# Build brief — a focused alternative to LOVO

> **Verdict:** Not faithfully · **Buildability:** 6/100 · **Category:** Voice AI
> **Source:** https://www.canitbevibecoded.com/lovo
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from LOVO. Verify current pricing and capabilities before acting.

## Context

**LOVO** — Produce labeled voiceovers from licensed local voices and user-authored scripts. It currently costs $24/mo.

A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For LOVO, produce labeled voiceovers from licensed local voices and user-authored scripts. The hard boundary is large proprietary voice catalog, editor, cloning, rights, and hosted rendering, plus models, compute, rights, and safety operations.

This brief describes a focused, single-operator replacement for the part of LOVO that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Turn user-authored scripts into clearly labeled synthetic voiceovers using licensed local voices and a local model, retaining provenance for every output.

- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- Local TTS model.
- Ffmpeg.
- GPU recommended.
- Voices the user has rights and consent to use.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build a closest honest personal substitute for LOVO in an empty repository.
Use Python 3.12, FastAPI, SQLite, ffmpeg, and a user-owned local TTS model; do not offer alternative stacks.
The core loop is: turn user-authored scripts into clearly labeled synthetic voiceovers using licensed local voices and a local model, retaining provenance for every output.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Require a project-level rights and consent acknowledgement before generating audio.
Ship with no celebrity, public-figure, or scraped voice assets and accept only explicitly licensed models.
Generate speech from text with voice, speed, pause, pronunciation, and segment controls.
Create a timeline for audio, captions, uploaded visuals, and simple transitions.
Embed project metadata and a visible synthetic-media disclosure in exported assets.
Store prompts, model identifiers, consent notes, and output hashes in a local provenance log.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out cloning a voice without clear consent.
Deliberately leave out impersonation or deceptive unlabeled media.
Deliberately leave out a frontier avatar model, public hosting, or enterprise rights clearance.
Finish by running the tests and listing the exact commands used.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- Large proprietary voice catalog, editor, cloning, rights, and hosted rendering.
- Frontier voice or avatar model.
- Licensed voice catalog.
- Real-time rendering fleet.
- Model quality and inference operations are part of the product.
- Licensed content and distribution rights are not reproducible with an LLM.

## What you still own after launch

- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [Piper](https://github.com/OHF-Voice/piper1-gpl) — Active community continuation of the fast local Piper text-to-speech engine

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Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/lovo