music-matcher

AI-assisted music matching analyzes videos to extract mood-based segments, then uses the MCP protocol to select the best music for each segment before composing a final video with synchronized audio.

AI video scoring works with Gemini and FFmpeg by using Gemini to analyze and extract mood-based segments from the video, MCP to match music to those segments, and FFmpeg to synthesize the final composed video with synchronized audio.

Yes, you can batch process multiple videos for automated music matching. The workflow supports batch processing and provides incremental outputs, allowing you to resume from partial runs for deterministic and reproducible results.

Yes, you need google-genai and python-dotenv dependencies to run this AI-assisted video production workflow, as they facilitate the Gemini-based video analysis phase and environment configuration required for mood-based music matching.

The best way to synchronize audio with video segments using MCP is through a phase-based workflow that extracts mood-based video segments, matches music via MCP, and uses FFmpeg to compose a final video with perfectly synchronized audio.

Batch video music matching should resume after interruption because the workflow supports resuming from partial runs with deterministic output, ensuring reproducible results across large video processing batches without losing progress.