Jeremy Voiceover Video

Jeremy Voiceover Video

Make a broadcast-quality narrated video end to end: write a scene-segmented script, narrate it in Jeremy's cloned voice, generate cinematic B-roll of Jeremy from his face LoRA for the "talking" moments, cut in real product screenshots, burn colorful animated subtitles, and render it all with ffmpeg. When asked for options, produce several distinct variant cuts and let a jury pick the winner. Always orchestrate the production with Fable (see Orchestration).

Segment by scene because the per-scene audio durations drive BOTH the subtitle timings AND how long each visual stays up — known text + measured duration = perfect sync and cuts that land on the beat.

1. The voice-clone TTS (LAN)

• Host: 192.168.1.5. ssh shoemoney@192.168.1.5. Toolkit: ~/Services/VoiceStudioLaravel.
• Engine: mlx-audio (Qwen3-TTS), OpenAI-style /v1/audio/speech on localhost:8200. Confirm up: curl -s -o /dev/null -w '%{http_code}' localhost:8200/v1/models → 200. If down, restart the mlx-audio server in the toolkit dir (see server.log) before synth.
• Synthesize: ./say <voice> "<text>" --no-play --mp3 --out <path.wav> — voices: jeremy, amber, brandi, trump. --no-play REQUIRED over SSH; --temperature default 0.8 (livelier).
• SRT/transcribe: ./transcribe <audio> --srt (local mlx-whisper) — quality check + fallback SRT.

2. Jeremy's face LoRA (Replicate)

Generate cinematic stills of Jeremy for the "when I'm talking" scenes with his Flux face LoRA:

• Model: shoemoney/jshoe-flux-v3 (private) · trigger word: jshoe · version pinned in scripts/gen_lora.py. This is the correct one — NOT any Hunyuan3D / "h3" avatar.
• Generate: REPLICATE_API_TOKEN=… scripts/gen_lora.py <out_dir> <prompts.txt> (one prompt/line, fires all in parallel, downloads jeremy-NN.png). Token lives in ~/.claude/aigate/mcp-keys.env.
• Prompt style: start with jshoe, then describe — bald, short beard, black t-shirt, dark studio, subtle green rim light (brand), cinematic, photorealistic, 16:9. Vary pose per scene (hero close-up, at desk with monitors, mid-gesture, arms-crossed CTA).
• Flux gives stills, not a lip-synced talking head. Animate them with Ken-Burns (zoompan) and fade/dissolve into the screenshot that follows — that's the "he's talking, then we cut to the product" rhythm the brand wants. (For a true talking head you'd add a lip-sync model — out of scope unless asked.)

3. Pipeline

3a. Script — one scene per line

First person for Jeremy ("I built this because…"). 150 wpm (2.5 w/s): a 12-word line ≈ 5s; a ~2-min video is ~280–320 words / ~8–12 scenes. AIR copy rule: say "AI", never "the machine"; never "corpus". Save one line per scene to script.txt.

3b. Voiceover + timing + SRT

scripts/build_vo.sh <voice> script.txt <out_dir> → scene-NN.wav, voice.mp3, voice.srt, manifest.json ([{scene,text,start,end,dur}] — the timing map everything else reads). Transcribe a scene back to catch garbled acronyms/numbers; respell in the script and regenerate just that scene.

3c. Jeremy B-roll stills

Generate the LoRA stills (§2) for the talking scenes into <out>/jeremy/.

3d. Scenes — hybrid, hyper-focused on the product

For each scene, build a clip exactly dur seconds long (read manifest.json):

• Jeremy scenes: his LoRA still, slow Ken-Burns, then fade/dissolve into the feature.
• Feature scenes: full-screen (1920×1080) animated HTML in brand identity built around the real screenshot — the product is the star. Callouts/highlights point at the exact feature named in the VO. Reuse brand tokens/fonts. No webfont links.
• Author scene HTML with subagents (Sonnet), render each with render_scene.js (Playwright recordVideo, full Chromium) → normalize to 1920×1080/30fps/h264 via ffmpeg at its dur.

3e. Colorful animated subtitles

Captions must be styled + animated, not plain white. Preferred (no ffmpeg deps): inject an HTML caption overlay into each scene before rendering — `scripts/inject_caption.py scene.html ""