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EPIC: AI Music Video Pipeline with Feedback Loop #248

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opened 2026-04-01 22:15:10 +00:00 by ezra · 1 comment
ezra commented 2026-04-01 22:15:10 +00:00
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Music Video Pipeline: Automatic Generation + Human Feedback

Vision

High-quality music videos with beautiful graphics, automatically generated, scored, and improved based on Alexander's feedback.

Core Workflow

1. GENERATE → AI creates music video (audio + visuals)
2. SCORE → Automatic quality metrics
3. DELIVER → Daily best + runners-up to Alexander
4. FEEDBACK → Alexander reviews, rates, comments
5. LEARN → System updates parameters from feedback
6. IMPROVE → Next generation uses learned parameters

Components

A. Generation Engine

  • Music generation (Suno API or local)
  • Visual generation (Stable Diffusion video, Luma, etc.)
  • Audio-reactive visuals (spectrogram sync)
  • Graphics overlay (lyrics, branding)

B. Quality Scoring (Automatic)

  • Audio quality metrics (clarity, dynamics, stereo)
  • Visual quality (sharpness, color, motion)
  • Audio-visual sync accuracy
  • Engagement prediction (from training data)
  • Technical compliance (format, resolution, bitrate)

C. Delivery System

  • Daily batch generation (overnight)
  • Rank by automatic score
  • Deliver top 3 + 5 runners-up
  • Telegram/DM delivery with preview

D. Feedback Collection

  • Rating (1-10)
  • Text feedback (what worked/didn't)
  • Tag selection (mood, genre, visual style)
  • Comparison voting (A/B tests)

E. Learning System

  • Parameter extraction from feedback
  • Preference model training
  • Generation prompt optimization
  • Score prediction calibration

Technical Stack

Layer Technology
Music Suno API / MusicGen / Riffusion
Visuals Stable Video Diffusion / Luma / Runway
Audio Analysis librosa, pydub, essentia
Video Processing ffmpeg, opencv
ML Pipeline Python, PyTorch, scikit-learn
Storage Local + Gitea artifacts
Delivery Telegram Bot API

Metrics to Track

Generation Metrics

  • Videos generated per day
  • Average generation time
  • Success rate
  • Storage used

Quality Metrics

  • Audio clarity score (0-100)
  • Visual sharpness (Laplacian variance)
  • Color vibrancy (saturation histogram)
  • Motion smoothness (optical flow)
  • Sync accuracy (beat detection vs visual)

Feedback Metrics

  • Alexander's average rating
  • Rating variance (consistency)
  • Common positive/negative tags
  • Preference drift over time

Learning Metrics

  • Prediction accuracy (score vs actual rating)
  • Parameter convergence
  • Generation improvement rate

Implementation Phases

Phase 1: Basic Pipeline (Week 1)

  • Music generation (Suno API)
  • Static visual generation (SD)
  • Basic video assembly (ffmpeg)
  • Manual delivery

Phase 2: Automation (Week 2)

  • Overnight batch generation
  • Automatic quality scoring
  • Telegram delivery
  • Simple feedback collection

Phase 3: Learning (Week 3-4)

  • Feedback parameter extraction
  • Preference model
  • Generation optimization
  • A/B testing framework

Phase 4: Polish (Week 5-6)

  • Advanced visuals (video diffusion)
  • Audio-reactive effects
  • Multiple genres/styles
  • Public showcase capability

Success Criteria

  • 3+ videos/day generated automatically
  • Alexander reviews daily
  • Average rating improves over 4 weeks
  • Automatic score correlates with human rating (r>0.7)
  • Pipeline runs without manual intervention

Owner

Alexander Whitestone — feedback provider
Ezra — architecture & implementation
Target: 6-week sprint

「The best art comes from the loop between creation and taste.」

# Music Video Pipeline: Automatic Generation + Human Feedback ## Vision High-quality music videos with beautiful graphics, automatically generated, scored, and improved based on Alexander's feedback. ## Core Workflow ``` 1. GENERATE → AI creates music video (audio + visuals) 2. SCORE → Automatic quality metrics 3. DELIVER → Daily best + runners-up to Alexander 4. FEEDBACK → Alexander reviews, rates, comments 5. LEARN → System updates parameters from feedback 6. IMPROVE → Next generation uses learned parameters ``` ## Components ### A. Generation Engine - [ ] Music generation (Suno API or local) - [ ] Visual generation (Stable Diffusion video, Luma, etc.) - [ ] Audio-reactive visuals (spectrogram sync) - [ ] Graphics overlay (lyrics, branding) ### B. Quality Scoring (Automatic) - [ ] Audio quality metrics (clarity, dynamics, stereo) - [ ] Visual quality (sharpness, color, motion) - [ ] Audio-visual sync accuracy - [ ] Engagement prediction (from training data) - [ ] Technical compliance (format, resolution, bitrate) ### C. Delivery System - [ ] Daily batch generation (overnight) - [ ] Rank by automatic score - [ ] Deliver top 3 + 5 runners-up - [ ] Telegram/DM delivery with preview ### D. Feedback Collection - [ ] Rating (1-10) - [ ] Text feedback (what worked/didn't) - [ ] Tag selection (mood, genre, visual style) - [ ] Comparison voting (A/B tests) ### E. Learning System - [ ] Parameter extraction from feedback - [ ] Preference model training - [ ] Generation prompt optimization - [ ] Score prediction calibration ## Technical Stack | Layer | Technology | |-------|------------| | Music | Suno API / MusicGen / Riffusion | | Visuals | Stable Video Diffusion / Luma / Runway | | Audio Analysis | librosa, pydub, essentia | | Video Processing | ffmpeg, opencv | | ML Pipeline | Python, PyTorch, scikit-learn | | Storage | Local + Gitea artifacts | | Delivery | Telegram Bot API | ## Metrics to Track ### Generation Metrics - Videos generated per day - Average generation time - Success rate - Storage used ### Quality Metrics - Audio clarity score (0-100) - Visual sharpness (Laplacian variance) - Color vibrancy (saturation histogram) - Motion smoothness (optical flow) - Sync accuracy (beat detection vs visual) ### Feedback Metrics - Alexander's average rating - Rating variance (consistency) - Common positive/negative tags - Preference drift over time ### Learning Metrics - Prediction accuracy (score vs actual rating) - Parameter convergence - Generation improvement rate ## Implementation Phases ### Phase 1: Basic Pipeline (Week 1) - [ ] Music generation (Suno API) - [ ] Static visual generation (SD) - [ ] Basic video assembly (ffmpeg) - [ ] Manual delivery ### Phase 2: Automation (Week 2) - [ ] Overnight batch generation - [ ] Automatic quality scoring - [ ] Telegram delivery - [ ] Simple feedback collection ### Phase 3: Learning (Week 3-4) - [ ] Feedback parameter extraction - [ ] Preference model - [ ] Generation optimization - [ ] A/B testing framework ### Phase 4: Polish (Week 5-6) - [ ] Advanced visuals (video diffusion) - [ ] Audio-reactive effects - [ ] Multiple genres/styles - [ ] Public showcase capability ## Success Criteria - [ ] 3+ videos/day generated automatically - [ ] Alexander reviews daily - [ ] Average rating improves over 4 weeks - [ ] Automatic score correlates with human rating (r>0.7) - [ ] Pipeline runs without manual intervention ## Owner Alexander Whitestone — feedback provider Ezra — architecture & implementation Target: 6-week sprint --- 「The best art comes from the loop between creation and taste.」
ezra commented 2026-04-01 22:27:00 +00:00
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🔄 REVISED: Local-Only Architecture

NO APIs. NO KEYS. LOCAL ONLY.

Stack (All Local)

  • Text/Concept: Ollama (qwen2.5:1.5b/7b) - already running
  • Music: MusicGen local (TBD) or Riffusion
  • Visuals: Stable Diffusion local (TBD) or placeholder
  • Queue: Auto-research work queue (existing)

Integration Points

  • /root/.hermes/auto-research/music_video_worker.py - new worker
  • Uses existing Ollama at localhost:11434
  • Outputs to /root/.hermes/auto-research/music_outputs/
  • Queues work via existing queue system

Current Status

✅ Ollama verified running (qwen2.5:1.5b, qwen2.5:7b, gemma3:4b)
✅ Worker scaffold ready
⏳ Need: Local SD setup OR alternative visuals
⏳ Need: Local MusicGen setup OR alternative audio

Next

  1. Generate lyrics/concepts (WORKING - Ollama)
  2. Visual integration (TODO - local SD)
  3. Music integration (TODO - local MusicGen)
  4. Queue integration (TODO - hook into work_queue.py)

**All local. No APIs. No keys.

## 🔄 REVISED: Local-Only Architecture **NO APIs. NO KEYS. LOCAL ONLY.** ### Stack (All Local) - **Text/Concept:** Ollama (qwen2.5:1.5b/7b) - already running - **Music:** MusicGen local (TBD) or Riffusion - **Visuals:** Stable Diffusion local (TBD) or placeholder - **Queue:** Auto-research work queue (existing) ### Integration Points - `/root/.hermes/auto-research/music_video_worker.py` - new worker - Uses existing Ollama at localhost:11434 - Outputs to `/root/.hermes/auto-research/music_outputs/` - Queues work via existing queue system ### Current Status ✅ Ollama verified running (qwen2.5:1.5b, qwen2.5:7b, gemma3:4b) ✅ Worker scaffold ready ⏳ Need: Local SD setup OR alternative visuals ⏳ Need: Local MusicGen setup OR alternative audio ### Next 1. Generate lyrics/concepts (WORKING - Ollama) 2. Visual integration (TODO - local SD) 3. Music integration (TODO - local MusicGen) 4. Queue integration (TODO - hook into work_queue.py) **All local. No APIs. No keys.
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Reference: Timmy_Foundation/timmy-home#248
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