# Local Cinematic Highlight PoC — Plan & Milestones

Owner: (Jaden) · Started: 2026-07-21 · Machine: Intel Mac (x86_64, 16 CPU, 32 GB, no GPU)

## Objective

Produce ONE genuinely cinematic highlight from a local source video, end-to-end through the existing
pipeline, using only local models resident in the service runtime:
Piper (voiceover) + MusicGen (music) + AudioLDM2 (SFX), with deliberate visual treatment and pacing,
and explicit human approval before any render.

A valid MP4 or a passing test is NOT success. Success = representative output passes measured media QA
and a human creative review.

## Non-negotiables (still in force during the PoC)

No external AI services in the media path · no placeholder silence/tones/OS `say` as a finished asset ·
no unlicensed assets · no rendering without explicit approval for the specific project · no inference-time
network (models load from local dirs, `HF_HUB_OFFLINE=1`). NOTE: the user authorized model DOWNLOADS on
2026-07-21 (one-time provisioning), reversing the earlier no-download stance; inference stays offline.

## Proven capability baseline (2026-07-21)

| Model | Runtime | Status | Evidence |
|---|---|---|---|
| Piper `en_US-lessac-medium` | piper-tts 1.5.0 | ✅ works | real speech, 22.05 kHz |
| MusicGen `musicgen-small` | transformers 4.44.2 + torch 2.2.2 | ✅ works | 5 s / 44.7 s CPU, mean −18 dB, 32 kHz |
| AudioLDM2 `cvssp/audioldm2` | diffusers 0.30.3 | ✅ works | 3 s / 22.3 s CPU, mean −21.8 dB, 16 kHz |

Environment traps (see memory `local-model-runtime-intel-mac`): torch capped at 2.2.2 (Intel-Mac),
numpy<2, transformers must be 4.x, audiocraft/xformers unusable here, `HF_HUB_DISABLE_XET=1` for downloads.

## Plan

### Phase 1 — Generate all three assets THROUGH the pipeline
- [x] 1.1 Rewrite `tools/local_asset_worker.py`: music→transformers MusicGen, sfx→diffusers AudioLDM2
      (resample to 48 kHz mono), voiceover→Piper (unchanged). audiocraft path removed. CLI + exit codes preserved.
- [x] 1.2 Materialize models into stable `models/` dirs via `save_pretrained` (`tools/provision_local_models.py`):
      `models/musicgen-small` (2.2 G), `models/audioldm2` (4.2 G); `models/piper` (60 M) already present.
- [x] 1.3 License/provenance `.license.txt` sidecars for each model marker file (config.json / model_index.json /
      voice .onnx). BOTH audio models are NON-COMMERCIAL (MusicGen CC-BY-NC-4.0, AudioLDM2 CC-BY-NC-SA-4.0) —
      flagged for production review. Honors the `AssetLicensePolicy` regular-file gate; no Java change.
- [x] 1.4 Smoke test: voiceover 3.67 s/−15.8 dB, music 4.94 s/−12.0 dB, sfx 3.00 s/−20.6 dB — all 48 kHz mono,
      real signal, exit 0.
- [x] 1.5 `mvn -o verify` → 245 tests / 62 classes / 0 failures / 0 errors / 0 skips (unchanged from baseline).

**Phase 1 COMPLETE (2026-07-21).**

### Phase 2 — One approved end-to-end highlight (STOP before render for explicit approval)
- [ ] 2.1 Opt-in `localpoc` Spring profile (base/production defaults untouched): venv python, model paths,
      offline flags, no bootstrap auto-start, heuristic fallback off, isolated PoC I/O dirs, render disabled.
- [ ] 2.2 Reprocess the DJI source so `category.json` + `highlight-candidates.json` are written.
- [ ] 2.3 Hand-author a director `edit-plan.json` grounded in persisted candidates (manual, local-only).
- [ ] 2.4 Explicit approval for the specific project → render → final + manifest + QA report.
- [ ] 2.5 Measure technical QA + structured human creative review.

### Phase 3 — Iterate to quality
- [ ] Improve selection, pacing, visual treatment, voice, music/SFX fit; keep experiments reproducible.

## Milestone log
- 2026-07-21: Models provisioned & individually proven (Piper, MusicGen, AudioLDM2). Plan approved. Phase 1 started.
- 2026-07-21: **Phase 1 complete.** Worker rewritten (audiocraft→transformers MusicGen + diffusers AudioLDM2),
  models materialized to `models/` with license sidecars, all 3 asset kinds generate 48 kHz mono real audio
  through the worker, `mvn -o verify` green (245/0/0/0). Paused for review before Phase 2.

## Deferred (until output-quality gate passes)
Production hardening: Spring Security/OIDC, PostgreSQL/Testcontainers, containers/K8s, CI/CD, distributed
ops, digest-bound authenticated approval. Recorded, not deleted.