_characters[i]] = item + + +def tranditional_to_simplified(text: str) -> str: + return "".join( + [t2s_dict[item] if item in t2s_dict else item for item in text]) + + +def simplified_to_traditional(text: str) -> str: + return "".join( + [s2t_dict[item] if item in s2t_dict else item for item in text]) + + +if __name__ == "__main__": + text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點" + print(text) + text_simple = tranditional_to_simplified(text) + print(text_simple) + text_traditional = simplified_to_traditional(text_simple) + print(text_traditional) \ No newline at end of file diff --git a/modules/utils/zh_normalization/chronology.py b/modules/utils/zh_normalization/chronology.py new file mode 100644 index 0000000000000000000000000000000000000000..a2c703d83150aa7f43c9d32ae156bc698092fdf4 --- /dev/null +++ b/modules/utils/zh_normalization/chronology.py @@ -0,0 +1,134 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re + +from .num import DIGITS +from .num import num2str +from .num import verbalize_cardinal +from .num import verbalize_digit + + +def _time_num2str(num_string: str) -> str: + """A special case for verbalizing number in time.""" + result = num2str(num_string.lstrip('0')) + if num_string.startswith('0'): + result = DIGITS['0'] + result + return result + + +# 时刻表达式 +RE_TIME = re.compile(r'([0-1]?[0-9]|2[0-3])' + r':([0-5][0-9])' + r'(:([0-5][0-9]))?') + +# 时间范围，如8:30-12:30 +RE_TIME_RANGE = re.compile(r'([0-1]?[0-9]|2[0-3])' + r':([0-5][0-9])' + r'(:([0-5][0-9]))?' + r'(~|-)' + r'([0-1]?[0-9]|2[0-3])' + r':([0-5][0-9])' + r'(:([0-5][0-9]))?') + + +def replace_time(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + + is_range = len(match.groups()) > 5 + + hour = match.group(1) + minute = match.group(2) + second = match.group(4) + + if is_range: + hour_2 = match.group(6) + minute_2 = match.group(7) + second_2 = match.group(9) + + result = f"{num2str(hour)}点" + if minute.lstrip('0'): + if int(minute) == 30: + result += "半" + else: + result += f"{_time_num2str(minute)}分" + if second and second.lstrip('0'): + result += f"{_time_num2str(second)}秒" + + if is_range: + result += "至" + result += f"{num2str(hour_2)}点" + if minute_2.lstrip('0'): + if int(minute) == 30: + result += "半" + else: + result += f"{_time_num2str(minute_2)}分" + if second_2 and second_2.lstrip('0'): + result += f"{_time_num2str(second_2)}秒" + + return result + + +RE_DATE = re.compile(r'(\d{4}|\d{2})年' + r'((0?[1-9]|1[0-2])月)?' + r'(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?') + + +def replace_date(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + year = match.group(1) + month = match.group(3) + day = match.group(5) + result = "" + if year: + result += f"{verbalize_digit(year)}年" + if month: + result += f"{verbalize_cardinal(month)}月" + if day: + result += f"{verbalize_cardinal(day)}{match.group(9)}" + return result + + +# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期 +RE_DATE2 = re.compile( + r'(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])') + + +def replace_date2(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + year = match.group(1) + month = match.group(3) + day = match.group(4) + result = "" + if year: + result += f"{verbalize_digit(year)}年" + if month: + result += f"{verbalize_cardinal(month)}月" + if day: + result += f"{verbalize_cardinal(day)}日" + return result \ No newline at end of file diff --git a/modules/utils/zh_normalization/constants.py b/modules/utils/zh_normalization/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..3078874d8bd3dfbff4f05620ff05872eab44a0a9 --- /dev/null +++ b/modules/utils/zh_normalization/constants.py @@ -0,0 +1,62 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re +import string + +from pypinyin.constants import SUPPORT_UCS4 + +# 全角半角转换 +# 英文字符全角 -> 半角映射表 (num: 52) +F2H_ASCII_LETTERS = { + ord(char) + 65248: ord(char) + for char in string.ascii_letters +} + +# 英文字符半角 -> 全角映射表 +H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()} + +# 数字字符全角 -> 半角映射表 (num: 10) +F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits} +# 数字字符半角 -> 全角映射表 +H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()} + +# 标点符号全角 -> 半角映射表 (num: 32) +F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation} +# 标点符号半角 -> 全角映射表 +H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()} + +# 空格 (num: 1) +F2H_SPACE = {'\u3000': ' '} +H2F_SPACE = {' ': '\u3000'} + +# 非"有拼音的汉字"的字符串，可用于NSW提取 +if SUPPORT_UCS4: + RE_NSW = re.compile(r'(?:[^' + r'\u3007' # 〇 + r'\u3400-\u4dbf' # CJK扩展A:[3400-4DBF] + r'\u4e00-\u9fff' # CJK基本:[4E00-9FFF] + r'\uf900-\ufaff' # CJK兼容:[F900-FAFF] + r'\U00020000-\U0002A6DF' # CJK扩展B:[20000-2A6DF] + r'\U0002A703-\U0002B73F' # CJK扩展C:[2A700-2B73F] + r'\U0002B740-\U0002B81D' # CJK扩展D:[2B740-2B81D] + r'\U0002F80A-\U0002FA1F' # CJK兼容扩展:[2F800-2FA1F] + r'])+') +else: + RE_NSW = re.compile( # pragma: no cover + r'(?:[^' + r'\u3007' # 〇 + r'\u3400-\u4dbf' # CJK扩展A:[3400-4DBF] + r'\u4e00-\u9fff' # CJK基本:[4E00-9FFF] + r'\uf900-\ufaff' # CJK兼容:[F900-FAFF] + r'])+') \ No newline at end of file diff --git a/modules/utils/zh_normalization/num.py b/modules/utils/zh_normalization/num.py new file mode 100644 index 0000000000000000000000000000000000000000..3b6b7fb6b2e7241a1d5ec9e75becbe4718275812 --- /dev/null +++ b/modules/utils/zh_normalization/num.py @@ -0,0 +1,244 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Rules to verbalize numbers into Chinese characters. +https://zh.wikipedia.org/wiki/中文数字#現代中文 +""" +import re +from collections import OrderedDict +from typing import List + +DIGITS = {str(i): tran for i, tran in enumerate("零一二三四五六七八九")} +UNITS = OrderedDict( + { + 1: "十", + 2: "百", + 3: "千", + 4: "万", + 8: "亿", + } +) + +COM_QUANTIFIERS = "(封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)" + +# 分数表达式 +RE_FRAC = re.compile(r"(-?)(\d+)/(\d+)") + + +def replace_frac(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + sign = match.group(1) + nominator = match.group(2) + denominator = match.group(3) + sign: str = "负" if sign else "" + nominator: str = num2str(nominator) + denominator: str = num2str(denominator) + result = f"{sign}{denominator}分之{nominator}" + return result + + +# 百分数表达式 +RE_PERCENTAGE = re.compile(r"(-?)(\d+(\.\d+)?)(%|％)") + + +def replace_percentage(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + sign = match.group(1) + percent = match.group(2) + sign: str = "负" if sign else "" + percent: str = num2str(percent) + result = f"{sign}百分之{percent}" + return result + + +# 整数表达式 +# 带负号的整数 -10 +RE_INTEGER = re.compile(r"(-)" r"(\d+)") + + +def replace_negative_num(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + sign = match.group(1) + number = match.group(2) + sign: str = "负" if sign else "" + number: str = num2str(number) + result = f"{sign}{number}" + return result + + +# 编号-无符号整形 +# 00078 +RE_DEFAULT_NUM = re.compile(r"\d{3}\d*") + + +def replace_default_num(match): + """ + Args: + match (re.Match) + Returns: + str + """ + number = match.group(0) + return verbalize_digit(number, alt_one=True) + + +# 数字表达式 +# 纯小数 +RE_DECIMAL_NUM = re.compile(r"(-?)((\d+)(\.\d+))" r"|(\.(\d+))") +# 正整数 + 量词 +RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几\+])?" + COM_QUANTIFIERS) +RE_NUMBER = re.compile(r"(-?)((\d+)(\.\d+)?)" r"|(\.(\d+))") + + +def replace_positive_quantifier(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + number = match.group(1) + match_2 = match.group(2) + if match_2 == "+": + match_2 = "多" + match_2: str = match_2 if match_2 else "" + quantifiers: str = match.group(3) + number: str = num2str(number) + result = f"{number}{match_2}{quantifiers}" + return result + + +def replace_number(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + sign = match.group(1) + number = match.group(2) + pure_decimal = match.group(5) + if pure_decimal: + result = num2str(pure_decimal) + else: + sign: str = "负" if sign else "" + number: str = num2str(number) + result = f"{sign}{number}" + return result + + +# 范围表达式 +# match.group(1) and match.group(8) are copy from RE_NUMBER + +RE_RANGE = re.compile( + r"((-?)((\d+)(\.\d+)?)|(\.(\d+)))[-~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))" +) + + +def replace_range(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + first, second = match.group(1), match.group(8) + first = RE_NUMBER.sub(replace_number, first) + second = RE_NUMBER.sub(replace_number, second) + result = f"{first}到{second}" + return result + + +def _get_value(value_string: str, use_zero: bool = True) -> List[str]: + stripped = value_string.lstrip("0") + if len(stripped) == 0: + return [] + elif len(stripped) == 1: + if use_zero and len(stripped) < len(value_string): + return [DIGITS["0"], DIGITS[stripped]] + else: + return [DIGITS[stripped]] + else: + largest_unit = next( + power for power in reversed(UNITS.keys()) if power < len(stripped) + ) + first_part = value_string[:-largest_unit] + second_part = value_string[-largest_unit:] + return _get_value(first_part) + [UNITS[largest_unit]] + _get_value(second_part) + + +def verbalize_cardinal(value_string: str) -> str: + if not value_string: + return "" + + # 000 -> '零' , 0 -> '零' + value_string = value_string.lstrip("0") + if len(value_string) == 0: + return DIGITS["0"] + + result_symbols = _get_value(value_string) + # verbalized number starting with '一十*' is abbreviated as `十*` + if ( + len(result_symbols) >= 2 + and result_symbols[0] == DIGITS["1"] + and result_symbols[1] == UNITS[1] + ): + result_symbols = result_symbols[1:] + return "".join(result_symbols) + + +def verbalize_digit(value_string: str, alt_one=False) -> str: + result_symbols = [DIGITS[digit] for digit in value_string] + result = "".join(result_symbols) + if alt_one: + result = result.replace("一", "幺") + return result + + +def num2str(value_string: str) -> str: + integer_decimal = value_string.split(".") + if len(integer_decimal) == 1: + integer = integer_decimal[0] + decimal = "" + elif len(integer_decimal) == 2: + integer, decimal = integer_decimal + else: + raise ValueError( + f"The value string: '${value_string}' has more than one point in it." + ) + + result = verbalize_cardinal(integer) + + decimal = decimal.rstrip("0") + if decimal: + # '.22' is verbalized as '零点二二' + # '3.20' is verbalized as '三点二 + result = result if result else "零" + result += "点" + verbalize_digit(decimal) + return result diff --git a/modules/utils/zh_normalization/phonecode.py b/modules/utils/zh_normalization/phonecode.py new file mode 100644 index 0000000000000000000000000000000000000000..51835112603c1a8c31052285276f138b8c5a74d6 --- /dev/null +++ b/modules/utils/zh_normalization/phonecode.py @@ -0,0 +1,63 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re + +from .num import verbalize_digit + +# 规范化固话/手机号码 +# 手机 +# http://www.jihaoba.com/news/show/13680 +# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198 +# 联通：130、131、132、156、155、186、185、176 +# 电信：133、153、189、180、181、177 +RE_MOBILE_PHONE = re.compile( + r"(? str: + if mobile: + sp_parts = phone_string.strip('+').split() + result = '，'.join( + [verbalize_digit(part, alt_one=True) for part in sp_parts]) + return result + else: + sil_parts = phone_string.split('-') + result = '，'.join( + [verbalize_digit(part, alt_one=True) for part in sil_parts]) + return result + + +def replace_phone(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + return phone2str(match.group(0), mobile=False) + + +def replace_mobile(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + return phone2str(match.group(0)) diff --git a/modules/utils/zh_normalization/quantifier.py b/modules/utils/zh_normalization/quantifier.py new file mode 100644 index 0000000000000000000000000000000000000000..5fc7e9f3ee1af44e4d5a37f7af1fc33c65d39e44 --- /dev/null +++ b/modules/utils/zh_normalization/quantifier.py @@ -0,0 +1,63 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re + +from .num import num2str + +# 温度表达式，温度会影响负号的读法 +# -3°C 零下三度 +RE_TEMPERATURE = re.compile(r"(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)") +measure_dict = { + "cm2": "平方厘米", + "cm²": "平方厘米", + "cm3": "立方厘米", + "cm³": "立方厘米", + "cm": "厘米", + "db": "分贝", + "ds": "毫秒", + "kg": "千克", + "km": "千米", + "m2": "平方米", + "m²": "平方米", + "m³": "立方米", + "m3": "立方米", + "ml": "毫升", + "m": "米", + "mm": "毫米", + "s": "秒", +} + + +def replace_temperature(match) -> str: + """ + Args: + match (re.Match) + Returns: + str + """ + sign = match.group(1) + temperature = match.group(2) + unit = match.group(3) + sign: str = "零下" if sign else "" + temperature: str = num2str(temperature) + unit: str = "摄氏度" if unit == "摄氏度" else "度" + result = f"{sign}{temperature}{unit}" + return result + + +def replace_measure(sentence) -> str: + for q_notation in measure_dict: + pattern = rf"(?<=\d){q_notation}" + sentence = re.sub(pattern, measure_dict[q_notation], sentence) + return sentence diff --git a/modules/utils/zh_normalization/text_normlization.py b/modules/utils/zh_normalization/text_normlization.py new file mode 100644 index 0000000000000000000000000000000000000000..1dbeff02a24ebef4080cc9c7c713f255c251a740 --- /dev/null +++ b/modules/utils/zh_normalization/text_normlization.py @@ -0,0 +1,155 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re +from typing import List + +from .char_convert import tranditional_to_simplified +from .chronology import RE_DATE +from .chronology import RE_DATE2 +from .chronology import RE_TIME +from .chronology import RE_TIME_RANGE +from .chronology import replace_date +from .chronology import replace_date2 +from .chronology import replace_time +from .constants import F2H_ASCII_LETTERS +from .constants import F2H_DIGITS +from .constants import F2H_SPACE +from .num import RE_DECIMAL_NUM +from .num import RE_DEFAULT_NUM +from .num import RE_FRAC +from .num import RE_INTEGER +from .num import RE_NUMBER +from .num import RE_PERCENTAGE +from .num import RE_POSITIVE_QUANTIFIERS +from .num import RE_RANGE +from .num import replace_default_num +from .num import replace_frac +from .num import replace_negative_num +from .num import replace_number +from .num import replace_percentage +from .num import replace_positive_quantifier +from .num import replace_range +from .phonecode import RE_MOBILE_PHONE +from .phonecode import RE_NATIONAL_UNIFORM_NUMBER +from .phonecode import RE_TELEPHONE +from .phonecode import replace_mobile +from .phonecode import replace_phone +from .quantifier import RE_TEMPERATURE +from .quantifier import replace_measure +from .quantifier import replace_temperature + + +class TextNormalizer(): + def __init__(self): + self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)') + + def _split(self, text: str, lang="zh") -> List[str]: + """Split long text into sentences with sentence-splitting punctuations. + Args: + text (str): The input text. + Returns: + List[str]: Sentences. + """ + # Only for pure Chinese here + if lang == "zh": + text = text.replace(" ", "") + # 过滤掉特殊字符 + text = re.sub(r'[——《》【】<=>{}()（）#&@“”^_|…\\]', '', text) + text = self.SENTENCE_SPLITOR.sub(r'\1\n', text) + text = text.strip() + sentences = [sentence.strip() for sentence in re.split(r'\n+', text)] + return sentences + + def _post_replace(self, sentence: str) -> str: + sentence = sentence.replace('/', '每') + sentence = sentence.replace('~', '至') + sentence = sentence.replace('～', '至') + sentence = sentence.replace('①', '一') + sentence = sentence.replace('②', '二') + sentence = sentence.replace('③', '三') + sentence = sentence.replace('④', '四') + sentence = sentence.replace('⑤', '五') + sentence = sentence.replace('⑥', '六') + sentence = sentence.replace('⑦', '七') + sentence = sentence.replace('⑧', '八') + sentence = sentence.replace('⑨', '九') + sentence = sentence.replace('⑩', '十') + sentence = sentence.replace('α', '阿尔法') + sentence = sentence.replace('β', '贝塔') + sentence = sentence.replace('γ', '伽玛').replace('Γ', '伽玛') + sentence = sentence.replace('δ', '德尔塔').replace('Δ', '德尔塔') + sentence = sentence.replace('ε', '艾普西龙') + sentence = sentence.replace('ζ', '捷塔') + sentence = sentence.replace('η', '依塔') + sentence = sentence.replace('θ', '西塔').replace('Θ', '西塔') + sentence = sentence.replace('ι', '艾欧塔') + sentence = sentence.replace('κ', '喀帕') + sentence = sentence.replace('λ', '拉姆达').replace('Λ', '拉姆达') + sentence = sentence.replace('μ', '缪') + sentence = sentence.replace('ν', '拗') + sentence = sentence.replace('ξ', '克西').replace('Ξ', '克西') + sentence = sentence.replace('ο', '欧米克伦') + sentence = sentence.replace('π', '派').replace('Π', '派') + sentence = sentence.replace('ρ', '肉') + sentence = sentence.replace('ς', '西格玛').replace('Σ', '西格玛').replace( + 'σ', '西格玛') + sentence = sentence.replace('τ', '套') + sentence = sentence.replace('υ', '宇普西龙') + sentence = sentence.replace('φ', '服艾').replace('Φ', '服艾') + sentence = sentence.replace('χ', '器') + sentence = sentence.replace('ψ', '普赛').replace('Ψ', '普赛') + sentence = sentence.replace('ω', '欧米伽').replace('Ω', '欧米伽') + # re filter special characters, have one more character "-" than line 68 + # sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|…\\]', '', sentence) + return sentence + + def normalize_sentence(self, sentence: str) -> str: + # basic character conversions + sentence = tranditional_to_simplified(sentence) + sentence = sentence.translate(F2H_ASCII_LETTERS).translate( + F2H_DIGITS).translate(F2H_SPACE) + + # number related NSW verbalization + sentence = RE_DATE.sub(replace_date, sentence) + sentence = RE_DATE2.sub(replace_date2, sentence) + + # range first + sentence = RE_TIME_RANGE.sub(replace_time, sentence) + sentence = RE_TIME.sub(replace_time, sentence) + + sentence = RE_TEMPERATURE.sub(replace_temperature, sentence) + sentence = replace_measure(sentence) + sentence = RE_FRAC.sub(replace_frac, sentence) + sentence = RE_PERCENTAGE.sub(replace_percentage, sentence) + sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence) + + sentence = RE_TELEPHONE.sub(replace_phone, sentence) + sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence) + + sentence = RE_RANGE.sub(replace_range, sentence) + sentence = RE_INTEGER.sub(replace_negative_num, sentence) + sentence = RE_DECIMAL_NUM.sub(replace_number, sentence) + sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier, + sentence) + sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence) + sentence = RE_NUMBER.sub(replace_number, sentence) + sentence = self._post_replace(sentence) + + return sentence + + def normalize(self, text: str, lang = "") -> List[str]: + sentences = self._split(text, lang) + sentences = [self.normalize_sentence(sent) for sent in sentences] + return sentences + \ No newline at end of file diff --git a/packages.txt b/packages.txt new file mode 100644 index 0000000000000000000000000000000000000000..862e49f79817882ba469b72f089779815bef92cd --- /dev/null +++ b/packages.txt @@ -0,0 +1 @@ +rubberband-cli \ No newline at end of file diff --git a/playground/client.mjs b/playground/client.mjs new file mode 100644 index 0000000000000000000000000000000000000000..22af4611d52737c46f73456d34cf85875e40c773 --- /dev/null +++ b/playground/client.mjs @@ -0,0 +1,167 @@ +import axios from "axios"; + +class APIClient { + constructor(baseURL) { + this.client = axios.create({ + baseURL: baseURL, + headers: { + "Content-Type": "application/json", + }, + }); + } + + async synthesizeTTS({ + text, + spk = "female2", + style = "chat", + temperature = 0.3, + top_P = 0.7, + top_K = 20, + seed = 34060637, + format = "mp3", + prompt1 = "", + prompt2 = "", + prefix = "", + }) { + try { + const response = await this.client.get("/v1/tts", { + params: { + text, + spk, + style, + temperature, + top_P, + top_K, + seed, + format, + prompt1, + prompt2, + prefix, + }, + responseType: "blob", // Important for handling binary data + }); + return response.data; + } catch (error) { + console.error("Error synthesizing TTS:", error); + throw error; + } + } + + /** + * + * @param {string} ssml - The SSML text to synthesize + * @param {string} format - The format of the synthesized audio (e.g. "mp3") + */ + async synthesizeSSML({ ssml, format = "mp3" }) { + try { + const response = await this.client.post( + "/v1/ssml", + { + ssml, + format, + }, + { + responseType: "blob", // Important for handling binary data + } + ); + return response.data; + } catch (error) { + console.error("Error synthesizing SSML:", error); + throw error; + } + } + + async openaiSpeechAPI({ + input, + model = "chattts-4w", + voice = "female2", + response_format = "mp3", + speed = 1, + }) { + try { + const response = await this.client.post( + "/v1/audio/speech", + { + input, + model, + voice, + response_format, + speed, + }, + { + responseType: "blob", // Important for handling binary data + } + ); + return response.data; + } catch (error) { + console.error("Error generating speech audio:", error); + throw error; + } + } + + async refineText({ + text, + prompt = "[oral_2][laugh_0][break_6]", + seed = -1, + top_P = 0.7, + top_K = 20, + temperature = 0.7, + repetition_penalty = 1.0, + max_new_token = 384, + }) { + try { + const response = await this.client.post("/v1/prompt/refine", { + text, + prompt, + seed, + top_P, + top_K, + temperature, + repetition_penalty, + max_new_token, + }); + return response.data; + } catch (error) { + console.error("Error refining text:", error); + throw error; + } + } + + async listStyles() { + try { + const response = await this.client.get("/v1/styles/list"); + return response.data; + } catch (error) { + console.error("Error listing styles:", error); + throw error; + } + } + + async listSpeakers() { + try { + const response = await this.client.get("/v1/speakers/list"); + return response.data; + } catch (error) { + console.error("Error listing speakers:", error); + throw error; + } + } + + async createSpeaker({ name, seed }) { + try { + const response = await this.client.post("/v1/speaker/create", { + name, + seed, + }); + return response.data; + } catch (error) { + console.error("Error creating speaker:", error); + throw error; + } + } +} + +export const client = new APIClient( + localStorage.getItem("__chattts_playground_api_base__") || + `${window.location.origin}` +); diff --git a/playground/index.html b/playground/index.html new file mode 100644 index 0000000000000000000000000000000000000000..ddd637cd56c3f0b08c0014119f2ad651d37ffaf8 --- /dev/null +++ b/playground/index.html @@ -0,0 +1,80 @@ + + + + + + + + ChatTTS Forge Playground + + + + + +
+ + + + + \ No newline at end of file diff --git a/playground/index.mjs b/playground/index.mjs new file mode 100644 index 0000000000000000000000000000000000000000..08222376479ac128b94b72297c77fe9ad21cfa0a --- /dev/null +++ b/playground/index.mjs @@ -0,0 +1,113 @@ +import { render } from "preact"; +import { html, create, styled } from "./misc.mjs"; + +import { pages } from "./pages.mjs"; + +const useStore = create((set, get) => ({ + page: Object.keys(pages).includes(location.hash.slice(1)) + ? location.hash.slice(1) + : Object.keys(pages)[0], + setPage: (page) => { + set({ page }); + location.hash = page; + }, +})); + +const NotFound = () => html`
Not Found
`; + +const Content = () => { + const { page } = useStore(); + const Page = pages[page]; + return Page ? html`<${Page} />` : html`<${NotFound} />`; +}; + +const NavButton = styled.button` + background-color: transparent; + border: none; + color: white; + cursor: pointer; + font-size: 1rem; + padding: 0.5rem 1rem; + border-bottom: 1px solid transparent; + + &:hover { + background-color: #444; + } + + &.--active { + border-bottom: 1px solid blue; + } +`; + +const HeaderNav = styled.nav` + display: flex; + padding: 4px 8px; + background-color: #333; + + gap: 8px; + + user-select: none; + + .nav-icon { + display: flex; + align-items: center; + justify-content: center; + } + + .nav-title { + display: flex; + align-items: center; + justify-content: center; + } + + .divider { + flex: 1; + } +`; + +const PageNav = () => { + const { setPage, page: current } = useStore(); + return html` + <${HeaderNav}> +
🗣️
+
ChatTTS Forge Playground
+ ${Object.keys(pages).map( + (page) => + html` + <${NavButton} + onClick=${() => setPage(page)} + className=${current === page ? "--active" : ""} + > + ${page} + + ` + )} +
+ <${NavButton} + onClick=${() => { + window.open("https://github.com/lenML/ChatTTS-Forge", "_blank"); + }} + > + github + + + `; +}; + +const AppContent = styled.div` + padding: 8px; + flex: 1; + overflow: auto; +`; + +const App = () => { + const { page } = useStore(); + return html` + <${PageNav} /> + <${AppContent} className="pg-scrollbar"> + <${Content} /> + + `; +}; + +render(html`<${App} />`, document.getElementById("app")); diff --git a/playground/misc.mjs b/playground/misc.mjs new file mode 100644 index 0000000000000000000000000000000000000000..2408cb9857c57d23cc876876d6ad5c79e5415e2d --- /dev/null +++ b/playground/misc.mjs @@ -0,0 +1,21 @@ +import { h } from "preact"; +import * as React from "preact/hooks"; +import htm from "htm"; +import { bindReact } from "@quik-fe/stand"; + +import * as goober from "goober"; + +goober.setup(h); + +export const html = htm.bind(h); +export const create = bindReact(React); + +/** + * @type {Record} + */ +export const styled = new Proxy( + {}, + { + get: (target, key) => goober.styled.call({}, key), + } +); diff --git a/playground/pages.mjs b/playground/pages.mjs new file mode 100644 index 0000000000000000000000000000000000000000..87854928821d70aec31ead114f720c10dd62af96 --- /dev/null +++ b/playground/pages.mjs @@ -0,0 +1,9 @@ +import { TTSPage } from "./tts.page.mjs"; +import { SSMLPage } from "./ssml.page.mjs"; +import { SpeakerPage } from "./speakers.page.mjs"; + +export const pages = { + tts: TTSPage, + ssml: SSMLPage, + speakers: SpeakerPage, +}; diff --git a/playground/speakers.page.mjs b/playground/speakers.page.mjs new file mode 100644 index 0000000000000000000000000000000000000000..3babcfa7369185a4592ed63eca3ebdd0cc7b0426 --- /dev/null +++ b/playground/speakers.page.mjs @@ -0,0 +1,219 @@ +import { client } from "./client.mjs"; +import { html, create, styled } from "./misc.mjs"; + +/** + * 管理 speaker + * + * 1. 显示 speaker 列表 + * 2. 创建 speaker + * 3. 可以删除修改 speaker + */ +const useStore = create((set, get) => ({ + /** + * @type {{ name: string, params: string }[]} + */ + speakers: [], + + setSpeakers: (speakers) => set({ speakers }), + + formData: { + seed: 42, + name: "", + }, + setFormData: (data) => set({ formData: data }), +})); + +window.addEventListener("load", async () => { + const speakers = await client.listSpeakers(); + useStore.get().setSpeakers(speakers.data); +}); + +const SpeakerFactory = () => { + // 调用接口创建 speaker + // 创建speaker需要设定seed和name + const { setSpeakers, formData, setFormData } = useStore(); + return html` + +
+ + + + +
+ + `; +}; + +const SpeakerList = () => { + // 显示 speaker 列表 + // 只是可以列出来，没有其他操作 + const { speakers } = useStore(); + // 以table + return html` +
+ Speakers + + + + + + + + + + ${speakers.map( + (speaker) => html` + + + + + ` + )} + +
idname
${speaker.id}${speaker.name}
+
+ `; +}; + +const SpeakerPageContainer = styled.div` + display: flex; + flex-direction: row; + + textarea { + width: 100%; + height: 10rem; + margin-bottom: 1rem; + + min-height: 10rem; + + resize: vertical; + } + + button { + padding: 0.5rem 1rem; + background-color: #007bff; + color: white; + border: none; + cursor: pointer; + } + + button:hover { + background-color: #0056b3; + } + + button:disabled { + background-color: #6c757d; + cursor: not-allowed; + } + + fieldset { + margin-top: 1rem; + padding: 1rem; + border: 1px solid #333; + } + + legend { + font-weight: bold; + } + + label { + display: block; + margin-bottom: 0.5rem; + } + + select, + input[type="range"], + input[type="number"] { + width: 100%; + margin-top: 0.25rem; + } + + input[type="range"] { + width: calc(100% - 2rem); + } + + input[type="number"] { + width: calc(100% - 2rem); + padding: 0.5rem; + } + + input[type="text"] { + width: 100%; + padding: 0.5rem; + } + + audio { + margin-top: 1rem; + } + + textarea, + input, + select { + background-color: #333; + color: white; + border: 1px solid #333; + border-radius: 0.25rem; + padding: 0.5rem; + } + + table { + width: 100%; + border-collapse: collapse; + } + + th, + td { + padding: 0.5rem; + border: 1px solid #333; + } + + th { + background-color: #333; + color: white; + } + + th:nth-child(2), + td:nth-child(2) { + width: 60%; + } + + .speaker-factory { + flex: 1; + } + + .spekaer-list { + width: 256px; + } +`; + +export const SpeakerPage = () => { + return html` + <${SpeakerPageContainer}> + <${SpeakerList} /> + <${SpeakerFactory} /> + + `; +}; diff --git a/playground/ssml.page.mjs b/playground/ssml.page.mjs new file mode 100644 index 0000000000000000000000000000000000000000..51206d59b08aab3fe86d50dc5b0351ab0c40df44 --- /dev/null +++ b/playground/ssml.page.mjs @@ -0,0 +1,269 @@ +import { client } from "./client.mjs"; +import { html, create, styled } from "./misc.mjs"; + +const default_ssml = ` + + + 这里是一个简单的 SSML 示例。 + + +`.trim(); + +const useStore = create((set, get) => ({ + params: { + ssml: default_ssml, + format: "mp3", + }, + setParams: (params) => set({ params }), + + loading: false, + + /** + * @type {Array<{ id: number, params: { ssml: string; format: string }, url: string }>} + */ + history: [], + setHistory: (history) => set({ history }), +})); + +const SSMLFormContainer = styled.div` + display: flex; + flex-direction: column; + + textarea { + width: 100%; + height: 10rem; + margin-bottom: 1rem; + + min-height: 10rem; + + resize: vertical; + } + + button { + padding: 0.5rem 1rem; + background-color: #007bff; + color: white; + border: none; + cursor: pointer; + } + + button:hover { + background-color: #0056b3; + } + + button:disabled { + background-color: #6c757d; + cursor: not-allowed; + } + + fieldset { + margin-top: 1rem; + padding: 1rem; + border: 1px solid #333; + } + + legend { + font-weight: bold; + } + + label { + display: block; + margin-bottom: 0.5rem; + } + + select, + input[type="range"], + input[type="number"] { + width: 100%; + margin-top: 0.25rem; + } + + input[type="range"] { + width: calc(100% - 2rem); + } + + input[type="number"] { + width: calc(100% - 2rem); + padding: 0.5rem; + } + + input[type="text"] { + width: 100%; + padding: 0.5rem; + } + + audio { + margin-top: 1rem; + } + + textarea, + input, + select { + background-color: #333; + color: white; + border: 1px solid #333; + border-radius: 0.25rem; + padding: 0.5rem; + } + + .ssml-body { + display: flex; + gap: 1rem; + } + + table { + width: 100%; + border-collapse: collapse; + } + + th, + td { + padding: 0.5rem; + border: 1px solid #333; + } + + th { + background-color: #333; + color: white; + } + + .btn-danger { + background-color: #dc3545; + color: white; + border: none; + cursor: pointer; + } + + .btn-danger:hover { + background-color: #bd2130; + } +`; + +const SSMLOptions = () => { + const { params, setParams } = useStore(); + return html` +
+ Options + +
+ `; +}; + +const SSMLHistory = () => { + const { history } = useStore(); + return html` +
+ History + + + + + + + + + + + ${[...history].reverse().map( + (item) => html` + + + + + + ` + )} + +
indexSSMLAudio
${item.id} + + + +
+
+ `; +}; + +let generate_index = 0; + +const SSMLForm = () => { + const { params, setParams, loading } = useStore(); + const request = async () => { + useStore.set({ loading: true }); + try { + const blob = await client.synthesizeSSML(params); + const blob_url = URL.createObjectURL(blob); + useStore.set({ + history: [ + ...useStore.get().history, + { + id: generate_index++, + params, + url: blob_url, + }, + ], + }); + } finally { + useStore.set({ loading: false }); + } + }; + return html` + <${SSMLFormContainer}> + + + + + + +
+
+ Options + + + + + + + + + + +
+ +
+ History + + + + + + + + + + ${[...ui.history].reverse().map( + (item, index) => html` + + + + + + ` + )} + +
idParamsAudio
${item.id} + 
${JSON.stringify(item.params, null, 2)}
+ 		+ +
+
+
+ + `; +}; diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bcdc929822057c8fb31cfb8f587aa990e45f1aaa --- /dev/null +++ b/requirements.txt @@ -0,0 +1,25 @@ +numpy +scipy +lxml +pydub +fastapi +soundfile +pyrubberband +omegaconf +pypinyin +pandas +vector_quantize_pytorch +einops +omegaconf~=2.3.0 +tqdm +huggingface_hub>=0.22.2,<1.0 +vocos==0.0.1 +transformers==4.41.2 +torch +torchvision +torchaudio +gradio +emojiswitch +python-dotenv +zhon +mistune \ No newline at end of file diff --git a/webui.py b/webui.py new file mode 100644 index 0000000000000000000000000000000000000000..8d35de10370b2212b855f0258814b7bf40273b70 --- /dev/null +++ b/webui.py @@ -0,0 +1,918 @@ +import spaces +import os +import logging + +from numpy import clip + +logging.basicConfig( + level=os.getenv("LOG_LEVEL", "INFO"), + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", +) + + +import gradio as gr +import io +import re +import numpy as np + +import torch + +from modules.ssml import parse_ssml +from modules.SynthesizeSegments import SynthesizeSegments, combine_audio_segments +from modules.generate_audio import generate_audio, generate_audio_batch + +from modules.speaker import speaker_mgr +from modules.data import styles_mgr + +from modules.api.utils import calc_spk_style + +from modules.normalization import text_normalize +from modules import refiner, config + +from modules.utils import env +from modules.SentenceSplitter import SentenceSplitter + +torch._dynamo.config.cache_size_limit = 64 +torch._dynamo.config.suppress_errors = True +torch.set_float32_matmul_precision("high") + +webui_config = { + "tts_max": 1000, + "ssml_max": 5000, + "spliter_threshold": 100, + "max_batch_size": 12, +} + + +def get_speakers(): + return speaker_mgr.list_speakers() + + +def get_styles(): + return styles_mgr.list_items() + + +def segments_length_limit(segments, total_max: int): + ret_segments = [] + total_len = 0 + for seg in segments: + total_len += len(seg["text"]) + if total_len > total_max: + break + ret_segments.append(seg) + return ret_segments + + +@torch.inference_mode() +@spaces.GPU +def synthesize_ssml(ssml: str, batch_size=8): + try: + batch_size = int(batch_size) + except Exception: + batch_size = 8 + + ssml = ssml.strip() + + if ssml == "": + return None + + segments = parse_ssml(ssml) + max_len = webui_config["ssml_max"] + segments = segments_length_limit(segments, max_len) + + if len(segments) == 0: + return None + + synthesize = SynthesizeSegments(batch_size=batch_size) + audio_segments = synthesize.synthesize_segments(segments) + combined_audio = combine_audio_segments(audio_segments) + + buffer = io.BytesIO() + combined_audio.export(buffer, format="wav") + + buffer.seek(0) + + return buffer.read() + + +@torch.inference_mode() +@spaces.GPU +def tts_generate( + text, + temperature, + top_p, + top_k, + spk, + infer_seed, + use_decoder, + prompt1, + prompt2, + prefix, + style, + disable_normalize=False, + batch_size=8, +): + try: + batch_size = int(batch_size) + except Exception: + batch_size = 8 + + max_len = webui_config["tts_max"] + text = text.strip()[0:max_len] + + if text == "": + return None + + if style == "*auto": + style = None + + if isinstance(top_k, float): + top_k = int(top_k) + + params = calc_spk_style(spk=spk, style=style) + + spk = params.get("spk", spk) + infer_seed = infer_seed or params.get("seed", infer_seed) + temperature = temperature or params.get("temperature", temperature) + prefix = prefix or params.get("prefix", prefix) + prompt1 = prompt1 or params.get("prompt1", "") + prompt2 = prompt2 or params.get("prompt2", "") + + infer_seed = clip(infer_seed, -1, 2**32 - 1) + infer_seed = int(infer_seed) + + if not disable_normalize: + text = text_normalize(text) + + if batch_size == 1: + sample_rate, audio_data = generate_audio( + text=text, + temperature=temperature, + top_P=top_p, + top_K=top_k, + spk=spk, + infer_seed=infer_seed, + use_decoder=use_decoder, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + ) + + return sample_rate, audio_data + else: + spliter = SentenceSplitter(webui_config["spliter_threshold"]) + sentences = spliter.parse(text) + sentences = [text_normalize(s) for s in sentences] + audio_data_batch = generate_audio_batch( + texts=sentences, + temperature=temperature, + top_P=top_p, + top_K=top_k, + spk=spk, + infer_seed=infer_seed, + use_decoder=use_decoder, + prompt1=prompt1, + prompt2=prompt2, + prefix=prefix, + ) + sample_rate = audio_data_batch[0][0] + audio_data = np.concatenate([data for _, data in audio_data_batch]) + + return sample_rate, audio_data + + +@torch.inference_mode() +@spaces.GPU +def refine_text(text: str, prompt: str): + text = text_normalize(text) + return refiner.refine_text(text, prompt=prompt) + + +def read_local_readme(): + with open("README.md", "r", encoding="utf-8") as file: + content = file.read() + content = content[content.index("# 🗣️ ChatTTS-Forge") :] + return content + + +# 演示示例文本 +sample_texts = [ + { + "text": "大🍌，一条大🍌，嘿，你的感觉真的很奇妙 [lbreak]", + }, + { + "text": "天气预报显示，今天会有小雨，请大家出门时记得带伞。降温的天气也提醒我们要适时添衣保暖 [lbreak]", + }, + { + "text": "公司的年度总结会议将在下周三举行，请各部门提前准备好相关材料，确保会议顺利进行 [lbreak]", + }, + { + "text": "今天的午餐菜单包括烤鸡、沙拉和蔬菜汤，大家可以根据自己的口味选择适合的菜品 [lbreak]", + }, + { + "text": "请注意，电梯将在下午两点进行例行维护，预计需要一个小时的时间，请大家在此期间使用楼梯 [lbreak]", + }, + { + "text": "图书馆新到了一批书籍，涵盖了文学、科学和历史等多个领域，欢迎大家前来借阅 [lbreak]", + }, + { + "text": "电影中梁朝伟扮演的陈永仁的编号27149 [lbreak]", + }, + { + "text": "这块黄金重达324.75克 [lbreak]", + }, + { + "text": "我们班的最高总分为583分 [lbreak]", + }, + { + "text": "12~23 [lbreak]", + }, + { + "text": "-1.5~2 [lbreak]", + }, + { + "text": "她出生于86年8月18日，她弟弟出生于1995年3月1日 [lbreak]", + }, + { + "text": "等会请在12:05请通知我 [lbreak]", + }, + { + "text": "今天的最低气温达到-10°C [lbreak]", + }, + { + "text": "现场有7/12的观众投出了赞成票 [lbreak]", + }, + { + "text": "明天有62％的概率降雨 [lbreak]", + }, + { + "text": "随便来几个价格12块5，34.5元，20.1万 [lbreak]", + }, + { + "text": "这是固话0421-33441122 [lbreak]", + }, + { + "text": "这是手机+86 18544139121 [lbreak]", + }, +] + +ssml_example1 = """ + + + 下面是一个 ChatTTS 用于合成多角色多情感的有声书示例[lbreak] + + + 黛玉冷笑道：[lbreak] + + + 我说呢 [uv_break] ，亏了绊住，不然，早就飞起来了[lbreak] + + + 宝玉道：[lbreak] + + + “只许和你玩 [uv_break] ，替你解闷。不过偶然到他那里，就说这些闲话。”[lbreak] + + + “好没意思的话！[uv_break] 去不去，关我什么事儿？ 又没叫你替我解闷儿 [uv_break]，还许你不理我呢” [lbreak] + + + 说着，便赌气回房去了 [lbreak] + + +""" +ssml_example2 = """ + + + 使用 prosody 控制生成文本的语速语调和音量，示例如下 [lbreak] + + + 无任何限制将会继承父级voice配置进行生成 [lbreak] + + + 设置 rate 大于1表示加速，小于1为减速 [lbreak] + + + 设置 pitch 调整音调，设置为6表示提高6个半音 [lbreak] + + + 设置 volume 调整音量，设置为2表示提高2个分贝 [lbreak] + + + 在 voice 中无prosody包裹的文本即为默认生成状态下的语音 [lbreak] + + +""" +ssml_example3 = """ + + + 使用 break 标签将会简单的 [lbreak] + + + + 插入一段空白到生成结果中 [lbreak] + + +""" + +ssml_example4 = """ + + + temperature for sampling (may be overridden by style or speaker) [lbreak] + + 温度值用于采样，这个值有可能被 style 或者 speaker 覆盖 [lbreak] + + temperature for sampling ，这个值有可能被 style 或者 speaker 覆盖 [lbreak] + + 温度值用于采样，(may be overridden by style or speaker) [lbreak] + + +""" + +default_ssml = """ + + + 这里是一个简单的 SSML 示例 [lbreak] + + +""" + + +def create_tts_interface(): + speakers = get_speakers() + + def get_speaker_show_name(spk): + if spk.gender == "*" or spk.gender == "": + return spk.name + return f"{spk.gender} : {spk.name}" + + speaker_names = ["*random"] + [ + get_speaker_show_name(speaker) for speaker in speakers + ] + + styles = ["*auto"] + [s.get("name") for s in get_styles()] + + history = [] + + with gr.Row(): + with gr.Column(scale=1): + with gr.Group(): + gr.Markdown("🎛️Sampling") + temperature_input = gr.Slider( + 0.01, 2.0, value=0.3, step=0.01, label="Temperature" + ) + top_p_input = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Top P") + top_k_input = gr.Slider(1, 50, value=20, step=1, label="Top K") + batch_size_input = gr.Slider( + 1, + webui_config["max_batch_size"], + value=8, + step=1, + label="Batch Size", + ) + + with gr.Row(): + with gr.Group(): + gr.Markdown("🎭Style") + gr.Markdown("- 后缀为 `_p` 表示带prompt，效果更强但是影响质量") + style_input_dropdown = gr.Dropdown( + choices=styles, + # label="Choose Style", + interactive=True, + show_label=False, + value="*auto", + ) + with gr.Row(): + with gr.Group(): + gr.Markdown("🗣️Speaker (Name or Seed)") + spk_input_text = gr.Textbox( + label="Speaker (Text or Seed)", + value="female2", + show_label=False, + ) + spk_input_dropdown = gr.Dropdown( + choices=speaker_names, + # label="Choose Speaker", + interactive=True, + value="female : female2", + show_label=False, + ) + spk_rand_button = gr.Button( + value="🎲", + # tooltip="Random Seed", + variant="secondary", + ) + spk_input_dropdown.change( + fn=lambda x: x.startswith("*") + and "-1" + or x.split(":")[-1].strip(), + inputs=[spk_input_dropdown], + outputs=[spk_input_text], + ) + spk_rand_button.click( + lambda x: str(torch.randint(0, 2**32 - 1, (1,)).item()), + inputs=[spk_input_text], + outputs=[spk_input_text], + ) + with gr.Group(): + gr.Markdown("💃Inference Seed") + infer_seed_input = gr.Number( + value=42, + label="Inference Seed", + show_label=False, + minimum=-1, + maximum=2**32 - 1, + ) + infer_seed_rand_button = gr.Button( + value="🎲", + # tooltip="Random Seed", + variant="secondary", + ) + use_decoder_input = gr.Checkbox( + value=True, label="Use Decoder", visible=False + ) + with gr.Group(): + gr.Markdown("🔧Prompt engineering") + prompt1_input = gr.Textbox(label="Prompt 1") + prompt2_input = gr.Textbox(label="Prompt 2") + prefix_input = gr.Textbox(label="Prefix") + + infer_seed_rand_button.click( + lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), + inputs=[infer_seed_input], + outputs=[infer_seed_input], + ) + with gr.Column(scale=3): + with gr.Row(): + with gr.Column(scale=4): + with gr.Group(): + input_title = gr.Markdown( + "📝Text Input", + elem_id="input-title", + ) + gr.Markdown("- 每个batch最长30s") + gr.Markdown("- batch size设置为1，即不使用批处理") + gr.Markdown("- 开启batch请配合设置Inference Seed") + gr.Markdown( + f"- 字数限制{webui_config['tts_max']:,}字，超过部分截断" + ) + gr.Markdown("- 如果尾字吞字不读，可以试试结尾加上 `[lbreak]`") + gr.Markdown( + "- If the input text is all in English, it is recommended to check disable_normalize" + ) + text_input = gr.Textbox( + show_label=False, + label="Text to Speech", + lines=10, + placeholder="输入文本或选择示例", + elem_id="text-input", + ) + # TODO 字数统计，其实实现很好写，但是就是会触发loading...并且还要和后端交互... + # text_input.change( + # fn=lambda x: ( + # f"📝Text Input ({len(x)} char)" + # if x + # else ( + # "📝Text Input (0 char)" + # if not x + # else "📝Text Input (0 char)" + # ) + # ), + # inputs=[text_input], + # outputs=[input_title], + # ) + with gr.Row(): + contorl_tokens = [ + "[laugh]", + "[uv_break]", + "[v_break]", + "[lbreak]", + ] + + for tk in contorl_tokens: + t_btn = gr.Button(tk) + t_btn.click( + lambda text, tk=tk: text + " " + tk, + inputs=[text_input], + outputs=[text_input], + ) + with gr.Column(scale=1): + with gr.Group(): + gr.Markdown("🎶Refiner") + refine_prompt_input = gr.Textbox( + label="Refine Prompt", + value="[oral_2][laugh_0][break_6]", + ) + refine_button = gr.Button("✍️Refine Text") + # TODO 分割句子，使用当前配置拼接为SSML，然后发送到SSML tab + # send_button = gr.Button("📩Split and send to SSML") + + with gr.Group(): + gr.Markdown("🔊Generate") + disable_normalize_input = gr.Checkbox( + value=False, label="Disable Normalize" + ) + tts_button = gr.Button( + "🔊Generate Audio", + variant="primary", + elem_classes="big-button", + ) + + with gr.Group(): + gr.Markdown("🎄Examples") + sample_dropdown = gr.Dropdown( + choices=[sample["text"] for sample in sample_texts], + show_label=False, + value=None, + interactive=True, + ) + sample_dropdown.change( + fn=lambda x: x, + inputs=[sample_dropdown], + outputs=[text_input], + ) + + with gr.Group(): + gr.Markdown("🎨Output") + tts_output = gr.Audio(label="Generated Audio") + + refine_button.click( + refine_text, + inputs=[text_input, refine_prompt_input], + outputs=[text_input], + ) + + tts_button.click( + tts_generate, + inputs=[ + text_input, + temperature_input, + top_p_input, + top_k_input, + spk_input_text, + infer_seed_input, + use_decoder_input, + prompt1_input, + prompt2_input, + prefix_input, + style_input_dropdown, + disable_normalize_input, + batch_size_input, + ], + outputs=tts_output, + ) + + +def create_ssml_interface(): + examples = [ + ssml_example1, + ssml_example2, + ssml_example3, + ssml_example4, + ] + + with gr.Row(): + with gr.Column(scale=3): + with gr.Group(): + gr.Markdown("📝SSML Input") + gr.Markdown(f"- 最长{webui_config['ssml_max']:,}字符，超过会被截断") + gr.Markdown("- 尽量保证使用相同的 seed") + gr.Markdown( + "- 关于SSML可以看这个 [文档](https://github.com/lenML/ChatTTS-Forge/blob/main/docs/SSML.md)" + ) + ssml_input = gr.Textbox( + label="SSML Input", + lines=10, + value=default_ssml, + placeholder="输入 SSML 或选择示例", + elem_id="ssml_input", + show_label=False, + ) + ssml_button = gr.Button("🔊Synthesize SSML", variant="primary") + with gr.Column(scale=1): + with gr.Group(): + # 参数 + gr.Markdown("🎛️Parameters") + # batch size + batch_size_input = gr.Slider( + label="Batch Size", + value=8, + minimum=1, + maximum=webui_config["max_batch_size"], + step=1, + ) + with gr.Group(): + gr.Markdown("🎄Examples") + gr.Examples( + examples=examples, + inputs=[ssml_input], + ) + + ssml_output = gr.Audio(label="Generated Audio") + + ssml_button.click( + synthesize_ssml, + inputs=[ssml_input, batch_size_input], + outputs=ssml_output, + ) + + return ssml_input + + +def split_long_text(long_text_input): + spliter = SentenceSplitter(webui_config["spliter_threshold"]) + sentences = spliter.parse(long_text_input) + sentences = [text_normalize(s) for s in sentences] + data = [] + for i, text in enumerate(sentences): + data.append([i, text, len(text)]) + return data + + +def merge_dataframe_to_ssml(dataframe, spk, style, seed): + if style == "*auto": + style = None + if spk == "-1" or spk == -1: + spk = None + if seed == -1 or seed == "-1": + seed = None + + ssml = "" + indent = " " * 2 + + for i, row in dataframe.iterrows(): + ssml += f"{indent}\n" + return f"\n{ssml}" + + +# 长文本处理 +# 可以输入长文本，并选择切割方法，切割之后可以将拼接的SSML发送到SSML tab +# 根据 。 句号切割，切割之后显示到 data table +def create_long_content_tab(ssml_input, tabs): + speakers = get_speakers() + + def get_speaker_show_name(spk): + if spk.gender == "*" or spk.gender == "": + return spk.name + return f"{spk.gender} : {spk.name}" + + speaker_names = ["*random"] + [ + get_speaker_show_name(speaker) for speaker in speakers + ] + + styles = ["*auto"] + [s.get("name") for s in get_styles()] + + with gr.Row(): + with gr.Column(scale=1): + # 选择说话人 选择风格 选择seed + with gr.Group(): + gr.Markdown("🗣️Speaker") + spk_input_text = gr.Textbox( + label="Speaker (Text or Seed)", + value="female2", + show_label=False, + ) + spk_input_dropdown = gr.Dropdown( + choices=speaker_names, + interactive=True, + value="female : female2", + show_label=False, + ) + spk_rand_button = gr.Button( + value="🎲", + variant="secondary", + ) + with gr.Group(): + gr.Markdown("🎭Style") + style_input_dropdown = gr.Dropdown( + choices=styles, + interactive=True, + show_label=False, + value="*auto", + ) + with gr.Group(): + gr.Markdown("🗣️Seed") + infer_seed_input = gr.Number( + value=42, + label="Inference Seed", + show_label=False, + minimum=-1, + maximum=2**32 - 1, + ) + infer_seed_rand_button = gr.Button( + value="🎲", + variant="secondary", + ) + + send_btn = gr.Button("📩Send to SSML", variant="primary") + + with gr.Column(scale=3): + with gr.Group(): + gr.Markdown("📝Long Text Input") + gr.Markdown("- 此页面用于处理超长文本") + gr.Markdown("- 切割后，可以选择说话人、风格、seed，然后发送到SSML") + long_text_input = gr.Textbox( + label="Long Text Input", + lines=10, + placeholder="输入长文本", + elem_id="long-text-input", + show_label=False, + ) + long_text_split_button = gr.Button("🔪Split Text") + + with gr.Row(): + with gr.Column(scale=3): + with gr.Group(): + gr.Markdown("🎨Output") + long_text_output = gr.DataFrame( + headers=["index", "text", "length"], + datatype=["number", "str", "number"], + elem_id="long-text-output", + interactive=False, + wrap=True, + value=[], + ) + + spk_input_dropdown.change( + fn=lambda x: x.startswith("*") and "-1" or x.split(":")[-1].strip(), + inputs=[spk_input_dropdown], + outputs=[spk_input_text], + ) + spk_rand_button.click( + lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), + inputs=[spk_input_text], + outputs=[spk_input_text], + ) + infer_seed_rand_button.click( + lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), + inputs=[infer_seed_input], + outputs=[infer_seed_input], + ) + long_text_split_button.click( + split_long_text, + inputs=[long_text_input], + outputs=[long_text_output], + ) + + infer_seed_rand_button.click( + lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), + inputs=[infer_seed_input], + outputs=[infer_seed_input], + ) + + send_btn.click( + merge_dataframe_to_ssml, + inputs=[ + long_text_output, + spk_input_text, + style_input_dropdown, + infer_seed_input, + ], + outputs=[ssml_input], + ) + + def change_tab(): + return gr.Tabs(selected="ssml") + + send_btn.click(change_tab, inputs=[], outputs=[tabs]) + + +def create_readme_tab(): + readme_content = read_local_readme() + gr.Markdown(readme_content) + + +def create_interface(): + + js_func = """ + function refresh() { + const url = new URL(window.location); + + if (url.searchParams.get('__theme') !== 'dark') { + url.searchParams.set('__theme', 'dark'); + window.location.href = url.href; + } + } + """ + + head_js = """ + + """ + + with gr.Blocks(js=js_func, head=head_js, title="ChatTTS Forge WebUI") as demo: + css = """ + + """ + + gr.HTML(css) + with gr.Tabs() as tabs: + with gr.TabItem("TTS"): + create_tts_interface() + + with gr.TabItem("SSML", id="ssml"): + ssml_input = create_ssml_interface() + + with gr.TabItem("Long Text"): + create_long_content_tab(ssml_input, tabs=tabs) + + with gr.TabItem("README"): + create_readme_tab() + + gr.Markdown( + "此项目基于 [ChatTTS-Forge](https://github.com/lenML/ChatTTS-Forge) " + ) + return demo + + +if __name__ == "__main__": + import argparse + import dotenv + + dotenv.load_dotenv( + dotenv_path=os.getenv("ENV_FILE", ".env.webui"), + ) + + parser = argparse.ArgumentParser(description="Gradio App") + parser.add_argument("--server_name", type=str, help="server name") + parser.add_argument("--server_port", type=int, help="server port") + parser.add_argument( + "--share", action="store_true", help="share the gradio interface" + ) + parser.add_argument("--debug", action="store_true", help="enable debug mode") + parser.add_argument("--auth", type=str, help="username:password for authentication") + parser.add_argument( + "--half", + action="store_true", + help="Enable half precision for model inference", + ) + parser.add_argument( + "--off_tqdm", + action="store_true", + help="Disable tqdm progress bar", + ) + parser.add_argument( + "--tts_max_len", + type=int, + help="Max length of text for TTS", + ) + parser.add_argument( + "--ssml_max_len", + type=int, + help="Max length of text for SSML", + ) + parser.add_argument( + "--max_batch_size", + type=int, + help="Max batch size for TTS", + ) + + args = parser.parse_args() + + server_name = env.get_env_or_arg(args, "server_name", "0.0.0.0", str) + server_port = env.get_env_or_arg(args, "server_port", 7860, int) + share = env.get_env_or_arg(args, "share", False, bool) + debug = env.get_env_or_arg(args, "debug", False, bool) + auth = env.get_env_or_arg(args, "auth", None, str) + half = env.get_env_or_arg(args, "half", False, bool) + off_tqdm = env.get_env_or_arg(args, "off_tqdm", False, bool) + + webui_config["tts_max"] = env.get_env_or_arg(args, "tts_max_len", 1000, int) + webui_config["ssml_max"] = env.get_env_or_arg(args, "ssml_max_len", 5000, int) + webui_config["max_batch_size"] = env.get_env_or_arg(args, "max_batch_size", 12, int) + + demo = create_interface() + + if auth: + auth = tuple(auth.split(":")) + + if half: + config.model_config["half"] = True + + if off_tqdm: + config.disable_tqdm = True + + demo.queue().launch( + server_name=server_name, + server_port=server_port, + share=share, + debug=debug, + auth=auth, + show_api=False, + )