Add CI workflow and a production README

- .github/workflows/ci.yml: build + test on every push/PR (JDK 21 + ffmpeg,
  mvn -B verify, uploads surefire reports). Closes the "no CI" gap.
- README.md: honest overview of the local offline highlight pipeline, the R1-R9
  cinematic quality rules, how to build/test/run, the local models and their
  (non-commercial) licenses, constraints, and limitations. Explicitly states it is
  a PoC, not production-hardened. Closes the "no README" gap.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MPuJXQyAeWpFcTtcnxo1UN
This commit is contained in:
JSLMPR 2026-07-24 12:17:08 +02:00
parent 2552a7cf06
commit 31efe33081
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name: CI
on:
push:
branches: ["**"]
pull_request:
branches: ["**"]
# Cancel superseded runs on the same ref.
concurrency:
group: ci-${{ github.ref }}
cancel-in-progress: true
jobs:
build:
name: Build & test (Java 21)
runs-on: ubuntu-latest
timeout-minutes: 30
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up JDK 21
uses: actions/setup-java@v4
with:
distribution: temurin
java-version: "21"
cache: maven
# Several integration tests shell out to ffmpeg/ffprobe; the local AI models are NOT needed for tests
# (asset workers are mocked), so no Python venv or model download is required in CI.
- name: Install ffmpeg
run: |
sudo apt-get update
sudo apt-get install -y ffmpeg
ffmpeg -version | head -n 1
ffprobe -version | head -n 1
# ubuntu-latest ships a recent Maven. (A pinned Maven Wrapper is a future hardening step; the wrapper
# plugin couldn't be provisioned offline in this environment.)
- name: Build and test
run: mvn -B -ntp verify
- name: Upload test reports
if: always()
uses: actions/upload-artifact@v4
with:
name: surefire-reports
path: target/surefire-reports/
if-no-files-found: ignore
retention-days: 7

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README.md Normal file
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# Video Editing Service — local cinematic highlight generator
Turn a single source video into a cinematic highlight **entirely with local, offline models**: it selects the
moment, cuts a story-structured montage, generates the music/SFX/voiceover, applies a cinematic grade, and
masters the audio — no external AI services, no runtime downloads.
> **Status:** a working, source-adaptive **proof of concept**. The cinematic quality ruleset (R1R9, below) is
> complete and content-agnostic. It is **not yet production-hardened** (no auth on the render endpoint,
> no containers/PostgreSQL/no-egress certification, and the local models are non-commercially licensed — see
> [Limitations](#limitations)). Do not deploy as-is.
## What it does
For one source clip the single-source highlight pipeline runs, fully offline:
```
ingest ─► analyze (ffprobe, scenes, audio, frames)
─► candidates + category
─► DIRECTOR (auto):
Tier 1 measure motion (YDIF) + audio (RMS) ─► story-structured shot list
Tier 2 local VLM (moondream2) captions beats ─► semantic payoff selection + overlay + music mood
─► generate assets (Piper voice · MusicGen music · AudioLDM2 SFX)
─► render (portrait/landscape-aware, exposure-normalized, push-in, crossfades, slow-mo ramp,
bold overlay, ducked source under a swelling score)
─► master loudness ─► QA probes ─► final.mp4
```
Rendering is gated: it stays off by default and requires an explicit approval flag per project.
## Cinematic quality rules (R1R9)
Every rule is **source-adaptive** — it measures the source and adapts, rather than hard-coding constants.
Full detail in [`docs/cinematic-quality-rules.md`](docs/cinematic-quality-rules.md).
| # | Rule | Measure → adapt |
|---|---|---|
| R1 | Exposure | frame luma → normalize; grade never crushes the subject |
| R2 | Orientation | source rotation → portrait/landscape output, no distortion |
| R3 | Audio balance + loudness | score leads, source ducked; measured loudness corrected to 16 LUFS |
| R4 | Duration | any length, story-driven |
| R5 | Motion push-in | per-shot motion (YDIF) → adaptive in-shot `zoompan` |
| R6 | Transitions | cross-dissolves between beats + ease into slow-motion |
| R7 | Overlays | bold, outlined, animated entrance |
| R8 | Music dynamics | volume swell builds into the payoff |
| R9 | Show the action | Tier-1 measured **+ Tier-2 VLM caption-driven** selection |
## Build & test
Requires **JDK 21** and **ffmpeg/ffprobe** on the PATH.
```bash
mvn -B verify # compile, run all tests, JaCoCo gate
```
CI (`.github/workflows/ci.yml`) runs this on every push.
## Run the highlight pipeline (local PoC)
The `localpoc` Spring profile wires the pipeline to pre-provisioned local model paths, isolates its
input/output trees, keeps rendering disabled + approval-required, and never starts a network-capable
bootstrap. The end-to-end command sequence (stage source → analyze → auto-direct → approve → render) is
documented step-by-step in the operator runbook. Outputs land under
`output/localpoc/highlight-projects/<project>/final.mp4`.
Local models used (each with a provenance sidecar under `models/`):
| Model | Role | License |
|---|---|---|
| Piper (`en_US-lessac-medium`) | voiceover | MIT / Blizzard dataset |
| MusicGen small | music | **CC-BY-NC** |
| AudioLDM2 | SFX | **CC-BY-NC-SA** |
| moondream2 | Tier-2 vision director | Apache-2.0 |
| YOLOv8n (optional CV) | visual analysis | **AGPL-3.0** |
## Non-negotiable constraints
- No automatic dependency/model downloads at runtime; models load offline from `models/` + the local cache.
- No external AI services in the media path.
- No unlicensed assets; no placeholder silence/tones passed off as generated audio (the pipeline fails closed).
- No rendering without an explicit approval flag.
## Limitations
- **Licensing:** MusicGen (CC-BY-NC), AudioLDM2 (CC-BY-NC-SA) and YOLOv8 (AGPL) are **non-commercial/copyleft**.
Commercial use requires swapping in commercially-licensed models/assets.
- **Not production-hardened:** no Spring Security/authN, no container/K8s/deployment manifests, REST persistence
defaults to in-memory, `POST /v1/edit-projects/{projectId}:render` has no approval gate, and no-egress
operation is not yet certified.
- **VLM quality:** on distant/small subjects the small local VLM is only weakly discriminative; a stronger
model or closer framing improves Tier-2 selection.
- A director can only cut what was filmed — it cannot show a moment the camera never captured.
## Repository map
- `src/main/java/org/example/videoclips/editing/` — highlight analysis, two-tier director, renderer, QA.
- `tools/` — local model workers (`local_asset_worker.py`, `vision_caption.py`).
- `src/main/resources/application-localpoc.yml` — the opt-in PoC profile.
- `docs/` — the PoC plan, the cinematic quality rules, acceptance review.