How LumiClip Finds the Best Moments in Your Video and Reframes Them for Mobile

import { DeepgramClient } from '@deepgram/sdk';

interface TranscriptionChunk {
  start: number;
  end: number;
  words: Array<{ text: string; start: number; end: number }>;
  speaker: string;
}

async function generateAudioSubstrate(audioUrl: string): Promise<TranscriptionChunk[]> {
  const deepgram = new DeepgramClient(process.env.DEEPGRAM_API_KEY!);
  // Nova-3 handles parallel chunking internally for long sources
  const response = await deepgram.listen.prerecorded.transcribeFile(
    { buffer: await fetch(audioUrl).then(r => r.arrayBuffer()) },
    { model: 'nova-3', smart_format: true, diarize: true, utterances: true }
  );
  
  return response.results.utterances.map(u => ({
    start: u.start,
    end: u.end,
    words: u.words,
    speaker: u.speaker
  }));
}

interface VideoFormat {
  type: 'dialogue' | 'screenshare' | 'gaming' | 'action';
  confidence: number;
}

async function classifyVideoFormat(videoPath: string): Promise<VideoFormat> {
  const frames = await extractKeyframes(videoPath, 7);
  const visionResponse = await callVisionClassifier(frames);
  return {
    type: visionResponse.label as VideoFormat['type'],
    confidence: visionResponse.score
  };
}

interface SegmentScore {
  id: string;
  selfContained: number;
  hookStrength: number;
  emotionalSalience: number;
  composite: number;
}

function evaluateSegment(transcript: string, format: VideoFormat['type']): SegmentScore {
  const baseScores = callScoringLLM(transcript, format);
  const thresholds = getThresholdsForFormat(format);
  
  const passes = 
    baseScores.selfContained >= thresholds.selfContained &&
    baseScores.hookStrength >= thresholds.hookStrength &&
    baseScores.emotionalSalience >= thresholds.emotionalSalience;
    
  return {
    id: crypto.randomUUID(),
    ...baseScores,
    composite: passes ? (baseScores.selfContained * 0.3 + baseScores.hookStrength * 0.4 + baseScores.emotionalSalience * 0.3) : 0
  };
}

function selectNonOverlappingCandidates(candidates: SegmentScore[], maxClips: number): Se

gmentScore[] { const sorted = [...candidates].sort((a, b) => b.composite - a.composite); const selected: SegmentScore[] = [];

for (const candidate of sorted) { const overlaps = selected.some(s => Math.abs(s.start - candidate.start) < 30 || // 30s minimum separation Math.abs(s.end - candidate.end) < 30 ); if (!overlaps && selected.length < maxClips) { selected.push(candidate); } } return selected; }

### Spatial Reframing Engine

Landscape-to-portrait conversion requires dynamic crop tracking that follows the active subject while maintaining cinematic stability.

**Step 1: Keyframe Sampling & Face Detection**
Processing every frame is computationally wasteful. Faces do not teleport between adjacent frames, so sampling at 2-4 FPS preserves tracking fidelity while reducing compute load by 75-90%.

```typescript
import { InsightFace } from 'insightface-node';

interface FaceDetection {
  frameIndex: number;
  bbox: [number, number, number, number];
  embedding: Float32Array;
}

async function detectFacesOnKeyframes(videoPath: string, fps: number = 3): Promise<FaceDetection[]> {
  const frames = await extractKeyframes(videoPath, Math.floor(getDuration(videoPath) * fps));
  const detector = new InsightFace({ model: 'buffalo_l' });
  
  const detections: FaceDetection[] = [];
  for (let i = 0; i < frames.length; i++) {
    const results = await detector.detect(frames[i]);
    detections.push(...results.map(r => ({
      frameIndex: i,
      bbox: r.bbox,
      embedding: r.embedding
    })));
  }
  return detections;
}

Step 2: Identity Clustering & Diarization Fusion Raw face detections lack temporal continuity. Clustering embeddings creates persistent identities. Fusing these identities with audio diarization data identifies the active speaker, which becomes the primary crop target.

function clusterIdentities(detections: FaceDetection[], diarization: TranscriptionChunk[]): Map<string, FaceDetection[]> {
  const identityMap = new Map<string, FaceDetection[]>();
  const threshold = 0.45; // cosine similarity threshold
  
  for (const det of detections) {
    let matchedId = '';
    for (const [id, cluster] of identityMap) {
      const avgEmbed = cluster.reduce((acc, d) => acc.map((v, i) => v + d.embedding[i]), new Float32Array(512));
      const similarity = cosineSimilarity(det.embedding, avgEmbed);
      if (similarity > threshold) {
        matchedId = id;
        break;
      }
    }
    if (!matchedId) matchedId = crypto.randomUUID();
    identityMap.get(matchedId)?.push(det) || identityMap.set(matchedId, [det]);
  }
  
  // Fuse with diarization to mark active speaker
  const activeSpeakerId = diarization.reduce((best, curr) => 
    curr.words.length > best.words.length ? curr : best
  ).speaker;
  
  return identityMap;
}

Step 3: Trajectory Smoothing Frame-by-frame crop targets produce jittery output. Applying an exponential moving average (EMA) or Kalman-inspired filter creates camera-operator-like motion.

function smoothCropTrajectory(rawTargets: number[][], alpha: number = 0.15): number[][] {
  const smoothed: number[][] = [];
  let prev = rawTargets[0];
  
  for (const target of rawTargets) {
    const current = target.map((val, i) => alpha * val + (1 - alpha) * prev[i]);
    smoothed.push(current);
    prev = current;
  }
  return smoothed;
}

Step 4: Gap Handling & Fallback Logic Faces disappear during cuts, B-roll, or downward glances. The system holds the last known crop position for short gaps, interpolates on reappearance, and falls back to content-aware center cropping for extended absences.

function handleDetectionGaps(smoothedTrajectory: number[][], maxGapFrames: number = 15): number[][] {
  return smoothedTrajectory.map((frame, i) => {
    if (frame.every(v => v === 0)) {
      const prevValid = smoothedTrajectory.slice(0, i).filter(f => f.some(v => v !== 0)).pop();
      if (prevValid && (i - smoothedTrajectory.lastIndexOf(prevValid)) <= maxGapFrames) {
        return prevValid;
      }
      return [0.5, 0.5, 0.5625, 1.0]; // 9:16 center fallback
    }
    return frame;
  });
}

Pitfall Guide

1. The Monolithic Prompt Fallacy

Explanation: Feeding an entire hour-long transcript and video frames to a single LLM assumes the model can maintain spatial awareness, pacing context, and temporal boundaries simultaneously. Current architectures lack the context window and reasoning depth to handle this without severe degradation. Fix: Implement a deterministic filtering stage. Use lightweight classifiers and rule-based segmenters to reduce the input to 15-20 high-probability candidates before invoking expensive scoring models.

2. Ignoring Format-Specific Heuristics

Explanation: Applying dialogue-optimized scoring to gaming footage or tutorials produces irrelevant clips. Gaming highlights rely on audio spikes and rapid visual changes, while tutorials depend on instructional clarity and screen stability. Fix: Run a format classification step early. Route candidates through format-specific scoring models or adjust threshold weights based on the detected category.

3. Jittery Crop Interpolation

Explanation: Applying raw bounding box coordinates directly to video frames creates micro-jitter that viewers perceive as unstable or unprofessional. Human vision is highly sensitive to high-frequency camera shake. Fix: Apply trajectory smoothing using exponential moving averages or a simplified Kalman filter. Tune the alpha parameter based on content type (lower alpha for slow-paced dialogue, higher for fast-paced action).

4. Diarization-Transcript Drift

Explanation: Audio diarization and visual face detection operate on different timelines. Misalignment causes the crop to follow a silent speaker or miss the active participant entirely. Fix: Fuse diarization confidence scores with face embedding clusters. Apply a temporal tolerance window (±2 seconds) when matching audio speakers to visual identities. Discard matches outside the window.

5. Overlapping Clip Generation

Explanation: Without explicit deduplication, scoring models often return multiple variations of the same moment. This wastes storage, inflates processing costs, and degrades user experience. Fix: Enforce a non-overlap constraint during final selection. Implement a minimum temporal separation threshold (e.g., 30 seconds) and cap the total output per project to prevent redundancy.

6. Static Fallback Traps

Explanation: Freezing the crop on the last known face position during long gaps creates awkward framing when the subject returns or the scene changes. Fix: Implement a gap-duration threshold. Hold position for short absences, interpolate smoothly on reappearance, and switch to content-aware center cropping for extended gaps. Use scene-change detection to trigger fallback resets.

7. Hook Generation Without Temporal Grounding

Explanation: Generating hooks before final clip selection often produces misleading or generic titles that don't match the actual visual content. Fix: Defer hook generation until after deduplication and final selection. Pass only the finalized segment's transcript to the hook model. Enforce length constraints (3-7 words) and prioritize declarative, attention-optimized phrasing.

Production Bundle

Action Checklist

• Implement parallel audio chunking for sources exceeding 30 minutes to prevent timeout errors
• Add format classification as a mandatory early-stage gate to route heuristics correctly
• Configure non-overlap constraints with a minimum 30-second separation threshold
• Apply trajectory smoothing with format-tuned alpha values before rendering crops
• Fuse diarization data with face embeddings using a ±2 second temporal tolerance window
• Defer hook generation until after final clip selection to ensure contextual accuracy
• Monitor compute costs per pipeline stage and adjust filtering thresholds to maintain ROI

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Single-speaker podcast	Diarization + static crop with EMA smoothing	Minimal movement, high audio dependency	Low
Multi-person interview	Active speaker tracking + dynamic crop fusion	Prevents center-crop failure between subjects	Medium
Gaming stream	Audio spike detection + action classifier	Visual pacing differs from dialogue	Medium
Tutorial/screenshare	Format classifier + center crop with UI detection	Faces are secondary to screen content	Low
Multi-camera production	Scene-change detection + per-camera reframing	Cuts break continuous tracking assumptions	High

Configuration Template

pipeline:
  audio:
    model: nova-3
    chunk_duration_sec: 600
    diarize: true
    utterances: true
    
  classification:
    keyframe_count: 7
    buckets: [dialogue, screenshare, gaming, action]
    confidence_threshold: 0.75
    
  segmentation:
    self_contained_min: 0.6
    hook_strength_min: 0.7
    emotional_salience_min: 0.5
    
  scoring:
    max_candidates: 20
    non_overlap_min_sec: 30
    max_clips_per_project: 10
    
  reframing:
    face_model: buffalo_l
    keyframe_fps: 3
    smoothing_alpha:
      dialogue: 0.12
      action: 0.25
    gap_hold_max_frames: 15
    fallback_strategy: content_aware_center

Quick Start Guide

1. Initialize the audio substrate: Run Deepgram Nova-3 on your source file with diarization enabled. Configure parallel chunking for files longer than 30 minutes to maintain throughput.
2. Classify and segment: Extract seven evenly spaced frames, pass them to a vision classifier, and route the transcript through a topic segmenter. Apply format-specific thresholds to filter low-quality candidates.
3. Score and deduplicate: Run the remaining candidates through a quality scoring model. Enforce non-overlap constraints and cap output to prevent redundancy. Generate hooks only for finalized segments.
4. Reframe spatially: Sample keyframes at 2-4 FPS, detect faces with InsightFace buffalo_l, cluster identities, and fuse with diarization data. Apply trajectory smoothing and gap handling before rendering the final vertical crop.
5. Validate and iterate: Compare output against a manually curated baseline. Adjust smoothing alpha values, scoring thresholds, and gap tolerance parameters based on content type. Monitor compute costs per stage to optimize pipeline efficiency.