tachidubb

name

tachidubb

description

Use when the user asks to dub a video, translate speech in a video, voice-clone across languages, build a multilingual reel, or compare dubbing quality. Wraps the TachiDUBB Studio local AI dubbing pipeline (whisperx → Ollama → VoxCPM2 → ffmpeg) via either an MCP server (preferred when the `tachidubb_*` tools are available) or a CLI fallback. Handles single-language dubs, side-by-side comparisons across N languages, stitched multilingual showcase reels, and re-dubs of previously processed sources.

TachiDUBB — agentic dubbing

A local, offline AI video dubbing studio. The user runs the server on their own machine — no upload to cloud, no per-minute fees. This skill makes the pipeline drivable through natural language.

For humans reading this file: copy this folder into ~/.claude/skills/ (so the path becomes ~/.claude/skills/tachidubb/SKILL.md) and Claude will know how to use the TachiDUBB MCP or CLI when you ask. After that, ask things like "dub this YouTube short into French and German" or "build a 5-language showcase reel of this clip" — Claude picks the right tool and waits for it.

Repo: https://github.com/TachikomaRed/tachidubb By @smolekoma and @smolemaru, built with Claude.

When this skill applies

Trigger on requests like:

• "Dub this video into French"
• "Translate the speech in https://… to Spanish and German"
• "Make a showcase reel of this short in 5 languages"
• "Re-dub job 5038e404 into Japanese"
• "What's the status of my dub?"
• "List my completed dubs"
• "Compare voice quality across 3 languages on this clip"

Two access paths — pick what's available

Path A — MCP (preferred). If tools named tachidubb_* are exposed by an MCP server, use them directly. They return structured JSON.

Path B — CLI fallback. If MCP isn't connected, shell out:

python tools/tachidubb_cli.py <subcommand> ...

Same capabilities, JSON output. Works from anywhere a shell can run.

Prerequisites — check first when a call fails

The user must have the TachiDUBB server running (default http://localhost:8910). If a tool call fails with a connection error, ask the user to start it:

python server.py        # from your tachidubb checkout (Windows: also start.bat)
./start.sh              # on Linux/macOS

Use tachidubb_system_status (MCP) or tachidubb system (CLI) to surface missing dependencies before submitting work. Common failures:

• ollama_binary.ok=False → "User needs Ollama installed and running"
• voxcpm.ok=False → falls back to edge_tts (lower quality, no cloning)
• gpu.ok=False → still works on CPU but slow

Core tools / commands

Task	MCP tool	CLI
Dub in one language	tachidubb_dub	tachidubb dub <src> --lang fr
N separate dubs (compare)	tachidubb_compare	tachidubb compare <src> --langs es,fr,de
Stitched multilingual reel	tachidubb_showcase	tachidubb showcase <src> --langs es,fr,de
Re-dub existing job	tachidubb_redub	tachidubb redub <jid> --langs ja,it --mode showcase
Job status	tachidubb_get_job	tachidubb status <jid>
List jobs	tachidubb_list_jobs	tachidubb jobs
Showcase status	tachidubb_get_showcase	tachidubb showcase-status <bid>
Rebuild showcase stitch	tachidubb_rebuild_showcase	tachidubb showcase-rebuild <bid>
System health	tachidubb_system_status	tachidubb system
Supported languages	tachidubb_list_languages	tachidubb languages
Installed translation models	tachidubb_list_models	tachidubb models

Source argument — URL or local path

Every submission takes a source:

• URL (starts with http:// or https://) — yt-dlp downloads it. Best for YouTube/Shorts.
• Local file path — uploaded to the server. Use absolute paths.

Language codes

en ru es pt fr de it pl tr ja ko zh ar hi nl — and auto for source language.

Models

Translation is via local Ollama. Default aya-expanse:8b (multilingual, ~5GB). If the user's machine doesn't have it, the server auto-falls back to whichever installed model is best (gemma3:12b, qwen3:8b, etc.). Check available models with tachidubb_list_models before suggesting a specific one in a prompt.

Blocking vs fire-and-forget

By default, submission tools queue work and return immediately with a job_id / batch_id. Pass wait=True (MCP) or --wait (CLI) when the user expects you to come back with a finished video URL in the same turn. Realistic timings on a single GPU:

• One 60s dub: ~2-4 min (most time in transcribe + TTS)
• Compare 5 langs × 60s: ~10-20 min (serial GPU queue)
• Showcase 5 langs × 60s: same as compare + ~10s stitch

For long jobs, prefer submit-then-poll-then-summarize over a 10-minute blocking wait inside a single tool call.

Showcase vs Compare — when to suggest which

• Compare (quick_test on the server): 5 separate mp4 files, one per language, full length. Use when the user wants to A/B/C/D listen to quality across languages.
• Showcase: one single mp4 that switches language every ~12s with a small · LL · badge in the top-right corner. Use when the user wants a deliverable to demo/share.

Re-dub from history

When the user references an existing dub ("the French one I made yesterday"), find it with tachidubb_list_jobs(status='complete'), then tachidubb_redub(job_id, target_langs=[...], mode=...). This skips re-upload and re-download.

Modes:

• single: one new language → one new job
• compare: 2-6 new languages → N separate jobs (batch_kind=quick_test)
• showcase: 2-6 new languages → N jobs + stitch into one reel

Failure recovery

• A job with status="error" and has_checkpoint=True can resume from the last completed pipeline stage — call POST /api/dub/<id>/continue (no MCP tool yet; suggest via CLI: curl to that endpoint or just hit Resume in the UI).
• Showcase batch where all child jobs completed but the stitch failed: use tachidubb_rebuild_showcase(batch_id) — much faster than rerunning the N dubs.

Example multi-step flow

User: "Take https://youtu.be/abc, give me a 60-second multilingual reel in 5 most popular languages and tell me when it's done."

1. tachidubb_system_status → confirm GPU + ollama_binary + voxcpm
2. tachidubb_list_models → pick a multilingual model from what's installed
3. tachidubb_showcase(source="https://youtu.be/abc", target_langs=["es","fr","de","ja","pt"], trim_seconds=60, wait=True)
4. Report URL: {result.url} to user
5. If wait_timeout exceeded → return batch_id and tell user to use tachidubb_get_showcase(batch_id) later.