Can I vibecode Colossyan?

Build a comprehensive local substitute for Colossyan, focusing on AI-powered corporate training and educational video generation, in an empty repository.
Tech Stack: Python 3.12, FastAPI (backend), React/Next.js (frontend), SQLite (database), and ffmpeg (video processing). Use local open-source models for AI features where possible.
Build a powerful, privacy-first, enterprise-grade alternative to Colossyan for generating training videos with synthetic narration, running entirely on a local machine. The architecture must prioritize security, data sovereignty, and a polished user experience.

Tech Stack:
- Backend: Python 3.12, FastAPI, SQLAlchemy (SQLite), ffmpeg (video orchestration).
- Frontend: React, Vite, Tailwind CSS, Shadcn/UI for a clean, professional aesthetic.
- AI Logic: Local TTS engine (e.g., Piper/Coqui), local LLM integration via Ollama, and basic image processing for slide management.

Core Features & Requirements:
1. Local-First Pipeline: The application must operate entirely offline. User data, uploaded documents (PDFs, PPTs), and source media must remain on the host machine. Include a "project vault" directory structure.
2. AI Narration Engine: Implement a robust TTS interface allowing voice selection, pitch/speed modulation, and prosody control. Support local model imports and provide a mechanism to test pronunciation for technical training jargon.
3. Timeline Editor: Develop a sophisticated browser-based timeline where users can drop in visual assets (slides, screenshots, stock images) and sync them with specific audio segments generated from scripts. Allow for basic cross-fades and text-overlay management.
4. Rendering System: Create an asynchronous task queue using Celery or simple background workers to handle video compilation via ffmpeg. The system must automatically burn in a "Synthetic Media" disclosure watermark on every frame to ensure professional compliance.
5. Provenance & Ethics: Implement a strict "consent-first" flow. Every project must generate a metadata sidecar file (JSON) logging the LLM version, TTS voice model, timestamp, and copyright declarations for the source content.
6. UX & Professionalism: The interface should emulate high-end design software—dark mode by default, keyboard shortcuts for common tasks (space to preview, J/K/L for timeline scrubbing), and clear progress states for heavy compute tasks.
7. Technical Rigor:
   - Provide a .env.example with strict security defaults.
   - Implement input sanitization for all file uploads and terminal command invocations (especially for ffmpeg arguments).
   - Write unit tests for the audio generation service and video compilation pipeline.
   - Include a setup script (e.g., install_deps.sh) that checks for ffmpeg, python, and required local model dependencies.
   - Ensure the project is entirely self-contained; do not build any authentication or cloud-hosted features.

Deliverable:
A full-stack repository with a production-ready file structure. Documentation should be extensive, focusing on how to configure the TTS engine, how the rendering pipeline handles temporary files, and a guide on extending the system for different video formats. Conclude the setup instructions by printing the precise commands needed to trigger the development environment for both the FastAPI server and the React frontend.