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一分鐘完成驗證Updates funscripts to make them estim-ready
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A structure-aware post-processor for funscripts. It analyzes the motion
structure of an existing script, lets you review and tag sections through an
interactive UI, and generates an improved script with smoother defaults,
expressive performance sections, and gentle breaks.
FunscriptForge now includes an eTransforms tab — the bridge between funscript editing and estim output generation.
Pick a character for the estim output. Each one controls how sensation moves and builds over time.檢視完整 README ↗A structure-aware post-processor for funscripts. It analyzes the motion structure of an existing script, lets you review and tag sections through an interactive UI, and generates an improved script with smoother defaults, expressive performance sections, and gentle breaks.
Download: latest release • Docs: liquid-releasing.github.io/funscriptforge • Community: Discord
FunscriptForge now includes an eTransforms tab — the bridge between funscript editing and estim output generation.
Pick a character for the estim output. Each one controls how sensation moves and builds over time.
| Character | What it means |
|---|---|
| Gentle | Soft, slow-building. Narrow arc, soft pulse onset. Good for intimate or slow content. |
| Reactive | Sharp, tracks action closely. Wide arc, instant response. Good for fast, intense content. |
| Scene Builder | Builds gradually over the scene. Circular arc, slow ramp. Rewards patience. |
| Unpredictable | Random direction changes, varied character. Keeps you guessing. |
| Balanced | Middle of everything. Good starting point for any content. |
Each character shows 1–2 contextual sliders most relevant to its personality, a live electrode path preview, and a plain-English "What you'll feel" summary.
The eTransforms tab connects directly to funscript-tools. The same five characters appear in both tools — pick a character in FunscriptForge, and funscript-tools applies it to generate the alpha / beta / pulse_frequency estim outputs.
The character name is the API between all three tools: Explorer analysis → FunscriptForge transforms → funscript-tools eTransform → restim playback.
Current scope: eTransforms apply globally to the full funscript. Per-section (phrase-level) character support is the next milestone.
FunscriptForge guides you through a structured workflow. Each tab builds on the previous one:
| Tab | Purpose |
|---|---|
| Project | Load funscript, set output folder, add media (video/audio/captions), author info |
| Device | Select target devices (Handy, OSR2, E-Stim FOC/Stereo, Generic/Intiface), apply device awareness |
| Tone | Apply a global mood — 6 tones from Tender to Dominant, with contextual sliders |
| Phrases | Per-phrase editing with transforms, live preview, Before/After charts |
| Patterns | Batch transforms by behavioral tag across all matching phrases |
| Catalogs | Reference guide: Behavior, Structural, Replacement, Tone, Stim, and Tag catalogs |
| Stim | E-stim character selection (Gentle, Reactive, Scene Builder, Unpredictable, Balanced) |
| Export | Quality gate, transform plan, device-specific folder export |
| Next Steps | Playback guides per device, credits, license |
State flows through the chain: Original → Device → Tone → Phrases → Export
.funscript file, set output folder, optional media (video, audio, captions).forge project files for save/resume — pick up where you left offSix catalog sections in one reference tab:
Each entry includes live interactive sliders and preview charts.
output/handy/, output/estim-foc/)_forge_log key records every transform for reproducibilityCtrl+Z undo, Ctrl+Y / Ctrl+Shift+Z redo, Ctrl+S saveassess, transform, customize, pipeline — full analysis and transform pipelinephrase-transform — apply any catalog transform to individual phrases from the command linefinalize — blend seams + final smooth as standalone post-processingexport-plan — mirror of the UI Export tab; supports --apply to write output directlycatalog — query and manage the cross-funscript pattern catalogvalidate-plugins — validate JSON recipe files and report Python plugin gate status without starting the appmeta — auto-derive pace, intensity, arc, mood, Hub tags, and tone suggestion from a funscriptsuggest-tone — print the auto-suggested Tone label and rationalebeats — extract beat timestamps from a video file; writes _beats.json + _beats.csvparse-captions — parse SRT or WebVTT captions; writes _captions.jsontest — run all testsThe analyzer reads a .funscript file and detects its motion structure,
working through five stages:
actions → phases → cycles → patterns → phrases → BPM transitions
The output is a single JSON file capturing the full structural picture.
Open the FunscriptForge desktop app (Tauri + React) and load your funscript. The Analyze view shows the full pipeline output — a colour-coded phrase timeline, BPM transitions table, and drill-down detail for patterns and phases.
The Phrase Editor tab shows the full funscript as a colour-coded chart with phrase bounding boxes. Click any phrase to open its detail panel where you can select a transform, tune its parameters with live sliders, and see a Before / After preview. Use Apply to all to copy the same transform to every instance of the same behavioral tag.
The Pattern Editor tab lets you fix behavioral issues phrase by phrase. Each phrase instance shows an original chart and a live preview as you adjust transforms. For phrases that span a long section (e.g. a single pattern covering most of the file), you can split the phrase into non-overlapping sub-ranges and apply a different transform to each one. Split boundaries are shown as dashed lines on both charts. Use Apply to all to copy the split structure — scaled proportionally — to every other instance of the same behavioral tag.
The Catalogs tab has six sections:
The Export tab aggregates every transform applied across the workflow.
Device-specific folders are created automatically (e.g. output/handy/, output/estim-foc/).
Tag-aware auto-suggestions (suggest_transform, checked in priority order):
| Tag | Suggested transform | Notes |
|---|---|---|
frantic | halve_tempo | BPM > 200 |
giggle, plateau, lazy | amplitude_scale | Amplify; scale computed to target peak hi ≈ 65 |
stingy | amplitude_scale | Reduce; scale computed to target peak hi ≈ 65 |
drift, half_stroke | recenter | target_center = 50 |
drone | beat_accent | Adds rhythmic variation |
ramp | funnel | Progressive center shift + amplitude scaling for energy arc shaping |
ambient | waiting | Low BPM + shallow amplitude + long duration |
Post-processing: blend seams (bilateral LPF at style boundaries) and final smooth (light global LPF).
After reviewing in the UI, run the full pipeline to produce the final funscript.
Open the Export tab and run the full pipeline. Adjust the BPM threshold and
amplitude scale, toggle whether to apply your Work Item windows, then write the result.
This produces a _pipeline.funscript file with an embedded _forge_log, independent of
any phrase-editor transforms.
# Step 1 — analyze (or use the UI; it saves a cached JSON automatically)
python cli.py assess input.funscript --output output/assessment.json
# Step 2 — transform (BPM-threshold baseline)
python cli.py transform input.funscript \
--assessment output/assessment.json \
--output output/transformed.funscript
# Step 3 — customize (apply your tagged windows)
python cli.py customize output/transformed.funscript \
--assessment output/assessment.json \
--perf output/input.performance.json \
--break output/input.break.json \
--raw output/input.raw.json \
--output output/final.funscript
# Or run both steps at once
python cli.py pipeline input.funscript --output-dir output/
Privacy first. FunscriptForge runs entirely on your machine. Your funscripts, media files, and edits never leave your computer — no account, no cloud sync, no telemetry.
FunscriptForge is a native Tauri 2 + React desktop app. The packaged Windows
installer bundles everything it needs (the forge-cli Python backend and a static
ffmpeg); there is no separate Python, pip, or browser install.
| Windows | |
|---|---|
| OS | Windows 10 64-bit (build 1903+) / Windows 11 |
| CPU | Any 64-bit dual-core x86 |
| RAM | 4 GB |
| Free disk | ~1 GB (app + bundled backend & ffmpeg) |
| Runtime | WebView2 (preinstalled on Windows 11; installer fetches it if missing) |
| Display | 1920 × 1080 (1080p) minimum |
Windows-first.
v0.1.0-alphaships a Windows installer. macOS and Linux builds are a post-beta follow-up — until then, run from source on those platforms.
| Resource | Recommendation | Why |
|---|---|---|
| RAM | 8 GB+ | Long funscripts (1+ hour) load the full action list into memory |
| CPU | 4-core, 3 GHz+ | Analysis runs single-threaded; faster clock speed = faster analysis |
| Free disk | 10 GB+ | Media files stay on disk during editing and are never modified — allow room for originals plus exports |
| Display | 2560 × 1440 (QHD) or larger | The Phrase and Polish tabs use a 3-pane layout with an embedded media player; QHD provides enough vertical space without scrolling |
An internet connection is only required once, to download the installer. After that the app runs completely offline — no calls home, no background updates.
Download the latest Windows installer (.msi or .exe / NSIS) from the
releases page
and run it. See docs/INSTALL.md for details.
FunscriptForge is a Tauri 2 + React app in ui/web/, driving the repo-root
Python backend (cli.py). You need the sibling repos checked out next to this one
(funscript-tools, forge-ui-components, videoflow, forgemoment).
# 1. Python backend env
python -m venv .venv
.venv/Scripts/activate # Windows (use source .venv/bin/activate elsewhere)
pip install -r requirements.txt
# 2. Frontend + full desktop app (from ui/web/)
cd ui/web
npm install
npm run tauri:dev # Rust shell + React + Python backend
# Production installers:
npx tauri build # → src-tauri/target/release/bundle/
In development the Rust shell invokes the backend through the .venv
(python cli.py <command>), with ffmpeg / ffprobe resolved on PATH. See
ui/web/README.md for prerequisites and the platform adapter.
python cli.py assess path/to/file.funscript --output output/assessment.json
Big Buck Bunny (Blender Foundation, 2008) is a recommended safe-for-work test video for trying the audio/video player feature. Community funscript sites carry demo scripts that pair with it, making it easy to exercise the Phrase Editor, Pattern Editor, and media player without needing private content.
The Blender Foundation released the video under the Creative Commons Attribution 2.5 license, so you can use, modify, and share it freely as long as you credit the creators.
© 2008 Blender Foundation | www.bigbuckbunny.org
funscriptforge/
├── assessment/ # Step 1: structural analysis + behavioral classification
│ ├── analyzer.py # FunscriptAnalyzer
│ ├── classifier.py # BehavioralTag, TAGS registry, annotate_phrases
│ └── readme.md
├── catalog/ # Cross-funscript pattern catalog
│ └── pattern_catalog.py # PatternCatalog (persistent JSON)
├── pattern_catalog/ # Step 2: BPM-threshold baseline transform
│ ├── transformer.py # FunscriptTransformer
│ ├── phrase_transforms.py # TRANSFORM_CATALOG (22 named transforms incl. funnel)
│ └── config.py # TransformerConfig
├── user_customization/ # Step 3: window-based fine-tuning
│ ├── customizer.py # WindowCustomizer
│ └── config.py # CustomizerConfig
├── visualizations/ # Plotly + matplotlib motion chart components
├── ui/ # UI code
│ ├── common/ # Framework-agnostic models and logic
│ │ ├── work_items.py # WorkItem + ItemType
│ │ ├── project.py # Project state
│ │ ├── pipeline.py # run_pipeline / run_pipeline_in_memory
│ │ └── tests/
│ └── web/ # Tauri 2 + React + Vite desktop app
│ ├── src/ # React source (App.jsx, api/forge.js, tabs)
│ └── src-tauri/ # Rust shell (commands.rs bridges to cli.py)
├── forge/ # Forge-layer modules (project, metadata, media analysis)
│ ├── metadata.py # derive_metadata() — auto-derive pace/intensity/arc/mood/tags/tone
│ ├── beats.py # extract_beats() — PyAV + librosa beat detection
│ ├── captions.py # parse_captions() — SRT + WebVTT parser
│ └── video.py # video_stats(), analyze_motion()
├── cli.py # Python backend / CLI (frozen to forge-cli for distribution)
├── forge-cli.spec # PyInstaller spec — freezes cli.py + scientific stack
├── docs/ # MkDocs user documentation site (in progress)
├── internal/ # Internal planning docs (gap analysis, backlogs, build notes)
├── media/ # App images, logos, icons
├── tests/ # Core pipeline unit tests
├── models.py # Shared dataclasses (Phrase now carries tags + metrics)
├── utils.py # Timestamp helpers, low-pass filter, writable_base_dir
└── requirements.txt
# Assess
python cli.py assess <funscript> [--output <path>] [--config <json>]
[--min-phrase-duration SECONDS] [--amplitude-tolerance FRACTION]
# Transform (BPM-threshold baseline)
python cli.py transform <funscript> --assessment <path>
[--output <path>] [--config <json>]
# Customize (window-based fine-tuning)
python cli.py customize <funscript> --assessment <path>
[--output <path>] [--config <json>]
[--perf <json>] [--break <json>] [--raw <json>] [--beats <json>]
# Full pipeline (assess → transform → customize in one step)
python cli.py pipeline <funscript> --output-dir <dir>
[--perf <json>] [--break <json>] [--raw <json>] [--beats <json>]
[--transformer-config <json>] [--customizer-config <json>]
# Phrase-level transform (applies a catalog transform to individual phrases)
python cli.py phrase-transform <funscript> --assessment <path>
--transform smooth --phrase 3 [--param strength=0.25]
--transform normalize --all
--suggest [--bpm-threshold 120] # tag-aware auto-pick
[--output <path>] [--dry-run]
# Finalize (blend seams + final smooth as post-processing)
python cli.py finalize <funscript> [--output <path>]
[--param seam_max_velocity=0.3] [--param smooth_strength=0.05]
[--skip-seams] [--skip-smooth]
# Export plan (mirror of the UI Export tab)
python cli.py export-plan <funscript> [--assessment <path>]
[--transforms overrides.json] [--no-recommended]
[--bpm-threshold BPM] [--format table|json]
[--apply] [--output <path>] [--dry-run]
# Catalog
python cli.py catalog [--catalog <path>] [--tag TAG] [--remove FUNSCRIPT] [--clear]
# Validate user-transform plugins (JSON schema check + Python plugin gate status)
python cli.py validate-plugins [--verbose] [--recipes-dir <path>] [--plugins-dir <path>]
# Auto-derive metadata (pace, intensity, arc, mood, Hub tags, tone suggestion)
python cli.py meta <funscript> [--assessment <path>] [--output <json>] [--format table|json]
# Print tone label + rationale only
python cli.py suggest-tone <funscript>
# Extract beat timestamps from video (requires: pip install av librosa)
python cli.py beats <video> [--audio <override>] [--output-dir <dir>]
# Writes: _beats.json, _beats.csv
# Parse SRT or WebVTT captions
python cli.py parse-captions <file.srt|.vtt> [--output-dir <dir>] [--print]
# Writes: _captions.json
# Utilities
python cli.py visualize <funscript> --assessment <path> [--output <path>]
python cli.py config [--customizer] [--analyzer] [--output <path>]
python cli.py test
# Python backend: core pipeline + integration + engine tests
python -m unittest discover -s tests -v
# Python UI-common layer
python -m unittest discover -s ui/common/tests -v
# All Python tests at once
python cli.py test
# React frontend tests
cd ui/web && npm test
| README | Description |
|---|---|
| assessment/readme.md | Structural analysis pipeline — phases, cycles, patterns, phrases, BPM transitions (Step 1) |
| pattern_catalog/README.md | BPM-threshold baseline transformer (Step 2) |
| pattern_catalog/EXTENDING_TRANSFORMS.md | Adding custom transforms via JSON recipes or Python plugins; security model |
| user_customization/README.md | Window-based fine-tuning customizer (Step 3) |
| ui/README.md | Frontend overview — the Tauri + React desktop app, backend, distribution |
| ui/web/README.md | Tauri + React app — prerequisites, run modes, forge.js adapter, tab flow |
| ui/common/README.md | Framework-agnostic business logic: Project, WorkItem |
| user_transforms/README.md | Adding custom transforms via JSON recipe files |
| plugins/README.md | Adding custom transforms via Python plugins |
© 2026 Liquid Releasing. Licensed under the MIT License. Written by human and Claude AI (Claude Sonnet).
FunscriptForge™ is a trademark of Liquid Releasing.
The .funscript file format is a community standard not owned by Liquid Releasing.
| Matplotlib motion chart components |
| tests/README.md | Test suite structure and coverage |
| internal/ACCESSIBILITY.md | WCAG 2.1 AA accessibility assessment — issues, severity, recommended fixes |
| docs/architecture/TONE_TAB_SPEC.md | Tone tab design spec — the 6 tones, card UI, beat envelopes, caption emotion, data model |
| docs/architecture/AUTO_METADATA_SPEC.md | Auto-derived metadata spec — pace/intensity/arc/mood/tags/tone suggestion |
| docs/architecture/HAPTIC_COMPOSITION_SPEC.md | Three-layer haptic composition — base funscript + beats + caption emotion |
| docs/INSTALL.md | End-user installation guide (Windows) |
| internal/BUILD.md | Building a standalone installer on Windows and macOS |
| internal/SECURITY.md | Threat analysis (T1–T5), mitigations implemented, Python plugin roadmap decision |