qwen-mm-plugins-omni-chatcut-video-translation

Translates video speech and produces speaker-preserving dubbed videos with timed translation plans.

2.9k|185|Updated Jul 29, 2026
One-click install
npx skills add https://github.com/QwenLM/Qwen-MM-Plugins --skill qwen-mm-plugins-omni-chatcut-video-translation-qwenlm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qwen-mm-plugins-omni-chatcut-video-translation
Source: https://github.com/QwenLM/Qwen-MM-Plugins/tree/main/src/capabilities/omni-chatcut/skill/video-translation
Command: npx skills add https://github.com/QwenLM/Qwen-MM-Plugins --skill qwen-mm-plugins-omni-chatcut-video-translation-qwenlm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Translating a video's spoken content into another language while preserving each speaker's voice requires coordinating transcription, translation timing, voice cloning, audio mixing, and quality review across many fragile steps. This Skill manages the entire dubbing pipeline as one resumable project, so a failed render or interrupted session can be inspected and resumed instead of restarted. ## Core Features & Use Cases - State-aware project routing: Inspect an existing project directory and automatically resume analysis, translation authoring, rendering, or delivery validation from the correct stage. - Evidence-reconciled transcription: Combine Omni multimodal analysis with mandatory VAD speech detection to author accurate speaker-labeled transcripts with reconciled timestamps. - Speaker-preserving dubbing: Generate reference-guided IndexTTS2 voices per segment, fit speech to time slots with bounded acceleration, mix with separated background audio, and remux while preserving the source video stream. - Use Case: Given a 20-minute Chinese product demo video, produce an English dubbed version where each original speaker's voice identity is preserved, speech fits the original timing, and a QA report flags segments needing manual listening review. ## Quick Start Translate the speech in my video file into English and produce a dubbed version that preserves each speaker's voice.

Frequently Asked Questions about qwen-mm-plugins-omni-chatcut-video-translation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I translate a video's speech into another language with dubbing?

Provide the source video and target language; the Skill analyzes speech and subtitles with Omni, authors a timed translation plan, synthesizes reference-guided voices via IndexTTS2, replaces the vocal track, and validates the final delivery. The project is resumable at every stage.

How do I set up the external dubbing service for video translation?

Deploy the bundled launch_dubbing_server.py reference server with IndexTTS2 checkpoints, Demucs, and TEN-VAD installed on a GPU host. Then set the QWEN_MM_DUBBING_SERVER_URL environment variable to the server's URL before running the Skill.

Can I resume an interrupted video translation project?

Yes. Run the inspect_video_translation_state.py script on the project directory to see its current state, then continue; the Skill routes to the correct stage whether that is transcript analysis, plan authoring, rendering, or delivery validation.

What happens if translated speech is too long for its time slot?

The renderer allows bounded acceleration up to 1.18x and may generate up to three TTS candidates per segment. If speech still does not fit, the translation must be shortened; the renderer never clips speech or overlaps adjacent segments.

Does video translation work for videos longer than 10 minutes?

Yes. Videos over 600 seconds use a bounded-windows analysis mode with ordered Omni windows, reconciling speaker labels across windows. Videos of 600 seconds or less are analyzed in a single full-video request.

How is dubbing quality verified before delivery?

The agent reviews the rendered video with omni_call, inspects flagged segments in the render report, and writes an agent_review.json with boolean checks for translation, timing, voice reference, delivery, and mix. Final validation also requires a full decode and matching hashes.