What is musical-mv-storyboard?

huangserva/musical-mv-storyboard — explained in plain English

Analysis updated 2026-05-18

★ 19PythonAudience · vibe coderComplexity · 3/5Setup · hard

Mindmap

mindmap
  root((Musical MV Storyboard))
    What it does
      Song driven shot planning
      Lip sync video generation
      Automated video assembly
    Tech stack
      Python
      Whisper
      ffmpeg
      Seedance
    Use cases
      AI music videos
      Lip sync sequences
      Narrative editing
    Audience
      Vibe coders
      AI video creators
    Workflow
      Beat analysis
      Voice direction gate
      Edit decision list

mindmap root((Musical MV Storyboard)) What it does Song driven shot planning Lip sync video generation Automated video assembly Tech stack Python Whisper ffmpeg Seedance Use cases AI music videos Lip sync sequences Narrative editing Audience Vibe coders AI video creators Workflow Beat analysis Voice direction gate Edit decision list

What do people build with it?

USE CASE 1

Plan an AI-generated music video's shots around a song's beat and structure.

USE CASE 2

Generate lip-sync video clips aligned to complete vocal phrases.

USE CASE 3

Assemble AI-generated clips into a final edit with an edit decision list.

USE CASE 4

Direct AI voice generation through a plan, audition, and approval workflow.

What is it built with?

PythonWhisperffmpegSeedance

How does it compare?

	huangserva/musical-mv-storyboard	16nic/comfyui-agnes-ai	6c696e68/gpt_signup_hybrid
Stars	19	19	19
Language	Python	Python	Python
Setup difficulty	hard	moderate	hard
Complexity	3/5	2/5	4/5
Audience	vibe coder	vibe coder	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1day+

Requires Whisper transcription, an external Seedance video generation service, and ffmpeg installed.

So what is it?

Musical MV Storyboard is a Python toolkit and workflow framework for producing AI-assisted music videos, using a song as the master timeline that drives all visual decisions. The central idea is that everything, shot choices, lip-sync moments, voice direction, and editing cuts, must be planned around the music's structure rather than assembled arbitrarily. The workflow starts by analyzing the song: a script transcribes lyrics with Whisper, identifies beat timing, detects energy peaks and climax windows, then builds a shot plan that maps musical phrases to visual types (close-ups, B-roll, group dance shots, atmospheric frames). For lip-sync sequences, the toolkit requires you to define phrase boundaries from complete vocal phrases before generating any video clips, preventing the common mistake of cutting reference audio at arbitrary time windows. Voice direction gets a dedicated step: you write a plan, audition a short sample, approve a template, then generate the full track. Scripts validate that all these gates are passed before video generation or final delivery. Video clips are generated via Seedance (an AI video generation service referenced in the README), then assembled using ffmpeg into a final edit with an EDL (edit decision list) that records all cut points and lip-sync offsets. A preview HTML page is auto-maintained as a director review board. You would use this if you are building an AI-generated music video, particularly one with live-action style performance footage, lip-sync, or narrative structure, and you want a disciplined, gate-driven process rather than ad-hoc clip generation. The toolkit is written in Python and designed to be driven by an AI coding agent.

Copy-paste prompts

Prompt 1

Walk me through generating a shot plan from a song using Musical MV Storyboard.

Prompt 2

Explain how the toolkit defines phrase boundaries for lip-sync video clips.

Prompt 3

Show me how the EDL is built and used to assemble the final video with ffmpeg.

Prompt 4

How does the voice direction gate work before generating a full audio track?

Frequently asked questions