-raw.wav`
- - Quiet room, consistent mic distance, natural conversational tone
-2. Pre-process: `ffmpeg -i raw.wav -ar 22050 -ac 1 aria.wav`
-3. Place at `~/voices/aria.wav`
-4. Update character JSON: `"voice_ref_path": "~/voices/aria.wav"`, `"engine": "chatterbox"`
-5. Test: run Chatterbox with the reference, verify voice quality
-6. If unsatisfactory, try Qwen3-TTS as alternative
-
----
-
-## Pipeline Integration
-
-### How P4 (OpenClaw) loads the character
-
-```python
-import json
-from pathlib import Path
-
-def load_character(name: str) -> dict:
- path = Path.home() / ".openclaw" / "characters" / f"{name}.json"
- config = json.loads(path.read_text())
- assert config["schema_version"] == 1, "Unsupported schema version"
- return config
-
-# System prompt injection
-character = load_character("aria")
-system_prompt = character["system_prompt"]
-# Pass to Ollama as system message
-```
-
-OpenClaw hot-reloads the character JSON on file change — no restart required.
-
-### How P3 selects TTS engine
-
-```python
-character = load_character(active_name)
-tts_cfg = character["tts"]
-
-if tts_cfg["engine"] == "chatterbox":
- tts = ChatterboxTTS(voice_ref=tts_cfg["voice_ref_path"])
-elif tts_cfg["engine"] == "qwen3":
- tts = Qwen3TTS()
-else: # kokoro (default)
- tts = KokoroWyomingClient(voice=tts_cfg.get("kokoro_voice", "af_heart"))
-```
-
----
-
-## Implementation Steps
-
-- [ ] Define and write `schema/character.schema.json` (v1)
-- [ ] Write `characters/aria.json` — default character with placeholder expression IDs
-- [ ] Set up Vite project in `src/` (install deps: `npm install`)
-- [ ] Integrate existing `character-manager.jsx` into new Vite project
-- [ ] Add schema validation on export (`ajv`)
-- [ ] Add expression mapping UI section
-- [ ] Add custom rules editor
-- [ ] Test full edit → export → validate → load cycle
-- [ ] Record or source voice reference audio for Aria
-- [ ] Pre-process audio and test with Chatterbox
-- [ ] Update `aria.json` with voice clone path if quality is good
-- [ ] Write `SchemaValidator.js` as standalone utility (used by P4 at runtime too)
-- [ ] Document schema in `schema/README.md`
-
----
-
-## Success Criteria
-
-- [ ] `aria.json` validates against `character.schema.json` without errors
-- [ ] Character Manager UI can load, edit, and export `aria.json`
-- [ ] OpenClaw loads `aria.json` system prompt and applies it to Ollama requests
-- [ ] P3 TTS engine selection correctly follows `tts.engine` field
-- [ ] Schema version check in P4 fails gracefully with a clear error message
-- [ ] Voice clone sounds natural (if Chatterbox path taken)
diff --git a/homeai-character/README.md b/homeai-character/README.md
new file mode 100644
index 0000000..18bc70e
--- /dev/null
+++ b/homeai-character/README.md
@@ -0,0 +1,16 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.
diff --git a/homeai-character/character-manager.jsx b/homeai-character/character-manager.jsx
deleted file mode 100644
index 33e063d..0000000
--- a/homeai-character/character-manager.jsx
+++ /dev/null
@@ -1,686 +0,0 @@
-import { useState, useEffect, useCallback } from "react";
-
-const STORAGE_KEY = "ai-character-profiles";
-
-const DEFAULT_MODELS = [
- "llama3.3:70b", "qwen2.5:72b", "mistral-large", "llama3.1:8b",
- "qwen2.5:14b", "gemma3:27b", "deepseek-r1:14b", "phi4:14b"
-];
-
-const TTS_MODELS = ["Kokoro", "Chatterbox", "F5-TTS", "Qwen3-TTS", "Piper"];
-const STT_MODELS = ["Whisper Large-v3", "Whisper Medium", "Whisper Small", "Whisper Turbo"];
-const IMAGE_MODELS = ["SDXL", "Flux.1-dev", "Flux.1-schnell", "SD 1.5", "Pony Diffusion"];
-
-const PERSONALITY_TRAITS = [
- "Warm", "Witty", "Calm", "Energetic", "Sarcastic", "Nurturing",
- "Curious", "Playful", "Formal", "Casual", "Empathetic", "Direct",
- "Creative", "Analytical", "Protective", "Mischievous"
-];
-
-const SPEAKING_STYLES = [
- "Conversational", "Poetic", "Concise", "Verbose", "Academic",
- "Informal", "Dramatic", "Deadpan", "Enthusiastic", "Measured"
-];
-
-const EMPTY_CHARACTER = {
- id: null,
- name: "",
- tagline: "",
- avatar: "",
- accentColor: "#7c6fff",
- personality: {
- traits: [],
- speakingStyle: "",
- coreValues: "",
- quirks: "",
- backstory: "",
- motivation: "",
- },
- prompts: {
- systemPrompt: "",
- wakeWordResponse: "",
- fallbackResponse: "",
- errorResponse: "",
- customPrompts: [],
- },
- models: {
- llm: "",
- tts: "",
- stt: "",
- imageGen: "",
- voiceCloneRef: "",
- ttsSpeed: 1.0,
- temperature: 0.7,
- },
- liveRepresentation: {
- live2dModel: "",
- idleExpression: "",
- speakingExpression: "",
- thinkingExpression: "",
- happyExpression: "",
- vtsTriggers: "",
- },
- userNotes: "",
- createdAt: null,
- updatedAt: null,
-};
-
-const TABS = ["Identity", "Personality", "Prompts", "Models", "Live2D", "Notes"];
-
-const TAB_ICONS = {
- Identity: "◈",
- Personality: "◉",
- Prompts: "◎",
- Models: "⬡",
- Live2D: "◇",
- Notes: "▣",
-};
-
-function generateId() {
- return Date.now().toString(36) + Math.random().toString(36).slice(2);
-}
-
-function ColorPicker({ value, onChange }) {
- const presets = [
- "#7c6fff","#ff6b9d","#00d4aa","#ff9f43","#48dbfb",
- "#ff6348","#a29bfe","#fd79a8","#55efc4","#fdcb6e"
- ];
- return (
-
- {presets.map(c => (
-
- );
-}
-
-function TagSelector({ options, selected, onChange, max = 6 }) {
- return (
-
- {options.map(opt => {
- const active = selected.includes(opt);
- return (
-
- );
- })}
-
- );
-}
-
-function Field({ label, hint, children }) {
- return (
-
-
- {hint &&
{hint}
}
- {children}
-