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Repository: Huanshere/VideoLingo
Branch: main
Commit: 29e240dcd2a1
Files: 132
Total size: 541.1 KB
Directory structure:
gitextract_wyfw8khg/
├── .cursorrules
├── .gitignore
├── .streamlit/
│ └── config.toml
├── Dockerfile
├── LICENSE
├── OneKeyStart.bat
├── README.md
├── VideoLingo_colab.ipynb
├── batch/
│ ├── OneKeyBatch.bat
│ ├── README.md
│ ├── README.zh.md
│ └── utils/
│ ├── batch_processor.py
│ ├── settings_check.py
│ └── video_processor.py
├── config.yaml
├── core/
│ ├── _10_gen_audio.py
│ ├── _11_merge_audio.py
│ ├── _12_dub_to_vid.py
│ ├── _1_ytdlp.py
│ ├── _2_asr.py
│ ├── _3_1_split_nlp.py
│ ├── _3_2_split_meaning.py
│ ├── _4_1_summarize.py
│ ├── _4_2_translate.py
│ ├── _5_split_sub.py
│ ├── _6_gen_sub.py
│ ├── _7_sub_into_vid.py
│ ├── _8_1_audio_task.py
│ ├── _8_2_dub_chunks.py
│ ├── _9_refer_audio.py
│ ├── __init__.py
│ ├── asr_backend/
│ │ ├── __init__.py
│ │ ├── audio_preprocess.py
│ │ ├── demucs_vl.py
│ │ ├── elevenlabs_asr.py
│ │ ├── whisperX_302.py
│ │ └── whisperX_local.py
│ ├── prompts.py
│ ├── spacy_utils/
│ │ ├── __init__.py
│ │ ├── load_nlp_model.py
│ │ ├── split_by_comma.py
│ │ ├── split_by_connector.py
│ │ ├── split_by_mark.py
│ │ └── split_long_by_root.py
│ ├── st_utils/
│ │ ├── __init__.py
│ │ ├── download_video_section.py
│ │ ├── imports_and_utils.py
│ │ └── sidebar_setting.py
│ ├── translate_lines.py
│ ├── tts_backend/
│ │ ├── _302_f5tts.py
│ │ ├── azure_tts.py
│ │ ├── custom_tts.py
│ │ ├── edge_tts.py
│ │ ├── estimate_duration.py
│ │ ├── fish_tts.py
│ │ ├── gpt_sovits_tts.py
│ │ ├── openai_tts.py
│ │ ├── sf_cosyvoice2.py
│ │ ├── sf_fishtts.py
│ │ └── tts_main.py
│ └── utils/
│ ├── __init__.py
│ ├── ask_gpt.py
│ ├── config_utils.py
│ ├── decorator.py
│ ├── delete_retry_dubbing.py
│ ├── models.py
│ ├── onekeycleanup.py
│ └── pypi_autochoose.py
├── custom_terms.xlsx
├── docs/
│ ├── .gitignore
│ ├── components/
│ │ ├── landing/
│ │ │ ├── comments.tsx
│ │ │ ├── faq.tsx
│ │ │ ├── features.tsx
│ │ │ ├── github-stats.tsx
│ │ │ ├── hero.tsx
│ │ │ └── index.tsx
│ │ └── ui/
│ │ ├── accordion.tsx
│ │ ├── badge.tsx
│ │ ├── button.tsx
│ │ ├── card.tsx
│ │ ├── hero-video-dialog.tsx
│ │ ├── rainbow-button.tsx
│ │ └── tooltip.tsx
│ ├── components.json
│ ├── lib/
│ │ └── utils.ts
│ ├── middleware.js
│ ├── next-env.d.ts
│ ├── next.config.js
│ ├── package.json
│ ├── pages/
│ │ ├── _app.mdx
│ │ ├── _meta.en-US.json
│ │ ├── _meta.ja.json
│ │ ├── _meta.zh-CN.json
│ │ ├── docs/
│ │ │ ├── _meta.en-US.json
│ │ │ ├── _meta.ja.json
│ │ │ ├── _meta.zh-CN.json
│ │ │ ├── docker.en-US.md
│ │ │ ├── docker.zh-CN.md
│ │ │ ├── introduction.en-US.md
│ │ │ ├── introduction.zh-CN.md
│ │ │ ├── start.en-US.md
│ │ │ ├── start.zh-CN.md
│ │ │ ├── tech.en-US.md
│ │ │ └── tech.zh-CN.md
│ │ ├── globals.css
│ │ ├── index.en-US.mdx
│ │ ├── index.ja.mdx
│ │ └── index.zh-CN.mdx
│ ├── postcss.config.js
│ ├── public/
│ │ └── site.webmanifest
│ ├── tailwind.config.js
│ ├── theme.config.jsx
│ └── tsconfig.json
├── install.py
├── launch.py
├── requirements.txt
├── setup.py
├── st.py
└── translations/
├── README.es.md
├── README.fr.md
├── README.ja.md
├── README.ru.md
├── README.zh-TW.md
├── README.zh.md
├── en.json
├── es.json
├── fr.json
├── ja.json
├── ru.json
├── translations.py
├── zh-CN.json
└── zh-HK.json
================================================
FILE CONTENTS
================================================
================================================
FILE: .cursorrules
================================================
2. 使用
# ------------
# comment
# ------------
进行大块的注释
3. 避免使用复杂的函数内注释，以及函数变量中不要有类型定义
4. 使用英文注释和print
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# history and output
/output/
/history/
_model_cache/
batch/input/
batch/output/
batch/tasks_setting.xlsx
# large files
/ffmpeg.exe
/ffmpeg
/ffprobe.exe
/ffprobe
.DS_Store
AllinOne.ipynb
config.backup.yaml
# runtime
runtime/
dev/
installer_files/
logs/
================================================
FILE: .streamlit/config.toml
================================================
[server]
maxUploadSize = 4096
================================================
FILE: Dockerfile
================================================
ARG CUDA_VERSION=12.4.1
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04
# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive
ARG PYTHON_VERSION=3.10
# Change software sources and install basic tools and system dependencies
RUN sed -i 's/archive.ubuntu.com/mirrors.aliyun.com/g' /etc/apt/sources.list && \
sed -i 's/security.ubuntu.com/mirrors.aliyun.com/g' /etc/apt/sources.list && \
apt-get update && apt-get install -y --no-install-recommends \
software-properties-common git curl sudo ffmpeg fonts-noto wget \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Clean apt cache
RUN apt-get clean && rm -rf /var/lib/apt/lists/*
# Workaround for CUDA compatibility issues
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# Set working directory and clone repository
WORKDIR /app
RUN git clone https://github.com/Huanshere/VideoLingo.git .
# Install PyTorch and torchaudio
RUN pip install torch==2.0.0 torchaudio==2.0.0 --index-url https://download.pytorch.org/whl/cu118
# Clean up unnecessary files
RUN rm -rf .git
# Upgrade pip and install basic dependencies
RUN pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
# Install dependencies
COPY requirements.txt .
RUN pip install -e .
# Set CUDA-related environment variables
ENV CUDA_HOME=/usr/local/cuda
ENV PATH=${CUDA_HOME}/bin:${PATH}
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
# Set CUDA architecture list
ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
ENV TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}
EXPOSE 8501
CMD ["streamlit", "run", "st.py"]
================================================
FILE: LICENSE
================================================
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================================================
FILE: OneKeyStart.bat
================================================
@echo off
chcp 65001 >nul 2>&1
call conda activate videolingo 2>nul
set PYTHONWARNINGS=ignore
python "%~dp0launch.py"
if %errorlevel% neq 0 (
echo.
echo Pre-flight checks or Streamlit failed. See logs\ for details.
echo.
)
pause
================================================
FILE: README.md
================================================
<div align="center">
<img src="/docs/logo.png" alt="VideoLingo Logo" height="140">
# Connect the World, Frame by Frame
<a href="https://trendshift.io/repositories/12200" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12200" alt="Huanshere%2FVideoLingo | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
[**English**](/README.md)｜[**简体中文**](/translations/README.zh.md)｜[**繁體中文**](/translations/README.zh-TW.md)｜[**日本語**](/translations/README.ja.md)｜[**Español**](/translations/README.es.md)｜[**Русский**](/translations/README.ru.md)｜[**Français**](/translations/README.fr.md)
</div>
## 🌟 Overview ([Try VL Now!](https://videolingo.io))
VideoLingo is an all-in-one video translation, localization, and dubbing tool aimed at generating Netflix-quality subtitles. It eliminates stiff machine translations and multi-line subtitles while adding high-quality dubbing, enabling global knowledge sharing across language barriers.
Key features:
- 🎥 YouTube video download via yt-dlp
- **🎙️ Word-level and Low-illusion subtitle recognition with WhisperX**
- **📝 NLP and AI-powered subtitle segmentation**
- **📚 Custom + AI-generated terminology for coherent translation**
- **🔄 3-step Translate-Reflect-Adaptation for cinematic quality**
- **✅ Netflix-standard, Single-line subtitles Only**
- **🗣️ Dubbing with GPT-SoVITS, Azure, OpenAI, and more**
- 🚀 One-click startup and processing in Streamlit
- 🌍 Multi-language support in Streamlit UI
- 📝 Detailed logging with progress resumption
Difference from similar projects: **Single-line subtitles only, superior translation quality, seamless dubbing experience**
## 🎥 Demo
<table>
<tr>
<td width="33%">
### Dual Subtitles
---
https://github.com/user-attachments/assets/a5c3d8d1-2b29-4ba9-b0d0-25896829d951
</td>
<td width="33%">
### Cosy2 Voice Clone
---
https://github.com/user-attachments/assets/e065fe4c-3694-477f-b4d6-316917df7c0a
</td>
<td width="33%">
### GPT-SoVITS with my voice
---
https://github.com/user-attachments/assets/47d965b2-b4ab-4a0b-9d08-b49a7bf3508c
</td>
</tr>
</table>
### Language Support
**Input Language Support(more to come):**
🇺🇸 English 🤩 | 🇷🇺 Russian 😊 | 🇫🇷 French 🤩 | 🇩🇪 German 🤩 | 🇮🇹 Italian 🤩 | 🇪🇸 Spanish 🤩 | 🇯🇵 Japanese 😐 | 🇨🇳 Chinese* 😊
> *Chinese uses a separate punctuation-enhanced whisper model, for now...
**Translation supports all languages, while dubbing language depends on the chosen TTS method.**
## Installation
Meet any problem? Chat with our free online AI agent [**here**](https://share.fastgpt.in/chat/share?shareId=066w11n3r9aq6879r4z0v9rh) to help you.
> **Note:** For Windows users with NVIDIA GPU, follow these steps before installation:
> 1. Install [CUDA Toolkit 12.6](https://developer.download.nvidia.com/compute/cuda/12.6.0/local_installers/cuda_12.6.0_560.76_windows.exe)
> 2. Install [CUDNN 9.3.0](https://developer.download.nvidia.com/compute/cudnn/9.3.0/local_installers/cudnn_9.3.0_windows.exe)
> 3. Add `C:\Program Files\NVIDIA\CUDNN\v9.3\bin\12.6` to your system PATH
> 4. Restart your computer
> **Note:** FFmpeg is required. Please install it via package managers:
> - Windows: ```choco install ffmpeg``` (via [Chocolatey](https://chocolatey.org/))
> - macOS: ```brew install ffmpeg``` (via [Homebrew](https://brew.sh/))
> - Linux: ```sudo apt install ffmpeg``` (Debian/Ubuntu)
1. Clone the repository
```bash
git clone https://github.com/Huanshere/VideoLingo.git
cd VideoLingo
```
2. Install dependencies(requires `python=3.10`)
```bash
conda create -n videolingo python=3.10.0 -y
conda activate videolingo
python install.py
```
3. Start the application
```bash
streamlit run st.py
```
### Docker
Alternatively, you can use Docker (requires CUDA 12.4 and NVIDIA Driver version >550), see [Docker docs](/docs/pages/docs/docker.en-US.md):
```bash
docker build -t videolingo .
docker run -d -p 8501:8501 --gpus all videolingo
```
## APIs
VideoLingo supports OpenAI-Like API format and various TTS interfaces:
- LLM: `claude-3-5-sonnet`, `gpt-4.1`, `deepseek-v3`, `gemini-2.0-flash`, ... (sorted by performance, be cautious with gemini-2.5-flash...)
- WhisperX: Run whisperX (large-v3) locally or use 302.ai API
- TTS: `azure-tts`, `openai-tts`, `siliconflow-fishtts`, **`fish-tts`**, `GPT-SoVITS`, `edge-tts`, `*custom-tts`(You can modify your own TTS in custom_tts.py!)
> **Note:** VideoLingo works with **[302.ai](https://gpt302.saaslink.net/C2oHR9)** - one API key for all services (LLM, WhisperX, TTS). Or run locally with Ollama and Edge-TTS for free, no API needed!
For detailed installation, API configuration, and batch mode instructions, please refer to the documentation: [English](/docs/pages/docs/start.en-US.md) | [中文](/docs/pages/docs/start.zh-CN.md)
## Current Limitations
1. WhisperX transcription performance may be affected by video background noise, as it uses wav2vac model for alignment. For videos with loud background music, please enable Voice Separation Enhancement. Additionally, subtitles ending with numbers or special characters may be truncated early due to wav2vac's inability to map numeric characters (e.g., "1") to their spoken form ("one").
2. Using weaker models can lead to errors during processes due to strict JSON format requirements for responses (tried my best to prompt llm😊). If this error occurs, please delete the `output` folder and retry with a different LLM, otherwise repeated execution will read the previous erroneous response causing the same error.
3. The dubbing feature may not be 100% perfect due to differences in speech rates and intonation between languages, as well as the impact of the translation step. However, this project has implemented extensive engineering processing for speech rates to ensure the best possible dubbing results.
4. **Multilingual video transcription recognition will only retain the main language**. This is because whisperX uses a specialized model for a single language when forcibly aligning word-level subtitles, and will delete unrecognized languages.
5. **For now, cannot dub multiple characters separately**, as whisperX's speaker distinction capability is not sufficiently reliable.
## 📄 License
This project is licensed under the Apache 2.0 License. Special thanks to the following open source projects for their contributions:
[whisperX](https://github.com/m-bain/whisperX), [yt-dlp](https://github.com/yt-dlp/yt-dlp), [json_repair](https://github.com/mangiucugna/json_repair), [BELLE](https://github.com/LianjiaTech/BELLE)
## 📬 Contact Me
- Submit [Issues](https://github.com/Huanshere/VideoLingo/issues) or [Pull Requests](https://github.com/Huanshere/VideoLingo/pulls) on GitHub
- DM me on Twitter: [@Huanshere](https://twitter.com/Huanshere)
- Email me at: team@videolingo.io
## ⭐ Star History
[](https://star-history.com/#Huanshere/VideoLingo&Timeline)
---
<p align="center">If you find VideoLingo helpful, please give me a ⭐️!</p>
================================================
FILE: VideoLingo_colab.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "RkeSbYF2HoM_"
},
"source": [
"# Welcome to VideoLingo! 🎉🚀\n",
"#### This colab file allows you to quickly experience the full functionality in just 5 minutes! ⏱️✨ Before you begin, you may need to prepare some keys. 🔑🗝️ Please read https://videolingo.io/docs/start to get ready. 📚👍\n",
"#### *Please use a T4 GPU to execute this colab for optimal performance."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h0jE67Gc-1nO"
},
"source": [
"## 1. Clone VideoLingo repo 📥"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NC3i2T7D51oS",
"outputId": "19821917-8ee4-4123-a099-fd35405fcdfe"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'VideoLingo'...\n",
"remote: Enumerating objects: 2578, done.\u001b[K\n",
"remote: Counting objects: 100% (595/595), done.\u001b[K\n",
"remote: Compressing objects: 100% (221/221), done.\u001b[K\n",
"remote: Total 2578 (delta 408), reused 378 (delta 374), pack-reused 1983 (from 1)\u001b[K\n",
"Receiving objects: 100% (2578/2578), 10.44 MiB | 12.60 MiB/s, done.\n",
"Resolving deltas: 100% (1644/1644), done.\n"
]
}
],
"source": [
"!git clone https://github.com/Huanshere/VideoLingo.git\n",
"%cd VideoLingo"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A5sIHzRs8JI1"
},
"source": [
"## 2. Installation 🚀\n",
"this takes around 4 mins"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tSymHAEg6Vzr",
"outputId": "8c5059e4-37d5-4540-cba5-9c7aa46fe226"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\u001b[1;35m│\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35mStarting installation...\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m│\u001b[0m\n",
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"┃\u001b[1m \u001b[0m\u001b[1mOption\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mModel \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mDescription \u001b[0m\u001b[1m \u001b[0m┃\n",
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"If you're unsure about the differences between models, please see \n",
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"\u001b[36m│\u001b[0m\u001b[36m \u001b[0m\u001b[36mInstalling PyTorch with CUDA support...\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m\u001b[36m│\u001b[0m\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"Installing collected packages: pydub, brotli, websockets, watchdog, tenacity, smmap, pycryptodomex, mutagen, json-repair, jiter, h11, azure-cognitiveservices-speech, yt-dlp, resampy, pydeck, pandas, httpcore, gitdb, httpx, gitpython, replicate, openai, streamlit\n",
" Attempting uninstall: tenacity\n",
" Found existing installation: tenacity 9.0.0\n",
" Uninstalling tenacity-9.0.0:\n",
" Successfully uninstalled tenacity-9.0.0\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"\u001b[0mSuccessfully installed azure-cognitiveservices-speech-1.40.0 brotli-1.1.0 gitdb-4.0.11 gitpython-3.1.43 h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 jiter-0.5.0 json-repair-0.29.7 mutagen-1.47.0 openai-1.47.0 pandas-2.2.3 pycryptodomex-3.21.0 pydeck-0.9.1 pydub-0.25.1 replicate-0.33.0 resampy-0.4.3 smmap-5.0.1 streamlit-1.38.0 tenacity-8.5.0 watchdog-4.0.2 websockets-13.1 yt-dlp-2024.8.6\n",
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"\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mAll installation steps are completed!\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
"\u001b[1;32m╰───────────────────────────────────────╯\u001b[0m\n",
"Please use the following command to start Streamlit:\n",
"\u001b[1;36mstreamlit run st.py\u001b[0m\n"
]
}
],
"source": [
"!python install.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5J2jjEY2BrM3"
},
"source": [
"## 3. Register and Obtain Ngrok Token 🔑\n",
"\n",
"1. Visit the [Ngrok official website](https://ngrok.com/) and register for an account.\n",
"2. After logging in, find the \"Your Authtoken\" section on the dashboard page, or directly visit [Ngrok Token](https://dashboard.ngrok.com/get-started/your-authtoken).\n",
"3. Copy your Ngrok Authtoken.\n",
"\n",
"After completing these steps, please fill in your ngrok token in the next section of code and proceed.\n",
"\n",
"---\n",
"\n",
"## 3. 注册并获取 Ngrok 令牌 🔑\n",
"\n",
"1. 访问 [Ngrok 官方网站](https://ngrok.com/) 并注册账户。\n",
"2. 登录后，在仪表板页面找到\"Your Authtoken\"部分，或直接访问 [Ngrok 令牌](https://dashboard.ngrok.com/get-started/your-authtoken)。\n",
"3. 复制您的 Ngrok Authtoken。\n",
"\n",
"完成这些步骤后，请在下一节代码中填入您的 ngrok 令牌并继续。"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tm25rblnBqhl",
"outputId": "93691228-1e7b-48e7-b7f3-c27007a4a13f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pyngrok in /usr/local/lib/python3.10/dist-packages (7.2.0)\n",
"Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.2)\n"
]
}
],
"source": [
"!pip install pyngrok\n",
"from pyngrok import ngrok\n",
"\n",
"#! SET Ngrok Authtoken Here\n",
"ngrok.set_auth_token(\"YOUR_TOKEN_HERE\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MDJXtgMuGXMH"
},
"source": [
"## 🎈 4. Streamlit GO !!!\n",
"Click the NgrokChannel URL to start your VideoLingo Journey.\n",
"\n",
"> tips: You can set your Language down the sidebar on the left."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 294
},
"id": "qr_a4mS29k5_",
"outputId": "e6915708-d030-45d0-d6a8-cb177960c137"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
"\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────────╮\n",
"│ Streamlit is available at Ngrok ⬇️ │\n",
"╰───────────────────────────────────╯\n",
"</pre>\n"
],
"text/plain": [
"╭───────────────────────────────────╮\n",
"│ Streamlit is available at Ngrok ⬇️ │\n",
"╰───────────────────────────────────╯\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Click 👉 NgrokTunnel: \"https://0308-34-125-196-161.ngrok-free.app\" -> \"http://localhost:8501\"\n",
"\n",
" You can now view your Streamlit app in your browser.\n",
"\n",
" Local URL: http://localhost:8501\n",
" Network URL: http://172.28.0.12:8501\n",
" External URL: http://34.125.196.161:8501\n",
"\n",
" Stopping...\n",
"Interrupted by user, shutting down...\n"
]
}
],
"source": [
"import subprocess\n",
"import threading\n",
"import sys\n",
"from pyngrok import ngrok\n",
"from rich import print as rprint\n",
"from rich.panel import Panel\n",
"\n",
"def print_output(process):\n",
" for line in iter(process.stdout.readline, ''):\n",
" sys.stdout.write(line)\n",
" for line in iter(process.stderr.readline, ''):\n",
" sys.stderr.write(line)\n",
"\n",
"# Start Streamlit\n",
"streamlit_process = subprocess.Popen(\n",
" [\"streamlit\", \"run\", \"st.py\"],\n",
" stdout=subprocess.PIPE,\n",
" stderr=subprocess.PIPE,\n",
" universal_newlines=True,\n",
" bufsize=1\n",
")\n",
"\n",
"# Create and start the output printing thread\n",
"output_thread = threading.Thread(target=print_output, args=(streamlit_process,))\n",
"output_thread.start()\n",
"\n",
"# Create a tunnel using ngrok\n",
"public_url = ngrok.connect(8501)\n",
"rprint(Panel(f\"Streamlit is available at Ngrok ⬇️\", expand=False))\n",
"print(f\"Click 👉 {public_url}\")\n",
"\n",
"# Keep the program running\n",
"ngrok_process = ngrok.get_ngrok_process()\n",
"try:\n",
" streamlit_process.wait()\n",
"except KeyboardInterrupt:\n",
" print(\"Interrupted by user, shutting down...\")\n",
"finally:\n",
" ngrok.kill()\n",
" streamlit_process.terminate()\n",
" output_thread.join()\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: batch/OneKeyBatch.bat
================================================
@echo off
cd /D "%~dp0"
cd ..
call conda activate videolingo
@rem 运行批处理脚本
call python batch\utils\batch_processor.py
:end
pause
================================================
FILE: batch/README.md
================================================
# VideoLingo Batch Mode
[English](./README.md) | [简体中文](./README.zh.md)
Before utilizing the batch mode, ensure you have used the Streamlit mode and properly configured the parameters in `config.yaml`.
## Usage Guide
### 1. Video File Preparation
- Place your video files in the `input` folder
- YouTube links can be specified in the next step
### 2. Task Configuration
Edit the `tasks_setting.xlsx` file:
| Field | Description | Acceptable Values |
|-------|-------------|-------------------|
| Video File | Video filename (without `input/` prefix) or YouTube URL | - |
| Source Language | Source language | 'en', 'zh', ... or leave empty for default |
| Target Language | Translation language | Use natural language description, or leave empty for default |
| Dubbing | Enable dubbing | 0 or empty: no dubbing; 1: enable dubbing |
Example:
| Video File | Source Language | Target Language | Dubbing |
|------------|-----------------|-----------------|---------|
| https://www.youtube.com/xxx | | German | |
| Kungfu Panda.mp4 | | | 1 |
### 3. Executing Batch Processing
1. Double-click to run `OneKeyBatch.bat`
2. Output files will be saved in the `output` folder
3. Task status can be monitored in the `Status` column of `tasks_setting.xlsx`
> Note: Keep `tasks_setting.xlsx` closed during execution to prevent interruptions due to file access conflicts.
## Important Considerations
### Handling Interruptions
If the command line is closed unexpectedly, language settings in `config.yaml` may be altered. Check settings before retrying.
### Error Management
- Failed files will be moved to the `output/ERROR` folder
- Error messages are recorded in the `Status` column of `tasks_setting.xlsx`
- To retry:
1. Move the single video folder from `ERROR` to the root directory
2. Rename it to `output`
3. Use Streamlit mode to process again
================================================
FILE: batch/README.zh.md
================================================
# VideoLingo Batch Mode
[English](./README.md) | [简体中文](./README.zh.md)
在使用批处理模式前，请确保你已经使用过 Streamlit 模式并正确设置了 `config.yaml` 中的参数。
## 使用方法
### 1. 准备视频文件
- 将要处理的视频文件放入 `input` 文件夹
- YouTube 链接可在下一步填写
### 2. 配置任务
编辑 `tasks_setting.xlsx` 文件：
| 字段 | 说明 | 可选值 |
|------|------|--------|
| Video File | 视频文件名（无需 `input/` 前缀）或 YouTube 链接 | - |
| Source Language | 源语言 | 'en', 'zh', ... 或留空使用默认设置 |
| Target Language | 翻译语言 | 使用自然语言描述，或留空使用默认设置 |
| Dubbing | 是否配音 | 0 或留空：不配音；1：配音 |
示例：
| Video File | Source Language | Target Language | Dubbing |
|------------|-----------------|-----------------|---------|
| https://www.youtube.com/xxx | | German | |
| Kungfu Panda.mp4 | | | 1 |
### 3. 运行批处理
1. 双击运行 `OneKeyBatch.bat`
2. 输出文件将保存在 `output` 文件夹
3. 任务状态可在 `tasks_setting.xlsx` 的 `Status` 列查看
> 注意在运行时保持 `tasks_setting.xlsx` 关闭，否则会因占用无法写入而中断。
## 注意事项
### 中断处理
如果中途关闭命令行，`config.yaml` 中的语言设置可能会改变。重试前请检查设置。
### 错误处理
- 处理失败的文件会被移至 `output/ERROR` 文件夹
- 错误信息记录在 `tasks_setting.xlsx` 的 `Status` 列
- 如需重试：
1. 将 `ERROR` 下的单个视频文件夹移至根目录
2. 重命名为 `output`
3. 使用 Streamlit 模式重新执行
================================================
FILE: batch/utils/batch_processor.py
================================================
import os
import gc
from batch.utils.settings_check import check_settings
from batch.utils.video_processor import process_video
from core.utils.config_utils import load_key, update_key
import pandas as pd
from rich.console import Console
from rich.panel import Panel
import time
import shutil
console = Console()
def record_and_update_config(source_language, target_language):
original_source_lang = load_key('whisper.language')
original_target_lang = load_key('target_language')
if source_language and not pd.isna(source_language):
update_key('whisper.language', source_language)
if target_language and not pd.isna(target_language):
update_key('target_language', target_language)
return original_source_lang, original_target_lang
def process_batch():
if not check_settings():
raise Exception("Settings check failed")
df = pd.read_excel('batch/tasks_setting.xlsx')
for index, row in df.iterrows():
if pd.isna(row['Status']) or 'Error' in str(row['Status']):
total_tasks = len(df)
video_file = row['Video File']
if not pd.isna(row['Status']) and 'Error' in str(row['Status']):
console.print(Panel(f"Retrying failed task: {video_file}\nTask {index + 1}/{total_tasks}",
title="[bold yellow]Retry Task", expand=False))
# Restore files from batch/output/ERROR to output
error_folder = os.path.join('batch', 'output', 'ERROR', os.path.splitext(video_file)[0])
if os.path.exists(error_folder):
# Ensure the output folder exists
os.makedirs('output', exist_ok=True)
# Copy all contents from ERROR folder for the specific video to output
for item in os.listdir(error_folder):
src_path = os.path.join(error_folder, item)
dst_path = os.path.join('output', item)
if os.path.isdir(src_path):
if os.path.exists(dst_path):
shutil.rmtree(dst_path)
shutil.copytree(src_path, dst_path)
else:
if os.path.exists(dst_path):
os.remove(dst_path)
shutil.copy2(src_path, dst_path)
console.print(f"[green]Restored files from ERROR folder for {video_file}")
else:
console.print(f"[yellow]Warning: Error folder not found: {error_folder}")
else:
console.print(Panel(f"Now processing task: {video_file}\nTask {index + 1}/{total_tasks}",
title="[bold blue]Current Task", expand=False))
source_language = row['Source Language']
target_language = row['Target Language']
original_source_lang, original_target_lang = record_and_update_config(source_language, target_language)
try:
dubbing = 0 if pd.isna(row['Dubbing']) else int(row['Dubbing'])
is_retry = not pd.isna(row['Status']) and 'Error' in str(row['Status'])
status, error_step, error_message = process_video(video_file, dubbing, is_retry)
status_msg = "Done" if status else f"Error: {error_step} - {error_message}"
except Exception as e:
status_msg = f"Error: Unhandled exception - {str(e)}"
console.print(f"[bold red]Error processing {video_file}: {status_msg}")
finally:
update_key('whisper.language', original_source_lang)
update_key('target_language', original_target_lang)
df.at[index, 'Status'] = status_msg
df.to_excel('batch/tasks_setting.xlsx', index=False)
gc.collect()
time.sleep(1)
else:
print(f"Skipping task: {row['Video File']} - Status: {row['Status']}")
console.print(Panel("All tasks processed!\nCheck out in `batch/output`!",
title="[bold green]Batch Processing Complete", expand=False))
if __name__ == "__main__":
process_batch()
================================================
FILE: batch/utils/settings_check.py
================================================
import os
import pandas as pd
from rich.console import Console
from rich.panel import Panel
# Constants
SETTINGS_FILE = 'batch/tasks_setting.xlsx'
INPUT_FOLDER = os.path.join('batch', 'input')
VALID_DUBBING_VALUES = [0, 1]
console = Console()
def check_settings():
os.makedirs(INPUT_FOLDER, exist_ok=True)
df = pd.read_excel(SETTINGS_FILE)
input_files = set(os.listdir(INPUT_FOLDER))
excel_files = set(df['Video File'].tolist())
files_not_in_excel = input_files - excel_files
all_passed = True
local_video_tasks = 0
url_tasks = 0
if files_not_in_excel:
console.print(Panel(
"\n".join([f"- {file}" for file in files_not_in_excel]),
title="[bold red]Warning: Files in input folder not mentioned in Excel sheet",
expand=False
))
all_passed = False
for index, row in df.iterrows():
video_file = row['Video File']
source_language = row['Source Language']
dubbing = row['Dubbing']
if video_file.startswith('http'):
url_tasks += 1
elif os.path.isfile(os.path.join(INPUT_FOLDER, video_file)):
local_video_tasks += 1
else:
console.print(Panel(f"Invalid video file or URL 「{video_file}」", title=f"[bold red]Error in row {index + 2}", expand=False))
all_passed = False
if not pd.isna(dubbing):
if int(dubbing) not in VALID_DUBBING_VALUES:
console.print(Panel(f"Invalid dubbing value 「{dubbing}」", title=f"[bold red]Error in row {index + 2}", expand=False))
all_passed = False
if all_passed:
console.print(Panel(f"✅ All settings passed the check!\nDetected {local_video_tasks} local video tasks and {url_tasks} URL tasks.", title="[bold green]Success", expand=False))
return all_passed
if __name__ == "__main__":
check_settings()
================================================
FILE: batch/utils/video_processor.py
================================================
import os
from core.st_utils.imports_and_utils import *
from core.utils.onekeycleanup import cleanup
from core.utils import load_key
import shutil
from functools import partial
from rich.panel import Panel
from rich.console import Console
from core import *
console = Console()
INPUT_DIR = 'batch/input'
OUTPUT_DIR = 'output'
SAVE_DIR = 'batch/output'
ERROR_OUTPUT_DIR = 'batch/output/ERROR'
YTB_RESOLUTION_KEY = "ytb_resolution"
def process_video(file, dubbing=False, is_retry=False):
if not is_retry:
prepare_output_folder(OUTPUT_DIR)
text_steps = [
("🎥 Processing input file", partial(process_input_file, file)),
("🎙️ Transcribing with Whisper", partial(_2_asr.transcribe)),
("✂️ Splitting sentences", split_sentences),
("📝 Summarizing and translating", summarize_and_translate),
("⚡ Processing and aligning subtitles", process_and_align_subtitles),
("🎬 Merging subtitles to video", _7_sub_into_vid.merge_subtitles_to_video),
]
if dubbing:
dubbing_steps = [
("🔊 Generating audio tasks", gen_audio_tasks),
("🎵 Extracting reference audio", _9_refer_audio.extract_refer_audio_main),
("🗣️ Generating audio", _10_gen_audio.gen_audio),
("🔄 Merging full audio", _11_merge_audio.merge_full_audio),
("🎞️ Merging dubbing to video", _12_dub_to_vid.merge_video_audio),
]
text_steps.extend(dubbing_steps)
current_step = ""
for step_name, step_func in text_steps:
current_step = step_name
for attempt in range(3):
try:
console.print(Panel(
f"[bold green]{step_name}[/]",
subtitle=f"Attempt {attempt + 1}/3" if attempt > 0 else None,
border_style="blue"
))
result = step_func()
if result is not None:
globals().update(result)
break
except Exception as e:
if attempt == 2:
error_panel = Panel(
f"[bold red]Error in step '{current_step}':[/]\n{str(e)}",
border_style="red"
)
console.print(error_panel)
cleanup(ERROR_OUTPUT_DIR)
return False, current_step, str(e)
console.print(Panel(
f"[yellow]Attempt {attempt + 1} failed. Retrying...[/]",
border_style="yellow"
))
console.print(Panel("[bold green]All steps completed successfully! 🎉[/]", border_style="green"))
cleanup(SAVE_DIR)
return True, "", ""
def prepare_output_folder(output_folder):
if os.path.exists(output_folder):
shutil.rmtree(output_folder)
os.makedirs(output_folder)
def process_input_file(file):
if file.startswith('http'):
_1_ytdlp.download_video_ytdlp(file, resolution=load_key(YTB_RESOLUTION_KEY))
video_file = _1_ytdlp.find_video_files()
else:
input_file = os.path.join('batch', 'input', file)
output_file = os.path.join(OUTPUT_DIR, file)
shutil.copy(input_file, output_file)
video_file = output_file
return {'video_file': video_file}
def split_sentences():
_3_1_split_nlp.split_by_spacy()
_3_2_split_meaning.split_sentences_by_meaning()
def summarize_and_translate():
_4_1_summarize.get_summary()
_4_2_translate.translate_all()
def process_and_align_subtitles():
_5_split_sub.split_for_sub_main()
_6_gen_sub.align_timestamp_main()
def gen_audio_tasks():
_8_1_audio_task.gen_audio_task_main()
_8_2_dub_chunks.gen_dub_chunks()
================================================
FILE: config.yaml
================================================
# * Settings marked with * are advanced settings that won't appear in the Streamlit page and can only be modified manually in config.py
# recommend to set in streamlit page
# -------------------
# version: "3.0.0"
# author: "Huanshere"
# -------------------
## ======================== Basic Settings ======================== ##
display_language: "zh-CN"
# API settings
api:
key: 'your-api-key'
base_url: 'https://yunwu.ai'
model: 'gpt-4.1-2025-04-14'
llm_support_json: false
# *Number of LLM multi-threaded accesses, set to 1 if using local LLM
max_workers: 4
# Language settings, written into the prompt, can be described in natural language
target_language: '简体中文'
# Whether to use Demucs for vocal separation before transcription
demucs: true
whisper:
# ["large-v3", "large-v3-turbo"]. Note: for zh model will force to use Belle/large-v3
model: 'large-v3'
# Whisper specified recognition language ISO 639-1
language: 'en'
detected_language: 'en'
# Whisper running mode ["local", "cloud", "elevenlabs"]. Specifies where to run, cloud uses 302.ai API
runtime: 'local'
# 302.ai API key
whisperX_302_api_key: 'your_302_api_key'
# ElevenLabs API key (experimental)
elevenlabs_api_key: 'your_elevenlabs_api_key'
# Whether to burn subtitles into the video
burn_subtitles: true
## ======================== Advanced Settings ======================== ##
# *🔬 h264_nvenc GPU acceleration for ffmpeg, make sure your GPU supports it
ffmpeg_gpu: false
# *Youtube settings
youtube:
cookies_path: ''
# *Default resolution for downloading YouTube videos [360, 1080, best]
ytb_resolution: '1080'
subtitle:
# *Maximum length of each subtitle line in characters
max_length: 75
# *Translated subtitles are slightly larger than source subtitles, affecting the reference length for subtitle splitting
target_multiplier: 1.2
# *Summary length, set low to 2k if using local LLM
summary_length: 8000
# *Maximum number of words for the first rough cut, below 18 will cut too finely affecting translation, above 22 is too long and will make subsequent subtitle splitting difficult to align
max_split_length: 20
# *Whether to reflect the translation result in the original text
reflect_translate: true
# *Whether to pause after extracting professional terms and before translation, allowing users to manually adjust the terminology table output\log\terminology.json
pause_before_translate: false
## ======================== Dubbing Settings ======================== ##
# TTS selection [sf_fish_tts, openai_tts, gpt_sovits, azure_tts, fish_tts, edge_tts, custom_tts]
tts_method: 'azure_tts'
# SiliconFlow FishTTS
sf_fish_tts:
# SiliconFlow API key
api_key: 'YOUR_API_KEY'
# only for mode "preset"
voice: 'anna'
# *only for mode "custom", dont set manually
custom_name: ''
voice_id: ''
# preset, custom, dynamic
mode: "preset"
# OpenAI TTS-1 API configuration, 302.ai API only
openai_tts:
api_key: 'YOUR_302_API_KEY'
voice: 'alloy'
# Azure configuration, 302.ai API only
azure_tts:
api_key: 'YOUR_302_API_KEY'
voice: 'zh-CN-YunfengNeural'
# FishTTS configuration, 302.ai API only
fish_tts:
api_key: 'YOUR_302_API_KEY'
character: 'AD学姐'
character_id_dict:
'AD学姐': '7f92f8afb8ec43bf81429cc1c9199cb1'
'丁真': '54a5170264694bfc8e9ad98df7bd89c3'
# SiliconFlow CosyVoice2 Clone
sf_cosyvoice2:
api_key: 'YOUR_SF_KEY'
# Edge TTS configuration
edge_tts:
voice: 'zh-CN-XiaoxiaoNeural'
# SoVITS configuration
gpt_sovits:
character: 'Huanyuv2'
refer_mode: 3
f5tts:
302_api: 'YOUR_302_API_KEY'
# *Audio speed range
speed_factor:
min: 1
accept: 1.2 # Maximum acceptable speed
max: 1.4
# *Merge audio configuration
min_subtitle_duration: 2.5 # Minimum subtitle duration, will be forcibly extended
min_trim_duration: 3.5 # Subtitles shorter than this value won't be split
tolerance: 1.5 # Allowed extension time to the next subtitle
## ======================== Additional settings ======================== ##
# Whisper model directory
model_dir: './_model_cache'
# Supported upload video formats
allowed_video_formats:
- 'mp4'
- 'mov'
- 'avi'
- 'mkv'
- 'flv'
- 'wmv'
- 'webm'
allowed_audio_formats:
- 'wav'
- 'mp3'
- 'flac'
- 'm4a'
# Spacy models
spacy_model_map:
en: 'en_core_web_md'
ru: 'ru_core_news_md'
fr: 'fr_core_news_md'
ja: 'ja_core_news_md'
es: 'es_core_news_md'
de: 'de_core_news_md'
it: 'it_core_news_md'
zh: 'zh_core_web_md'
# Languages that use space as separator
language_split_with_space:
- 'en'
- 'es'
- 'fr'
- 'de'
- 'it'
- 'ru'
# Languages that do not use space as separator
language_split_without_space:
- 'zh'
- 'ja'
================================================
FILE: core/_10_gen_audio.py
================================================
import os
import time
import shutil
import subprocess
from typing import Tuple
import pandas as pd
from pydub import AudioSegment
from rich.console import Console
from rich.progress import Progress
from concurrent.futures import ThreadPoolExecutor, as_completed
from core.utils import *
from core.utils.models import *
from core.asr_backend.audio_preprocess import get_audio_duration
from core.tts_backend.tts_main import tts_main
console = Console()
TEMP_FILE_TEMPLATE = f"{_AUDIO_TMP_DIR}/{{}}_temp.wav"
OUTPUT_FILE_TEMPLATE = f"{_AUDIO_SEGS_DIR}/{{}}.wav"
WARMUP_SIZE = 5
def parse_df_srt_time(time_str: str) -> float:
"""Convert SRT time format to seconds"""
hours, minutes, seconds = time_str.strip().split(':')
seconds, milliseconds = seconds.split('.')
return int(hours) * 3600 + int(minutes) * 60 + int(seconds) + int(milliseconds) / 1000
def adjust_audio_speed(input_file: str, output_file: str, speed_factor: float) -> None:
"""Adjust audio speed and handle edge cases"""
# If the speed factor is close to 1, directly copy the file
if abs(speed_factor - 1.0) < 0.001:
shutil.copy2(input_file, output_file)
return
atempo = speed_factor
cmd = ['ffmpeg', '-i', input_file, '-filter:a', f'atempo={atempo}', '-y', output_file]
input_duration = get_audio_duration(input_file)
max_retries = 2
for attempt in range(max_retries):
try:
subprocess.run(cmd, check=True, stderr=subprocess.PIPE)
output_duration = get_audio_duration(output_file)
expected_duration = input_duration / speed_factor
diff = output_duration - expected_duration
# If the output duration exceeds the expected duration, but the input audio is less than 3 seconds, and the error is within 0.1 seconds, truncate to the expected length
if output_duration >= expected_duration * 1.02 and input_duration < 3 and diff <= 0.1:
audio = AudioSegment.from_wav(output_file)
trimmed_audio = audio[:(expected_duration * 1000)] # pydub uses milliseconds
trimmed_audio.export(output_file, format="wav")
print(f"✂️ Trimmed to expected duration: {expected_duration:.2f} seconds")
return
elif output_duration >= expected_duration * 1.02:
raise Exception(f"Audio duration abnormal: input file={input_file}, output file={output_file}, speed factor={speed_factor}, input duration={input_duration:.2f}s, output duration={output_duration:.2f}s")
return
except subprocess.CalledProcessError as e:
if attempt < max_retries - 1:
rprint(f"[yellow]⚠️ Audio speed adjustment failed, retrying in 1s ({attempt + 1}/{max_retries})[/yellow]")
time.sleep(1)
else:
rprint(f"[red]❌ Audio speed adjustment failed, max retries reached ({max_retries})[/red]")
raise e
def process_row(row: pd.Series, tasks_df: pd.DataFrame) -> Tuple[int, float]:
"""Helper function for processing single row data"""
number = row['number']
lines = eval(row['lines']) if isinstance(row['lines'], str) else row['lines']
real_dur = 0
for line_index, line in enumerate(lines):
temp_file = TEMP_FILE_TEMPLATE.format(f"{number}_{line_index}")
tts_main(line, temp_file, number, tasks_df)
real_dur += get_audio_duration(temp_file)
return number, real_dur
def generate_tts_audio(tasks_df: pd.DataFrame) -> pd.DataFrame:
"""Generate TTS audio sequentially and calculate actual duration"""
tasks_df['real_dur'] = 0
rprint("[bold green]🎯 Starting TTS audio generation...[/bold green]")
with Progress() as progress:
task = progress.add_task("[cyan]🔄 Generating TTS audio...", total=len(tasks_df))
# warm up for first 5 rows
warmup_size = min(WARMUP_SIZE, len(tasks_df))
for _, row in tasks_df.head(warmup_size).iterrows():
try:
number, real_dur = process_row(row, tasks_df)
tasks_df.loc[tasks_df['number'] == number, 'real_dur'] = real_dur
progress.advance(task)
except Exception as e:
rprint(f"[red]❌ Error in warmup: {str(e)}[/red]")
raise e
# for gpt_sovits, do not use parallel to avoid mistakes
max_workers = load_key("max_workers") if load_key("tts_method") != "gpt_sovits" else 1
# parallel processing for remaining tasks
if len(tasks_df) > warmup_size:
remaining_tasks = tasks_df.iloc[warmup_size:].copy()
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = [
executor.submit(process_row, row, tasks_df.copy())
for _, row in remaining_tasks.iterrows()
]
for future in as_completed(futures):
try:
number, real_dur = future.result()
tasks_df.loc[tasks_df['number'] == number, 'real_dur'] = real_dur
progress.advance(task)
except Exception as e:
rprint(f"[red]❌ Error: {str(e)}[/red]")
raise e
rprint("[bold green]✨ TTS audio generation completed![/bold green]")
return tasks_df
def process_chunk(chunk_df: pd.DataFrame, accept: float, min_speed: float) -> tuple[float, bool]:
"""Process audio chunk and calculate speed factor"""
chunk_durs = chunk_df['real_dur'].sum()
tol_durs = chunk_df['tol_dur'].sum()
durations = tol_durs - chunk_df.iloc[-1]['tolerance']
all_gaps = chunk_df['gap'].sum() - chunk_df.iloc[-1]['gap']
keep_gaps = True
speed_var_error = 0.1
if (chunk_durs + all_gaps) / accept < durations:
speed_factor = max(min_speed, (chunk_durs + all_gaps) / (durations-speed_var_error))
elif chunk_durs / accept < durations:
speed_factor = max(min_speed, chunk_durs / (durations-speed_var_error))
keep_gaps = False
elif (chunk_durs + all_gaps) / accept < tol_durs:
speed_factor = max(min_speed, (chunk_durs + all_gaps) / (tol_durs-speed_var_error))
else:
speed_factor = chunk_durs / (tol_durs-speed_var_error)
keep_gaps = False
return round(speed_factor, 3), keep_gaps
def merge_chunks(tasks_df: pd.DataFrame) -> pd.DataFrame:
"""Merge audio chunks and adjust timeline"""
rprint("[bold blue]🔄 Starting audio chunks processing...[/bold blue]")
accept = load_key("speed_factor.accept")
min_speed = load_key("speed_factor.min")
chunk_start = 0
tasks_df['new_sub_times'] = None
for index, row in tasks_df.iterrows():
if row['cut_off'] == 1:
chunk_df = tasks_df.iloc[chunk_start:index+1].reset_index(drop=True)
speed_factor, keep_gaps = process_chunk(chunk_df, accept, min_speed)
# 🎯 Step1: Start processing new timeline
chunk_start_time = parse_df_srt_time(chunk_df.iloc[0]['start_time'])
chunk_end_time = parse_df_srt_time(chunk_df.iloc[-1]['end_time']) + chunk_df.iloc[-1]['tolerance'] # 加上tolerance才是这一块的结束
cur_time = chunk_start_time
for i, row in chunk_df.iterrows():
# If i is not 0, which is not the first row of the chunk, cur_time needs to be added with the gap of the previous row, remember to divide by speed_factor
if i != 0 and keep_gaps:
cur_time += chunk_df.iloc[i-1]['gap']/speed_factor
new_sub_times = []
number = row['number']
lines = eval(row['lines']) if isinstance(row['lines'], str) else row['lines']
for line_index, line in enumerate(lines):
# 🔄 Step2: Start speed change and save as OUTPUT_FILE_TEMPLATE
temp_file = TEMP_FILE_TEMPLATE.format(f"{number}_{line_index}")
output_file = OUTPUT_FILE_TEMPLATE.format(f"{number}_{line_index}")
adjust_audio_speed(temp_file, output_file, speed_factor)
ad_dur = get_audio_duration(output_file)
new_sub_times.append([cur_time, cur_time+ad_dur])
cur_time += ad_dur
# 🔄 Step3: Find corresponding main DataFrame index and update new_sub_times
main_df_idx = tasks_df[tasks_df['number'] == row['number']].index[0]
tasks_df.at[main_df_idx, 'new_sub_times'] = new_sub_times
# 🎯 Step4: Choose emoji based on speed_factor and accept comparison
emoji = "⚡" if speed_factor <= accept else "⚠️"
rprint(f"[cyan]{emoji} Processed chunk {chunk_start} to {index} with speed factor {speed_factor}[/cyan]")
# 🔄 Step5: Check if the last row exceeds the range
if cur_time > chunk_end_time:
time_diff = cur_time - chunk_end_time
if time_diff <= 0.6: # If exceeding time is within 0.6 seconds, truncate the last audio
rprint(f"[yellow]⚠️ Chunk {chunk_start} to {index} exceeds by {time_diff:.3f}s, truncating last audio[/yellow]")
# Get the last audio file
last_number = tasks_df.iloc[index]['number']
last_lines = eval(tasks_df.iloc[index]['lines']) if isinstance(tasks_df.iloc[index]['lines'], str) else tasks_df.iloc[index]['lines']
last_line_index = len(last_lines) - 1
last_file = OUTPUT_FILE_TEMPLATE.format(f"{last_number}_{last_line_index}")
# Calculate the duration to keep
audio = AudioSegment.from_wav(last_file)
original_duration = len(audio) / 1000 # Convert to seconds
new_duration = original_duration - time_diff
trimmed_audio = audio[:(new_duration * 1000)] # pydub uses milliseconds
trimmed_audio.export(last_file, format="wav")
# Update the last timestamp
last_times = tasks_df.at[index, 'new_sub_times']
last_times[-1][1] = chunk_end_time
tasks_df.at[index, 'new_sub_times'] = last_times
else:
raise Exception(f"Chunk {chunk_start} to {index} exceeds the chunk end time {chunk_end_time:.2f} seconds with current time {cur_time:.2f} seconds")
chunk_start = index+1
rprint("[bold green]✅ Audio chunks processing completed![/bold green]")
return tasks_df
def gen_audio() -> None:
"""Main function: Generate audio and process timeline"""
rprint("[bold magenta]🚀 Starting audio generation process...[/bold magenta]")
# 🎯 Step1: Create necessary directories
os.makedirs(_AUDIO_TMP_DIR, exist_ok=True)
os.makedirs(_AUDIO_SEGS_DIR, exist_ok=True)
# 📝 Step2: Load task file
tasks_df = pd.read_excel(_8_1_AUDIO_TASK)
rprint("[green]📊 Loaded task file successfully[/green]")
# 🔊 Step3: Generate TTS audio
tasks_df = generate_tts_audio(tasks_df)
# 🔄 Step4: Merge audio chunks
tasks_df = merge_chunks(tasks_df)
# 💾 Step5: Save results
tasks_df.to_excel(_8_1_AUDIO_TASK, index=False)
rprint("[bold green]🎉 Audio generation completed successfully![/bold green]")
if __name__ == "__main__":
gen_audio()
================================================
FILE: core/_11_merge_audio.py
================================================
import os
import pandas as pd
import subprocess
from pydub import AudioSegment
from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn
from rich.console import Console
from core.utils import *
from core.utils.models import *
console = Console()
DUB_VOCAL_FILE = 'output/dub.mp3'
DUB_SUB_FILE = 'output/dub.srt'
OUTPUT_FILE_TEMPLATE = f"{_AUDIO_SEGS_DIR}/{{}}.wav"
def load_and_flatten_data(excel_file):
"""Load and flatten Excel data"""
df = pd.read_excel(excel_file)
lines = [eval(line) if isinstance(line, str) else line for line in df['lines'].tolist()]
lines = [item for sublist in lines for item in sublist]
new_sub_times = [eval(time) if isinstance(time, str) else time for time in df['new_sub_times'].tolist()]
new_sub_times = [item for sublist in new_sub_times for item in sublist]
return df, lines, new_sub_times
def get_audio_files(df):
"""Generate a list of audio file paths"""
audios = []
for index, row in df.iterrows():
number = row['number']
line_count = len(eval(row['lines']) if isinstance(row['lines'], str) else row['lines'])
for line_index in range(line_count):
temp_file = OUTPUT_FILE_TEMPLATE.format(f"{number}_{line_index}")
audios.append(temp_file)
return audios
def process_audio_segment(audio_file):
"""Process a single audio segment with MP3 compression"""
temp_file = f"{audio_file}_temp.mp3"
ffmpeg_cmd = [
'ffmpeg', '-y',
'-i', audio_file,
'-ar', '16000',
'-ac', '1',
'-b:a', '64k',
temp_file
]
subprocess.run(ffmpeg_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
audio_segment = AudioSegment.from_mp3(temp_file)
os.remove(temp_file)
return audio_segment
def merge_audio_segments(audios, new_sub_times, sample_rate):
merged_audio = AudioSegment.silent(duration=0, frame_rate=sample_rate)
with Progress(SpinnerColumn(), TextColumn("[progress.description]{task.description}"), BarColumn(), TaskProgressColumn()) as progress:
merge_task = progress.add_task("🎵 Merging audio segments...", total=len(audios))
for i, (audio_file, time_range) in enumerate(zip(audios, new_sub_times)):
if not os.path.exists(audio_file):
console.print(f"[bold yellow]⚠️ Warning: File {audio_file} does not exist, skipping...[/bold yellow]")
progress.advance(merge_task)
continue
audio_segment = process_audio_segment(audio_file)
start_time, end_time = time_range
# Add silence segment
if i > 0:
prev_end = new_sub_times[i-1][1]
silence_duration = start_time - prev_end
if silence_duration > 0:
silence = AudioSegment.silent(duration=int(silence_duration * 1000), frame_rate=sample_rate)
merged_audio += silence
elif start_time > 0:
silence = AudioSegment.silent(duration=int(start_time * 1000), frame_rate=sample_rate)
merged_audio += silence
merged_audio += audio_segment
progress.advance(merge_task)
return merged_audio
def create_srt_subtitle():
df, lines, new_sub_times = load_and_flatten_data(_8_1_AUDIO_TASK)
with open(DUB_SUB_FILE, 'w', encoding='utf-8') as f:
for i, ((start_time, end_time), line) in enumerate(zip(new_sub_times, lines), 1):
start_str = f"{int(start_time//3600):02d}:{int((start_time%3600)//60):02d}:{int(start_time%60):02d},{int((start_time*1000)%1000):03d}"
end_str = f"{int(end_time//3600):02d}:{int((end_time%3600)//60):02d}:{int(end_time%60):02d},{int((end_time*1000)%1000):03d}"
f.write(f"{i}\n")
f.write(f"{start_str} --> {end_str}\n")
f.write(f"{line}\n\n")
rprint(f"[bold green]✅ Subtitle file created: {DUB_SUB_FILE}[/bold green]")
def merge_full_audio():
"""Main function: Process the complete audio merging process"""
console.print("\n[bold cyan]🎬 Starting audio merging process...[/bold cyan]")
with console.status("[bold cyan]📊 Loading data from Excel...[/bold cyan]"):
df, lines, new_sub_times = load_and_flatten_data(_8_1_AUDIO_TASK)
console.print("[bold green]✅ Data loaded successfully[/bold green]")
with console.status("[bold cyan]🔍 Getting audio file list...[/bold cyan]"):
audios = get_audio_files(df)
console.print(f"[bold green]✅ Found {len(audios)} audio segments[/bold green]")
with console.status("[bold cyan]📝 Generating subtitle file...[/bold cyan]"):
create_srt_subtitle()
if not os.path.exists(audios[0]):
console.print(f"[bold red]❌ Error: First audio file {audios[0]} does not exist![/bold red]")
return
sample_rate = 16000
console.print(f"[bold green]✅ Sample rate: {sample_rate}Hz[/bold green]")
console.print("[bold cyan]🔄 Starting audio merge process...[/bold cyan]")
merged_audio = merge_audio_segments(audios, new_sub_times, sample_rate)
with console.status("[bold cyan]💾 Exporting final audio file...[/bold cyan]"):
merged_audio = merged_audio.set_frame_rate(16000).set_channels(1)
merged_audio.export(DUB_VOCAL_FILE, format="mp3", parameters=["-b:a", "64k"])
console.print(f"[bold green]✅ Audio file successfully merged![/bold green]")
console.print(f"[bold green]📁 Output file: {DUB_VOCAL_FILE}[/bold green]")
if __name__ == "__main__":
merge_full_audio()
================================================
FILE: core/_12_dub_to_vid.py
================================================
import platform
import subprocess
import cv2
import numpy as np
from rich.console import Console
from core._1_ytdlp import find_video_files
from core.asr_backend.audio_preprocess import normalize_audio_volume
from core.utils import *
from core.utils.models import *
console = Console()
DUB_VIDEO = "output/output_dub.mp4"
DUB_SUB_FILE = 'output/dub.srt'
DUB_AUDIO = 'output/dub.mp3'
TRANS_FONT_SIZE = 17
TRANS_FONT_NAME = 'Arial'
if platform.system() == 'Linux':
TRANS_FONT_NAME = 'NotoSansCJK-Regular'
if platform.system() == 'Darwin':
TRANS_FONT_NAME = 'Arial Unicode MS'
TRANS_FONT_COLOR = '&H00FFFF'
TRANS_OUTLINE_COLOR = '&H000000'
TRANS_OUTLINE_WIDTH = 1
TRANS_BACK_COLOR = '&H33000000'
def merge_video_audio():
"""Merge video and audio, and reduce video volume"""
VIDEO_FILE = find_video_files()
background_file = _BACKGROUND_AUDIO_FILE
if not load_key("burn_subtitles"):
rprint("[bold yellow]Warning: A 0-second black video will be generated as a placeholder as subtitles are not burned in.[/bold yellow]")
# Create a black frame
frame = np.zeros((1080, 1920, 3), dtype=np.uint8)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(DUB_VIDEO, fourcc, 1, (1920, 1080))
out.write(frame)
out.release()
rprint("[bold green]Placeholder video has been generated.[/bold green]")
return
# Normalize dub audio
normalized_dub_audio = 'output/normalized_dub.wav'
normalize_audio_volume(DUB_AUDIO, normalized_dub_audio)
# Merge video and audio with translated subtitles
video = cv2.VideoCapture(VIDEO_FILE)
TARGET_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
TARGET_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
video.release()
rprint(f"[bold green]Video resolution: {TARGET_WIDTH}x{TARGET_HEIGHT}[/bold green]")
subtitle_filter = (
f"subtitles={DUB_SUB_FILE}:force_style='FontSize={TRANS_FONT_SIZE},"
f"FontName={TRANS_FONT_NAME},PrimaryColour={TRANS_FONT_COLOR},"
f"OutlineColour={TRANS_OUTLINE_COLOR},OutlineWidth={TRANS_OUTLINE_WIDTH},"
f"BackColour={TRANS_BACK_COLOR},Alignment=2,MarginV=27,BorderStyle=4'"
)
cmd = [
'ffmpeg', '-y', '-i', VIDEO_FILE, '-i', background_file, '-i', normalized_dub_audio,
'-filter_complex',
f'[0:v]scale={TARGET_WIDTH}:{TARGET_HEIGHT}:force_original_aspect_ratio=decrease,'
f'pad={TARGET_WIDTH}:{TARGET_HEIGHT}:(ow-iw)/2:(oh-ih)/2,'
f'{subtitle_filter}[v];'
f'[1:a][2:a]amix=inputs=2:duration=first:dropout_transition=3[a]'
]
if load_key("ffmpeg_gpu"):
rprint("[bold green]Using GPU acceleration...[/bold green]")
cmd.extend(['-map', '[v]', '-map', '[a]', '-c:v', 'h264_nvenc'])
else:
cmd.extend(['-map', '[v]', '-map', '[a]'])
cmd.extend(['-c:a', 'aac', '-b:a', '96k', DUB_VIDEO])
subprocess.run(cmd)
rprint(f"[bold green]Video and audio successfully merged into {DUB_VIDEO}[/bold green]")
if __name__ == '__main__':
merge_video_audio()
================================================
FILE: core/_1_ytdlp.py
================================================
import os,sys
import glob
import re
import subprocess
from core.utils import *
def sanitize_filename(filename):
# Remove or replace illegal characters
filename = re.sub(r'[<>:"/\\|?*]', '', filename)
# Ensure filename doesn't start or end with a dot or space
filename = filename.strip('. ')
# Use default name if filename is empty
return filename if filename else 'video'
def update_ytdlp():
try:
subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "yt-dlp"])
if 'yt_dlp' in sys.modules:
del sys.modules['yt_dlp']
rprint("[green]yt-dlp updated[/green]")
except subprocess.CalledProcessError as e:
rprint("[yellow]Warning: Failed to update yt-dlp: {e}[/yellow]")
from yt_dlp import YoutubeDL
return YoutubeDL
def download_video_ytdlp(url, save_path='output', resolution='1080'):
os.makedirs(save_path, exist_ok=True)
ydl_opts = {
'format': 'bestvideo+bestaudio/best' if resolution == 'best' else f'bestvideo[height<={resolution}]+bestaudio/best[height<={resolution}]',
'outtmpl': f'{save_path}/%(title)s.%(ext)s',
'noplaylist': True,
'writethumbnail': True,
'postprocessors': [{'key': 'FFmpegThumbnailsConvertor', 'format': 'jpg'}],
}
# Read Youtube Cookie File
cookies_path = load_key("youtube.cookies_path")
if os.path.exists(cookies_path):
ydl_opts["cookiefile"] = str(cookies_path)
# Get YoutubeDL class after updating
YoutubeDL = update_ytdlp()
with YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
# Check and rename files after download
for file in os.listdir(save_path):
if os.path.isfile(os.path.join(save_path, file)):
filename, ext = os.path.splitext(file)
new_filename = sanitize_filename(filename)
if new_filename != filename:
os.rename(os.path.join(save_path, file), os.path.join(save_path, new_filename + ext))
def find_video_files(save_path='output'):
video_files = [file for file in glob.glob(save_path + "/*") if os.path.splitext(file)[1][1:].lower() in load_key("allowed_video_formats")]
# change \\ to /, this happen on windows
if sys.platform.startswith('win'):
video_files = [file.replace("\\", "/") for file in video_files]
video_files = [file for file in video_files if not file.startswith("output/output")]
if len(video_files) != 1:
raise ValueError(f"Number of videos found {len(video_files)} is not unique. Please check.")
return video_files[0]
if __name__ == '__main__':
# Example usage
url = input('Please enter the URL of the video you want to download: ')
resolution = input('Please enter the desired resolution (360/480/720/1080, default 1080): ')
resolution = int(resolution) if resolution.isdigit() else 1080
download_video_ytdlp(url, resolution=resolution)
print(f"🎥 Video has been downloaded to {find_video_files()}")
================================================
FILE: core/_2_asr.py
================================================
from core.utils import *
from core.asr_backend.demucs_vl import demucs_audio
from core.asr_backend.audio_preprocess import process_transcription, convert_video_to_audio, split_audio, save_results, normalize_audio_volume
from core._1_ytdlp import find_video_files
from core.utils.models import *
@check_file_exists(_2_CLEANED_CHUNKS)
def transcribe():
# 1. video to audio
video_file = find_video_files()
convert_video_to_audio(video_file)
# 2. Demucs vocal separation:
if load_key("demucs"):
demucs_audio()
vocal_audio = normalize_audio_volume(_VOCAL_AUDIO_FILE, _VOCAL_AUDIO_FILE, format="mp3")
else:
vocal_audio = _RAW_AUDIO_FILE
# 3. Extract audio
segments = split_audio(_RAW_AUDIO_FILE)
# 4. Transcribe audio by clips
all_results = []
runtime = load_key("whisper.runtime")
if runtime == "local":
from core.asr_backend.whisperX_local import transcribe_audio as ts
rprint("[cyan]🎤 Transcribing audio with local model...[/cyan]")
elif runtime == "cloud":
from core.asr_backend.whisperX_302 import transcribe_audio_302 as ts
rprint("[cyan]🎤 Transcribing audio with 302 API...[/cyan]")
elif runtime == "elevenlabs":
from core.asr_backend.elevenlabs_asr import transcribe_audio_elevenlabs as ts
rprint("[cyan]🎤 Transcribing audio with ElevenLabs API...[/cyan]")
for start, end in segments:
result = ts(_RAW_AUDIO_FILE, vocal_audio, start, end)
all_results.append(result)
# 5. Combine results
combined_result = {'segments': []}
for result in all_results:
combined_result['segments'].extend(result['segments'])
# 6. Process df
df = process_transcription(combined_result)
save_results(df)
if __name__ == "__main__":
transcribe()
================================================
FILE: core/_3_1_split_nlp.py
================================================
from core.spacy_utils import *
from core.utils.models import _3_1_SPLIT_BY_NLP
from core.utils import check_file_exists
@check_file_exists(_3_1_SPLIT_BY_NLP)
def split_by_spacy():
nlp = init_nlp()
split_by_mark(nlp)
split_by_comma_main(nlp)
split_sentences_main(nlp)
split_long_by_root_main(nlp)
return
if __name__ == '__main__':
split_by_spacy()
================================================
FILE: core/_3_2_split_meaning.py
================================================
import concurrent.futures
from difflib import SequenceMatcher
import math
from core.prompts import get_split_prompt
from core.spacy_utils.load_nlp_model import init_nlp
from core.utils import *
from rich.console import Console
from rich.table import Table
from core.utils.models import _3_1_SPLIT_BY_NLP, _3_2_SPLIT_BY_MEANING
console = Console()
def tokenize_sentence(sentence, nlp):
doc = nlp(sentence)
return [token.text for token in doc]
def find_split_positions(original, modified):
split_positions = []
parts = modified.split('[br]')
start = 0
whisper_language = load_key("whisper.language")
language = load_key("whisper.detected_language") if whisper_language == 'auto' else whisper_language
joiner = get_joiner(language)
for i in range(len(parts) - 1):
max_similarity = 0
best_split = None
for j in range(start, len(original)):
original_left = original[start:j]
modified_left = joiner.join(parts[i].split())
left_similarity = SequenceMatcher(None, original_left, modified_left).ratio()
if left_similarity > max_similarity:
max_similarity = left_similarity
best_split = j
if max_similarity < 0.9:
console.print(f"[yellow]Warning: low similarity found at the best split point: {max_similarity}[/yellow]")
if best_split is not None:
split_positions.append(best_split)
start = best_split
else:
console.print(f"[yellow]Warning: Unable to find a suitable split point for the {i+1}th part.[/yellow]")
return split_positions
def split_sentence(sentence, num_parts, word_limit=20, index=-1, retry_attempt=0):
"""Split a long sentence using GPT and return the result as a string."""
split_prompt = get_split_prompt(sentence, num_parts, word_limit)
def valid_split(response_data):
choice = response_data["choice"]
if f'split{choice}' not in response_data:
return {"status": "error", "message": "Missing required key: `split`"}
if "[br]" not in response_data[f"split{choice}"]:
return {"status": "error", "message": "Split failed, no [br] found"}
return {"status": "success", "message": "Split completed"}
response_data = ask_gpt(split_prompt + " " * retry_attempt, resp_type='json', valid_def=valid_split, log_title='split_by_meaning')
choice = response_data["choice"]
best_split = response_data[f"split{choice}"]
split_points = find_split_positions(sentence, best_split)
# split the sentence based on the split points
for i, split_point in enumerate(split_points):
if i == 0:
best_split = sentence[:split_point] + '\n' + sentence[split_point:]
else:
parts = best_split.split('\n')
last_part = parts[-1]
parts[-1] = last_part[:split_point - split_points[i-1]] + '\n' + last_part[split_point - split_points[i-1]:]
best_split = '\n'.join(parts)
if index != -1:
console.print(f'[green]✅ Sentence {index} has been successfully split[/green]')
table = Table(title="")
table.add_column("Type", style="cyan")
table.add_column("Sentence")
table.add_row("Original", sentence, style="yellow")
table.add_row("Split", best_split.replace('\n', ' ||'), style="yellow")
console.print(table)
return best_split
def parallel_split_sentences(sentences, max_length, max_workers, nlp, retry_attempt=0):
"""Split sentences in parallel using a thread pool."""
new_sentences = [None] * len(sentences)
futures = []
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
for index, sentence in enumerate(sentences):
# Use tokenizer to split the sentence
tokens = tokenize_sentence(sentence, nlp)
# print("Tokenization result:", tokens)
num_parts = math.ceil(len(tokens) / max_length)
if len(tokens) > max_length:
future = executor.submit(split_sentence, sentence, num_parts, max_length, index=index, retry_attempt=retry_attempt)
futures.append((future, index, num_parts, sentence))
else:
new_sentences[index] = [sentence]
for future, index, num_parts, sentence in futures:
split_result = future.result()
if split_result:
split_lines = split_result.strip().split('\n')
new_sentences[index] = [line.strip() for line in split_lines]
else:
new_sentences[index] = [sentence]
return [sentence for sublist in new_sentences for sentence in sublist]
@check_file_exists(_3_2_SPLIT_BY_MEANING)
def split_sentences_by_meaning():
"""The main function to split sentences by meaning."""
# read input sentences
with open(_3_1_SPLIT_BY_NLP, 'r', encoding='utf-8') as f:
sentences = [line.strip() for line in f.readlines()]
nlp = init_nlp()
# 🔄 process sentences multiple times to ensure all are split
for retry_attempt in range(3):
sentences = parallel_split_sentences(sentences, max_length=load_key("max_split_length"), max_workers=load_key("max_workers"), nlp=nlp, retry_attempt=retry_attempt)
# 💾 save results
with open(_3_2_SPLIT_BY_MEANING, 'w', encoding='utf-8') as f:
f.write('\n'.join(sentences))
console.print('[green]✅ All sentences have been successfully split![/green]')
if __name__ == '__main__':
# print(split_sentence('Which makes no sense to the... average guy who always pushes the character creation slider all the way to the right.', 2, 22))
split_sentences_by_meaning()
================================================
FILE: core/_4_1_summarize.py
================================================
import json
from core.prompts import get_summary_prompt
import pandas as pd
from core.utils import *
from core.utils.models import _3_2_SPLIT_BY_MEANING, _4_1_TERMINOLOGY
CUSTOM_TERMS_PATH = 'custom_terms.xlsx'
def combine_chunks():
"""Combine the text chunks identified by whisper into a single long text"""
with open(_3_2_SPLIT_BY_MEANING, 'r', encoding='utf-8') as file:
sentences = file.readlines()
cleaned_sentences = [line.strip() for line in sentences]
combined_text = ' '.join(cleaned_sentences)
return combined_text[:load_key('summary_length')] #! Return only the first x characters
def search_things_to_note_in_prompt(sentence):
"""Search for terms to note in the given sentence"""
with open(_4_1_TERMINOLOGY, 'r', encoding='utf-8') as file:
things_to_note = json.load(file)
things_to_note_list = [term['src'] for term in things_to_note['terms'] if term['src'].lower() in sentence.lower()]
if things_to_note_list:
prompt = '\n'.join(
f'{i+1}. "{term["src"]}": "{term["tgt"]}",'
f' meaning: {term["note"]}'
for i, term in enumerate(things_to_note['terms'])
if term['src'] in things_to_note_list
)
return prompt
else:
return None
def get_summary():
src_content = combine_chunks()
custom_terms = pd.read_excel(CUSTOM_TERMS_PATH)
custom_terms_json = {
"terms":
[
{
"src": str(row.iloc[0]),
"tgt": str(row.iloc[1]),
"note": str(row.iloc[2])
}
for _, row in custom_terms.iterrows()
]
}
if len(custom_terms) > 0:
rprint(f"📖 Custom Terms Loaded: {len(custom_terms)} terms")
rprint("📝 Terms Content:", json.dumps(custom_terms_json, indent=2, ensure_ascii=False))
summary_prompt = get_summary_prompt(src_content, custom_terms_json)
rprint("📝 Summarizing and extracting terminology ...")
def valid_summary(response_data):
required_keys = {'src', 'tgt', 'note'}
if 'terms' not in response_data:
return {"status": "error", "message": "Invalid response format"}
for term in response_data['terms']:
if not all(key in term for key in required_keys):
return {"status": "error", "message": "Invalid response format"}
return {"status": "success", "message": "Summary completed"}
summary = ask_gpt(summary_prompt, resp_type='json', valid_def=valid_summary, log_title='summary')
summary['terms'].extend(custom_terms_json['terms'])
with open(_4_1_TERMINOLOGY, 'w', encoding='utf-8') as f:
json.dump(summary, f, ensure_ascii=False, indent=4)
rprint(f'💾 Summary log saved to → `{_4_1_TERMINOLOGY}`')
if __name__ == '__main__':
get_summary()
================================================
FILE: core/_4_2_translate.py
================================================
import pandas as pd
import json
import concurrent.futures
from core.translate_lines import translate_lines
from core._4_1_summarize import search_things_to_note_in_prompt
from core._8_1_audio_task import check_len_then_trim
from core._6_gen_sub import align_timestamp
from core.utils import *
from rich.console import Console
from rich.progress import Progress, SpinnerColumn, TextColumn
from difflib import SequenceMatcher
from core.utils.models import *
console = Console()
# Function to split text into chunks
def split_chunks_by_chars(chunk_size, max_i):
"""Split text into chunks based on character count, return a list of multi-line text chunks"""
with open(_3_2_SPLIT_BY_MEANING, "r", encoding="utf-8") as file:
sentences = file.read().strip().split('\n')
chunks = []
chunk = ''
sentence_count = 0
for sentence in sentences:
if len(chunk) + len(sentence + '\n') > chunk_size or sentence_count == max_i:
chunks.append(chunk.strip())
chunk = sentence + '\n'
sentence_count = 1
else:
chunk += sentence + '\n'
sentence_count += 1
chunks.append(chunk.strip())
return chunks
# Get context from surrounding chunks
def get_previous_content(chunks, chunk_index):
return None if chunk_index == 0 else chunks[chunk_index - 1].split('\n')[-3:] # Get last 3 lines
def get_after_content(chunks, chunk_index):
return None if chunk_index == len(chunks) - 1 else chunks[chunk_index + 1].split('\n')[:2] # Get first 2 lines
# 🔍 Translate a single chunk
def translate_chunk(chunk, chunks, theme_prompt, i):
things_to_note_prompt = search_things_to_note_in_prompt(chunk)
previous_content_prompt = get_previous_content(chunks, i)
after_content_prompt = get_after_content(chunks, i)
translation, english_result = translate_lines(chunk, previous_content_prompt, after_content_prompt, things_to_note_prompt, theme_prompt, i)
return i, english_result, translation
# Add similarity calculation function
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
# 🚀 Main function to translate all chunks
@check_file_exists(_4_2_TRANSLATION)
def translate_all():
console.print("[bold green]Start Translating All...[/bold green]")
chunks = split_chunks_by_chars(chunk_size=600, max_i=10)
with open(_4_1_TERMINOLOGY, 'r', encoding='utf-8') as file:
theme_prompt = json.load(file).get('theme')
# 🔄 Use concurrent execution for translation
with Progress(SpinnerColumn(), TextColumn("[progress.description]{task.description}"), transient=True) as progress:
task = progress.add_task("[cyan]Translating chunks...", total=len(chunks))
with concurrent.futures.ThreadPoolExecutor(max_workers=load_key("max_workers")) as executor:
futures = []
for i, chunk in enumerate(chunks):
future = executor.submit(translate_chunk, chunk, chunks, theme_prompt, i)
futures.append(future)
results = []
for future in concurrent.futures.as_completed(futures):
results.append(future.result())
progress.update(task, advance=1)
results.sort(key=lambda x: x[0]) # Sort results based on original order
# 💾 Save results to lists and Excel file
src_text, trans_text = [], []
for i, chunk in enumerate(chunks):
chunk_lines = chunk.split('\n')
src_text.extend(chunk_lines)
# Calculate similarity between current chunk and translation results
chunk_text = ''.join(chunk_lines).lower()
matching_results = [(r, similar(''.join(r[1].split('\n')).lower(), chunk_text))
for r in results]
best_match = max(matching_results, key=lambda x: x[1])
# Check similarity and handle exceptions
if best_match[1] < 0.9:
console.print(f"[yellow]Warning: No matching translation found for chunk {i}[/yellow]")
raise ValueError(f"Translation matching failed (chunk {i})")
elif best_match[1] < 1.0:
console.print(f"[yellow]Warning: Similar match found (chunk {i}, similarity: {best_match[1]:.3f})[/yellow]")
trans_text.extend(best_match[0][2].split('\n'))
# Trim long translation text
df_text = pd.read_excel(_2_CLEANED_CHUNKS)
df_text['text'] = df_text['text'].str.strip('"').str.strip()
df_translate = pd.DataFrame({'Source': src_text, 'Translation': trans_text})
subtitle_output_configs = [('trans_subs_for_audio.srt', ['Translation'])]
df_time = align_timestamp(df_text, df_translate, subtitle_output_configs, output_dir=None, for_display=False)
console.print(df_time)
# apply check_len_then_trim to df_time['Translation'], only when duration > MIN_TRIM_DURATION.
df_time['Translation'] = df_time.apply(lambda x: check_len_then_trim(x['Translation'], x['duration']) if x['duration'] > load_key("min_trim_duration") else x['Translation'], axis=1)
console.print(df_time)
df_time.to_excel(_4_2_TRANSLATION, index=False)
console.print("[bold green]✅ Translation completed and results saved.[/bold green]")
if __name__ == '__main__':
translate_all()
================================================
FILE: core/_5_split_sub.py
================================================
import pandas as pd
from typing import List, Tuple
import concurrent.futures
from core._3_2_split_meaning import split_sentence
from core.prompts import get_align_prompt
from rich.panel import Panel
from rich.console import Console
from rich.table import Table
from core.utils import *
from core.utils.models import *
console = Console()
# ! You can modify your own weights here
# Chinese and Japanese 2.5 characters, Korean 2 characters, Thai 1.5 characters, full-width symbols 2 characters, other English-based and half-width symbols 1 character
def calc_len(text: str) -> float:
text = str(text) # force convert
def char_weight(char):
code = ord(char)
if 0x4E00 <= code <= 0x9FFF or 0x3040 <= code <= 0x30FF: # Chinese and Japanese
return 1.75
elif 0xAC00 <= code <= 0xD7A3 or 0x1100 <= code <= 0x11FF: # Korean
return 1.5
elif 0x0E00 <= code <= 0x0E7F: # Thai
return 1
elif 0xFF01 <= code <= 0xFF5E: # full-width symbols
return 1.75
else: # other characters (e.g. English and half-width symbols)
return 1
return sum(char_weight(char) for char in text)
def align_subs(src_sub: str, tr_sub: str, src_part: str) -> Tuple[List[str], List[str], str]:
align_prompt = get_align_prompt(src_sub, tr_sub, src_part)
def valid_align(response_data):
if 'align' not in response_data:
return {"status": "error", "message": "Missing required key: `align`"}
if len(response_data['align']) < 2:
return {"status": "error", "message": "Align does not contain more than 1 part as expected!"}
return {"status": "success", "message": "Align completed"}
parsed = ask_gpt(align_prompt, resp_type='json', valid_def=valid_align, log_title='align_subs')
align_data = parsed['align']
src_parts = src_part.split('\n')
tr_parts = [item[f'target_part_{i+1}'].strip() for i, item in enumerate(align_data)]
whisper_language = load_key("whisper.language")
language = load_key("whisper.detected_language") if whisper_language == 'auto' else whisper_language
joiner = get_joiner(language)
tr_remerged = joiner.join(tr_parts)
table = Table(title="🔗 Aligned parts")
table.add_column("Language", style="cyan")
table.add_column("Parts", style="magenta")
table.add_row("SRC_LANG", "\n".join(src_parts))
table.add_row("TARGET_LANG", "\n".join(tr_parts))
console.print(table)
return src_parts, tr_parts, tr_remerged
def split_align_subs(src_lines: List[str], tr_lines: List[str]):
subtitle_set = load_key("subtitle")
MAX_SUB_LENGTH = subtitle_set["max_length"]
TARGET_SUB_MULTIPLIER = subtitle_set["target_multiplier"]
remerged_tr_lines = tr_lines.copy()
to_split = []
for i, (src, tr) in enumerate(zip(src_lines, tr_lines)):
src, tr = str(src), str(tr)
if len(src) > MAX_SUB_LENGTH or calc_len(tr) * TARGET_SUB_MULTIPLIER > MAX_SUB_LENGTH:
to_split.append(i)
table = Table(title=f"📏 Line {i} needs to be split")
table.add_column("Type", style="cyan")
table.add_column("Content", style="magenta")
table.add_row("Source Line", src)
table.add_row("Target Line", tr)
console.print(table)
@except_handler("Error in split_align_subs")
def process(i):
split_src = split_sentence(src_lines[i], num_parts=2).strip()
src_parts, tr_parts, tr_remerged = align_subs(src_lines[i], tr_lines[i], split_src)
src_lines[i] = src_parts
tr_lines[i] = tr_parts
remerged_tr_lines[i] = tr_remerged
with concurrent.futures.ThreadPoolExecutor(max_workers=load_key("max_workers")) as executor:
executor.map(process, to_split)
# Flatten `src_lines` and `tr_lines`
src_lines = [item for sublist in src_lines for item in (sublist if isinstance(sublist, list) else [sublist])]
tr_lines = [item for sublist in tr_lines for item in (sublist if isinstance(sublist, list) else [sublist])]
return src_lines, tr_lines, remerged_tr_lines
def split_for_sub_main():
console.print("[bold green]🚀 Start splitting subtitles...[/bold green]")
df = pd.read_excel(_4_2_TRANSLATION)
src = df['Source'].tolist()
trans = df['Translation'].tolist()
subtitle_set = load_key("subtitle")
MAX_SUB_LENGTH = subtitle_set["max_length"]
TARGET_SUB_MULTIPLIER = subtitle_set["target_multiplier"]
for attempt in range(3): # 多次切割
console.print(Panel(f"🔄 Split attempt {attempt + 1}", expand=False))
split_src, split_trans, remerged = split_align_subs(src.copy(), trans)
# 检查是否所有字幕都符合长度要求
if all(len(src) <= MAX_SUB_LENGTH for src in split_src) and \
all(calc_len(tr) * TARGET_SUB_MULTIPLIER <= MAX_SUB_LENGTH for tr in split_trans):
break
# 更新源数据继续下一轮分割
src, trans = split_src, split_trans
# 确保二者有相同的长度，防止报错
if len(src) > len(remerged):
remerged += [None] * (len(src) - len(remerged))
elif len(remerged) > len(src):
src += [None] * (len(remerged) - len(src))
pd.DataFrame({'Source': split_src, 'Translation': split_trans}).to_excel(_5_SPLIT_SUB, index=False)
pd.DataFrame({'Source': src, 'Translation': remerged}).to_excel(_5_REMERGED, index=False)
if __name__ == '__main__':
split_for_sub_main()
================================================
FILE: core/_6_gen_sub.py
================================================
import pandas as pd
import os
import re
from rich.panel import Panel
from rich.console import Console
import autocorrect_py as autocorrect
from core.utils import *
from core.utils.models import *
console = Console()
SUBTITLE_OUTPUT_CONFIGS = [
('src.srt', ['Source']),
('trans.srt', ['Translation']),
('src_trans.srt', ['Source', 'Translation']),
('trans_src.srt', ['Translation', 'Source'])
]
AUDIO_SUBTITLE_OUTPUT_CONFIGS = [
('src_subs_for_audio.srt', ['Source']),
('trans_subs_for_audio.srt', ['Translation'])
]
def convert_to_srt_format(start_time, end_time):
"""Convert time (in seconds) to the format: hours:minutes:seconds,milliseconds"""
def seconds_to_hmsm(seconds):
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
seconds = seconds % 60
milliseconds = int(seconds * 1000) % 1000
return f"{hours:02d}:{minutes:02d}:{int(seconds):02d},{milliseconds:03d}"
start_srt = seconds_to_hmsm(start_time)
end_srt = seconds_to_hmsm(end_time)
return f"{start_srt} --> {end_srt}"
def remove_punctuation(text):
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'[^\w\s]', '', text)
return text.strip()
def show_difference(str1, str2):
"""Show the difference positions between two strings"""
min_len = min(len(str1), len(str2))
diff_positions = []
for i in range(min_len):
if str1[i] != str2[i]:
diff_positions.append(i)
if len(str1) != len(str2):
diff_positions.extend(range(min_len, max(len(str1), len(str2))))
print("Difference positions:")
print(f"Expected sentence: {str1}")
print(f"Actual match: {str2}")
print("Position markers: " + "".join("^" if i in diff_positions else " " for i in range(max(len(str1), len(str2)))))
print(f"Difference indices: {diff_positions}")
def get_sentence_timestamps(df_words, df_sentences):
time_stamp_list = []
# Build complete string and position mapping
full_words_str = ''
position_to_word_idx = {}
for idx, word in enumerate(df_words['text']):
clean_word = remove_punctuation(word.lower())
start_pos = len(full_words_str)
full_words_str += clean_word
for pos in range(start_pos, len(full_words_str)):
position_to_word_idx[pos] = idx
current_pos = 0
for idx, sentence in df_sentences['Source'].items():
clean_sentence = remove_punctuation(sentence.lower()).replace(" ", "")
sentence_len = len(clean_sentence)
match_found = False
while current_pos <= len(full_words_str) - sentence_len:
if full_words_str[current_pos:current_pos+sentence_len] == clean_sentence:
start_word_idx = position_to_word_idx[current_pos]
end_word_idx = position_to_word_idx[current_pos + sentence_len - 1]
time_stamp_list.append((
float(df_words['start'][start_word_idx]),
float(df_words['end'][end_word_idx])
))
current_pos += sentence_len
match_found = True
break
current_pos += 1
if not match_found:
print(f"\n⚠️ Warning: No exact match found for sentence: {sentence}")
show_difference(clean_sentence,
full_words_str[current_pos:current_pos+len(clean_sentence)])
print("\nOriginal sentence:", df_sentences['Source'][idx])
raise ValueError("❎ No match found for sentence.")
return time_stamp_list
def align_timestamp(df_text, df_translate, subtitle_output_configs: list, output_dir: str, for_display: bool = True):
"""Align timestamps and add a new timestamp column to df_translate"""
df_trans_time = df_translate.copy()
# Assign an ID to each word in df_text['text'] and create a new DataFrame
words = df_text['text'].str.split(expand=True).stack().reset_index(level=1, drop=True).reset_index()
words.columns = ['id', 'word']
words['id'] = words['id'].astype(int)
# Process timestamps ⏰
time_stamp_list = get_sentence_timestamps(df_text, df_translate)
df_trans_time['timestamp'] = time_stamp_list
df_trans_time['duration'] = df_trans_time['timestamp'].apply(lambda x: x[1] - x[0])
# Remove gaps 🕳️
for i in range(len(df_trans_time)-1):
delta_time = df_trans_time.loc[i+1, 'timestamp'][0] - df_trans_time.loc[i, 'timestamp'][1]
if 0 < delta_time < 1:
df_trans_time.at[i, 'timestamp'] = (df_trans_time.loc[i, 'timestamp'][0], df_trans_time.loc[i+1, 'timestamp'][0])
# Convert start and end timestamps to SRT format
df_trans_time['timestamp'] = df_trans_time['timestamp'].apply(lambda x: convert_to_srt_format(x[0], x[1]))
# Polish subtitles: replace punctuation in Translation if for_display
if for_display:
df_trans_time['Translation'] = df_trans_time['Translation'].apply(lambda x: re.sub(r'[，。]', ' ', x).strip())
# Output subtitles 📜
def generate_subtitle_string(df, columns):
return ''.join([f"{i+1}\n{row['timestamp']}\n{row[columns[0]].strip()}\n{row[columns[1]].strip() if len(columns) > 1 else ''}\n\n" for i, row in df.iterrows()]).strip()
if output_dir:
os.makedirs(output_dir, exist_ok=True)
for filename, columns in subtitle_output_configs:
subtitle_str = generate_subtitle_string(df_trans_time, columns)
with open(os.path.join(output_dir, filename), 'w', encoding='utf-8') as f:
f.write(subtitle_str)
return df_trans_time
# ✨ Beautify the translation
def clean_translation(x):
if pd.isna(x):
return ''
cleaned = str(x).strip('。').strip('，')
return autocorrect.format(cleaned)
def align_timestamp_main():
df_text = pd.read_excel(_2_CLEANED_CHUNKS)
df_text['text'] = df_text['text'].str.strip('"').str.strip()
df_translate = pd.read_excel(_5_SPLIT_SUB)
df_translate['Translation'] = df_translate['Translation'].apply(clean_translation)
align_timestamp(df_text, df_translate, SUBTITLE_OUTPUT_CONFIGS, _OUTPUT_DIR)
console.print(Panel("[bold green]🎉📝 Subtitles generation completed! Please check in the `output` folder 👀[/bold green]"))
# for audio
df_translate_for_audio = pd.read_excel(_5_REMERGED) # use remerged file to avoid unmatched lines when dubbing
df_translate_for_audio['Translation'] = df_translate_for_audio['Translation'].apply(clean_translation)
align_timestamp(df_text, df_translate_for_audio, AUDIO_SUBTITLE_OUTPUT_CONFIGS, _AUDIO_DIR)
console.print(Panel(f"[bold green]🎉📝 Audio subtitles generation completed! Please check in the `{_AUDIO_DIR}` folder 👀[/bold green]"))
if __name__ == '__main__':
align_timestamp_main()
================================================
FILE: core/_7_sub_into_vid.py
================================================
import os, subprocess, time
from core._1_ytdlp import find_video_files
import cv2
import numpy as np
import platform
from core.utils import *
SRC_FONT_SIZE = 15
TRANS_FONT_SIZE = 17
FONT_NAME = 'Arial'
TRANS_FONT_NAME = 'Arial'
# Linux need to install google noto fonts: apt-get install fonts-noto
if platform.system() == 'Linux':
FONT_NAME = 'NotoSansCJK-Regular'
TRANS_FONT_NAME = 'NotoSansCJK-Regular'
# Mac OS has different font names
elif platform.system() == 'Darwin':
FONT_NAME = 'Arial Unicode MS'
TRANS_FONT_NAME = 'Arial Unicode MS'
SRC_FONT_COLOR = '&HFFFFFF'
SRC_OUTLINE_COLOR = '&H000000'
SRC_OUTLINE_WIDTH = 1
SRC_SHADOW_COLOR = '&H80000000'
TRANS_FONT_COLOR = '&H00FFFF'
TRANS_OUTLINE_COLOR = '&H000000'
TRANS_OUTLINE_WIDTH = 1
TRANS_BACK_COLOR = '&H33000000'
OUTPUT_DIR = "output"
OUTPUT_VIDEO = f"{OUTPUT_DIR}/output_sub.mp4"
SRC_SRT = f"{OUTPUT_DIR}/src.srt"
TRANS_SRT = f"{OUTPUT_DIR}/trans.srt"
def check_gpu_available():
try:
result = subprocess.run(['ffmpeg', '-encoders'], capture_output=True, text=True)
return 'h264_nvenc' in result.stdout
except:
return False
def merge_subtitles_to_video():
video_file = find_video_files()
os.makedirs(os.path.dirname(OUTPUT_VIDEO), exist_ok=True)
# Check resolution
if not load_key("burn_subtitles"):
rprint("[bold yellow]Warning: A 0-second black video will be generated as a placeholder as subtitles are not burned in.[/bold yellow]")
# Create a black frame
frame = np.zeros((1080, 1920, 3), dtype=np.uint8)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(OUTPUT_VIDEO, fourcc, 1, (1920, 1080))
out.write(frame)
out.release()
rprint("[bold green]Placeholder video has been generated.[/bold green]")
return
if not os.path.exists(SRC_SRT) or not os.path.exists(TRANS_SRT):
rprint("Subtitle files not found in the 'output' directory.")
exit(1)
video = cv2.VideoCapture(video_file)
TARGET_WIDTH = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
TARGET_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
video.release()
rprint(f"[bold green]Video resolution: {TARGET_WIDTH}x{TARGET_HEIGHT}[/bold green]")
ffmpeg_cmd = [
'ffmpeg', '-i', video_file,
'-vf', (
f"scale={TARGET_WIDTH}:{TARGET_HEIGHT}:force_original_aspect_ratio=decrease,"
f"pad={TARGET_WIDTH}:{TARGET_HEIGHT}:(ow-iw)/2:(oh-ih)/2,"
f"subtitles={SRC_SRT}:force_style='FontSize={SRC_FONT_SIZE},FontName={FONT_NAME},"
f"PrimaryColour={SRC_FONT_COLOR},OutlineColour={SRC_OUTLINE_COLOR},OutlineWidth={SRC_OUTLINE_WIDTH},"
f"ShadowColour={SRC_SHADOW_COLOR},BorderStyle=1',"
f"subtitles={TRANS_SRT}:force_style='FontSize={TRANS_FONT_SIZE},FontName={TRANS_FONT_NAME},"
f"PrimaryColour={TRANS_FONT_COLOR},OutlineColour={TRANS_OUTLINE_COLOR},OutlineWidth={TRANS_OUTLINE_WIDTH},"
f"BackColour={TRANS_BACK_COLOR},Alignment=2,MarginV=27,BorderStyle=4'"
).encode('utf-8'),
]
ffmpeg_gpu = load_key("ffmpeg_gpu")
if ffmpeg_gpu:
rprint("[bold green]will use GPU acceleration.[/bold green]")
ffmpeg_cmd.extend(['-c:v', 'h264_nvenc'])
ffmpeg_cmd.extend(['-y', OUTPUT_VIDEO])
rprint("🎬 Start merging subtitles to video...")
start_time = time.time()
process = subprocess.Popen(ffmpeg_cmd)
try:
process.wait()
if process.returncode == 0:
rprint(f"\n✅ Done! Time taken: {time.time() - start_time:.2f} seconds")
else:
rprint("\n❌ FFmpeg execution error")
except Exception as e:
rprint(f"\n❌ Error occurred: {e}")
if process.poll() is None:
process.kill()
if __name__ == "__main__":
merge_subtitles_to_video()
================================================
FILE: core/_8_1_audio_task.py
================================================
import datetime
import re
import pandas as pd
from rich.console import Console
from rich.panel import Panel
from core.prompts import get_subtitle_trim_prompt
from core.tts_backend.estimate_duration import init_estimator, estimate_duration
from core.utils import *
from core.utils.models import *
console = Console()
speed_factor = load_key("speed_factor")
TRANS_SUBS_FOR_AUDIO_FILE = 'output/audio/trans_subs_for_audio.srt'
SRC_SUBS_FOR_AUDIO_FILE = 'output/audio/src_subs_for_audio.srt'
ESTIMATOR = None
def check_len_then_trim(text, duration):
global ESTIMATOR
if ESTIMATOR is None:
ESTIMATOR = init_estimator()
estimated_duration = estimate_duration(text, ESTIMATOR) / speed_factor['max']
console.print(f"Subtitle text: {text}, "
f"[bold green]Estimated reading duration: {estimated_duration:.2f} seconds[/bold green]")
if estimated_duration > duration:
rprint(Panel(f"Estimated reading duration {estimated_duration:.2f} seconds exceeds given duration {duration:.2f} seconds, shortening...", title="Processing", border_style="yellow"))
original_text = text
prompt = get_subtitle_trim_prompt(text, duration)
def valid_trim(response):
if 'result' not in response:
return {'status': 'error', 'message': 'No result in response'}
return {'status': 'success', 'message': ''}
try:
response = ask_gpt(prompt, resp_type='json', log_title='sub_trim', valid_def=valid_trim)
shortened_text = response['result']
except Exception:
rprint("[bold red]🚫 AI refused to answer due to sensitivity, so manually remove punctuation[/bold red]")
shortened_text = re.sub(r'[,.!?;:，。！？；：]', ' ', text).strip()
rprint(Panel(f"Subtitle before shortening: {original_text}\nSubtitle after shortening: {shortened_text}", title="Subtitle Shortening Result", border_style="green"))
return shortened_text
else:
return text
def time_diff_seconds(t1, t2, base_date):
"""Calculate the difference in seconds between two time objects"""
dt1 = datetime.datetime.combine(base_date, t1)
dt2 = datetime.datetime.combine(base_date, t2)
return (dt2 - dt1).total_seconds()
def process_srt():
"""Process srt file, generate audio tasks"""
with open(TRANS_SUBS_FOR_AUDIO_FILE, 'r', encoding='utf-8') as file:
content = file.read()
with open(SRC_SUBS_FOR_AUDIO_FILE, 'r', encoding='utf-8') as src_file:
src_content = src_file.read()
subtitles = []
src_subtitles = {}
for block in src_content.strip().split('\n\n'):
lines = [line.strip() for line in block.split('\n') if line.strip()]
if len(lines) < 3:
continue
number = int(lines[0])
src_text = ' '.join(lines[2:])
src_subtitles[number] = src_text
for block in content.strip().split('\n\n'):
lines = [line.strip() for line in block.split('\n') if line.strip()]
if len(lines) < 3:
continue
try:
number = int(lines[0])
start_time, end_time = lines[1].split(' --> ')
start_time = datetime.datetime.strptime(start_time, '%H:%M:%S,%f').time()
end_time = datetime.datetime.strptime(end_time, '%H:%M:%S,%f').time()
duration = time_diff_seconds(start_time, end_time, datetime.date.today())
text = ' '.join(lines[2:])
# Remove content within parentheses (including English and Chinese parentheses)
text = re.sub(r'\([^)]*\)', '', text).strip()
text = re.sub(r'（[^）]*）', '', text).strip()
# Remove '-' character, can continue to add illegal characters that cause errors
text = text.replace('-', '')
# Add the original text from src_subs_for_audio.srt
origin = src_subtitles.get(number, '')
except ValueError as e:
rprint(Panel(f"Unable to parse subtitle block '{block}', error: {str(e)}, skipping this subtitle block.", title="Error", border_style="red"))
continue
subtitles.append({'number': number, 'start_time': start_time, 'end_time': end_time, 'duration': duration, 'text': text, 'origin': origin})
df = pd.DataFrame(subtitles)
i = 0
MIN_SUB_DUR = load_key("min_subtitle_duration")
while i < len(df):
today = datetime.date.today()
if df.loc[i, 'duration'] < MIN_SUB_DUR:
if i < len(df) - 1 and time_diff_seconds(df.loc[i, 'start_time'],df.loc[i+1, 'start_time'],today) < MIN_SUB_DUR:
rprint(f"[bold yellow]Merging subtitles {i+1} and {i+2}[/bold yellow]")
df.loc[i, 'text'] += ' ' + df.loc[i+1, 'text']
df.loc[i, 'origin'] += ' ' + df.loc[i+1, 'origin']
df.loc[i, 'end_time'] = df.loc[i+1, 'end_time']
df.loc[i, 'duration'] = time_diff_seconds(df.loc[i, 'start_time'],df.loc[i, 'end_time'],today)
df = df.drop(i+1).reset_index(drop=True)
else:
if i < len(df) - 1: # Not the last audio
rprint(f"[bold blue]Extending subtitle {i+1} duration to {MIN_SUB_DUR} seconds[/bold blue]")
df.loc[i, 'end_time'] = (datetime.datetime.combine(today, df.loc[i, 'start_time']) +
datetime.timedelta(seconds=MIN_SUB_DUR)).time()
df.loc[i, 'duration'] = MIN_SUB_DUR
else:
rprint(f"[bold red]The last subtitle {i+1} duration is less than {MIN_SUB_DUR} seconds, but not extending[/bold red]")
i += 1
else:
i += 1
df['start_time'] = df['start_time'].apply(lambda x: x.strftime('%H:%M:%S.%f')[:-3])
df['end_time'] = df['end_time'].apply(lambda x: x.strftime('%H:%M:%S.%f')[:-3])
##! No longer perform secondary trim
# check and trim subtitle length, for twice to ensure the subtitle length is within the limit, 允许tolerance
# df['text'] = df.apply(lambda x: check_len_then_trim(x['text'], x['duration']+x['tolerance']), axis=1)
return df
@check_file_exists(_8_1_AUDIO_TASK)
def gen_audio_task_main():
df = process_srt()
console.print(df)
df.to_excel(_8_1_AUDIO_TASK, index=False)
rprint(Panel(f"Successfully generated {_8_1_AUDIO_TASK}", title="Success", border_style="green"))
if __name__ == '__main__':
gen_audio_task_main()
================================================
FILE: core/_8_2_dub_chunks.py
================================================
import datetime
import re
import pandas as pd
from core._8_1_audio_task import time_diff_seconds
from core.asr_backend.audio_preprocess import get_audio_duration
from core.tts_backend.estimate_duration import init_estimator, estimate_duration
from core.utils import *
from core.utils.models import *
SRC_SRT = "output/src.srt"
TRANS_SRT = "output/trans.srt"
MAX_MERGE_COUNT = 5
ESTIMATOR = None
def calc_if_too_fast(est_dur, tol_dur, duration, tolerance):
accept = load_key("speed_factor.accept") # Maximum acceptable speed factor
if est_dur / accept > tol_dur: # Even max speed factor cannot adapt
return 2
elif est_dur > tol_dur: # Speed adjustment needed within acceptable range
return 1
elif est_dur < duration - tolerance: # Speaking speed too slow
return -1
else: # Normal speaking speed
return 0
def merge_rows(df, start_idx, merge_count):
"""Merge multiple rows and calculate cumulative values"""
merged = {
'est_dur': df.iloc[start_idx]['est_dur'],
'tol_dur': df.iloc[start_idx]['tol_dur'],
'duration': df.iloc[start_idx]['duration']
}
while merge_count < MAX_MERGE_COUNT and (start_idx + merge_count) < len(df):
next_row = df.iloc[start_idx + merge_count]
merged['est_dur'] += next_row['est_dur']
merged['tol_dur'] += next_row['tol_dur']
merged['duration'] += next_row['duration']
speed_flag = calc_if_too_fast(
merged['est_dur'],
merged['tol_dur'],
merged['duration'],
df.iloc[start_idx + merge_count]['tolerance']
)
if speed_flag <= 0 or merge_count == 2:
df.at[start_idx + merge_count, 'cut_off'] = 1
return merge_count + 1
merge_count += 1
# If no suitable merge point is found
if merge_count >= MAX_MERGE_COUNT or (start_idx + merge_count) >= len(df):
df.at[start_idx + merge_count - 1, 'cut_off'] = 1
return merge_count
def analyze_subtitle_timing_and_speed(df):
rprint("[🔍 Analyzing] Calculating subtitle timing and speed...")
global ESTIMATOR
if ESTIMATOR is None:
ESTIMATOR = init_estimator()
TOLERANCE = load_key("tolerance")
whole_dur = get_audio_duration(_RAW_AUDIO_FILE)
df['gap'] = 0.0 # Initialize gap column
for i in range(len(df) - 1):
current_end = datetime.datetime.strptime(df.loc[i, 'end_time'], '%H:%M:%S.%f').time()
next_start = datetime.datetime.strptime(df.loc[i + 1, 'start_time'], '%H:%M:%S.%f').time()
df.loc[i, 'gap'] = time_diff_seconds(current_end, next_start, datetime.date.today())
# Set the gap for the last line
last_end = datetime.datetime.strptime(df.iloc[-1]['end_time'], '%H:%M:%S.%f').time()
last_end_seconds = (last_end.hour * 3600 + last_end.minute * 60 +
last_end.second + last_end.microsecond / 1000000)
df.iloc[-1, df.columns.get_loc('gap')] = whole_dur - last_end_seconds
df['tolerance'] = df['gap'].apply(lambda x: TOLERANCE if x > TOLERANCE else x)
df['tol_dur'] = df['duration'] + df['tolerance']
df['est_dur'] = df.apply(lambda x: estimate_duration(x['text'], ESTIMATOR), axis=1)
## Calculate speed indicators
accept = load_key("speed_factor.accept") # Maximum acceptable speed factor
def calc_if_too_fast(row):
est_dur = row['est_dur']
tol_dur = row['tol_dur']
duration = row['duration']
tolerance = row['tolerance']
if est_dur / accept > tol_dur: # Even max speed factor cannot adapt
return 2
elif est_dur > tol_dur: # Speed adjustment needed within acceptable range
return 1
elif est_dur < duration - tolerance: # Speaking speed too slow
return -1
else: # Normal speaking speed
return 0
df['if_too_fast'] = df.apply(calc_if_too_fast, axis=1)
return df
def process_cutoffs(df):
rprint("[✂️ Processing] Generating cutoff points...")
df['cut_off'] = 0 # Initialize cut_off column
df.loc[df['gap'] >= load_key("tolerance"), 'cut_off'] = 1 # Set to 1 when gap is greater than TOLERANCE
idx = 0
while idx < len(df):
# Process marked split points
if df.iloc[idx]['cut_off'] == 1:
if df.iloc[idx]['if_too_fast'] == 2:
rprint(f"[⚠️ Warning] Line {idx} is too fast and cannot be fixed by speed adjustment")
idx += 1
continue
# Process the last line
if idx + 1 >= len(df):
df.at[idx, 'cut_off'] = 1
break
# Process normal or slow lines
if df.iloc[idx]['if_too_fast'] <= 0:
if df.iloc[idx + 1]['if_too_fast'] <= 0:
df.at[idx, 'cut_off'] = 1
idx += 1
else:
idx += merge_rows(df, idx, 1)
# Process fast lines
else:
idx += merge_rows(df, idx, 1)
return df
def gen_dub_chunks():
rprint("[🎬 Starting] Generating dubbing chunks...")
df = pd.read_excel(_8_1_AUDIO_TASK)
rprint("[📊 Processing] Analyzing timing and speed...")
df = analyze_subtitle_timing_and_speed(df)
rprint("[✂️ Processing] Processing cutoffs...")
df = process_cutoffs(df)
rprint("[📝 Reading] Loading transcript files...")
content = open(TRANS_SRT, "r", encoding="utf-8").read()
ori_content = open(SRC_SRT, "r", encoding="utf-8").read()
# Process subtitle content
content_lines = []
ori_content_lines = []
# Process translated subtitles
for block in content.strip().split('\n\n'):
lines = [line.strip() for line in block.split('\n') if line.strip()]
if len(lines) >= 3:
text = ' '.join(lines[2:])
text = re.sub(r'\([^)]*\)|（[^）]*）', '', text).strip().replace('-', '')
content_lines.append(text)
# Process source subtitles (same structure)
for block in ori_content.strip().split('\n\n'):
lines = [line.strip() for line in block.split('\n') if line.strip()]
if len(lines) >= 3:
text = ' '.join(lines[2:])
text = re.sub(r'\([^)]*\)|（[^）]*）', '', text).strip().replace('-', '')
ori_content_lines.append(text)
# Match processing
df['lines'] = None
df['src_lines'] = None
last_idx = 0
def clean_text(text):
"""clean space and punctuation"""
if not text or not isinstance(text, str):
return ''
return re.sub(r'[^\w\s]|[\s]', '', text)
for idx, row in df.iterrows():
target = clean_text(row['text'])
matches = []
current = ''
match_indices = [] # Store indices for matching lines
for i in range(last_idx, len(content_lines)):
line = content_lines[i]
cleaned_line = clean_text
gitextract_wyfw8khg/
├── .cursorrules
├── .gitignore
├── .streamlit/
│ └── config.toml
├── Dockerfile
├── LICENSE
├── OneKeyStart.bat
├── README.md
├── VideoLingo_colab.ipynb
├── batch/
│ ├── OneKeyBatch.bat
│ ├── README.md
│ ├── README.zh.md
│ └── utils/
│ ├── batch_processor.py
│ ├── settings_check.py
│ └── video_processor.py
├── config.yaml
├── core/
│ ├── _10_gen_audio.py
│ ├── _11_merge_audio.py
│ ├── _12_dub_to_vid.py
│ ├── _1_ytdlp.py
│ ├── _2_asr.py
│ ├── _3_1_split_nlp.py
│ ├── _3_2_split_meaning.py
│ ├── _4_1_summarize.py
│ ├── _4_2_translate.py
│ ├── _5_split_sub.py
│ ├── _6_gen_sub.py
│ ├── _7_sub_into_vid.py
│ ├── _8_1_audio_task.py
│ ├── _8_2_dub_chunks.py
│ ├── _9_refer_audio.py
│ ├── __init__.py
│ ├── asr_backend/
│ │ ├── __init__.py
│ │ ├── audio_preprocess.py
│ │ ├── demucs_vl.py
│ │ ├── elevenlabs_asr.py
│ │ ├── whisperX_302.py
│ │ └── whisperX_local.py
│ ├── prompts.py
│ ├── spacy_utils/
│ │ ├── __init__.py
│ │ ├── load_nlp_model.py
│ │ ├── split_by_comma.py
│ │ ├── split_by_connector.py
│ │ ├── split_by_mark.py
│ │ └── split_long_by_root.py
│ ├── st_utils/
│ │ ├── __init__.py
│ │ ├── download_video_section.py
│ │ ├── imports_and_utils.py
│ │ └── sidebar_setting.py
│ ├── translate_lines.py
│ ├── tts_backend/
│ │ ├── _302_f5tts.py
│ │ ├── azure_tts.py
│ │ ├── custom_tts.py
│ │ ├── edge_tts.py
│ │ ├── estimate_duration.py
│ │ ├── fish_tts.py
│ │ ├── gpt_sovits_tts.py
│ │ ├── openai_tts.py
│ │ ├── sf_cosyvoice2.py
│ │ ├── sf_fishtts.py
│ │ └── tts_main.py
│ └── utils/
│ ├── __init__.py
│ ├── ask_gpt.py
│ ├── config_utils.py
│ ├── decorator.py
│ ├── delete_retry_dubbing.py
│ ├── models.py
│ ├── onekeycleanup.py
│ └── pypi_autochoose.py
├── custom_terms.xlsx
├── docs/
│ ├── .gitignore
│ ├── components/
│ │ ├── landing/
│ │ │ ├── comments.tsx
│ │ │ ├── faq.tsx
│ │ │ ├── features.tsx
│ │ │ ├── github-stats.tsx
│ │ │ ├── hero.tsx
│ │ │ └── index.tsx
│ │ └── ui/
│ │ ├── accordion.tsx
│ │ ├── badge.tsx
│ │ ├── button.tsx
│ │ ├── card.tsx
│ │ ├── hero-video-dialog.tsx
│ │ ├── rainbow-button.tsx
│ │ └── tooltip.tsx
│ ├── components.json
│ ├── lib/
│ │ └── utils.ts
│ ├── middleware.js
│ ├── next-env.d.ts
│ ├── next.config.js
│ ├── package.json
│ ├── pages/
│ │ ├── _app.mdx
│ │ ├── _meta.en-US.json
│ │ ├── _meta.ja.json
│ │ ├── _meta.zh-CN.json
│ │ ├── docs/
│ │ │ ├── _meta.en-US.json
│ │ │ ├── _meta.ja.json
│ │ │ ├── _meta.zh-CN.json
│ │ │ ├── docker.en-US.md
│ │ │ ├── docker.zh-CN.md
│ │ │ ├── introduction.en-US.md
│ │ │ ├── introduction.zh-CN.md
│ │ │ ├── start.en-US.md
│ │ │ ├── start.zh-CN.md
│ │ │ ├── tech.en-US.md
│ │ │ └── tech.zh-CN.md
│ │ ├── globals.css
│ │ ├── index.en-US.mdx
│ │ ├── index.ja.mdx
│ │ └── index.zh-CN.mdx
│ ├── postcss.config.js
│ ├── public/
│ │ └── site.webmanifest
│ ├── tailwind.config.js
│ ├── theme.config.jsx
│ └── tsconfig.json
├── install.py
├── launch.py
├── requirements.txt
├── setup.py
├── st.py
└── translations/
├── README.es.md
├── README.fr.md
├── README.ja.md
├── README.ru.md
├── README.zh-TW.md
├── README.zh.md
├── en.json
├── es.json
├── fr.json
├── ja.json
├── ru.json
├── translations.py
├── zh-CN.json
└── zh-HK.json
SYMBOL INDEX (205 symbols across 66 files)
FILE: batch/utils/batch_processor.py
function record_and_update_config (line 14) | def record_and_update_config(source_language, target_language):
function process_batch (line 25) | def process_batch():
FILE: batch/utils/settings_check.py
function check_settings (line 13) | def check_settings():
FILE: batch/utils/video_processor.py
function process_video (line 19) | def process_video(file, dubbing=False, is_retry=False):
function prepare_output_folder (line 74) | def prepare_output_folder(output_folder):
function process_input_file (line 79) | def process_input_file(file):
function split_sentences (line 90) | def split_sentences():
function summarize_and_translate (line 94) | def summarize_and_translate():
function process_and_align_subtitles (line 98) | def process_and_align_subtitles():
function gen_audio_tasks (line 102) | def gen_audio_tasks():
FILE: core/_10_gen_audio.py
function parse_df_srt_time (line 24) | def parse_df_srt_time(time_str: str) -> float:
function adjust_audio_speed (line 30) | def adjust_audio_speed(input_file: str, output_file: str, speed_factor: ...
function process_row (line 65) | def process_row(row: pd.Series, tasks_df: pd.DataFrame) -> Tuple[int, fl...
function generate_tts_audio (line 76) | def generate_tts_audio(tasks_df: pd.DataFrame) -> pd.DataFrame:
function process_chunk (line 118) | def process_chunk(chunk_df: pd.DataFrame, accept: float, min_speed: floa...
function merge_chunks (line 141) | def merge_chunks(tasks_df: pd.DataFrame) -> pd.DataFrame:
function gen_audio (line 209) | def gen_audio() -> None:
FILE: core/_11_merge_audio.py
function load_and_flatten_data (line 16) | def load_and_flatten_data(excel_file):
function get_audio_files (line 27) | def get_audio_files(df):
function process_audio_segment (line 38) | def process_audio_segment(audio_file):
function merge_audio_segments (line 54) | def merge_audio_segments(audios, new_sub_times, sample_rate):
function create_srt_subtitle (line 85) | def create_srt_subtitle():
function merge_full_audio (line 99) | def merge_full_audio():
FILE: core/_12_dub_to_vid.py
function merge_video_audio (line 31) | def merge_video_audio():
FILE: core/_1_ytdlp.py
function sanitize_filename (line 7) | def sanitize_filename(filename):
function update_ytdlp (line 15) | def update_ytdlp():
function download_video_ytdlp (line 26) | def download_video_ytdlp(url, save_path='output', resolution='1080'):
function find_video_files (line 54) | def find_video_files(save_path='output'):
FILE: core/_2_asr.py
function transcribe (line 8) | def transcribe():
FILE: core/_3_1_split_nlp.py
function split_by_spacy (line 6) | def split_by_spacy():
FILE: core/_3_2_split_meaning.py
function tokenize_sentence (line 12) | def tokenize_sentence(sentence, nlp):
function find_split_positions (line 16) | def find_split_positions(original, modified):
function split_sentence (line 48) | def split_sentence(sentence, num_parts, word_limit=20, index=-1, retry_a...
function parallel_split_sentences (line 83) | def parallel_split_sentences(sentences, max_length, max_workers, nlp, re...
function split_sentences_by_meaning (line 111) | def split_sentences_by_meaning():
FILE: core/_4_1_summarize.py
function combine_chunks (line 9) | def combine_chunks():
function search_things_to_note_in_prompt (line 17) | def search_things_to_note_in_prompt(sentence):
function get_summary (line 33) | def get_summary():
FILE: core/_4_2_translate.py
function split_chunks_by_chars (line 16) | def split_chunks_by_chars(chunk_size, max_i):
function get_previous_content (line 36) | def get_previous_content(chunks, chunk_index):
function get_after_content (line 38) | def get_after_content(chunks, chunk_index):
function translate_chunk (line 42) | def translate_chunk(chunk, chunks, theme_prompt, i):
function similar (line 50) | def similar(a, b):
function translate_all (line 55) | def translate_all():
FILE: core/_5_split_sub.py
function calc_len (line 16) | def calc_len(text: str) -> float:
function align_subs (line 33) | def align_subs(src_sub: str, tr_sub: str, src_part: str) -> Tuple[List[s...
function split_align_subs (line 61) | def split_align_subs(src_lines: List[str], tr_lines: List[str]):
function split_for_sub_main (line 96) | def split_for_sub_main():
FILE: core/_6_gen_sub.py
function convert_to_srt_format (line 23) | def convert_to_srt_format(start_time, end_time):
function remove_punctuation (line 36) | def remove_punctuation(text):
function show_difference (line 41) | def show_difference(str1, str2):
function get_sentence_timestamps (line 59) | def get_sentence_timestamps(df_words, df_sentences):
function align_timestamp (line 103) | def align_timestamp(df_text, df_translate, subtitle_output_configs: list...
function clean_translation (line 144) | def clean_translation(x):
function align_timestamp_main (line 150) | def align_timestamp_main():
FILE: core/_7_sub_into_vid.py
function check_gpu_available (line 36) | def check_gpu_available():
function merge_subtitles_to_video (line 43) | def merge_subtitles_to_video():
FILE: core/_8_1_audio_task.py
function check_len_then_trim (line 18) | def check_len_then_trim(text, duration):
function time_diff_seconds (line 46) | def time_diff_seconds(t1, t2, base_date):
function process_srt (line 52) | def process_srt():
function gen_audio_task_main (line 136) | def gen_audio_task_main():
FILE: core/_8_2_dub_chunks.py
function calc_if_too_fast (line 15) | def calc_if_too_fast(est_dur, tol_dur, duration, tolerance):
function merge_rows (line 26) | def merge_rows(df, start_idx, merge_count):
function analyze_subtitle_timing_and_speed (line 58) | def analyze_subtitle_timing_and_speed(df):
function process_cutoffs (line 101) | def process_cutoffs(df):
function gen_dub_chunks (line 132) | def gen_dub_chunks():
FILE: core/_9_refer_audio.py
function time_to_samples (line 13) | def time_to_samples(time_str, sr):
function extract_audio (line 20) | def extract_audio(audio_data, sr, start_time, end_time, out_file):
function extract_refer_audio_main (line 26) | def extract_refer_audio_main():
FILE: core/asr_backend/audio_preprocess.py
function _ffmpeg_has_encoder (line 12) | def _ffmpeg_has_encoder(encoder_name: str) -> bool:
function normalize_audio_volume (line 22) | def normalize_audio_volume(audio_path, output_path, target_db = -20.0, f...
function convert_video_to_audio (line 30) | def convert_video_to_audio(video_file: str):
function get_audio_duration (line 55) | def get_audio_duration(audio_file: str) -> float:
function split_audio (line 71) | def split_audio(audio_file: str, target_len: float = 30*60, win: float =...
function process_transcription (line 109) | def process_transcription(result: Dict) -> pd.DataFrame:
function save_results (line 160) | def save_results(df: pd.DataFrame):
function save_language (line 180) | def save_language(language: str):
FILE: core/asr_backend/demucs_vl.py
class PreloadedSeparator (line 14) | class PreloadedSeparator(Separator):
method __init__ (line 15) | def __init__(self, model: BagOfModels, shifts: int = 1, overlap: float...
function demucs_audio (line 22) | def demucs_audio():
FILE: core/asr_backend/elevenlabs_asr.py
function elev2whisper (line 33) | def elev2whisper(elev_json, word_level_timestamp = False):
function transcribe_audio_elevenlabs (line 67) | def transcribe_audio_elevenlabs(raw_audio_path, vocal_audio_path, start ...
FILE: core/asr_backend/whisperX_302.py
function transcribe_audio_302 (line 13) | def transcribe_audio_302(raw_audio_path: str, vocal_audio_path: str, sta...
FILE: core/asr_backend/whisperX_local.py
function _patched_torch_load (line 22) | def _patched_torch_load(*args, **kwargs):
function check_hf_mirror (line 38) | def check_hf_mirror():
function transcribe_audio (line 62) | def transcribe_audio(raw_audio_file, vocal_audio_file, start, end):
FILE: core/prompts.py
function get_split_prompt (line 6) | def get_split_prompt(sentence, num_parts = 2, word_limit = 20):
function get_summary_prompt (line 53) | def get_summary_prompt(source_content, custom_terms_json=None):
function generate_shared_prompt (line 128) | def generate_shared_prompt(previous_content_prompt, after_content_prompt...
function get_prompt_faithfulness (line 144) | def get_prompt_faithfulness(lines, shared_prompt):
function get_prompt_expressiveness (line 190) | def get_prompt_expressiveness(faithfulness_result, lines, shared_prompt):
function get_align_prompt (line 252) | def get_align_prompt(src_sub, tr_sub, src_part):
function get_subtitle_trim_prompt (line 302) | def get_subtitle_trim_prompt(text, duration):
function get_correct_text_prompt (line 343) | def get_correct_text_prompt(text):
FILE: core/spacy_utils/load_nlp_model.py
function get_spacy_model (line 7) | def get_spacy_model(language: str):
function init_nlp (line 14) | def init_nlp():
FILE: core/spacy_utils/split_by_comma.py
function is_valid_phrase (line 9) | def is_valid_phrase(phrase):
function analyze_comma (line 15) | def analyze_comma(start, doc, token):
function split_by_comma (line 30) | def split_by_comma(text, nlp):
function split_by_comma_main (line 47) | def split_by_comma_main(nlp):
FILE: core/spacy_utils/split_by_connector.py
function analyze_connectors (line 8) | def analyze_connectors(doc, token):
function split_by_connectors (line 84) | def split_by_connectors(text, context_words=5, nlp=None):
function split_sentences_main (line 127) | def split_sentences_main(nlp):
FILE: core/spacy_utils/split_by_mark.py
function split_by_mark (line 10) | def split_by_mark(nlp):
FILE: core/spacy_utils/split_long_by_root.py
function split_long_sentence (line 10) | def split_long_sentence(doc):
function split_extremely_long_sentence (line 43) | def split_extremely_long_sentence(doc):
function split_long_by_root_main (line 64) | def split_long_by_root_main(nlp):
FILE: core/st_utils/download_video_section.py
function download_video_section (line 14) | def download_video_section():
function convert_audio_to_video (line 67) | def convert_audio_to_video(audio_file: str) -> str:
FILE: core/st_utils/imports_and_utils.py
function download_subtitle_zip_button (line 8) | def download_subtitle_zip_button(text: str):
FILE: core/st_utils/sidebar_setting.py
function config_input (line 6) | def config_input(label, key, help=None):
function page_setting (line 13) | def page_setting():
function check_api (line 155) | def check_api():
FILE: core/translate_lines.py
function valid_translate_result (line 9) | def valid_translate_result(result: dict, required_keys: list, required_s...
function translate_lines (line 21) | def translate_lines(lines, previous_content_prompt, after_cotent_prompt,...
FILE: core/tts_backend/_302_f5tts.py
function upload_file_to_302 (line 13) | def upload_file_to_302(file_path):
function _f5_tts (line 29) | def _f5_tts(text: str, refer_url: str, save_path: str) -> bool:
function _merge_audio (line 53) | def _merge_audio(files, output: str) -> bool:
function _get_ref_audio (line 78) | def _get_ref_audio(task_df, min_duration=8, max_duration=14.5) -> str:
function f5_tts_for_videolingo (line 120) | def f5_tts_for_videolingo(text: str, save_as: str, number: int, task_df):
FILE: core/tts_backend/azure_tts.py
function azure_tts (line 4) | def azure_tts(text: str, save_path: str) -> None:
FILE: core/tts_backend/custom_tts.py
function custom_tts (line 3) | def custom_tts(text, save_path):
FILE: core/tts_backend/edge_tts.py
function edge_tts (line 15) | def edge_tts(text, save_path):
FILE: core/tts_backend/estimate_duration.py
class AdvancedSyllableEstimator (line 7) | class AdvancedSyllableEstimator:
method __init__ (line 8) | def __init__(self):
method estimate_duration (line 20) | def estimate_duration(self, text: str, lang: Optional[str] = None) -> ...
method count_syllables (line 24) | def count_syllables(self, text: str, lang: Optional[str] = None) -> int:
method _count_english_syllables (line 49) | def _count_english_syllables(self, text: str) -> int:
method _detect_language (line 59) | def _detect_language(self, text: str) -> str:
method process_mixed_text (line 64) | def process_mixed_text(self, text: str) -> dict:
function init_estimator (line 106) | def init_estimator():
function estimate_duration (line 109) | def estimate_duration(text: str, estimator: AdvancedSyllableEstimator):
FILE: core/tts_backend/fish_tts.py
function fish_tts (line 6) | def fish_tts(text: str, save_as: str) -> bool:
FILE: core/tts_backend/gpt_sovits_tts.py
function check_lang (line 9) | def check_lang(text_lang, prompt_lang):
function gpt_sovits_tts (line 27) | def gpt_sovits_tts(text, text_lang, save_path, ref_audio_path, prompt_la...
function gpt_sovits_tts_for_videolingo (line 56) | def gpt_sovits_tts_for_videolingo(text, save_as, number, task_df):
function find_and_check_config_path (line 109) | def find_and_check_config_path(dubbing_character):
function start_gpt_sovits_server (line 125) | def start_gpt_sovits_server():
FILE: core/tts_backend/openai_tts.py
function openai_tts (line 11) | def openai_tts(text, save_path):
FILE: core/tts_backend/sf_cosyvoice2.py
function wav_to_base64 (line 6) | def wav_to_base64(wav_file_path):
function cosyvoice_tts_for_videolingo (line 13) | def cosyvoice_tts_for_videolingo(text, save_as, number, task_df):
FILE: core/tts_backend/sf_fishtts.py
function siliconflow_fish_tts (line 23) | def siliconflow_fish_tts(text, save_path, mode="preset", voice_id=None, ...
function create_custom_voice (line 66) | def create_custom_voice(audio_path, text, custom_name=None):
function merge_audio (line 101) | def merge_audio(files, output):
function get_ref_audio (line 122) | def get_ref_audio(task_df):
function siliconflow_fish_tts_for_videolingo (line 180) | def siliconflow_fish_tts_for_videolingo(text, save_as, number, task_df):
FILE: core/tts_backend/tts_main.py
function clean_text_for_tts (line 18) | def clean_text_for_tts(text):
function tts_main (line 25) | def tts_main(text, save_as, number, task_df):
FILE: core/utils/ask_gpt.py
function _save_cache (line 17) | def _save_cache(model, prompt, resp_content, resp_type, resp, message=No...
function _load_cache (line 29) | def _load_cache(prompt, resp_type, log_title):
function ask_gpt (line 44) | def ask_gpt(prompt, resp_type=None, valid_def=None, log_title="default"):
FILE: core/utils/config_utils.py
function load_key (line 14) | def load_key(key):
function update_key (line 28) | def update_key(key, new_value):
function get_joiner (line 50) | def get_joiner(language):
FILE: core/utils/decorator.py
function except_handler (line 10) | def except_handler(error_msg, retry=0, delay=1, default_return=None):
function check_file_exists (line 34) | def check_file_exists(file_path):
function test_function (line 47) | def test_function():
FILE: core/utils/delete_retry_dubbing.py
function delete_dubbing_files (line 4) | def delete_dubbing_files():
FILE: core/utils/onekeycleanup.py
function cleanup (line 6) | def cleanup(history_dir="history"):
function move_file (line 42) | def move_file(src, dst):
function sanitize_filename (line 72) | def sanitize_filename(filename):
FILE: core/utils/pypi_autochoose.py
function get_optimal_thread_count (line 22) | def get_optimal_thread_count():
function test_mirror_speed (line 29) | def test_mirror_speed(name, url):
function set_pip_mirror (line 42) | def set_pip_mirror(url):
function get_current_pip_mirror (line 52) | def get_current_pip_mirror():
function main (line 60) | def main():
FILE: docs/components/landing/comments.tsx
type Comment (line 3) | type Comment = {
type Props (line 9) | type Props = {
function Comments (line 14) | function Comments({ items, title }: Props) {
FILE: docs/components/landing/faq.tsx
type FAQItem (line 3) | interface FAQItem {
type FAQProps (line 8) | interface FAQProps {
function FAQ (line 13) | function FAQ({ items, title }: FAQProps) {
FILE: docs/components/landing/features.tsx
type Feature (line 5) | type Feature = {
type FeaturesProps (line 12) | type FeaturesProps = {
function Features (line 18) | function Features({ items, title }: FeaturesProps) {
FILE: docs/components/landing/github-stats.tsx
function GitHubStats (line 5) | function GitHubStats({ stars, recentStargazers }) {
FILE: docs/components/landing/hero.tsx
type HeroProps (line 6) | interface HeroProps {
function Hero (line 12) | function Hero({ title, description, videoSrc }: HeroProps) {
FILE: docs/components/landing/index.tsx
function Landing (line 18) | function Landing({ data }) {
FILE: docs/components/ui/badge.tsx
type BadgeProps (line 26) | interface BadgeProps
function Badge (line 30) | function Badge({ className, variant, ...props }: BadgeProps) {
FILE: docs/components/ui/button.tsx
type ButtonProps (line 37) | interface ButtonProps
FILE: docs/components/ui/hero-video-dialog.tsx
type AnimationStyle (line 9) | type AnimationStyle =
type HeroVideoProps (line 19) | interface HeroVideoProps {
function HeroVideoDialog (line 70) | function HeroVideoDialog({
FILE: docs/components/ui/rainbow-button.tsx
type RainbowButtonProps (line 5) | interface RainbowButtonProps
function RainbowButton (line 8) | function RainbowButton({ children, ...props }: RainbowButtonProps) {
FILE: docs/lib/utils.ts
function cn (line 4) | function cn(...inputs: ClassValue[]) {
FILE: docs/theme.config.jsx
method useNextSeoProps (line 40) | useNextSeoProps() {
FILE: install.py
function install_package (line 15) | def install_package(*packages):
function check_nvidia_gpu (line 18) | def check_nvidia_gpu():
function check_ffmpeg (line 44) | def check_ffmpeg():
function _detect_cuda_version_from_smi (line 96) | def _detect_cuda_version_from_smi():
function _detect_cuda_index (line 111) | def _detect_cuda_index():
function main (line 143) | def main():
FILE: launch.py
function log (line 11) | def log(msg):
function check_package (line 16) | def check_package(name, import_name=None):
function main (line 24) | def main():
FILE: st.py
function text_processing_section (line 16) | def text_processing_section():
function process_text (line 45) | def process_text():
function audio_processing_section (line 65) | def audio_processing_section():
function process_audio (line 92) | def process_audio():
function main (line 108) | def main():
FILE: translations/translations.py
function load_translations (line 14) | def load_translations(language="en"):
function translate (line 19) | def translate(key):
Condensed preview — 132 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (587K chars).
[
{
"path": ".cursorrules",
"chars": 101,
"preview": "2. 使用\n# ------------\n# comment\n# ------------ \n进行大块的注释\n3. 避免使用复杂的函数内注释，以及函数变量中不要有类型定义\n4. 使用英文注释和print"
},
{
"path": ".gitignore",
"chars": 2943,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": ".streamlit/config.toml",
"chars": 29,
"preview": "[server]\nmaxUploadSize = 4096"
},
{
"path": "Dockerfile",
"chars": 2125,
"preview": "ARG CUDA_VERSION=12.4.1\nFROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04\n\n# Set environment variables\nENV DEBIAN_FRONT"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "OneKeyStart.bat",
"chars": 242,
"preview": "@echo off\nchcp 65001 >nul 2>&1\ncall conda activate videolingo 2>nul\nset PYTHONWARNINGS=ignore\npython \"%~dp0launch.py\"\nif"
},
{
"path": "README.md",
"chars": 7050,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# Connect the World, Frame by Frame"
},
{
"path": "VideoLingo_colab.ipynb",
"chars": 85535,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"RkeSbYF2HoM_\"\n },\n \"sou"
},
{
"path": "batch/OneKeyBatch.bat",
"chars": 131,
"preview": "@echo off\ncd /D \"%~dp0\"\ncd ..\n\ncall conda activate videolingo\n\n@rem 运行批处理脚本\ncall python batch\\utils\\batch_processor.py\n\n"
},
{
"path": "batch/README.md",
"chars": 1864,
"preview": "# VideoLingo Batch Mode\n\n[English](./README.md) | [简体中文](./README.zh.md)\n\nBefore utilizing the batch mode, ensure you ha"
},
{
"path": "batch/README.zh.md",
"chars": 1085,
"preview": "# VideoLingo Batch Mode\n\n[English](./README.md) | [简体中文](./README.zh.md)\n\n在使用批处理模式前，请确保你已经使用过 Streamlit 模式并正确设置了 `config"
},
{
"path": "batch/utils/batch_processor.py",
"chars": 4485,
"preview": "import os\nimport gc\nfrom batch.utils.settings_check import check_settings\nfrom batch.utils.video_processor import proces"
},
{
"path": "batch/utils/settings_check.py",
"chars": 1899,
"preview": "import os\nimport pandas as pd\nfrom rich.console import Console\nfrom rich.panel import Panel\n\n# Constants\nSETTINGS_FILE ="
},
{
"path": "batch/utils/video_processor.py",
"chars": 3732,
"preview": "import os\nfrom core.st_utils.imports_and_utils import *\nfrom core.utils.onekeycleanup import cleanup\nfrom core.utils imp"
},
{
"path": "config.yaml",
"chars": 4642,
"preview": "# * Settings marked with * are advanced settings that won't appear in the Streamlit page and can only be modified manual"
},
{
"path": "core/_10_gen_audio.py",
"chars": 11588,
"preview": "import os\nimport time\nimport shutil\nimport subprocess\nfrom typing import Tuple\n\nimport pandas as pd\nfrom pydub import Au"
},
{
"path": "core/_11_merge_audio.py",
"chars": 5674,
"preview": "import os\nimport pandas as pd\nimport subprocess\nfrom pydub import AudioSegment\nfrom rich.progress import Progress, Spinn"
},
{
"path": "core/_12_dub_to_vid.py",
"chars": 3108,
"preview": "import platform\nimport subprocess\n\nimport cv2\nimport numpy as np\nfrom rich.console import Console\n\nfrom core._1_ytdlp im"
},
{
"path": "core/_1_ytdlp.py",
"chars": 2996,
"preview": "import os,sys\nimport glob\nimport re\nimport subprocess\nfrom core.utils import *\n\ndef sanitize_filename(filename):\n # R"
},
{
"path": "core/_2_asr.py",
"chars": 1834,
"preview": "from core.utils import *\nfrom core.asr_backend.demucs_vl import demucs_audio\nfrom core.asr_backend.audio_preprocess impo"
},
{
"path": "core/_3_1_split_nlp.py",
"chars": 376,
"preview": "from core.spacy_utils import *\nfrom core.utils.models import _3_1_SPLIT_BY_NLP\nfrom core.utils import check_file_exists\n"
},
{
"path": "core/_3_2_split_meaning.py",
"chars": 5728,
"preview": "import concurrent.futures\nfrom difflib import SequenceMatcher\nimport math\nfrom core.prompts import get_split_prompt\nfrom"
},
{
"path": "core/_4_1_summarize.py",
"chars": 2873,
"preview": "import json\nfrom core.prompts import get_summary_prompt\nimport pandas as pd\nfrom core.utils import *\nfrom core.utils.mod"
},
{
"path": "core/_4_2_translate.py",
"chars": 5246,
"preview": "import pandas as pd\nimport json\nimport concurrent.futures\nfrom core.translate_lines import translate_lines\nfrom core._4_"
},
{
"path": "core/_5_split_sub.py",
"chars": 5473,
"preview": "import pandas as pd\nfrom typing import List, Tuple\nimport concurrent.futures\n\nfrom core._3_2_split_meaning import split_"
},
{
"path": "core/_6_gen_sub.py",
"chars": 6885,
"preview": "import pandas as pd\nimport os\nimport re\nfrom rich.panel import Panel\nfrom rich.console import Console\nimport autocorrect"
},
{
"path": "core/_7_sub_into_vid.py",
"chars": 3865,
"preview": "import os, subprocess, time\nfrom core._1_ytdlp import find_video_files\nimport cv2\nimport numpy as np\nimport platform\nfro"
},
{
"path": "core/_8_1_audio_task.py",
"chars": 6533,
"preview": "import datetime\nimport re\nimport pandas as pd\nfrom rich.console import Console\nfrom rich.panel import Panel\nfrom core.pr"
},
{
"path": "core/_8_2_dub_chunks.py",
"chars": 7712,
"preview": "import datetime\nimport re\nimport pandas as pd\nfrom core._8_1_audio_task import time_diff_seconds\nfrom core.asr_backend.a"
},
{
"path": "core/_9_refer_audio.py",
"chars": 2065,
"preview": "import os\nfrom rich.panel import Panel\nfrom rich.console import Console\nfrom rich.progress import Progress, SpinnerColum"
},
{
"path": "core/__init__.py",
"chars": 1012,
"preview": "# use try-except to avoid error when installing\ntry:\n from . import (\n _1_ytdlp,\n _2_asr,\n _3_1_"
},
{
"path": "core/asr_backend/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "core/asr_backend/audio_preprocess.py",
"chars": 8100,
"preview": "import os, subprocess\nimport pandas as pd\nfrom typing import Dict, List, Tuple\nfrom pydub import AudioSegment\nfrom core."
},
{
"path": "core/asr_backend/demucs_vl.py",
"chars": 2262,
"preview": "import os\nimport torch\nfrom rich.console import Console\nfrom rich import print as rprint\nfrom demucs.pretrained import g"
},
{
"path": "core/asr_backend/elevenlabs_asr.py",
"chars": 5283,
"preview": "import os\nimport json\nimport time\nimport requests\nimport tempfile\nimport librosa\nimport soundfile as sf\nfrom rich import"
},
{
"path": "core/asr_backend/whisperX_302.py",
"chars": 2440,
"preview": "import os\nimport io\nimport json\nimport time\nimport requests\nimport librosa\nimport soundfile as sf\nfrom rich import print"
},
{
"path": "core/asr_backend/whisperX_local.py",
"chars": 6823,
"preview": "import os\nimport warnings\nimport time\nimport subprocess\nimport torch\nimport functools\n\nwarnings.filterwarnings(\"ignore\")"
},
{
"path": "core/prompts.py",
"chars": 13271,
"preview": "import json\nfrom core.utils import *\n\n## ================================================================\n# @ step4_spli"
},
{
"path": "core/spacy_utils/__init__.py",
"chars": 372,
"preview": "from .split_by_comma import split_by_comma_main\nfrom .split_by_connector import split_sentences_main\nfrom .split_by_mark"
},
{
"path": "core/spacy_utils/load_nlp_model.py",
"chars": 1307,
"preview": "import spacy\nfrom spacy.cli import download\nfrom core.utils import rprint, load_key, except_handler\n\nSPACY_MODEL_MAP = l"
},
{
"path": "core/spacy_utils/split_by_comma.py",
"chars": 2719,
"preview": "import itertools\nimport os\nimport warnings\nfrom core.utils import *\nfrom core.spacy_utils.load_nlp_model import init_nlp"
},
{
"path": "core/spacy_utils/split_by_connector.py",
"chars": 6063,
"preview": "import os\nimport warnings\nfrom core.spacy_utils.load_nlp_model import init_nlp, SPLIT_BY_COMMA_FILE, SPLIT_BY_CONNECTOR_"
},
{
"path": "core/spacy_utils/split_by_mark.py",
"chars": 2458,
"preview": "import os\nimport pandas as pd\nimport warnings\nfrom core.spacy_utils.load_nlp_model import init_nlp, SPLIT_BY_MARK_FILE\nf"
},
{
"path": "core/spacy_utils/split_long_by_root.py",
"chars": 4483,
"preview": "import os\nimport string\nimport warnings\nfrom core.spacy_utils.load_nlp_model import init_nlp, SPLIT_BY_CONNECTOR_FILE\nfr"
},
{
"path": "core/st_utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "core/st_utils/download_video_section.py",
"chars": 3298,
"preview": "import os\nimport re\nimport shutil\nimport subprocess\nfrom time import sleep\n\nimport streamlit as st\nfrom core._1_ytdlp im"
},
{
"path": "core/st_utils/imports_and_utils.py",
"chars": 3356,
"preview": "import os\nimport streamlit as st\nimport io, zipfile\nfrom core.st_utils.download_video_section import download_video_sect"
},
{
"path": "core/st_utils/sidebar_setting.py",
"chars": 8120,
"preview": "import streamlit as st\nfrom translations.translations import translate as t\nfrom translations.translations import DISPLA"
},
{
"path": "core/translate_lines.py",
"chars": 5485,
"preview": "from core.prompts import generate_shared_prompt, get_prompt_faithfulness, get_prompt_expressiveness\nfrom rich.panel impo"
},
{
"path": "core/tts_backend/_302_f5tts.py",
"chars": 5347,
"preview": "import http.client\nimport json\nimport os\nimport requests\nfrom pydub import AudioSegment\nfrom core.asr_backend.audio_prep"
},
{
"path": "core/tts_backend/azure_tts.py",
"chars": 799,
"preview": "import requests\nfrom core.utils import load_key\n\ndef azure_tts(text: str, save_path: str) -> None:\n url = \"https://ap"
},
{
"path": "core/tts_backend/custom_tts.py",
"chars": 948,
"preview": "from pathlib import Path\n\ndef custom_tts(text, save_path):\n \"\"\"\n Custom TTS (Text-to-Speech) interface\n \n Ar"
},
{
"path": "core/tts_backend/edge_tts.py",
"chars": 970,
"preview": "from pathlib import Path\nimport edge_tts\nfrom core.utils import *\nimport subprocess\n\n# Available voices can be listed us"
},
{
"path": "core/tts_backend/estimate_duration.py",
"chars": 6201,
"preview": "import syllables\nfrom pypinyin import pinyin, Style\nfrom g2p_en import G2p\nfrom typing import Optional\nimport re\n\nclass "
},
{
"path": "core/tts_backend/fish_tts.py",
"chars": 1261,
"preview": "import requests\nfrom core.utils import *\nimport json\n\n@except_handler(\"Failed to generate audio using 302.ai Fish TTS\", "
},
{
"path": "core/tts_backend/gpt_sovits_tts.py",
"chars": 7853,
"preview": "from pathlib import Path\nimport requests\nimport os, sys\nimport subprocess\nimport socket\nimport time\nfrom core.utils impo"
},
{
"path": "core/tts_backend/openai_tts.py",
"chars": 1437,
"preview": "from pathlib import Path\nimport requests\nimport json\nfrom core.utils import load_key, except_handler\n\nBASE_URL = \"https:"
},
{
"path": "core/tts_backend/sf_cosyvoice2.py",
"chars": 1780,
"preview": "from openai import OpenAI\nfrom pathlib import Path\nimport base64\nfrom core.utils import *\n\ndef wav_to_base64(wav_file_pa"
},
{
"path": "core/tts_backend/sf_fishtts.py",
"chars": 9808,
"preview": "import os\nimport time\nimport uuid\nimport base64\nimport hashlib\nimport requests\nfrom pathlib import Path\nfrom pydub impor"
},
{
"path": "core/tts_backend/tts_main.py",
"chars": 3753,
"preview": "import os\nimport re\nfrom pydub import AudioSegment\n\nfrom core.asr_backend.audio_preprocess import get_audio_duration\nfro"
},
{
"path": "core/utils/__init__.py",
"chars": 387,
"preview": "# use try-except to avoid error when installing\ntry:\n from .ask_gpt import ask_gpt\n from .decorator import except_"
},
{
"path": "core/utils/ask_gpt.py",
"chars": 3342,
"preview": "import os\nimport json\nfrom threading import Lock\nimport json_repair\nfrom openai import OpenAI\nfrom core.utils.config_uti"
},
{
"path": "core/utils/config_utils.py",
"chars": 1648,
"preview": "from ruamel.yaml import YAML\nimport threading\n\nCONFIG_PATH = 'config.yaml'\nlock = threading.Lock()\n\nyaml = YAML()\nyaml.p"
},
{
"path": "core/utils/decorator.py",
"chars": 1570,
"preview": "import functools\nimport time\nimport os\nfrom rich import print as rprint\n\n# ------------------------------\n# retry decora"
},
{
"path": "core/utils/delete_retry_dubbing.py",
"chars": 948,
"preview": "import os\nimport shutil\n\ndef delete_dubbing_files():\n files_to_delete = [\n os.path.join(\"output\", \"dub.wav\"),\n"
},
{
"path": "core/utils/models.py",
"chars": 1439,
"preview": "# ------------------------------------------\n# 定义中间产出文件\n# ------------------------------------------\n\n_2_CLEANED_CHUNKS "
},
{
"path": "core/utils/onekeycleanup.py",
"chars": 2673,
"preview": "import os\nimport glob\nfrom core._1_ytdlp import find_video_files\nimport shutil\n\ndef cleanup(history_dir=\"history\"):\n "
},
{
"path": "core/utils/pypi_autochoose.py",
"chars": 3754,
"preview": "import subprocess\nimport time\nimport requests\nimport os\nimport concurrent.futures\nfrom rich.console import Console\nfrom "
},
{
"path": "docs/.gitignore",
"chars": 234,
"preview": ".DS_Store\n.next/\nnode_modules/\n*.log\ndist/\n.turbo/\nout/\n# Theme styles\npackages/nextra-theme-*/style.css\n\n# Stork relate"
},
{
"path": "docs/components/landing/comments.tsx",
"chars": 960,
"preview": "import { Card, CardContent } from '@/components/ui/card'\n\ntype Comment = {\n\tcontent: string\n\tauthor: string\n\ttitle: stri"
},
{
"path": "docs/components/landing/faq.tsx",
"chars": 932,
"preview": "import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from '@/components/ui/accordion'\n\nexport interfa"
},
{
"path": "docs/components/landing/features.tsx",
"chars": 1393,
"preview": "import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card'\nimport { CheckCircle, ArrowRight } from "
},
{
"path": "docs/components/landing/github-stats.tsx",
"chars": 2564,
"preview": "import { Tooltip, TooltipContent, TooltipProvider, TooltipTrigger } from '@/components/ui/tooltip'\nimport { Button } fro"
},
{
"path": "docs/components/landing/hero.tsx",
"chars": 1430,
"preview": "import Link from 'next/link'\nimport { Button } from '@/components/ui/button'\nimport HeroVideoDialog from \"@/components/u"
},
{
"path": "docs/components/landing/index.tsx",
"chars": 982,
"preview": "import Hero from '@/components/landing/hero'\nimport Features from '@/components/landing/features'\nimport Comments from '"
},
{
"path": "docs/components/ui/accordion.tsx",
"chars": 2023,
"preview": "// @ts-nocheck\nimport * as React from \"react\"\nimport * as AccordionPrimitive from \"@radix-ui/react-accordion\"\nimport { C"
},
{
"path": "docs/components/ui/badge.tsx",
"chars": 1140,
"preview": "import * as React from \"react\"\nimport { cva, type VariantProps } from \"class-variance-authority\"\n\nimport { cn } from \"@/"
},
{
"path": "docs/components/ui/button.tsx",
"chars": 1799,
"preview": "import * as React from \"react\"\nimport { Slot } from \"@radix-ui/react-slot\"\nimport { cva, type VariantProps } from \"class"
},
{
"path": "docs/components/ui/card.tsx",
"chars": 1847,
"preview": "import * as React from \"react\"\n\nimport { cn } from \"@/lib/utils\"\n\nconst Card = React.forwardRef<\n HTMLDivElement,\n Rea"
},
{
"path": "docs/components/ui/hero-video-dialog.tsx",
"chars": 4628,
"preview": "\"use client\";\n\nimport { useState } from \"react\";\nimport { AnimatePresence, motion } from \"framer-motion\";\nimport { Play,"
},
{
"path": "docs/components/ui/rainbow-button.tsx",
"chars": 1700,
"preview": "import React from \"react\";\n\nimport { cn } from \"@/lib/utils\";\n\ninterface RainbowButtonProps\n extends React.ButtonHTMLAt"
},
{
"path": "docs/components/ui/tooltip.tsx",
"chars": 1145,
"preview": "import * as React from \"react\"\nimport * as TooltipPrimitive from \"@radix-ui/react-tooltip\"\n\nimport { cn } from \"@/lib/ut"
},
{
"path": "docs/components.json",
"chars": 412,
"preview": "{\n \"$schema\": \"https://ui.shadcn.com/schema.json\",\n \"style\": \"default\",\n \"rsc\": false,\n \"tsx\": true,\n \"tailwind\": {"
},
{
"path": "docs/lib/utils.ts",
"chars": 166,
"preview": "import { type ClassValue, clsx } from \"clsx\"\nimport { twMerge } from \"tailwind-merge\"\n\nexport function cn(...inputs: Cla"
},
{
"path": "docs/middleware.js",
"chars": 56,
"preview": "export { locales as middleware } from 'nextra/locales'\n\n"
},
{
"path": "docs/next-env.d.ts",
"chars": 230,
"preview": "/// <reference types=\"next\" />\n/// <reference types=\"next/image-types/global\" />\n\n// NOTE: This file should not be edite"
},
{
"path": "docs/next.config.js",
"chars": 462,
"preview": "const withNextra = require('nextra')({\n theme: 'nextra-theme-docs',\n themeConfig: './theme.config.jsx',\n})\n\nconst "
},
{
"path": "docs/package.json",
"chars": 961,
"preview": "{\n \"name\": \"docs\",\n \"version\": \"1.0.0\",\n \"description\": \"\",\n \"main\": \"index.js\",\n \"scripts\": {\n \"dev\": \"next\",\n "
},
{
"path": "docs/pages/_app.mdx",
"chars": 2198,
"preview": "import './globals.css'\nimport { GoogleAnalytics } from '@next/third-parties/google'\n\nexport default function App({ Compo"
},
{
"path": "docs/pages/_meta.en-US.json",
"chars": 223,
"preview": "{\n \"index\": {\n \"type\": \"page\",\n \"title\": \"VideoLingo\",\n \"display\": \"hidden\",\n \"theme\": {\n "
},
{
"path": "docs/pages/_meta.ja.json",
"chars": 221,
"preview": "{\n \"index\": {\n \"type\": \"page\",\n \"title\": \"VideoLingo\",\n \"display\": \"hidden\",\n \"theme\": {\n "
},
{
"path": "docs/pages/_meta.zh-CN.json",
"chars": 216,
"preview": "{\n \"index\": {\n \"type\": \"page\",\n \"title\": \"VideoLingo\",\n \"display\": \"hidden\",\n \"theme\": {\n "
},
{
"path": "docs/pages/docs/_meta.en-US.json",
"chars": 212,
"preview": "{ \n \"introduction\": {\n \"title\": \"Introduction\"\n },\n \"start\": {\n \"title\": \"Start\"\n },\n \"tech\": {"
},
{
"path": "docs/pages/docs/_meta.ja.json",
"chars": 139,
"preview": "{\n \"introduction\": {\n \"title\": \"紹介\"\n },\n \"start\": {\n \"title\": \"スタート\"\n },\n \"tech\": {\n \"titl"
},
{
"path": "docs/pages/docs/_meta.zh-CN.json",
"chars": 190,
"preview": "{\n \"introduction\": {\n \"title\": \"介绍\"\n },\n \"start\": {\n \"title\": \"使用文档\"\n },\n \"tech\": {\n \"titl"
},
{
"path": "docs/pages/docs/docker.en-US.md",
"chars": 2724,
"preview": "# Docker Installation\n\nVideoLingo provides a Dockerfile that you can use to build the current VideoLingo package. Here a"
},
{
"path": "docs/pages/docs/docker.zh-CN.md",
"chars": 1516,
"preview": "# Docker安装\n\nVideoLingo 提供了Dockerfile,可自行使用Dockerfile打包目前VideoLingo。以下是打包和运行的详细说明:\n\n## 系统要求\n\n- CUDA版本 > 12.4\n- NVIDIA Dri"
},
{
"path": "docs/pages/docs/introduction.en-US.md",
"chars": 5920,
"preview": "# VideoLingo: Connecting the World, Frame by Frame\n\n## 🌟 Overview ([Try VideoLingo Now!](https://videolingo.io))\n\nVideoL"
},
{
"path": "docs/pages/docs/introduction.zh-CN.md",
"chars": 3562,
"preview": "# VideoLingo: 连接世界的每一帧\n\n**QQ 群：875297969**\n\n## 🌟 简介（[在线体验！](https://videolingo.io)）\n\nVideoLingo 是一站式视频翻译本地化配音工具，能够一键生成 N"
},
{
"path": "docs/pages/docs/start.en-US.md",
"chars": 12715,
"preview": "# 🚀 Getting Started\n\n## 📋 API Configuration\nVideoLingo requires an LLM and TTS(optional). For the best quality, use clau"
},
{
"path": "docs/pages/docs/start.zh-CN.md",
"chars": 8441,
"preview": "# 🚀 开始使用\n\n## 📋 API 配置指南\n本项目需使用大模型和 TTS。追求最佳质量请使用 claude-3-5-sonnet-20240620 与 Azure TTS。也可以选择完全本地化体验，使用 Ollama 作为大模型，Edg"
},
{
"path": "docs/pages/docs/tech.en-US.md",
"chars": 16187,
"preview": "## Videolingo Video Translation System Technical Documentation\n\nVideolingo is a highly integrated video translation syst"
},
{
"path": "docs/pages/docs/tech.zh-CN.md",
"chars": 8040,
"preview": "## Videolingo 视频翻译系统技术文档\n\nVideolingo 是一个高度集成的视频翻译系统，能够自动执行一系列复杂的操作，包括视频下载、音频提取、语音识别、文本处理、翻译、字幕生成、文本到语音合成以及音视频合成。该系统利用 AI"
},
{
"path": "docs/pages/globals.css",
"chars": 2067,
"preview": "@tailwind base;\n@tailwind components;\n@tailwind utilities;\n\n@layer base {\n\t:root {\n\t\t--background: 0 0% 100%;\n\t\t--foregr"
},
{
"path": "docs/pages/index.en-US.mdx",
"chars": 4711,
"preview": "---\ntitle: VideoLingo\n---\n\nimport Landing from '@/components/landing'\n\nexport const getStaticProps = ({ params }) => {\n "
},
{
"path": "docs/pages/index.ja.mdx",
"chars": 3562,
"preview": "---\ntitle: VideoLingo\n---\n\nimport Landing from '@/components/landing'\n\nexport const getStaticProps = ({ params }) => {\n "
},
{
"path": "docs/pages/index.zh-CN.mdx",
"chars": 3326,
"preview": "---\ntitle: VideoLingo\n---\n\nimport Landing from '@/components/landing'\n\nexport const getStaticProps = async ({ params }) "
},
{
"path": "docs/postcss.config.js",
"chars": 82,
"preview": "module.exports = {\n plugins: {\n tailwindcss: {},\n autoprefixer: {},\n },\n}\n"
},
{
"path": "docs/public/site.webmanifest",
"chars": 436,
"preview": "{\n \"name\": \"MyWebSite\",\n \"short_name\": \"MySite\",\n \"icons\": [\n {\n \"src\": \"/web-app-manifest-192x192.png\",\n "
},
{
"path": "docs/tailwind.config.js",
"chars": 2755,
"preview": "/** @type {import('tailwindcss').Config} */\nmodule.exports = {\n darkMode: [\"class\"],\n content: [\n './pages/**/*.{js"
},
{
"path": "docs/theme.config.jsx",
"chars": 1369,
"preview": "import { useRouter } from 'next/router'\nimport { useConfig } from 'nextra-theme-docs'\n\nconst title = 'VideoLingo'\n\nexpor"
},
{
"path": "docs/tsconfig.json",
"chars": 887,
"preview": "{\n \"compilerOptions\": {\n \"target\": \"es5\",\n \"lib\": [\"dom\", \"dom.iterable\", \"esnext\"],\n \"allowJs\": true,"
},
{
"path": "install.py",
"chars": 11237,
"preview": "import os, sys\nimport platform\nimport subprocess\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\n\nascii_logo"
},
{
"path": "launch.py",
"chars": 2949,
"preview": "\"\"\"VideoLingo Enhanced Launcher - Pre-flight checks + logging.\"\"\"\nimport subprocess, sys, os, shutil, socket\nfrom pathli"
},
{
"path": "requirements.txt",
"chars": 676,
"preview": "librosa==0.11.0\npytorch-lightning==2.6.1\nlightning==2.6.1\ntransformers>=4.48.0\nmoviepy==1.0.3\nnumpy>=2.0.2\nopenai>=1.55."
},
{
"path": "setup.py",
"chars": 316,
"preview": "from setuptools import setup, find_packages\n\nNAME = 'VideoLingo'\nVERSION = '3.0.0'\n\nwith open('requirements.txt', encodi"
},
{
"path": "st.py",
"chars": 5248,
"preview": "import streamlit as st\nimport os, sys\nfrom core.st_utils.imports_and_utils import *\nfrom core import *\n\n# SET PATH\ncurre"
},
{
"path": "translations/README.es.md",
"chars": 7632,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# Conectando el Mundo, Cuadro por C"
},
{
"path": "translations/README.fr.md",
"chars": 7871,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# Connecter le Monde, Image par Ima"
},
{
"path": "translations/README.ja.md",
"chars": 5180,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# フレームごとに世界をつなぐ\n\n<a href=\"https://t"
},
{
"path": "translations/README.ru.md",
"chars": 5727,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# Объединяя Мир, Кадр за Кадром\n\n<a"
},
{
"path": "translations/README.zh-TW.md",
"chars": 4634,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# 連結世界，逐格前行\n\n<a href=\"https://trend"
},
{
"path": "translations/README.zh.md",
"chars": 4689,
"preview": "<div align=\"center\">\n\n<img src=\"/docs/logo.png\" alt=\"VideoLingo Logo\" height=\"140\">\n\n# 连接世界每一帧\n\n<a href=\"https://trendsh"
},
{
"path": "translations/en.json",
"chars": 8925,
"preview": "{\n \"a. Download or Upload Video\": \"a. Download or Upload Video\",\n \"Delete and Reselect\": \"Delete and Reselect\",\n "
},
{
"path": "translations/es.json",
"chars": 9540,
"preview": "{\n \"a. Download or Upload Video\": \"a. Descargar o subir video\",\n \"Delete and Reselect\": \"Eliminar y volver a selec"
},
{
"path": "translations/fr.json",
"chars": 9699,
"preview": "{\n \"a. Download or Upload Video\": \"a. Télécharger ou importer une vidéo\",\n \"Delete and Reselect\": \"Supprimer et re"
},
{
"path": "translations/ja.json",
"chars": 7315,
"preview": "{\n \"a. Download or Upload Video\": \"a. 動画のダウンロードまたはアップロード\",\n \"Delete and Reselect\": \"削除して再選択\",\n \"Enter YouTube l"
},
{
"path": "translations/ru.json",
"chars": 9170,
"preview": "{\n \"a. Download or Upload Video\": \"a. Скачать или загрузить видео\",\n \"Delete and Reselect\": \"Удалить и выбрать зан"
},
{
"path": "translations/translations.py",
"chars": 889,
"preview": "import json\n\nDISPLAY_LANGUAGES = {\n \"🇬🇧 English\": \"en\",\n \"🇨🇳 简体中文\": \"zh-CN\",\n \"🇭🇰 繁体中文\": \"zh-HK\",\n \"🇯🇵 日本語\":"
},
{
"path": "translations/zh-CN.json",
"chars": 6846,
"preview": "{\n \"a. Download or Upload Video\": \"a. 下载或上传视频\",\n \"Delete and Reselect\": \"删除并重新选择\",\n \"Enter YouTube link:\": \"输入Y"
},
{
"path": "translations/zh-HK.json",
"chars": 6850,
"preview": "{\n \"a. Download or Upload Video\": \"a. 下載或上傳影片\",\n \"Delete and Reselect\": \"刪除並重新選擇\",\n \"Enter YouTube link:\": \"輸入Y"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the Huanshere/VideoLingo GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 132 files (541.1 KB), approximately 165.2k tokens, and a symbol index with 205 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.