Mazinger Dubber

End-to-end video dubbing pipeline. Download a video, transcribe it, translate the subtitles, clone a voice, and produce a fully dubbed audio or video — in one command.

▶️ Watch Demo Video (with audio)

🚀 Get Started in 2 Steps

Prerequisites: Python 3.10+ and ffmpeg on your PATH (apt install ffmpeg / brew install ffmpeg).

1. Install

⚠️ Use uv — plain pip will fail. mazinger[all] bundles packages with conflicting dependency pins that only uv can resolve. Install uv once: pip install uv

uv pip install "mazinger[all]"

2. Launch the Web UI

mazinger web --with-ollama --with-faster-whisper

A local URL opens in your browser. Paste a video link, pick a voice, and click Start. The flags install a free local LLM (Ollama) and download the speech-recognition model on first run — no API keys required.

The flags only move the wait to launch time: if you run plain mazinger web, Studio installs Ollama and pulls the model by itself the first time you start a mission with the Ollama (Local — Free) provider. Set OLLAMA_HOST to use an Ollama server running elsewhere.

💡 No GPU? Run it on a free Colab T4 in two clicks:

Prefer the command line or Python? Skip ahead to Common Tasks or the Python API.

✨ What You Get

mazinger[all] is a single, GPU-friendly install that includes everything needed for the full pipeline and the Studio web UI:

CohereX needs a one-time Hugging Face sign-in — the Cohere models are gated. In the Studio, open 🤗 Hugging Face → Sign in with Hugging Face; from the CLI, set HF_TOKEN. Not signed in, or working in a language CohereX does not cover? Switch the transcription method to Faster Whisper, which needs neither.

Need Chatterbox, MLX (Apple Silicon), or a lighter install? See the Installation Guide — mazinger[all] now bundles CohereX, which brings the pyannote stack with it (~50 extra packages).

🛠️ Common Tasks

Dub a video — auto-clone the original speaker

No voice files needed. Mazinger picks the best 20–60 s of the source as the cloning reference.

mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --target-language Spanish

Dub with a ready-made voice theme

16 built-in themes — no files, no profile downloads.

mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --voice-theme narrator-m \
    --target-language Spanish

Themes: narrator-m/f · young-m/f · deep-m/f · warm-m/f · news-m/f · storyteller-m/f · kid-m/f · teen-m/f

Dub with a HuggingFace voice profile

mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --clone-profile abubakr \
    --target-language Arabic

Available out-of-the-box: abubakr · daheeh-v1 · 3b1b · italian-v1 · morgan-freeman · trump-v1 — full list in Voice Profiles.

Dub with your own voice sample

mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --voice-sample speaker.m4a \
    --voice-script speaker_transcript.txt \
    --target-language Spanish

Output a video with burned-in subtitles

mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --voice-theme narrator-m \
    --target-language Arabic \
    --output-type video \
    --embed-subtitles \
    --subtitle-google-font "Noto Sans Arabic"

See Subtitle Styling for fonts, colors, positioning, and RTL options.

Transcribe with CohereX (Cohere Transcribe)

14 languages with word-level alignment, plus a dedicated Arabic model that is selected automatically for Arabic sources. Included in mazinger[all], and the default in Mazinger Studio — nothing to install separately.

The Cohere models are gated on HuggingFace, so it needs a one-time sign-in. In the Studio, open 🤗 Hugging Face → Sign in with Hugging Face and follow the link it shows — no token to copy. From the CLI, set HF_TOKEN instead.

The CLI still defaults to Faster Whisper; pass --transcribe-method coherex to use CohereX there:

export HF_TOKEN=hf_your_token

mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --transcribe-method coherex \
    --source-language Arabic \
    --voice-theme narrator-m \
    --target-language English

⚠️ Cohere Transcribe cannot detect the source language on its own. Given one, it transcribes confidently in whatever language you name; given none, it probes each supported language first, which is slower and less reliable. Always pass --source-language (or pick one in the Studio).

Use Deepgram instead of a local GPU

export DEEPGRAM_API_KEY=your_key_here
mazinger dub "https://youtube.com/watch?v=VIDEO_ID" \
    --transcribe-method deepgram \
    --voice-theme narrator-m \
    --target-language English

Run a single stage

mazinger download   "https://youtube.com/watch?v=VIDEO_ID"
mazinger transcribe ./output/projects/my-video/source/audio.mp3 -o subs.srt
mazinger translate  --srt subs.srt --target-language French -o translated.srt
mazinger subtitle   video.mp4 --srt translated.srt -o output.mp4

Every stage caches its output. Re-running resumes where it stopped. Full command list in the CLI Reference.

🐍 Python API

from mazinger import MazingerDubber

dubber = MazingerDubber(openai_api_key="sk-...", base_dir="./output")

proj = dubber.dub(
    source="https://youtube.com/watch?v=VIDEO_ID",
    voice_theme="narrator-m",
    target_language="Spanish",
    output_type="video",
)

print(proj.final_video)   # ./output/projects/<slug>/tts/dubbed.mp4

Full reference: Python API.

🔧 How It Works

Mazinger chains ten resumable stages: Download → Transcribe → Thumbnails → Describe → Review → Translate → Re-segment → Speak → Assemble → Subtitle. Every stage runs standalone or as part of the full pipeline; completed stages and individual TTS segments are cached and skipped on re-runs.

See the Pipeline Overview for a diagram and the data flow between stages.

📚 Documentation

License

MIT