av-sync-workflow

name

av-sync-workflow

description

Audio-to-video synchronization workflow: analyze audio (beats, tempo, emotion, mood), find/match video clips to match scene and feeling, sync cuts to music beats, generate beat-marked videos. Use when user wants to: (1) turn a song into a music video, (2) sync video clips to music beats, (3) create a video that matches audio mood/scene/rhythm, (4) do beat-matching video editing. Triggers: "制作音乐视频", "音频转视频", "beat matching", "卡点视频", "音视频同步", "视频踩点", "music video creation", "sync video to audio"

AV-Sync Workflow

Transform audio into a professionally edited video synchronized to beats, mood, and scene.

Workflow Overview

Audio → Analysis → Clip Matching → Beat Sync → Video Assembly → Export

Step 1: Analyze Audio

Use scripts/audio_analysis.py to extract:

• Beats/BPM: Timestamp of each beat, overall tempo (BPM)
• Sections: Verse, chorus, bridge, outro markers
• Emotion/Mood: Energy level, valence (happy/sad), tempo category
• Key moments: High-impact points (drops, climaxes, transitions)

python3 scripts/audio_analysis.py /path/to/song.mp3 --output /tmp/analysis.json

Output structure:

{
  "bpm": 120,
  "duration": 214,
  "beats": [0.0, 0.5, 1.0, ...],
  "sections": [
    {"type": "intro", "start": 0, "end": 15},
    {"type": "verse", "start": 15, "end": 45},
    {"type": "chorus", "start": 45, "end": 75}
  ],
  "mood": {"energy": 0.7, "valence": 0.6, "danceability": 0.8},
  "key_moments": [
    {"time": 45.0, "type": "chorus_drop", "intensity": 1.0}
  ]
}

Step 2: Gather Video Clips

User provides video clips OR search for stock footage:

Stock footage sources:

• Pexels: https://www.pexels.com/search/videos/{query}/
• Pixabay: https://pixabay.com/videos/search/{query}/
• Coverr: https://coverr.co/search/{query}

Download stock video:

# Via yt-dlp (for pexels/pixabay)
yt-dlp -f "best[height<=1080]" -o "/tmp/clip_%(id)s.%(ext)s" "https://pexels.com/video/12345"

# Via direct URL
ffmpeg -i "https://example.com/video.mp4" -c copy /tmp/clip.mp4

Step 3: Analyze Each Clip

For each clip, extract:

• Scene type (indoor/outdoor, city/nature, close-up/wide)
• Mood/style (energetic/calm, happy/sad)
• Duration and cut points
• Visual elements (faces, motion, colors)

python3 scripts/video_analysis.py /tmp/clip.mp4 --output /tmp/clip_analysis.json

Step 4: Match Clips to Audio Sections

Algorithm: Map clips to audio sections based on:

1. Emotion matching: High-energy chorus → energetic clips
2. Scene continuity: Smooth transitions between scenes
3. Beat alignment: Cut on beats for rhythm
4. Length fit: Clip duration matches section duration

python3 scripts/match_clips.py \
  --audio-analysis /tmp/analysis.json \
  --clips /tmp/clip1.mp4,/tmp/clip2.mp4 \
  --clip-analyses /tmp/clip1_analysis.json,/tmp/clip2_analysis.json \
  --output /tmp/edit_plan.json

Step 5: Generate Beat-Synced Video

python3 scripts/assemble_video.py \
  --edit-plan /tmp/edit_plan.json \
  --audio /path/to/song.mp3 \
  --output /tmp/final_video.mp4 \
  --format mp4 \
  --codec h264 \
  --quality high

Reference Scripts

scripts/audio_analysis.py

Analyzes audio file using librosa. Extracts:

• Beat timestamps (per-beat and bar-level)
• BPM
• Onset strength envelope
• Spectral features for mood
• librosa-beat-grid output option

scripts/video_analysis.py

Analyzes video clip:

• Dominant colors / color mood
• Scene type classification (urban, nature, indoor, etc.)
• Motion level (static, moderate, high)
• Detected faces / people
• Suggested cut points (scene changes)

scripts/match_clips.py

Intelligent clip-to-audio matching:

• Emotion/mood alignment scoring
• Scene variety ensuring no repetitive cuts
• Beat-synced cut point optimization
• Output: detailed edit decision list (EDL)

scripts/assemble_video.py

Final video assembly:

• Apply cut points from edit plan
• Add smooth transitions (dissolve, fade)
• Add slow-motion on climactic beats
• Mix audio track
• Export at specified quality

Beat-Sync Cut Points

For every beat in the audio, consider:

• Strong beat (bar 1): Major cut or transition
• Weak beat (bar 2-4): Minor cut or no cut
• Off-beat: Effect triggers (zoom, flash)

Standard cut cadence:

• 4-beat bars: Cut every 4 or 8 beats
• Chorus: Cut every 2 beats for high energy
• Outro: Gradual slowdown, fade

Quick Start (Minimal)

If user provides just audio + one video:

# 1. Detect beats
python3 scripts/audio_analysis.py song.mp3 -o beats.json

# 2. Simple beat-sync assembly
python3 scripts/simple_sync.py --audio song.mp3 --clip video.mp4 --beats beats.json -o output.mp4

Quality Settings

Quality	Resolution	Bitrate	Use Case
draft	720p	2Mbps	Quick preview
standard	1080p	5Mbps	Social media
high	1080p	10Mbps	YouTube
premium	4K	20Mbps	Final output

Key Notes

• FFmpeg required: Most scripts depend on ffmpeg being installed
• Audio duration vs video clips: If clips shorter than audio, loop or find more clips
• BPM > 140: Consider half-time editing for drop-songs
• Transitions: Default is cut-only (beat-sync), add dissolves for chorus sections
• Mood input: If user specifies mood (e.g., "sad, rainy, nostalgic"), prioritize that over automatic analysis

Troubleshooting

• No beats detected: Audio may be recorded poorly; try --spectral mode
• Clip too short: Auto-loop small clips up to 3x original length
• Aspect ratio mismatch: Automatically crop/pad to 16:9 or 9:16 for reels