sceneflow 0.2.4

Smart video cut point detection for AI-generated talking head videos using multi-factor visual analysis and speech detection

SceneFlow

Smart cut point detection for AI-generated talking head videos using advanced speech detection and multi-factor visual analysis.

Overview

SceneFlow automatically identifies the optimal point to cut AI-generated talking head videos by analyzing speech patterns and visual features. It eliminates awkward mid-speech cuts and unwanted motion at the end of videos.

Key Features

• Speech Detection - Silero VAD for precise speech boundary identification
• Energy-Based Refinement - Frame-accurate speech end detection using audio energy analysis
• Visual Analysis - Multi-factor ranking based on eye openness, motion stability, expression neutrality, pose stability, and visual sharpness
• Multi-Face Support - Analyzes all faces with center-weighted scoring
• LLM Integration - Optional GPT-4o vision for selecting the best frame
• URL Support - Direct video URL processing via HTTP GET
• Airtable Integration - Upload results and videos for tracking
• InsightFace-Powered - 106-landmark facial detection for precise analysis

Installation

pip install sceneflow

Requirements

• Python 3.9 or higher
• FFmpeg (for video processing)
• OpenAI API key (optional, for LLM-powered frame selection)

Quick Start

Command Line Interface

Basic Usage

# Get cut timestamp
sceneflow video.mp4

# Verbose output with detailed analysis
sceneflow video.mp4 --verbose

# Get top 5 best cut points
sceneflow video.mp4 --top-n 5

# Process video from URL
sceneflow "https://example.com/video.mp4" --verbose

Advanced Options

# Save outputs (frames, video, logs)
sceneflow video.mp4 --save-frames --save-video --save-logs

# Custom output path for video
sceneflow video.mp4 --output /path/to/output.mp4

# Use LLM for frame selection
sceneflow video.mp4 --use-llm-selection

# Upload results to Airtable
sceneflow video.mp4 --airtable

# Disable energy refinement
sceneflow video.mp4 --no-energy-refinement

# Adjust energy refinement parameters
sceneflow video.mp4 --energy-threshold-db 10.0 --energy-lookback-frames 25

# Disable visual analysis (faster, speech detection only)
sceneflow video.mp4 --disable-visual-analysis

Python API

Simple API

from sceneflow import get_cut_frame, get_ranked_cut_frames

# Get the best cut point
best_time = get_cut_frame("video.mp4")
print(f"Cut at: {best_time:.2f}s")

# Get top 5 cut points
top_5 = get_ranked_cut_frames("video.mp4", n=5)
for i, time in enumerate(top_5, 1):
    print(f"{i}. {time:.2f}s")

# With LLM-powered frame selection
best_time = get_cut_frame(
    "video.mp4",
    use_llm_selection=True,
    openai_api_key="your-api-key"
)

# Upload to Airtable
best_time = get_cut_frame(
    "video.mp4",
    upload_to_airtable=True,
    save_video=True
)

# Disable energy refinement
best_time = get_cut_frame(
    "video.mp4",
    use_energy_refinement=False
)

Advanced API

from sceneflow import CutPointRanker
from sceneflow.speech_detector import SpeechDetector
import cv2

# Detect speech end time
detector = SpeechDetector()
speech_end_time, confidence = detector.get_speech_end_time(
    "video.mp4",
    return_confidence=True
)

# Get video duration
cap = cv2.VideoCapture("video.mp4")
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration = frame_count / fps
cap.release()

# Rank frames after speech ends
ranker = CutPointRanker()
ranked_frames = ranker.rank_frames(
    video_path="video.mp4",
    start_time=speech_end_time,
    end_time=duration,
    sample_rate=2
)

# Get best cut point
best_cut = ranked_frames[0]
print(f"Best cut point: {best_cut.timestamp:.2f}s (score: {best_cut.score:.4f})")

API Reference

Common Parameters

Parameter	Type	Default	Used By	Description
source	str	required	All	Video file path or URL
output_path	str	required	cut_video	Output path for cut video
n	int	5	get_ranked	Number of timestamps to return
sample_rate	int	2	All	Process every Nth frame
save_video	bool	False	get_cut_frame	Save cut video
save_frames	bool	False	All	Save annotated frames
save_logs	bool	False	All	Save analysis logs
upload_to_airtable	bool	False	All	Upload to Airtable
use_llm_selection	bool	False	All	Use GPT-4o for selection
use_energy_refinement	bool	True	All	Refine VAD with energy analysis
energy_threshold_db	float	8.0	All	Minimum dB drop for refinement
energy_lookback_frames	int	20	All	Max frames to search backward
disable_visual_analysis	bool	False	All	Skip visual ranking, use speech end only
openai_api_key	str	None	All	OpenAI API key (or use env var)
airtable_access_token	str	None	All	Airtable token (or use env var)
airtable_base_id	str	None	All	Airtable base ID (or use env var)
airtable_table_name	str	None	All	Airtable table name (or use env var)

Functions:

• get_cut_frame(source, **params) - Returns best cut timestamp (float)
• get_ranked_cut_frames(source, n=5, **params) - Returns top N timestamps (list)
• cut_video(source, output_path, **params) - Cuts video and returns timestamp (float)

CLI Reference

sceneflow SOURCE [OPTIONS]

Arguments:
  SOURCE                         Path to video file or URL

Output Options:
  --verbose                      Show detailed analysis
  --json-output PATH             Save analysis to JSON (directory path)
  --top-n INT                    Return top N timestamps with scores

Processing Options:
  --sample-rate INT              Process every Nth frame (default: 2)
  --save-frames                  Save annotated frames with landmarks
  --save-video                   Save cut video
  --output PATH                  Custom output path for video
  --save-logs                    Save detailed logs

Speech Detection Options:
  --no-energy-refinement         Disable energy-based refinement
  --energy-threshold-db FLOAT    Minimum dB drop (default: 8.0)
  --energy-lookback-frames INT   Max frames to search backward (default: 20)

Visual Analysis Options:
  --disable-visual-analysis      Disable visual analysis and return speech end time only (faster)

Advanced Options:
  --use-llm-selection            Use GPT-4o for frame selection
  --airtable                     Upload results to Airtable

  --help                         Show help message
  --version                      Show version

Environment Variables

For optional features, set these environment variables:

# Airtable Integration
AIRTABLE_ACCESS_TOKEN     # Your Airtable access token
AIRTABLE_BASE_ID          # Your Airtable base ID
AIRTABLE_TABLE_NAME       # Table name (optional, defaults to "SceneFlow Analysis")

# LLM-Powered Selection
OPENAI_API_KEY            # OpenAI API key for GPT-4o integration

Quick setup:

cp .env.example .env
# Edit .env with your credentials

How It Works

Speech Detection

Uses Silero VAD (Voice Activity Detection) for accurate speech/silence detection. Energy-based refinement analyzes audio energy around the VAD timestamp to find the exact frame where speech ends, typically adjusting by 3-5 frames for frame-accurate boundaries.

Visual Analysis

After identifying speech end, SceneFlow analyzes frames using InsightFace (106-landmark facial detection) and ranks them based on:

Factor	Weight	Description
Expression Neutrality	30%	Calm, neutral facial expressions
Motion Stability	25%	Minimal optical flow between frames
Eye Openness	20%	Natural eye openness
Pose Stability	15%	Steady head position
Visual Sharpness	10%	Clear frame quality

When multiple faces are detected, center-weighted averaging prioritizes faces closer to the frame center.

Ranking System

1. Extract raw features from all frames
2. Normalize metrics across the entire frame set
3. Apply weighted scoring based on configuration
4. Use temporal context windows for stable sequences
5. Return ranked list of candidates

Examples

Check the examples/ directory:

• basic_usage.py - Simple API usage
• custom_config.py - Custom configuration
• ranked_results.py - Multiple cut points
• url_download.py - Working with URLs
• batch_processing.py - Processing multiple videos
• save_logs_example.py - Detailed analysis logs

Technical Details

• Speech Detection: Silero VAD
• Energy Refinement: Audio energy analysis with dB-based drop detection
• Facial Analysis: InsightFace with 106-landmark detection
• Multi-Face Support: Center-weighted averaging
• Motion Analysis: Farneback optical flow
• Eye Detection: Eye Aspect Ratio (EAR) using 7-point eye landmarks
• Expression Analysis: Mouth Aspect Ratio (MAR) using 20-point mouth landmarks
• Frame Quality: Laplacian variance
• Ranking: Multi-factor scoring with temporal context windows
• LLM Selection: GPT-4o vision analysis

Contributing

Contributions are welcome. See CONTRIBUTING.md for details.

License

MIT License - see LICENSE for details.

Links

• Repository: https://github.com/vertexcover-io/sceneflow
• Issues: https://github.com/vertexcover-io/sceneflow/issues
• PyPI: https://pypi.org/project/sceneflow/

Changelog

See CHANGELOG.md for version history.