huangserva

huangserva / musical-mv-storyboard

Public
19
2
100% credibility
Found May 17, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project is a complete music-first workflow for creating AI-assisted music videos. It takes a song, analyzes its musical structure (lyrics, beats, energy, climax points), and helps a filmmaker plan and produce a music video from the song outward. The system guides users through shot planning, validates lip-sync timing, scores different edit options, and helps assemble the final video—always keeping the music as the master timeline that visuals must serve.

How It Works

1
🎵 You start with a song

You have a song you want to turn into a music video, and you feed it into the system.

2
🔬 The system listens to every beat

It analyzes your song to find the lyrics, beats, energy levels, and the most exciting climax moments.

3
🎬 A storyboard builds itself

Based on the music analysis, the system suggests what kind of shots go where—a close-up during the chorus, a dance scene during the beat drop.

4
✍️ You review and direct the plan

You look over the suggested shots, add your creative vision, and lock in the exact moments for lip-sync and key scenes.

5
Two paths to your video
🤖
AI video generation

The system creates video clips from your prompts, ready for editing.

📋
Export for external tools

You take your shot plan and prompts to use with your preferred video software.

6
🎞️ Everything assembles into your edit

All your clips come together in a timeline, synced perfectly to your song's rhythm.

🎉 Your music video is ready

You have a complete, polished music video where every shot serves the music.

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AI-Generated Review

What is musical-mv-storyboard?

This is a production workflow for creating AI-assisted music videos and MV-style short films. The project treats audio as the master timeline—every visual decision, edit, and shot classification flows from analyzing the song. It provides Python scripts that handle the full pipeline: extracting beats and energy curves via Whisper and librosa, detecting musical climax windows with ffmpeg, auto-classifying shots based on vocal density and section type, generating Seedance video prompts, and validating that the final edit locks correctly to the music. Lip-sync clips get special treatment with phrase-boundary gates, and the system includes preview generation so directors can review the assembled timeline as an HTML board.

Why is it gaining traction?

The hook is the hard production gates. Instead of treating video generation as a free-for-all, this project enforces sequencing: director score before prompts, visual duration planning before generation, lip-sync gate before output, rhythm audit after any edit change. This means fewer wasted generations and fewer "close enough" clips that break the music. The workflow also natively supports ElevenLabs for song generation and Seedance for I2V video, which covers the most common Chinese MV production stack without requiring duct-tape scripting.

Who should use this?

This is for indie MV producers and AI video agents running end-to-end music video projects. If you are generating songs with ElevenLabs and stitching together AI video clips with lip-sync and B-roll, this gives you the scaffolding to plan shots around audio peaks instead of guessing. Small studios with limited testing infrastructure will benefit most from the validation gates that catch timing drift before delivery.

Verdict

At 19 stars and a 1.0% credibility score, this project is a specialized tool rather than a community standard. The documentation is thorough and the gating system is genuinely well-thought-out, but it lacks broader adoption, test coverage, and external validation. Use it if you are already working in this production stack; wait for more community traction if you need a battle-tested, widely-supported solution.

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