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<a id="readme-top"></a>
<!-- PROJECT SHIELDS -->
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[![LinkedIn][linkedin-shield]][linkedin-url]
<!-- PROJECT LOGO -->
<br />
<div align="center">
<img src="demo_and_images/logo.png" alt="Logo" width="400" height="80">
</div>
<div>
<h3 align="center">Brainrotinator</h3>
### Podcast Clip Automation Using AI
- Edits long form content into clips with subtitles using FFmpeg (libass for burn-in)
- Web UI built with Gradio for editing — CLI still works for headless / cron use
- Transcribes audio using Vosk or Whisper Models (your choice)
- Mutes audio where profanity is detected using FFmpeg's `volume` filter driven by SRT timestamps
- Uses TinyLlamma LLM to generate titles based on transcription for YouTube and Instagram.
- Automatically uploads to YouTube, Instagram, and Tiktok based on schedule given in config file using Selenium Firefox.
- Downloads videos from youtube using given URL using Pytube
- Thank you Timofei for the inspiration and name of the project.
</div>
<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li><a href="#about-the-project">About The Project</a></li>
<li><a href="#architecture">Architecture</a></li>
<li><a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#install-without-docker">Install without Docker</a></li>
<li><a href="#install-with-docker">Install with Docker</a></li>
</ul>
</li>
<li><a href="#running-the-program">Running the Program</a>
<ul>
<li><a href="#gradio-ui">Gradio UI</a></li>
<li><a href="#cli">CLI</a></li>
<li><a href="#config">Config</a></li>
</ul>
</li>
<li><a href="#things-to-note">Things to note</a></li>
<li><a href="#vosk-or-whisper">Vosk or Whisper</a></li>
<li><a href="#license">License</a></li>
<li><a href="#contact">Contact</a></li>
<li><a href="#acknowledgments">Acknowledgments</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
## About The Project
### Video Created and Uploaded Using Brainrotinator
https://github.com/user-attachments/assets/36b5d927-ecde-4099-b9f5-687d2b0108e5
[Watch the demo on youtube](https://www.youtube.com/shorts/p__GGpKI9-w)
[Original Video](https://www.youtube.com/watch?v=Ue_jnmeBO_I)
I initially made this as a joke.
I have been editing youtube videos myself for around 10 years now. I wanted to see if I could automate the horrible podcast clips I see on youtube shorts using python.
It was really fun working with AI models to make some cool features for this project
## New Gradio Layout:
<img width="3085" height="1710" alt="image" src="https://github.com/user-attachments/assets/bdd28f0d-db84-4953-8147-0bc8afaaf1e9" />
<!-- ARCHITECTURE -->
## Architecture
The editor is **FFmpeg-only** as of the v2 rewrite. moviepy, ImageMagick, and cleanvid have all been removed. Setup is dramatically simpler — `pip install -r requirements.txt` and a working `ffmpeg` binary is enough to edit videos.
### Application Flow
```mermaid
flowchart TD
subgraph Input
A1[Upload MP4]
A2[YouTube URL\nyt-dlp download]
A3[Existing file\nin to_split/]
end
subgraph UI["Entry Points"]
B1[app.py\nGradio Web UI]
B2[main.py\nCLI]
end
subgraph Editor["brainrotinator/ — VideoEditor"]
C1[Split into chunks\nvideo_editor.py]
C2{Blur mode?}
C3[Blur letterbox\nffmpeg_ops.py]
C4[Center crop 9:16\nffmpeg_ops.py]
C5[Transcribe audio\ntranscribe.py]
C6{Whisper\nor Vosk?}
C7[Whisper model]
C8[Vosk model]
C9[Generate SRT\nsubtitles.py]
C10[Detect profanity\nprofanity.py / swears.txt]
C11[Burn subtitles\nlibass / ffmpeg_ops.py]
C12[Mute profanity\nFFmpeg volume filter]
C13[Generate title\nTinyLlama LLM]
end
subgraph Output["done_split/"]
D1[Final MP4 clips\nwith subtitles]
end
subgraph Uploaders
E1[YouTube\nyoutube_uploader_selenium]
E2[Instagram\nInstagram_Uploader]
E3[TikTok\nupload_tiktok.py]
end
A1 & A2 & A3 --> B1
A1 & A2 & A3 --> B2
B1 & B2 --> C1
C1 --> C2
C2 -->|Yes| C3
C2 -->|No| C4
C3 & C4 --> C5
C5 --> C6
C6 -->|Whisper| C7
C6 -->|Vosk| C8
C7 & C8 --> C9
C9 --> C10
C10 --> C11
C10 --> C12
C11 & C12 --> C13
C13 --> D1
D1 --> E1 & E2 & E3
```
### Repo Layout
```text
brainrotinator/ # Editor package — pure FFmpeg
ffmpeg_ops.py # Cut, crop, blur, burn-in, mute wrappers
subtitles.py # SRT → styled ASS, profanity → mute-range list
transcribe.py # Vosk / Whisper / TinyLlama (lazy, resumable)
video_editor.py # Per-chunk orchestration
profanity.py # Text-profanity censor
uploaders/ # Selenium-based uploaders
login.py, uploader_selenium.py, upload_tiktok.py
Instagram_Uploader/, youtube_uploader_selenium/
downloader/ # yt-dlp downloader module
downloadVid.py, combineAudioVideo.py
assets/ # Static files
fonts/, swears.txt, title.txt
to_split/, done_split/, subtitles/ # Runtime media directories
models/ # Persistent AI models storage (Vosk/TinyLllama/Whisper)
app.py # Gradio Web UI entrypoint
main.py # CLI entrypoint
config.py / config.json # Pydantic configuration model
Dockerfile / docker-compose.yml # Containerization setup
```
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- GETTING STARTED -->
## Getting Started
Editing requires only Python, FFmpeg (with libass), and ~8 GB of disk for models on first run. The selenium uploaders additionally need Firefox + geckodriver and per-platform cookies.
### Install with Docker
The best way to run Brainrotinator with Docker is using **Docker Compose**. This automatically handles mounting the `to_split`, `done_split`, and `models` directories so your files and AI models are saved locally on your machine, working seamlessly across Windows, Mac, and Linux without complex path variables.
1. Ensure your `config.json` points the AI models to the synced `models` folder:
```json
"voskModelDir": "models",
"tinyLlamaDir": "models"
```
2. Build and start the container in the background. (Use `--build` the first time you run this, or after pulling in new code updates):
```sh
docker compose up -d --build
```
*Note: On subsequent runs, you can just use `docker compose up -d` to start the container instantly without docker checking for build updates.*
Then open http://localhost:7860.
To view logs or access the container shell:
* **Logs:** `docker compose logs -f`
* **Shell:** `docker compose exec brainrotinator bash`
If you'll use the uploader, run `python login.py` **on your host** first (it needs a GUI) so cookies are present in the mounted volume before the container starts.
#### Save output locally without Gradio
You can also run the CLI editor directly via Docker Compose (bypassing the web UI). Finished clips land in `done_split/` on your host machine, so no browser download is needed:
```sh
docker compose run --rm brainrotinator python main.py -e
```
Drop your source mp4s into `to_split/` before running.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
### Install without Docker
**Prerequisites**
* Python 3.10+
* FFmpeg with libass (`ffmpeg -filters | grep " ass "` should list it; most distro packages and the official Windows builds include it)
* ~10 GB VRAM if you'll use Whisper; CPU is fine for Vosk
* ~4 GB for the TinyLlama model, ~4 GB for the Vosk model (downloaded automatically on first use)
* Firefox + geckodriver — only if you'll use the uploader
**Steps**
1. Clone the repo and `cd` in.
2. Install Python deps:
```sh
pip install -r requirements.txt
```
3. Make sure `ffmpeg` is on your `PATH`. No `IMAGEMAGICK_BINARY` / `FFMPEG_BINARY` env vars are needed anymore.
4. *(Uploader only)* install geckodriver v0.32.0 and put it on your `PATH`, then run `python login.py` once on a machine with a GUI to capture cookies.
5. Launch:
* Gradio UI: `python app.py` → http://localhost:7860
* Or CLI: `python main.py` (see [CLI](#cli) below)
## Running the Program
### Gradio UI
```sh
python app.py
```
Tabs:
* **Edit** — upload an mp4 or paste a YouTube URL, set chunk length / blur / Vosk-vs-Whisper / profanity filter, watch logs stream as the splitter runs.
* **Library** — list everything in `done_split/`. **Click a filename to download it** to your computer. Delete clips you don't want.
* **Settings** — edit `config.json` in-browser, validated against the pydantic schema before save.
### CLI
```sh
python main.py # default loop: edit one video → upload from done_split → repeat
python main.py -e # edit only (consume to_split/, write to done_split/)
python main.py -u # upload only (consume done_split/ on the schedule in config.json)
```
When the editor runs out of videos in `to_split/`, the CLI prompts for a YouTube URL and downloads it via `yt-dlp`.
### Config
`config.json` is now validated by `config.Config` (`config.py`). Defaults are filled in for any missing keys.
```json
{
"tags": ["chuckle Sandwich", "jschlatt", "ted nivison", "slimecicle", "gaming", "comedy"],
"description": "#shorts",
"howManyUploads": 1,
"howManyHoursBetweenSchedule": 0,
"howManyMinsBetweenUpload": 5,
"howManyHoursLongToSleep": 23,
"sleepXMinsBeforeStartingUploader": 0,
"chunkDuration": 58,
"blurTopBottomOfClip": true,
"useWhisperForTranscription": false,
"filterProfanityInSubtitles": false,
"uploadToYoutube": true,
"uploadToInstagram": true,
"uploadToTiktok": false,
"firefoxHeadless": true,
"voskModelDir": "",
"tinyLlamaDir": ""
}
```
| Key | Meaning |
|---|---|
| `tags` | YouTube tags; also used as #hashtags appended to the IG/TikTok caption |
| `description` | YouTube description; prepended before tags for IG/TikTok |
| `howManyUploads` | Uploads per cycle before sleeping `howManyHoursLongToSleep` |
| `howManyHoursBetweenSchedule` | Hours between each scheduled upload (YouTube/TikTok only — IG ignores) |
| `howManyMinsBetweenUpload` | Base delay between uploads, plus a 0–5 min jitter |
| `chunkDuration` | Clip length in seconds |
| `blurTopBottomOfClip` | `true` = blurred letterbox; `false` = center-crop to 9:16 |
| `useWhisperForTranscription` | `true` = Whisper, `false` = Vosk |
| `filterProfanityInSubtitles` | Censor swears in burned-in subtitles (audio is muted regardless) |
| `firefoxHeadless` | Must be `true` inside Docker (no display) |
| `voskModelDir`, `tinyLlamaDir` | Where to cache models. Empty = current working directory |
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## Things to note
* **Editor vs uploader**: the editor is FFmpeg-only and should keep working indefinitely. The selenium uploaders depend on YouTube/IG/TikTok DOM layout and **will break** when those sites change. I am not maintaining them.
* **`swears.txt`** is the source of truth for what gets muted. Add or remove words to taste. Matching is word-bounded so `ass` won't match `class`.
* **TikTok uploads** require cookies from https://github.com/wkaisertexas/tiktok-uploader — and you will hit captchas. A paid solver like sadcaptcha can fix it; this repo doesn't include one.
* **Headless selenium**: cookies must already exist or the upload will crash. Run `login.py` on a machine with a GUI first.
* **Models** download lazily on first transcription. Vosk shows a `tqdm` progress bar; TinyLlama uses `huggingface_hub.snapshot_download` (resumable).
* **libass fonts**: the burn-in filter is invoked with `fontsdir=fonts/`, so any `.ttf` you drop in `fonts/` is available. Default is `Bangers.ttf`.
## Vosk or whisper
### Whisper
**Pros:**
- Really accurate
- Better profanity filter due to accuracy
**Cons:**
- Subtitles linger/timing is bad
- Late/early profanity mute due to timing
### Vosk
**Pros:**
- Timing is really good
- Timing of muting profanity very good
**Cons:**
- Not very accurate, so words might not get filtered
- A lot of the words are not accurate
### Notes
Maybe adapt whisper to use https://github.com/m-bain/whisperX for better timing
* I used vosk for the example video in readme
* Vosk vs whisper comparison in the demo_and_images folder
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- LICENSE -->
## License
Do not Sell this program. Do not use it for your own cloud service you are selling like [this](https://www.opus.pro/).
Other than that do what you like with it.
<!-- ACKNOWLEDGMENTS -->
## Acknowledgments
Thank you to the following projects for making this possible.
* [Vosk](https://alphacephei.com/vosk/)
* [Whisper](https://github.com/openai/whisper)
* [TinyLlama](https://huggingface.co/TinyLlama)
* [Text Profanity Filter](https://github.com/ben174/profanity)
* [CleanVid (mute profanity in audio)](https://github.com/mmguero/cleanvid)
* [FFmpeg](https://ffmpeg.org/) + [libass](https://github.com/libass/libass)
* [pysubs2](https://github.com/tkarabela/pysubs2) (SRT → ASS conversion)
* [Gradio](https://www.gradio.app/)
* [Youtube selenium uploader (also what I used to make the instagram uploader)](https://github.com/linouk23/youtube_uploader_selenium)
* [yt-dlp for downloading youtube videos](https://github.com/ytdl-org/youtube-dl)
* [TiktokUploader](https://github.com/wkaisertexas/tiktok-uploader)
* [readme Template](https://github.com/othneildrew/Best-README-Template/blob/main/README.md)
### TODO
- Add support for different models (current ones are outdated)
- Change preview of subtitles, maybe run subtitles through llm to get emojis or custom colors per line
- Color param for text
- CV to focus crop around face/person talking
<p align="right">(<a href="#readme-top">back to top</a>)</p>
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