narractive 2.2.0

Modular framework for automating desktop application demo video production

Narractive

A modular Python framework for automated video production — from narration to final cut.

Narractive orchestrates the full pipeline: UI interaction (PyAutoGUI), screen recording (OBS or headless), text-to-speech narration, Mermaid diagram generation, subtitle generation, and FFmpeg assembly. Script your sequences, define narration cues, and let the framework produce polished demo videos hands-free.

Features

• Dual recording backends: OBS WebSocket (desktop) or headless frame capture (Docker/Xvfb)
• Multi-engine TTS narration: edge-tts (free), ElevenLabs (premium), F5-TTS (voice cloning), XTTS v2 (multilingual cloning)
• Timeline-synchronized sequences: Narration cues paired with UI actions
• Mermaid diagram slides: HTML + PNG via Playwright, mmdc, or mermaid.ink API (zero-dep)
• SRT subtitle generation: WPM-based timing from narration text, multilingual defaults
• Multilingual diagram labels: i18n base class with automatic language fallback
• FFmpeg post-production: Quality presets (draft/final), subtitle burn, intro/outro from images, duration matching
• Interactive calibration: Record UI element positions for pixel-perfect automation
• Docker support: Reproducible headless production in CI/CD

Quick Start

# Install from PyPI
pip install narractive

# Or install from source
pip install -e .

# Copy and configure
cp config.template.yaml config.yaml

# Calibrate UI positions (interactive)
narractive --calibrate --config config.yaml

# Generate subtitles from narrations (multilingual)
narractive --subtitles --narrations-dir narrations/ --config config.yaml

# Generate subtitles (single language)
narractive --subtitles --lang fr --narrations-dir narrations/

# Generate diagrams
narractive --diagrams --diagrams-module my_project.diagrams.mermaid_definitions

# Record all sequences
narractive --all --sequences-package my_project.sequences --config config.yaml

# Assemble final video (fast preview)
narractive --assemble --quality draft --project-name "My Project"

# Assemble final video (publication quality)
narractive --assemble --quality final --project-name "My Project"

# Or headless (Docker)
docker compose run --rm video --all --sequences-package my_project.sequences

Architecture

narractive/
├── video_automation/              # Framework (pip-installable)
│   ├── core/                      # Generic modules
│   │   ├── app_automator.py      # PyAutoGUI + window control
│   │   ├── obs_controller.py     # OBS WebSocket 5.x
│   │   ├── frame_capturer.py     # Headless Xvfb capture
│   │   ├── narrator.py           # TTS (edge-tts/ElevenLabs/F5-TTS/XTTS v2)
│   │   ├── subtitles.py          # SRT generation from narration text
│   │   ├── timeline.py           # Narration-synchronized cues
│   │   ├── diagram_generator.py  # Mermaid → HTML/PNG (Playwright/mmdc/API)
│   │   └── video_assembler.py    # FFmpeg post-production + quality presets
│   ├── sequences/
│   │   └── base.py               # VideoSequence + TimelineSequence
│   ├── bridges/
│   │   ├── f5_tts_bridge.py      # F5-TTS subprocess bridge
│   │   └── xtts_bridge.py        # XTTS v2 (Coqui TTS) subprocess bridge
│   ├── diagrams/
│   │   ├── i18n.py               # Multilingual diagram labels
│   │   └── template.html         # Mermaid HTML template
│   ├── scripts/
│   │   ├── calibrate.py          # Interactive UI calibration
│   │   └── setup_obs.py          # OBS auto-configuration
│   └── cli.py                    # Click-based CLI
│
├── examples/
│   └── filtermate/               # Example project (QGIS plugin demo)
│
├── config.template.yaml          # Configuration template
├── Dockerfile                    # Headless Docker image
├── docker-compose.yml
└── pyproject.toml

Creating Sequences for Your App

1. Simple sequence (manual timing)

from video_automation.sequences.base import VideoSequence

class MyIntro(VideoSequence):
    name = "Introduction"
    sequence_id = "seq00"
    duration_estimate = 30.0
    obs_scene = "Main"

    def execute(self, obs, app, config):
        app.focus_app()
        app.click_at("my_button")
        app.wait(2.0)
        app.scroll_down(3)

2. Timeline sequence (narration-synchronized)

from video_automation.sequences.base import TimelineSequence
from video_automation.core.timeline import NarrationCue

class MyDemo(TimelineSequence):
    name = "Live Demo"
    sequence_id = "seq01"
    duration_estimate = 60.0

    def build_timeline(self, obs, app, config):
        return [
            NarrationCue(
                text="Welcome to the demo.",
                actions=lambda: app.wait(1.0),
                sync="during",
            ),
            NarrationCue(
                text="Let's open the settings.",
                actions=lambda: app.click_at("settings_button"),
                sync="after",
            ),
        ]

3. Multilingual diagram labels

from video_automation.diagrams.i18n import DiagramLabels

labels = DiagramLabels(
    labels={
        "server": {"fr": "Serveur", "en": "Server", "pt": "Servidor"},
        "client": {"fr": "Client", "en": "Client", "pt": "Cliente"},
    },
    titles={
        "architecture": {"fr": "Architecture", "en": "Architecture"},
    },
    default_lang="fr",
)

name = labels.l("server", "en")  # "Server"

4. Register sequences

Create my_project/sequences/__init__.py:

from my_project.sequences.seq00_intro import MyIntro
from my_project.sequences.seq01_demo import MyDemo

SEQUENCES = [MyIntro, MyDemo]

Then run:

narractive --list --sequences-package my_project.sequences
narractive --all --sequences-package my_project.sequences

Configuration

See config.template.yaml for all available options. Key sections:

Section	Purpose
obs	OBS WebSocket connection, scenes, output directory
app	Window title, panel name, calibrated UI positions
timing	Click/type/scroll delays, transition pauses
diagrams	Mermaid rendering (resolution, theme, colors)
narration	TTS engine, voice, speed, F5-TTS/XTTS options
subtitles	SRT generation (enabled, max chars, max lines)
capture	Headless frame capture (FPS, resolution, display)
output	Final video encoding (resolution, fps, codec, quality preset)

TTS Engines

Engine	Cost	Quality	Multilingual	Setup
edge-tts	Free	Good	Yes	pip install edge-tts (included)
ElevenLabs	Paid	Excellent	Yes	pip install elevenlabs + API key
F5-TTS	Free	Excellent	No	Conda env + GPU recommended
XTTS v2	Free	Excellent	Yes	pip install TTS + GPU recommended

Requirements

• Python 3.10+
• FFmpeg (for video assembly)
• OBS Studio (desktop mode) or Docker (headless mode)
• Your target application installed and running

License

MIT