Dubbing has a standard pipeline and everyone in the industry knows it: transcribe the dialogue, translate it, clone each speaker from one clip, then speed up whatever runs long so it fits the timing. Every step is a compromise and the compromises compound. A sped-up line sounds rushed, a voice cloned from a single clip loses the performance, and a line that had to be shortened loses words.
Dub Anything is built as an argument against that pipeline. It runs on your own PC, uses each actor's own voice, keeps the original music and effects, and puts an AI coding agent in the role of dubbing director.
What it rejects, specifically
The README lists the four steps of the fixed pipeline and then lists what it does instead. That structure is worth taking seriously, because it names the failure mode before claiming a fix.
An agent directs, line by line. It works out who says each line from voice matching, video frames and the dialogue itself, fixes what speech recognition misheard, writes a translation timed to the actor, and triages every QA flag. It also tells you which lines are guesses instead of hiding them.
Each line is acted like the original. The cloned voice is prompted with the actor's own delivery of that very line, so a shout stays a shout and a murmur stays a murmur. Cloning from a single reference clip is exactly what this avoids.
Each line takes the actor's time. Every take is re-timed to end where the actor stops, so turns land where they did in the original without dead air. Line length is predicted before rendering, so the translation is written to fill the time rather than squeezed into it afterwards.
Every line is checked. Each rendered line is transcribed back. A garbled one gets fresh takes, up to three rounds, and the best take is kept.
That last point is the difference between a demo and a deliverable. A dubbing tool that does not listen to its own output will ship a line that says the wrong thing in the right voice, and nobody notices until the review.
It reads the picture
Two details are easy to miss. Burned-in captions become the transcript, and they often name the speaker, which solves part of the attribution problem for free. Text on screen, from title cards to signs, can be translated as subtitles placed beside it.
Between lines you hear the original soundtrack. Songs, laughter and names stay the actor's own, which is the part that makes a dub feel like the same film rather than a replacement.
By default each actor keeps their native accent, using a faster engine with more accurate cloning. You can ask for a different accent, such as American English for an English dub, at the cost of an extra render pass.
What you need before you start
Hardware is the easy part. It runs on macOS with Apple Silicon or Intel, Windows with an NVIDIA GPU of 6 GB or more, Windows with an AMD Radeon of RDNA 3 or newer or a Ryzen AI chip, or Linux with an NVIDIA GPU of 6 GB or more. AMD on Linux should work but is untested.
The hard requirement is an AI coding agent, and there is no way around it. The supported routes are Claude Pro or Max with Claude Code or the Claude desktop app, ChatGPT Plus or Pro with OpenAI Codex, or OpenCode with an API key or a local model that can read images. This is not a web app you click through; the agent installs the voice engine, runs the renders and fixes what went wrong.
Disk is around 40 GB plus working room. A long video in both voices used about 85 GB before it was cleaned up. The first run downloads VoiceStudio as the voice engine along with the models, and may pause for you to sign in or accept a licence.
The timings, without rounding
These numbers are the most useful thing in the README because they are specific and unflattering where it matters.
A 90-minute video with the actors' native accents took 4.1 hours of machine time on a laptop RTX 4090, nearly all of it on the GPU: about half preparing the parts, two fifths rendering and checking the voices. Adding an optional American English track brings the total to about seven hours.
A 16 GB NVIDIA card runs everything at once. Apple Silicon uses Metal through MPS. AMD runs through AMD's ROCm build of PyTorch and is markedly slower: on a laptop's integrated Radeon 890M, 15 minutes of video took about three hours, and the American English track about 2.7 hours more, a third of which was checking takes with Whisper on the CPU. A 90-minute video would run overnight or longer. Integrated graphics share system memory, so give them 32 GB of RAM or more.
Subtitles alone take roughly half the machine time of a dub, since no voice is rendered or checked and the voice engine's own transcription is skipped. A 30-minute video took 41 minutes.
What you get back
A video file named with the dubbed suffix: the dub plays by default, the original audio is one track away, and subtitles in both languages are included. Ask for subtitles only and you get a subtitled file plus an SRT beside it.
Alongside it, a short list of moments to check with timestamps. If something is wrong, you tell the agent what and where, and it re-renders the affected parts rather than the whole file. That is the practical benefit of a line-by-line pipeline: the unit of repair is one line.
Where it strains
It needs a coding agent and a terminal, which excludes anyone who wants a browser tab. First runs involve large downloads. Long videos take hours and integrated GPUs take a night. The defaults were chosen by listening to alternatives side by side, not against an objective benchmark, and the README says so, which is honest and also means quality is a judgement call you should verify on your own footage before committing a feature-length project.
It is MIT licensed, written in Python, and the demos are a Japanese animation and a Japanese live-action clip dubbed into English, which is clearly the workload it was built for.
