Update CircleButton component to include default button type

- Added a default `type` prop set to 'button' in the CircleButton component to ensure proper button behavior.
- Enhanced the component's flexibility by allowing the type to be overridden through props.

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.7
+current_version = 0.1.11
 commit = True
 tag = True
 tag_name = v{new_version}
@@ -34,6 +34,6 @@ replace = "version": "{new_version}"
 search = "version": "{current_version}"
 replace = "version": "{new_version}"
 
-[bumpversion:file:backend/main.py]
+[bumpversion:file:backend/__init__.py]
-search = "version": "{current_version}"
+search = __version__ = "{current_version}"
-replace = "version": "{new_version}"
+replace = __version__ = "{new_version}"

.github/workflows/release.yml

@@ -17,15 +17,19 @@ jobs:
           - platform: 'macos-latest'
             args: '--target aarch64-apple-darwin'
             python-version: '3.12'
+            backend: 'mlx'
           - platform: 'macos-15-intel'
             args: '--target x86_64-apple-darwin'
             python-version: '3.12'
+            backend: 'pytorch'
           # - platform: 'ubuntu-22.04'
           #   args: ''
           #   python-version: '3.12'
+          #   backend: 'pytorch'
           - platform: 'windows-latest'
             args: ''
             python-version: '3.12'
+            backend: 'pytorch'
 
     runs-on: ${{ matrix.platform }}
 
@@ -57,6 +61,11 @@ jobs:
           pip install pyinstaller
           pip install -r backend/requirements.txt
 
+      - name: Install MLX dependencies (Apple Silicon only)
+        if: matrix.backend == 'mlx'
+        run: |
+          pip install -r backend/requirements-mlx.txt
+
       - name: Build Python server (Linux/macOS)
         if: matrix.platform != 'windows-latest'
         run: |
@@ -133,7 +142,8 @@ jobs:
             See the assets below to download and install this version.
 
             ### Installation
-            - **macOS**: Download the `.dmg` file
+            - **macOS (Apple Silicon)**: Download the `aarch64.dmg` file - uses MLX for fast native inference
+            - **macOS (Intel)**: Download the `x64.dmg` file - uses PyTorch
             - **Windows**: Download the `.msi` installer
             - **Linux**: Download the `.AppImage` or `.deb` package
 

.npmrc

@@ -0,0 +1,2 @@
+# Force bun usage
+engine-strict=true

CHANGELOG.md

@@ -53,6 +53,20 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+- **Makefile** - Comprehensive development workflow automation with commands for setup, development, building, testing, and code quality checks
+  - Includes Python version detection and compatibility warnings
+  - Self-documenting help system with `make help`
+  - Colored output for better readability
+  - Supports parallel development server execution
+
+### Changed
+- **README** - Added Makefile reference and updated Quick Start with Makefile-based setup instructions alongside manual setup
+
+---
+
+## [Unreleased - Planned]
+
 ### Planned
 - Real-time streaming synthesis
 - Conversation mode with multiple speakers

CONTRIBUTING.md

@@ -32,6 +32,10 @@ Thank you for your interest in contributing to Voicebox! This document provides
 
 ### Development Setup
 
+**Using the Makefile (recommended for macOS/Linux):** Run `make setup` to install all dependencies, then `make dev` to start development servers. See `make help` for all available commands.
+
+**Manual setup (required for Windows):**
+
 1. **Fork and clone the repository**
    ```bash
    git clone https://github.com/YOUR_USERNAME/voicebox.git
@@ -62,37 +66,43 @@ Thank you for your interest in contributing to Voicebox! This document provides
    # Install Python dependencies
    pip install -r requirements.txt
    
+   # Install MLX dependencies (Apple Silicon only - for faster inference)
+   # On Apple Silicon, this enables native Metal acceleration
+   if [[ $(uname -m) == "arm64" ]]; then
+     pip install -r requirements-mlx.txt
+   fi
+   
    # Install Qwen3-TTS (required for voice synthesis)
    pip install git+https://github.com/QwenLM/Qwen3-TTS.git
    ```
 
-4. **Initialize database**
+4. **Start development servers**
-   ```bash
-   cd backend
-   python -c "from database import init_db; init_db()"
-   ```
-   This creates the SQLite database at `data/voicebox.db`.
 
-5. **Start development servers**
+   Development requires two terminals: one for the Python backend, one for the Tauri app.
-   
+
-   **Terminal 1: Backend server**
+   **Terminal 1: Backend server** (start this first)
    ```bash
    cd backend
    source venv/bin/activate  # Activate venv if not already active
    bun run dev:server
-   # Or manually: uvicorn main:app --reload --port 8000
+   # Or manually: uvicorn main:app --reload --port 17493
    ```
-   Backend will be available at `http://localhost:8000`
+   Backend will be available at `http://localhost:17493`
-   
+
    **Terminal 2: Desktop app**
    ```bash
    bun run dev
    ```
    This will:
+   - Create a placeholder sidecar binary (for Tauri compilation)
    - Start Vite dev server on port 5173
    - Launch Tauri window pointing to localhost:5173
+   - Connect to the Python server you started in Terminal 1
    - Enable hot reload
 
+   > **Note:** In dev mode, the app connects to your manually-started Python server.
+   > The bundled server binary is only used in production builds.
+
    **Optional: Web app**
    ```bash
    bun run dev:web
@@ -109,18 +119,36 @@ First-time usage will be slower due to model downloads, but subsequent runs will
 
 ### Building
 
-**Build Python server binary:**
+**Build everything (recommended):**
 ```bash
+bun run build
+```
+This automatically:
+1. Builds the Python server binary (`./scripts/build-server.sh`)
+2. Builds the Tauri desktop app (`cd tauri && bun run tauri build`)
+
+Creates platform-specific installers (`.dmg`, `.msi`, `.AppImage`) in `tauri/src-tauri/target/release/bundle/`.
+
+**Note:** The build process detects your platform and includes the appropriate backend (MLX for Apple Silicon, PyTorch for others).
+
+**Build server binary only:**
+```bash
+bun run build:server
+# or
 ./scripts/build-server.sh
 ```
 Creates platform-specific binary in `tauri/src-tauri/binaries/`
 
-**Build Tauri desktop app:**
+**Building with local Qwen3-TTS development version:**
+
+If you're actively developing or modifying the Qwen3-TTS library, set the `QWEN_TTS_PATH` environment variable to point to your local clone:
+
 ```bash
-cd tauri
+export QWEN_TTS_PATH=~/path/to/your/Qwen3-TTS
-bun run tauri build
+bun run build:server
 ```
-Creates platform-specific installers (`.dmg`, `.msi`, `.AppImage`)
+
+This makes PyInstaller use your local qwen-tts version instead of the pip-installed package. Useful when testing changes to the TTS library before they're published to PyPI or when using an editable install (`pip install -e`).
 
 **Build web app:**
 ```bash
@@ -137,6 +165,26 @@ After starting the backend server:
 ```
 This downloads the OpenAPI schema and generates the TypeScript client in `app/src/lib/api/`
 
+### Convert Assets to Web Formats
+
+To optimize images and videos for the web, run:
+```bash
+bun run convert:assets
+```
+
+This script:
+- Converts PNG → WebP (better compression, same quality)
+- Converts MOV → WebM (VP9 codec, smaller file size)
+- Processes files in `landing/public/` and `docs/public/`
+- **Deletes original files** after successful conversion
+
+**Requirements:** Install `webp` and `ffmpeg`:
+```bash
+brew install webp ffmpeg
+```
+
+> **Note:** Run this before committing new images or videos to keep the repository size small.
+
 ## Development Workflow
 
 ### 1. Create a Branch

Makefile

@@ -0,0 +1,245 @@
+# Voicebox Makefile
+# Unix-only (macOS/Linux). Windows users should use WSL.
+
+SHELL := /bin/bash
+.DEFAULT_GOAL := help
+
+# Directories
+BACKEND_DIR := backend
+TAURI_DIR := tauri
+WEB_DIR := web
+APP_DIR := app
+
+# Python (prefer 3.12, fallback to 3.13, then python3)
+PYTHON := $(shell command -v python3.12 2>/dev/null || command -v python3.13 2>/dev/null || echo python3)
+VENV := $(CURDIR)/$(BACKEND_DIR)/venv
+VENV_BIN := $(VENV)/bin
+PIP := $(VENV_BIN)/pip
+PYTHON_VENV := $(VENV_BIN)/python
+
+# Colors for output
+BLUE := \033[0;34m
+GREEN := \033[0;32m
+YELLOW := \033[0;33m
+NC := \033[0m # No Color
+
+.PHONY: help
+help: ## Show this help message
+	@echo -e "$(BLUE)Voicebox$(NC) - Development Commands"
+	@echo ""
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | \
+		awk 'BEGIN {FS = ":.*?## "}; {printf "  $(GREEN)%-20s$(NC) %s\n", $$1, $$2}'
+
+# =============================================================================
+# SETUP
+# =============================================================================
+
+.PHONY: setup setup-js setup-python setup-rust
+
+setup: setup-js setup-python ## Full project setup (all dependencies)
+	@echo -e "$(GREEN)✓ Setup complete!$(NC)"
+	@echo -e "  Run $(YELLOW)make dev$(NC) to start development servers"
+
+setup-js: ## Install JavaScript dependencies (bun)
+	@echo -e "$(BLUE)Installing JavaScript dependencies...$(NC)"
+	bun install
+
+setup-python: $(VENV)/bin/activate ## Set up Python virtual environment and dependencies
+	@echo -e "$(BLUE)Installing Python dependencies...$(NC)"
+	$(PIP) install --upgrade pip
+	$(PIP) install -r $(BACKEND_DIR)/requirements.txt
+	@if [ "$$(uname -m)" = "arm64" ] && [ "$$(uname)" = "Darwin" ]; then \
+		echo -e "$(BLUE)Detected Apple Silicon - installing MLX dependencies...$(NC)"; \
+		$(PIP) install -r $(BACKEND_DIR)/requirements-mlx.txt; \
+		echo -e "$(GREEN)✓ MLX backend enabled (native Metal acceleration)$(NC)"; \
+	fi
+	$(PIP) install git+https://github.com/QwenLM/Qwen3-TTS.git
+	@echo -e "$(GREEN)✓ Python environment ready$(NC)"
+
+$(VENV)/bin/activate:
+	@echo -e "$(BLUE)Creating Python virtual environment...$(NC)"
+	@PY_MINOR=$$($(PYTHON) -c "import sys; print(sys.version_info[1])"); \
+	if [ "$$PY_MINOR" -gt 13 ]; then \
+		echo -e "$(YELLOW)Warning: Python 3.$$PY_MINOR detected. ML packages may not be compatible.$(NC)"; \
+		echo -e "$(YELLOW)Recommended: Use Python 3.12 or 3.13 (brew install [email protected])$(NC)"; \
+	fi
+	$(PYTHON) -m venv $(VENV)
+
+setup-rust: ## Install Rust toolchain (if not present)
+	@command -v rustc >/dev/null 2>&1 || curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+
+# =============================================================================
+# DEVELOPMENT
+# =============================================================================
+
+.PHONY: dev dev-backend dev-frontend dev-web kill-dev
+
+dev: ## Start backend + desktop app (parallel)
+	@echo -e "$(BLUE)Starting development servers...$(NC)"
+	@echo -e "$(YELLOW)Note: If Tauri fails, run 'make build-server' first or use separate terminals$(NC)"
+	@trap 'kill 0' EXIT; \
+		$(MAKE) dev-backend & \
+		sleep 2 && $(MAKE) dev-frontend & \
+		wait
+
+dev-backend: ## Start FastAPI backend server
+	@echo -e "$(BLUE)Starting backend server on http://localhost:17493$(NC)"
+	$(VENV_BIN)/uvicorn backend.main:app --reload --port 17493
+
+dev-frontend: ## Start Tauri desktop app
+	@echo -e "$(BLUE)Starting Tauri desktop app...$(NC)"
+	bun run dev
+
+dev-web: ## Start backend + web app (parallel)
+	@echo -e "$(BLUE)Starting web development servers...$(NC)"
+	@trap 'kill 0' EXIT; \
+		$(MAKE) dev-backend & \
+		sleep 2 && cd $(WEB_DIR) && bun run dev & \
+		wait
+
+kill-dev: ## Kill all development processes
+	@echo -e "$(YELLOW)Killing development processes...$(NC)"
+	-pkill -f "uvicorn main:app" 2>/dev/null || true
+	-pkill -f "vite" 2>/dev/null || true
+	@echo -e "$(GREEN)✓ Processes killed$(NC)"
+
+# =============================================================================
+# BUILD
+# =============================================================================
+
+.PHONY: build build-server build-tauri build-web
+
+build: build-server build-tauri ## Build everything (server binary + desktop app)
+	@echo -e "$(GREEN)✓ Build complete!$(NC)"
+
+build-server: ## Build Python server binary
+	@echo -e "$(BLUE)Building server binary...$(NC)"
+	PATH="$(VENV_BIN):$$PATH" ./scripts/build-server.sh
+
+build-tauri: ## Build Tauri desktop app
+	@echo -e "$(BLUE)Building Tauri desktop app...$(NC)"
+	cd $(TAURI_DIR) && bun run tauri build
+
+build-web: ## Build web app
+	@echo -e "$(BLUE)Building web app...$(NC)"
+	cd $(WEB_DIR) && bun run build
+	@echo -e "$(GREEN)✓ Web build output in $(WEB_DIR)/dist/$(NC)"
+
+# =============================================================================
+# DATABASE & API
+# =============================================================================
+
+.PHONY: db-init db-reset generate-api
+
+db-init: $(VENV)/bin/activate ## Initialize SQLite database
+	@echo -e "$(BLUE)Initializing database...$(NC)"
+	cd $(BACKEND_DIR) && $(PYTHON_VENV) -c "from database import init_db; init_db()"
+	@echo -e "$(GREEN)✓ Database created at $(BACKEND_DIR)/data/voicebox.db$(NC)"
+
+db-reset: ## Reset database (delete and reinitialize)
+	@echo -e "$(YELLOW)Resetting database...$(NC)"
+	rm -f $(BACKEND_DIR)/data/voicebox.db
+	$(MAKE) db-init
+
+generate-api: ## Generate TypeScript API client from OpenAPI schema
+	@echo -e "$(BLUE)Generating API client...$(NC)"
+	@echo -e "$(YELLOW)Note: Backend must be running (make dev-backend)$(NC)"
+	./scripts/generate-api.sh
+	@echo -e "$(GREEN)✓ API client generated in $(APP_DIR)/src/lib/api/$(NC)"
+
+# =============================================================================
+# CODE QUALITY
+# =============================================================================
+
+.PHONY: lint format typecheck check
+
+lint: ## Run linter (Biome)
+	@echo -e "$(BLUE)Linting...$(NC)"
+	bun run lint
+
+format: ## Format code (Biome)
+	@echo -e "$(BLUE)Formatting...$(NC)"
+	bun run format
+
+typecheck: ## Run TypeScript type checking
+	@echo -e "$(BLUE)Type checking...$(NC)"
+	bun run tsc --noEmit
+
+check: ## Run all checks (Biome lint + format + type check)
+	@echo -e "$(BLUE)Running all checks...$(NC)"
+	bun run check
+	@echo -e "$(GREEN)✓ All checks passed$(NC)"
+
+# =============================================================================
+# TESTING
+# =============================================================================
+
+.PHONY: test test-backend test-frontend
+
+test: test-backend test-frontend ## Run all tests
+	@echo -e "$(GREEN)✓ All tests passed$(NC)"
+
+test-backend: ## Run Python backend tests (requires pytest)
+	@echo -e "$(BLUE)Running backend tests...$(NC)"
+	@if [ -f "$(VENV_BIN)/pytest" ]; then \
+		cd $(BACKEND_DIR) && $(VENV_BIN)/pytest -v; \
+	else \
+		echo -e "$(YELLOW)pytest not installed. Run: $(PIP) install pytest$(NC)"; \
+		exit 1; \
+	fi
+
+test-frontend: ## Run frontend tests (requires test script in package.json)
+	@echo -e "$(BLUE)Running frontend tests...$(NC)"
+	@if bun run test --help >/dev/null 2>&1; then \
+		bun run test; \
+	else \
+		echo -e "$(YELLOW)No test script configured$(NC)"; \
+		exit 1; \
+	fi
+
+# =============================================================================
+# LOGS & DEBUGGING
+# =============================================================================
+
+.PHONY: logs docs
+
+logs: ## Tail backend logs
+	@echo -e "$(BLUE)Tailing logs (Ctrl+C to stop)...$(NC)"
+	tail -f $(BACKEND_DIR)/logs/*.log 2>/dev/null || echo "No log files found"
+
+docs: ## Open API documentation (backend must be running)
+	@echo -e "$(BLUE)Opening API docs...$(NC)"
+	open http://localhost:17493/docs 2>/dev/null || xdg-open http://localhost:17493/docs
+
+# =============================================================================
+# CLEAN
+# =============================================================================
+
+.PHONY: clean clean-python clean-build clean-all
+
+clean: ## Clean build artifacts
+	@echo -e "$(BLUE)Cleaning build artifacts...$(NC)"
+	rm -rf $(TAURI_DIR)/src-tauri/target/release
+	rm -rf $(WEB_DIR)/dist
+	rm -rf $(APP_DIR)/dist
+	@echo -e "$(GREEN)✓ Build artifacts cleaned$(NC)"
+
+clean-python: ## Clean Python cache and virtual environment
+	@echo -e "$(BLUE)Cleaning Python files...$(NC)"
+	rm -rf $(VENV)
+	find $(BACKEND_DIR) -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true
+	find $(BACKEND_DIR) -type f -name "*.pyc" -delete 2>/dev/null || true
+	@echo -e "$(GREEN)✓ Python environment cleaned$(NC)"
+
+clean-build: ## Clean Rust/Tauri build cache
+	@echo -e "$(BLUE)Cleaning Rust build cache...$(NC)"
+	cd $(TAURI_DIR)/src-tauri && cargo clean
+	@echo -e "$(GREEN)✓ Rust cache cleaned$(NC)"
+
+clean-all: clean clean-python clean-build ## Nuclear clean (everything)
+	@echo -e "$(BLUE)Cleaning node_modules...$(NC)"
+	rm -rf node_modules
+	rm -rf $(APP_DIR)/node_modules
+	rm -rf $(TAURI_DIR)/node_modules
+	rm -rf $(WEB_DIR)/node_modules
+	@echo -e "$(GREEN)✓ Full clean complete$(NC)"

README.md

@@ -10,6 +10,21 @@
   All running locally on your machine.
 </p>
 
+<p align="center">
+  <a href="https://github.com/jamiepine/voicebox/releases">
+    <img src="https://img.shields.io/github/downloads/jamiepine/voicebox/total?style=flat&color=blue" alt="Downloads" />
+  </a>
+  <a href="https://github.com/jamiepine/voicebox/releases/latest">
+    <img src="https://img.shields.io/github/v/release/jamiepine/voicebox?style=flat" alt="Release" />
+  </a>
+  <a href="https://github.com/jamiepine/voicebox/stargazers">
+    <img src="https://img.shields.io/github/stars/jamiepine/voicebox?style=flat" alt="Stars" />
+  </a>
+  <a href="https://github.com/jamiepine/voicebox/blob/main/LICENSE">
+    <img src="https://img.shields.io/github/license/jamiepine/voicebox?style=flat" alt="License" />
+  </a>
+</p>
+
 <p align="center">
   <a href="https://voicebox.sh">voicebox.sh</a> •
   <a href="#download">Download</a> •
@@ -22,7 +37,7 @@
 
 <p align="center">
   <a href="https://voicebox.sh">
-    <img src=".github/assets/screenshot.webp" alt="Voicebox App Screenshot" width="800" />
+    <img src="landing/public/assets/app-screenshot-1.webp" alt="Voicebox App Screenshot" width="800" />
   </a>
 </p>
 
@@ -32,17 +47,30 @@
 
 <br/>
 
-## Why Voicebox?
+<p align="center">
+  <img src="landing/public/assets/app-screenshot-2.webp" alt="Voicebox Screenshot 2" width="800" />
+</p>
 
-Voice AI is exploding, but most tools are either cloud-locked, expensive, or a nightmare to set up. Voicebox is different:
+<p align="center">
+  <img src="landing/public/assets/app-screenshot-3.webp" alt="Voicebox Screenshot 3" width="800" />
+</p>
 
-- **100% Local** — Your voice data never leaves your machine
+<br/>
-- **Lightweight** — No bloated Electron, native Tauri performance
-- **Fast** — Near-instant on CUDA, optimized for Apple Silicon
-- **Flexible** — Use the app, integrate the API, or both
-- **Open Source** — No subscriptions, no limits, no lock-in
 
-Built with **Tauri** (Rust), **TypeScript**, **React**, and **Python**. Native performance meets modern DX.
+## What is Voicebox?
+
+Voicebox is a **local-first voice cloning studio** with DAW-like features for professional voice synthesis. Think of it as the **Ollama for voice** — download models, clone voices, and generate speech entirely on your machine.
+
+Unlike cloud services that lock your voice data behind subscriptions, Voicebox gives you:
+
+- **Complete privacy** — models and voice data stay on your machine
+- **Professional tools** — multi-track timeline editor, audio trimming, conversation mixing
+- **Model flexibility** — currently powered by Qwen3-TTS, with support for XTTS, Bark, and other models coming soon
+- **API-first** — use the desktop app or integrate voice synthesis into your own projects
+- **Native performance** — built with Tauri (Rust), not Electron
+- **Super fast on Mac** — MLX backend with native Metal acceleration for 4-5x faster inference on Apple Silicon
+
+Download a voice model, clone any voice from a few seconds of audio, and compose multi-voice projects with studio-grade editing tools. No Python install required, no cloud dependency, no limits.
 
 ---
 
@@ -70,11 +98,13 @@ Powered by Alibaba's **Qwen3-TTS** — a breakthrough model that achieves near-p
 - **Instant cloning** — Upload a sample, get a voice profile
 - **High fidelity** — Natural prosody, emotion, and cadence
 - **Multi-language** — English, Chinese, and more coming
+- **Lightning fast on Mac** — MLX backend leverages Apple Silicon's Neural Engine for super fast generation
 
 ### Voice Profile Management
 
 - **Create profiles** from audio files or record directly in-app
 - **Import/Export** profiles to share or backup
+- **Multi-sample support** — combine multiple samples for higher quality cloning
 - **Organize** with descriptions and language tags
 
 ### Speech Generation
@@ -83,9 +113,19 @@ Powered by Alibaba's **Qwen3-TTS** — a breakthrough model that achieves near-p
 - **Batch generation** for long-form content
 - **Smart caching** — regenerate instantly with voice prompt caching
 
+### Stories Editor
+
+Create multi-voice narratives, podcasts, and conversations with a timeline-based editor.
+
+- **Multi-track composition** — arrange multiple voice tracks in a single project
+- **Inline audio editing** — trim and split clips directly in the timeline
+- **Auto-playback** — preview stories with synchronized playhead
+- **Voice mixing** — build conversations with multiple participants
+
 ### Recording & Transcription
 
 - **In-app recording** with waveform visualization
+- **System audio capture** — record desktop audio on macOS and Windows
 - **Automatic transcription** powered by Whisper
 - **Export recordings** in multiple formats
 
@@ -109,17 +149,17 @@ Voicebox exposes a full REST API, so you can integrate voice synthesis into your
 
 ```bash
 # Generate speech
-curl -X POST http://localhost:8000/api/generate \
+curl -X POST http://localhost:8000/generate \
   -H "Content-Type: application/json" \
-  -d '{"text": "Hello world", "profile_id": "abc123"}'
+  -d '{"text": "Hello world", "profile_id": "abc123", "language": "en"}'
 
 # List voice profiles
-curl http://localhost:8000/api/profiles
+curl http://localhost:8000/profiles
 
-# Create a profile from audio
+# Create a profile
-curl -X POST http://localhost:8000/api/profiles \
+curl -X POST http://localhost:8000/profiles \
-  -F "[email protected]" \
+  -H "Content-Type: application/json" \
-  -F "name=My Voice"
+  -d '{"name": "My Voice", "language": "en"}'
 ```
 
 **Use cases:**
@@ -142,8 +182,9 @@ Full API documentation available at `http://localhost:8000/docs` when running.
 | Frontend | React, TypeScript, Tailwind CSS |
 | State | Zustand, React Query |
 | Backend | FastAPI (Python) |
-| Voice Model | Qwen3-TTS |
+| Voice Model | Qwen3-TTS (PyTorch or MLX) |
-| Transcription | Whisper |
+| Transcription | Whisper (PyTorch or MLX) |
+| Inference Engine | MLX (Apple Silicon) / PyTorch (Windows/Linux/Intel) |
 | Database | SQLite |
 | Audio | WaveSurfer.js, librosa |
 
@@ -184,8 +225,26 @@ Voicebox aims to be the **one-stop shop for everything voice** — cloning, synt
 
 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed setup and contribution guidelines.
 
+**Using the Makefile (recommended):** Run `make help` to see all available commands for setup, development, building, and testing.
+
 ### Quick Start
 
+**With Makefile (Unix/macOS/Linux):**
+
+```bash
+# Clone the repo
+git clone https://github.com/voicebox-sh/voicebox.git
+cd voicebox
+
+# Setup everything
+make setup
+
+# Start development
+make dev
+```
+
+**Manual setup (all platforms):**
+
 ```bash
 # Clone the repo
 git clone https://github.com/voicebox-sh/voicebox.git
@@ -201,7 +260,11 @@ cd backend && pip install -r requirements.txt && cd ..
 bun run dev
 ```
 
-**Prerequisites:** [Bun](https://bun.sh), [Rust](https://rustup.rs), [Python 3.11+](https://python.org). CUDA-capable GPU recommended (CPU inference supported but slower).
+**Prerequisites:** [Bun](https://bun.sh), [Rust](https://rustup.rs), [Python 3.11+](https://python.org). 
+
+**Performance:** 
+- **Apple Silicon (M1/M2/M3)**: Uses MLX backend with native Metal acceleration for 4-5x faster inference
+- **Windows/Linux/Intel Mac**: Uses PyTorch backend (CUDA GPU recommended, CPU supported but slower)
 
 ### Project Structure
 

app/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@voicebox/app",
-  "version": "0.1.7",
+  "version": "0.1.11",
   "private": true,
   "type": "module",
   "scripts": {
@@ -48,6 +48,7 @@
     "react": "^18.3.0",
     "react-dom": "^18.3.0",
     "react-hook-form": "^7.53.0",
+    "react-sound-visualizer": "^1.4.0",
     "tailwind-merge": "^2.5.4",
     "wavesurfer.js": "^7.0.0",
     "zod": "^3.23.8",

app/src/App.tsx

@@ -4,15 +4,10 @@ import voiceboxLogo from '@/assets/voicebox-logo.png';
 import ShinyText from '@/components/ShinyText';
 import { TitleBarDragRegion } from '@/components/TitleBarDragRegion';
 import { TOP_SAFE_AREA_PADDING } from '@/lib/constants/ui';
-import {
-  isTauri,
-  setKeepServerRunning,
-  setupWindowCloseHandler,
-  startServer,
-} from '@/lib/tauri';
 import { cn } from '@/lib/utils/cn';
 import { router } from '@/router';
 import { useServerStore } from '@/stores/serverStore';
+import { usePlatform } from '@/platform/PlatformContext';
 
 const LOADING_MESSAGES = [
   'Warming up tensors...',
@@ -38,29 +33,38 @@ const LOADING_MESSAGES = [
 ];
 
 function App() {
+  const platform = usePlatform();
   const [serverReady, setServerReady] = useState(false);
   const [loadingMessageIndex, setLoadingMessageIndex] = useState(0);
   const serverStartingRef = useRef(false);
 
   // Sync stored setting to Rust on startup
   useEffect(() => {
-    if (isTauri()) {
+    if (platform.metadata.isTauri) {
       const keepRunning = useServerStore.getState().keepServerRunningOnClose;
-      setKeepServerRunning(keepRunning).catch((error) => {
+      platform.lifecycle.setKeepServerRunning(keepRunning).catch((error) => {
         console.error('Failed to sync initial setting to Rust:', error);
       });
     }
-  }, []);
+  }, [platform]);
+
+  // Setup lifecycle callbacks
+  useEffect(() => {
+    platform.lifecycle.onServerReady = () => {
+      setServerReady(true);
+    };
+  }, [platform]);
 
   // Setup window close handler and auto-start server when running in Tauri (production only)
   useEffect(() => {
-    if (!isTauri()) {
+    if (!platform.metadata.isTauri) {
+      setServerReady(true); // Web assumes server is running
       return;
     }
 
     // Setup window close handler to check setting and stop server if needed
     // This works in both dev and prod, but will only stop server if it was started by the app
-    setupWindowCloseHandler().catch((error) => {
+    platform.lifecycle.setupWindowCloseHandler().catch((error) => {
       console.error('Failed to setup window close handler:', error);
     });
 
@@ -83,18 +87,21 @@ function App() {
     serverStartingRef.current = true;
     console.log('Production mode: Starting bundled server...');
 
-    startServer(false)
+    platform.lifecycle
+      .startServer(false)
       .then((serverUrl) => {
         console.log('Server is ready at:', serverUrl);
         // Update the server URL in the store with the dynamically assigned port
         useServerStore.getState().setServerUrl(serverUrl);
         setServerReady(true);
         // Mark that we started the server (so we know to stop it on close)
+        // @ts-expect-error - adding property to window
         window.__voiceboxServerStartedByApp = true;
       })
       .catch((error) => {
         console.error('Failed to auto-start server:', error);
         serverStartingRef.current = false;
+        // @ts-expect-error - adding property to window
         window.__voiceboxServerStartedByApp = false;
       });
 
@@ -104,11 +111,11 @@ function App() {
       // Window close event handles server shutdown based on setting
       serverStartingRef.current = false;
     };
-  }, []);
+  }, [platform]);
 
   // Cycle through loading messages every 3 seconds
   useEffect(() => {
-    if (!isTauri() || serverReady) {
+    if (!platform.metadata.isTauri || serverReady) {
       return;
     }
 
@@ -117,10 +124,10 @@ function App() {
     }, 3000);
 
     return () => clearInterval(interval);
-  }, [serverReady]);
+  }, [serverReady, platform.metadata.isTauri]);
 
   // Show loading screen while server is starting in Tauri
-  if (isTauri() && !serverReady) {
+  if (platform.metadata.isTauri && !serverReady) {
     return (
       <div
         className={cn(

app/src/components/AudioPlayer/AudioPlayer.tsx

@@ -1,17 +1,17 @@
 import { useQuery } from '@tanstack/react-query';
-import { invoke } from '@tauri-apps/api/core';
 import { Pause, Play, Repeat, Volume2, VolumeX, X } from 'lucide-react';
 import { useEffect, useMemo, useRef, useState } from 'react';
 import WaveSurfer from 'wavesurfer.js';
 import { Button } from '@/components/ui/button';
 import { Slider } from '@/components/ui/slider';
 import { apiClient } from '@/lib/api/client';
-import { isTauri } from '@/lib/tauri';
 import { formatAudioDuration } from '@/lib/utils/audio';
 import { debug } from '@/lib/utils/debug';
 import { usePlayerStore } from '@/stores/playerStore';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function AudioPlayer() {
+  const platform = usePlatform();
   const {
     audioUrl,
     audioId,
@@ -39,7 +39,7 @@ export function AudioPlayer() {
       if (!profileId) return { channel_ids: [] };
       return apiClient.getProfileChannels(profileId);
     },
-    enabled: !!profileId && isTauri(),
+    enabled: !!profileId && platform.metadata.isTauri,
   });
 
   const { data: channels } = useQuery({
@@ -50,7 +50,7 @@ export function AudioPlayer() {
 
   // Determine if we should use native playback
   const useNativePlayback = useMemo(() => {
-    if (!isTauri() || !profileChannels || !channels) {
+    if (!platform.metadata.isTauri || !profileChannels || !channels) {
       return false;
     }
 
@@ -195,7 +195,7 @@ export function AudioPlayer() {
         let runtimeProfileChannels = null;
         let runtimeChannels = null;
 
-        if (isTauri() && currentProfileId) {
+        if (platform.metadata.isTauri && currentProfileId) {
           try {
             runtimeProfileChannels = await apiClient.getProfileChannels(currentProfileId);
             debug.log('Runtime profileChannels:', runtimeProfileChannels);
@@ -210,7 +210,7 @@ export function AudioPlayer() {
         }
 
         debug.log('Auto-play check:', {
-          isTauri: isTauri(),
+          isTauri: platform.metadata.isTauri,
           currentAudioUrl,
           currentProfileId,
           hasProfileChannels: !!runtimeProfileChannels,
@@ -218,7 +218,7 @@ export function AudioPlayer() {
         });
 
         if (
-          isTauri() &&
+          platform.metadata.isTauri &&
           currentAudioUrl &&
           currentProfileId &&
           runtimeProfileChannels &&
@@ -229,7 +229,7 @@ export function AudioPlayer() {
           // Stop any existing native playback first
           if (isUsingNativePlaybackRef.current) {
             try {
-              await invoke('stop_audio_playback');
+              platform.audio.stopPlayback();
               debug.log('Stopped existing native playback before starting new one');
             } catch (error) {
               debug.error('Failed to stop existing playback:', error);
@@ -279,11 +279,8 @@ export function AudioPlayer() {
                 // Play via native audio
                 debug.log('Invoking play_audio_to_devices...');
                 try {
-                  const result = await invoke('play_audio_to_devices', {
+                  await platform.audio.playToDevices(audioData, deviceIds);
-                    audioData: Array.from(audioData),
+                  debug.log('play_audio_to_devices completed successfully');
-                    deviceIds: deviceIds,
-                  });
-                  debug.log('play_audio_to_devices completed successfully, result:', result);
 
                   // Mark that we're using native playback
                   isUsingNativePlaybackRef.current = true;
@@ -516,15 +513,13 @@ export function AudioPlayer() {
     }
 
     // Stop native playback if it was active
-    if (isUsingNativePlaybackRef.current && isTauri()) {
+    if (isUsingNativePlaybackRef.current && platform.metadata.isTauri) {
-      (async () => {
+      try {
-        try {
+        platform.audio.stopPlayback();
-          await invoke('stop_audio_playback');
+        debug.log('Stopped native audio playback');
-          debug.log('Stopped native audio playback');
+      } catch (error) {
-        } catch (error) {
+        debug.error('Failed to stop native playback:', error);
-          debug.error('Failed to stop native playback:', error);
+      }
-        }
-      })();
     }
 
     // Reset native playback flag when loading new audio
@@ -711,7 +706,7 @@ export function AudioPlayer() {
       if (isPlaying) {
         // Pause: stop native playback and pause WaveSurfer visualization
         try {
-          await invoke('stop_audio_playback');
+          platform.audio.stopPlayback();
           debug.log('Stopped native audio playback');
         } catch (error) {
           debug.error('Failed to stop native playback:', error);
@@ -724,7 +719,7 @@ export function AudioPlayer() {
       try {
         // Stop any existing native playback first
         try {
-          await invoke('stop_audio_playback');
+          platform.audio.stopPlayback();
         } catch (_error) {
           // Ignore errors when stopping (might not be playing)
           debug.log('No existing playback to stop');
@@ -742,10 +737,7 @@ export function AudioPlayer() {
           const audioData = new Uint8Array(await response.arrayBuffer());
 
           // Play via native audio
-          await invoke('play_audio_to_devices', {
+          await platform.audio.playToDevices(audioData, deviceIds);
-            audioData: Array.from(audioData),
-            deviceIds: deviceIds,
-          });
 
           // Mark that we're using native playback
           isUsingNativePlaybackRef.current = true;
@@ -806,10 +798,12 @@ export function AudioPlayer() {
 
   const handleClose = () => {
     // Stop any native playback
-    if (isUsingNativePlaybackRef.current && isTauri()) {
+    if (isUsingNativePlaybackRef.current && platform.metadata.isTauri) {
-      invoke('stop_audio_playback').catch((error) => {
+      try {
+        platform.audio.stopPlayback();
+      } catch (error) {
         debug.error('Failed to stop native playback:', error);
-      });
+      }
     }
     // Stop WaveSurfer
     if (wavesurferRef.current) {

app/src/components/AudioTab/AudioTab.tsx

@@ -1,5 +1,4 @@
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
-import { invoke } from '@tauri-apps/api/core';
 import { Check, CheckCircle2, Edit, Plus, Speaker, Trash2 } from 'lucide-react';
 import { useState } from 'react';
 import { Badge } from '@/components/ui/badge';
@@ -23,9 +22,9 @@ import {
 } from '@/components/ui/select';
 import { apiClient } from '@/lib/api/client';
 import { BOTTOM_SAFE_AREA_PADDING } from '@/lib/constants/ui';
-import { isTauri } from '@/lib/tauri';
 import { cn } from '@/lib/utils/cn';
 import { usePlayerStore } from '@/stores/playerStore';
+import { usePlatform } from '@/platform/PlatformContext';
 
 interface AudioDevice {
   id: string;
@@ -34,6 +33,7 @@ interface AudioDevice {
 }
 
 export function AudioTab() {
+  const platform = usePlatform();
   const [createDialogOpen, setCreateDialogOpen] = useState(false);
   const [editingChannel, setEditingChannel] = useState<string | null>(null);
   const [selectedChannelId, setSelectedChannelId] = useState<string | null>(null);
@@ -49,18 +49,17 @@ export function AudioTab() {
   const { data: devices, isLoading: devicesLoading } = useQuery({
     queryKey: ['audio-devices'],
     queryFn: async () => {
-      if (!isTauri()) {
+      if (!platform.metadata.isTauri) {
         return [];
       }
       try {
-        const result = await invoke<AudioDevice[]>('list_audio_output_devices');
+        return await platform.audio.listOutputDevices();
-        return result;
       } catch (error) {
         console.error('Failed to list audio devices:', error);
         return [];
       }
     },
-    enabled: isTauri(),
+    enabled: platform.metadata.isTauri,
   });
 
   const { data: profiles } = useQuery({
@@ -342,7 +341,7 @@ export function AudioTab() {
             <div className="flex flex-col items-center justify-center py-12 border-2 border-dashed border-muted rounded-md">
               <CheckCircle2 className="h-12 w-12 text-muted-foreground mb-4" />
               <p className="text-muted-foreground text-center">
-                {isTauri() ? 'No audio devices found' : 'Audio device selection requires Tauri'}
+                {platform.metadata.isTauri ? 'No audio devices found' : 'Audio device selection requires Tauri'}
               </p>
             </div>
           )}

app/src/components/Generation/FloatingGenerateBox.tsx

@@ -112,9 +112,6 @@ export function FloatingGenerateBox({
     }
   }, [selectedProfileId, profiles, setSelectedProfileId]);
 
-  // Get current form value to trigger resize when it changes
-  const formValue = form.watch(isInstructMode ? 'instruct' : 'text');
-
   // Auto-resize textarea based on content (only when expanded)
   useEffect(() => {
     if (!isExpanded) {
@@ -196,59 +193,104 @@ export function FloatingGenerateBox({
         <Form {...form}>
           <form onSubmit={form.handleSubmit(onSubmit)}>
             <div className="flex gap-2">
-              <motion.div className="flex-1" transition={{ duration: 0.3, ease: 'easeOut' }}>
+              <motion.div
-                {isInstructMode && (
+                className={cn('flex-1', isExpanded && 'mr-12')}
-                  <span className="text-xs text-accent font-medium mb-1 block">
+                transition={{ duration: 0.3, ease: 'easeOut' }}
-                    Delivery instructions:
+              >
-                  </span>
+                {/* Text field - hidden when in instruct mode */}
-                )}
+                <div style={{ display: isInstructMode ? 'none' : 'block' }}>
-                <FormField
+                  <FormField
-                  control={form.control}
+                    control={form.control}
-                  name={isInstructMode ? 'instruct' : 'text'}
+                    name="text"
-                  render={({ field }) => (
+                    render={({ field }) => (
-                    <FormItem>
+                      <FormItem>
-                      <FormControl>
+                        <FormControl>
-                        <motion.div
+                          <motion.div
-                          animate={{
+                            animate={{
-                            height: isExpanded ? 'auto' : '32px',
+                              height: isExpanded ? 'auto' : '32px',
-                          }}
-                          transition={{ duration: 0.15, ease: 'easeOut' }}
-                          style={{ overflow: 'hidden' }}
-                        >
-                          <Textarea
-                            {...field}
-                            ref={(node: HTMLTextAreaElement | null) => {
-                              // Store ref for auto-resize
-                              textareaRef.current = node;
-                              // Forward ref to react-hook-form
-                              if (typeof field.ref === 'function') {
-                                field.ref(node);
-                              }
                             }}
-                            placeholder={
+                            transition={{ duration: 0.15, ease: 'easeOut' }}
-                              isInstructMode
+                            style={{ overflow: 'hidden' }}
-                                ? 'Add delivery instructions...'
+                          >
-                                : isStoriesRoute && currentStory
+                            <Textarea
+                              {...field}
+                              ref={(node: HTMLTextAreaElement | null) => {
+                                // Store ref for auto-resize (only for active field)
+                                if (!isInstructMode) {
+                                  textareaRef.current = node;
+                                }
+                                // Forward ref to react-hook-form
+                                if (typeof field.ref === 'function') {
+                                  field.ref(node);
+                                }
+                              }}
+                              placeholder={
+                                isStoriesRoute && currentStory
                                   ? `Generate speech for "${currentStory.name}"...`
                                   : selectedProfile
                                     ? `Generate speech using ${selectedProfile.name}...`
                                     : 'Select a voice profile above...'
-                            }
+                              }
-                            className="resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full"
+                              className="resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full"
-                            style={{
+                              style={{
-                              minHeight: isExpanded ? '100px' : '32px',
+                                minHeight: isExpanded ? '100px' : '32px',
-                              maxHeight: '300px',
+                                maxHeight: '300px',
+                              }}
+                              disabled={!selectedProfileId}
+                              onClick={() => setIsExpanded(true)}
+                              onFocus={() => setIsExpanded(true)}
+                            />
+                          </motion.div>
+                        </FormControl>
+                        <FormMessage className="text-xs" />
+                      </FormItem>
+                    )}
+                  />
+                </div>
+                {/* Instruct field - hidden when in text mode */}
+                <div style={{ display: isInstructMode ? 'block' : 'none' }}>
+                  <FormField
+                    control={form.control}
+                    name="instruct"
+                    render={({ field }) => (
+                      <FormItem>
+                        <FormControl>
+                          <motion.div
+                            animate={{
+                              height: isExpanded ? 'auto' : '32px',
                             }}
-                            disabled={!selectedProfileId}
+                            transition={{ duration: 0.15, ease: 'easeOut' }}
-                            onClick={() => setIsExpanded(true)}
+                            style={{ overflow: 'hidden' }}
-                            onFocus={() => setIsExpanded(true)}
+                          >
-                          />
+                            <Textarea
-                        </motion.div>
+                              {...field}
-                      </FormControl>
+                              ref={(node: HTMLTextAreaElement | null) => {
-                      <FormMessage className="text-xs" />
+                                // Store ref for auto-resize (only for active field)
-                    </FormItem>
+                                if (isInstructMode) {
-                  )}
+                                  textareaRef.current = node;
-                />
+                                }
+                                // Forward ref to react-hook-form
+                                if (typeof field.ref === 'function') {
+                                  field.ref(node);
+                                }
+                              }}
+                              placeholder="Add delivery instructions..."
+                              className="resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full"
+                              style={{
+                                minHeight: isExpanded ? '100px' : '32px',
+                                maxHeight: '300px',
+                              }}
+                              disabled={!selectedProfileId}
+                              onClick={() => setIsExpanded(true)}
+                              onFocus={() => setIsExpanded(true)}
+                            />
+                          </motion.div>
+                        </FormControl>
+                        <FormMessage className="text-xs" />
+                      </FormItem>
+                    )}
+                  />
+                </div>
               </motion.div>
 
               <div className="relative shrink-0">
@@ -278,9 +320,12 @@ export function FloatingGenerateBox({
                         variant="ghost"
                         size="icon"
                         onClick={() => setIsInstructMode(!isInstructMode)}
-                        className={`h-10 w-10 rounded-full bg-card border border-border hover:bg-background/50 transition-all duration-200 ${
+                        className={cn(
-                          isInstructMode ? 'text-accent' : ''
+                          'h-10 w-10 rounded-full transition-all duration-200',
-                        }`}
+                          isInstructMode
+                            ? 'bg-accent text-accent-foreground border border-accent hover:bg-accent/90'
+                            : 'bg-card border border-border hover:bg-background/50',
+                        )}
                       >
                         <MessageSquare className="h-4 w-4" />
                       </Button>

app/src/components/History/HistoryTable.tsx

@@ -1,5 +1,6 @@
-import { AudioWaveform, Download, FileArchive, MoreHorizontal, Play, Trash2 } from 'lucide-react';
+import { AudioWaveform, Download, FileArchive, Loader2, MoreHorizontal, Play, Trash2 } from 'lucide-react';
 import { useEffect, useRef, useState } from 'react';
+import type { HistoryResponse } from '@/lib/api/types';
 import { Button } from '@/components/ui/button';
 import {
   Dialog,
@@ -33,18 +34,23 @@ import { usePlayerStore } from '@/stores/playerStore';
 // OLD TABLE-BASED COMPONENT - REMOVED (can be found in git history)
 // This is the new alternate history view with fixed height rows
 
-// NEW ALTERNATE HISTORY VIEW - FIXED HEIGHT ROWS
+// NEW ALTERNATE HISTORY VIEW - FIXED HEIGHT ROWS WITH INFINITE SCROLL
 export function HistoryTable() {
-  const [page, _setPage] = useState(0);
+  const [page, setPage] = useState(0);
+  const [allHistory, setAllHistory] = useState<HistoryResponse[]>([]);
+  const [total, setTotal] = useState(0);
   const [isScrolled, setIsScrolled] = useState(false);
   const scrollRef = useRef<HTMLDivElement>(null);
+  const loadMoreRef = useRef<HTMLDivElement>(null);
   const fileInputRef = useRef<HTMLInputElement>(null);
   const [importDialogOpen, setImportDialogOpen] = useState(false);
   const [selectedFile, setSelectedFile] = useState<File | null>(null);
+  const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
+  const [generationToDelete, setGenerationToDelete] = useState<{ id: string; name: string } | null>(null);
   const limit = 20;
   const { toast } = useToast();
 
-  const { data: historyData, isLoading } = useHistory({
+  const { data: historyData, isLoading, isFetching } = useHistory({
     limit,
     offset: page * limit,
   });
@@ -60,6 +66,56 @@ export function HistoryTable() {
   const audioUrl = usePlayerStore((state) => state.audioUrl);
   const isPlayerVisible = !!audioUrl;
 
+  // Update accumulated history when new data arrives
+  useEffect(() => {
+    if (historyData?.items) {
+      setTotal(historyData.total);
+      if (page === 0) {
+        // Reset to first page
+        setAllHistory(historyData.items);
+      } else {
+        // Append new items, avoiding duplicates
+        setAllHistory((prev) => {
+          const existingIds = new Set(prev.map((item) => item.id));
+          const newItems = historyData.items.filter((item) => !existingIds.has(item.id));
+          return [...prev, ...newItems];
+        });
+      }
+    }
+  }, [historyData, page]);
+
+  // Reset to page 0 when deletions or imports occur
+  useEffect(() => {
+    if (deleteGeneration.isSuccess || importGeneration.isSuccess) {
+      setPage(0);
+      setAllHistory([]);
+    }
+  }, [deleteGeneration.isSuccess, importGeneration.isSuccess]);
+
+  // Intersection Observer for infinite scroll
+  useEffect(() => {
+    const loadMoreEl = loadMoreRef.current;
+    if (!loadMoreEl) return;
+
+    const observer = new IntersectionObserver(
+      (entries) => {
+        const target = entries[0];
+        if (target.isIntersecting && !isFetching && allHistory.length < total) {
+          setPage((prev) => prev + 1);
+        }
+      },
+      {
+        root: scrollRef.current,
+        rootMargin: '100px',
+        threshold: 0.1,
+      },
+    );
+
+    observer.observe(loadMoreEl);
+    return () => observer.disconnect();
+  }, [isFetching, allHistory.length, total]);
+
+  // Track scroll position for gradient effect
   useEffect(() => {
     const scrollEl = scrollRef.current;
     if (!scrollEl) return;
@@ -113,24 +169,16 @@ export function HistoryTable() {
     );
   };
 
-  const _handleImportClick = () => {
+  const handleDeleteClick = (generationId: string, profileName: string) => {
-    file_handleImportClickk.click();
+    setGenerationToDelete({ id: generationId, name: profileName });
+    setDeleteDialogOpen(true);
   };
 
-  const _handleFileChange = (_e: React.ChangeEvent<HTMLInputElement>) => {
+  const handleDeleteConfirm = () => {
-    cons_handleFileChangeet.files?.[0];
+    if (generationToDelete) {
-    if (file) {
+      deleteGeneration.mutate(generationToDelete.id);
-      // Validate file extension
+      setDeleteDialogOpen(false);
-      if (!file.name.endsWith('.voicebox.zip')) {
+      setGenerationToDelete(null);
-        toast({
-          title: 'Invalid file type',
-          description: 'Please select a valid .voicebox.zip file',
-          variant: 'destructive',
-        });
-        return;
-      }
-      setSelectedFile(file);
-      setImportDialogOpen(true);
     }
   };
 
@@ -159,13 +207,16 @@ export function HistoryTable() {
     }
   };
 
-  if (isLoading) {
+  if (isLoading && page === 0) {
-    return null;
+    return (
+      <div className="flex items-center justify-center h-full">
+        <Loader2 className="h-8 w-8 animate-spin text-muted-foreground" />
+      </div>
+    );
   }
 
-  const history = historyData?.items || [];
+  const history = allHistory;
-  const total = historyData?.total || 0;
+  const hasMore = allHistory.length < total;
-  const _hasMore = history.length === limit && (page + 1) * limit < total;
 
   return (
     <div className="flex flex-col h-full min-h-0 relative">
@@ -271,7 +322,7 @@ export function HistoryTable() {
                           Export Package
                         </DropdownMenuItem>
                         <DropdownMenuItem
-                          onClick={() => deleteGeneration.mutate(gen.id)}
+                          onClick={() => handleDeleteClick(gen.id, gen.profile_name)}
                           disabled={deleteGeneration.isPending}
                           className="text-destructive focus:text-destructive"
                         >
@@ -284,10 +335,53 @@ export function HistoryTable() {
                 </div>
               );
             })}
+
+            {/* Load more trigger element */}
+            {hasMore && (
+              <div ref={loadMoreRef} className="flex items-center justify-center py-4">
+                {isFetching && <Loader2 className="h-6 w-6 animate-spin text-muted-foreground" />}
+              </div>
+            )}
+
+            {/* End of list indicator */}
+            {!hasMore && history.length > 0 && (
+              <div className="text-center py-4 text-xs text-muted-foreground">
+                You've reached the end
+              </div>
+            )}
           </div>
         </>
       )}
 
+      <Dialog open={deleteDialogOpen} onOpenChange={setDeleteDialogOpen}>
+        <DialogContent>
+          <DialogHeader>
+            <DialogTitle>Delete Generation</DialogTitle>
+            <DialogDescription>
+              Are you sure you want to delete this generation from "{generationToDelete?.name}"? This action cannot be undone.
+            </DialogDescription>
+          </DialogHeader>
+          <DialogFooter>
+            <Button
+              variant="outline"
+              onClick={() => {
+                setDeleteDialogOpen(false);
+                setGenerationToDelete(null);
+              }}
+            >
+              Cancel
+            </Button>
+            <Button
+              variant="destructive"
+              onClick={handleDeleteConfirm}
+              disabled={deleteGeneration.isPending}
+            >
+              {deleteGeneration.isPending ? 'Deleting...' : 'Delete'}
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
+
       <Dialog open={importDialogOpen} onOpenChange={setImportDialogOpen}>
         <DialogContent>
           <DialogHeader>

app/src/components/ServerSettings/ConnectionForm.tsx

@@ -17,7 +17,7 @@ import { Input } from '@/components/ui/input';
 import { Checkbox } from '@/components/ui/checkbox';
 import { useToast } from '@/components/ui/use-toast';
 import { useServerStore } from '@/stores/serverStore';
-import { setKeepServerRunning } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 
 const connectionSchema = z.object({
   serverUrl: z.string().url('Please enter a valid URL'),
@@ -26,6 +26,7 @@ const connectionSchema = z.object({
 type ConnectionFormValues = z.infer<typeof connectionSchema>;
 
 export function ConnectionForm() {
+  const platform = usePlatform();
   const serverUrl = useServerStore((state) => state.serverUrl);
   const setServerUrl = useServerStore((state) => state.setServerUrl);
   const keepServerRunningOnClose = useServerStore((state) => state.keepServerRunningOnClose);
@@ -89,7 +90,7 @@ export function ConnectionForm() {
               checked={keepServerRunningOnClose}
               onCheckedChange={(checked: boolean) => {
                 setKeepServerRunningOnClose(checked);
-                setKeepServerRunning(checked).catch((error) => {
+                platform.lifecycle.setKeepServerRunning(checked).catch((error) => {
                   console.error('Failed to sync setting to Rust:', error);
                 });
                 toast({

app/src/components/ServerSettings/ModelProgress.tsx

@@ -12,11 +12,10 @@ interface ModelProgressProps {
 
 export function ModelProgress({ modelName, displayName }: ModelProgressProps) {
   const [progress, setProgress] = useState<ModelProgressType | null>(null);
-  const [isSubscribed, setIsSubscribed] = useState(false);
   const serverUrl = useServerStore((state) => state.serverUrl);
 
   useEffect(() => {
-    if (!serverUrl || isSubscribed) return;
+    if (!serverUrl) return;
 
     // Subscribe to progress updates via Server-Sent Events
     const eventSource = new EventSource(`${serverUrl}/models/progress/${modelName}`);
@@ -29,7 +28,6 @@ export function ModelProgress({ modelName, displayName }: ModelProgressProps) {
         // Close connection if complete or error
         if (data.status === 'complete' || data.status === 'error') {
           eventSource.close();
-          setIsSubscribed(false);
         }
       } catch (error) {
         console.error('Error parsing progress event:', error);
@@ -39,16 +37,12 @@ export function ModelProgress({ modelName, displayName }: ModelProgressProps) {
     eventSource.onerror = (error) => {
       console.error('SSE error:', error);
       eventSource.close();
-      setIsSubscribed(false);
     };
 
-    setIsSubscribed(true);
-
     return () => {
       eventSource.close();
-      setIsSubscribed(false);
     };
-  }, [serverUrl, modelName, isSubscribed]);
+  }, [serverUrl, modelName]);
 
   // Don't render if no progress or if complete/error and some time has passed
   if (

app/src/components/ServerSettings/UpdateStatus.tsx

@@ -1,4 +1,3 @@
-import { getVersion } from '@tauri-apps/api/app';
 import { AlertCircle, Download, RefreshCw } from 'lucide-react';
 import { useEffect, useState } from 'react';
 import { Badge } from '@/components/ui/badge';
@@ -6,16 +5,18 @@ import { Button } from '@/components/ui/button';
 import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
 import { Progress } from '@/components/ui/progress';
 import { useAutoUpdater } from '@/hooks/useAutoUpdater';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function UpdateStatus() {
+  const platform = usePlatform();
   const { status, checkForUpdates, downloadAndInstall, restartAndInstall } = useAutoUpdater(false);
   const [currentVersion, setCurrentVersion] = useState<string>('');
 
   useEffect(() => {
-    getVersion()
+    platform.metadata.getVersion()
       .then(setCurrentVersion)
       .catch(() => setCurrentVersion('0.1.0'));
-  }, []);
+  }, [platform]);
 
   return (
     <Card>

app/src/components/ServerTab/ServerTab.tsx

@@ -1,16 +1,17 @@
 import { ConnectionForm } from '@/components/ServerSettings/ConnectionForm';
 import { ServerStatus } from '@/components/ServerSettings/ServerStatus';
 import { UpdateStatus } from '@/components/ServerSettings/UpdateStatus';
-import { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function ServerTab() {
+  const platform = usePlatform();
   return (
     <div className="space-y-4 overflow-y-auto flex flex-col">
       <div className="grid gap-4 md:grid-cols-2">
         <ConnectionForm />
         <ServerStatus />
       </div>
-      {isTauri() && <UpdateStatus />}
+      {platform.metadata.isTauri && <UpdateStatus />}
       <div className="py-8 text-center text-sm text-muted-foreground">
         Created by{' '}
         <a

app/src/components/StoriesTab/StoryChatItem.tsx

@@ -1,6 +1,7 @@
 import { useSortable } from '@dnd-kit/sortable';
 import { CSS } from '@dnd-kit/utilities';
 import { GripVertical, Mic, MoreHorizontal, Play, Trash2 } from 'lucide-react';
+import { useState } from 'react';
 import { Button } from '@/components/ui/button';
 import {
   DropdownMenu,
@@ -12,6 +13,7 @@ import { Textarea } from '@/components/ui/textarea';
 import type { StoryItemDetail } from '@/lib/api/types';
 import { cn } from '@/lib/utils/cn';
 import { useStoryStore } from '@/stores/storyStore';
+import { useServerStore } from '@/stores/serverStore';
 
 interface StoryChatItemProps {
   item: StoryItemDetail;
@@ -33,6 +35,10 @@ export function StoryChatItem({
   isDragging,
 }: StoryChatItemProps) {
   const seek = useStoryStore((state) => state.seek);
+  const serverUrl = useServerStore((state) => state.serverUrl);
+  const [avatarError, setAvatarError] = useState(false);
+
+  const avatarUrl = `${serverUrl}/profiles/${item.profile_id}/avatar`;
 
   // Check if this item is currently playing based on timecode
   const itemStartMs = item.start_time_ms;
@@ -72,10 +78,22 @@ export function StoryChatItem({
         </button>
       )}
 
-      {/* Voice Icon */}
+      {/* Voice Avatar */}
       <div className="shrink-0">
-        <div className="h-10 w-10 rounded-full bg-muted flex items-center justify-center">
+        <div className="h-10 w-10 rounded-full bg-muted flex items-center justify-center overflow-hidden">
-          <Mic className="h-5 w-5 text-muted-foreground" />
+          {!avatarError ? (
+            <img
+              src={avatarUrl}
+              alt={`${item.profile_name} avatar`}
+              className={cn(
+                'h-full w-full object-cover transition-all duration-200',
+                !isCurrentlyPlaying && 'grayscale'
+              )}
+              onError={() => setAvatarError(true)}
+            />
+          ) : (
+            <Mic className="h-5 w-5 text-muted-foreground" />
+          )}
         </div>
       </div>
 

app/src/components/StoriesTab/StoryList.tsx

@@ -1,14 +1,5 @@
 import { Plus, BookOpen, MoreHorizontal, Pencil, Trash2 } from 'lucide-react';
 import { useState } from 'react';
-import { Button } from '@/components/ui/button';
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogFooter,
-  DialogHeader,
-  DialogTitle,
-} from '@/components/ui/dialog';
 import {
   AlertDialog,
   AlertDialogAction,
@@ -19,6 +10,15 @@ import {
   AlertDialogHeader,
   AlertDialogTitle,
 } from '@/components/ui/alert-dialog';
+import { Button } from '@/components/ui/button';
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogFooter,
+  DialogHeader,
+  DialogTitle,
+} from '@/components/ui/dialog';
 import {
   DropdownMenu,
   DropdownMenuContent,
@@ -26,18 +26,13 @@ import {
   DropdownMenuTrigger,
 } from '@/components/ui/dropdown-menu';
 import { Input } from '@/components/ui/input';
-import { Textarea } from '@/components/ui/textarea';
 import { Label } from '@/components/ui/label';
+import { Textarea } from '@/components/ui/textarea';
 import { useToast } from '@/components/ui/use-toast';
-import {
+import { useStories, useCreateStory, useUpdateStory, useDeleteStory } from '@/lib/hooks/useStories';
-  useStories,
-  useCreateStory,
-  useUpdateStory,
-  useDeleteStory,
-} from '@/lib/hooks/useStories';
-import { useStoryStore } from '@/stores/storyStore';
 import { cn } from '@/lib/utils/cn';
 import { formatDate } from '@/lib/utils/format';
+import { useStoryStore } from '@/stores/storyStore';
 
 export function StoryList() {
   const { data: stories, isLoading } = useStories();
@@ -49,7 +44,11 @@ export function StoryList() {
   const [createDialogOpen, setCreateDialogOpen] = useState(false);
   const [editDialogOpen, setEditDialogOpen] = useState(false);
   const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
-  const [editingStory, setEditingStory] = useState<{ id: string; name: string; description?: string } | null>(null);
+  const [editingStory, setEditingStory] = useState<{
+    id: string;
+    name: string;
+    description?: string;
+  } | null>(null);
   const [deletingStoryId, setDeletingStoryId] = useState<string | null>(null);
   const [newStoryName, setNewStoryName] = useState('');
   const [newStoryDescription, setNewStoryDescription] = useState('');
@@ -186,7 +185,7 @@ export function StoryList() {
       {/* Story List */}
       <div className="flex-1 min-h-0 overflow-y-auto space-y-2">
         {storyList.length === 0 ? (
-          <div className="text-center py-12 px-5 border-2 border-dashed border-muted rounded-md text-muted-foreground">
+          <div className="text-center py-12 px-5 border-2 border-dashed border-muted rounded-2xl text-muted-foreground">
             <BookOpen className="h-12 w-12 mx-auto mb-4 opacity-50" />
             <p className="text-sm">No stories yet</p>
             <p className="text-xs mt-2">Create your first story to get started</p>
@@ -196,7 +195,7 @@ export function StoryList() {
             <div
               key={story.id}
               className={cn(
-                'h-24 p-4 border rounded-md transition-colors group flex items-center',
+                'h-24 p-4 border rounded-2xl transition-colors group flex items-center',
                 selectedStoryId === story.id && 'bg-muted border-primary',
               )}
             >
@@ -213,7 +212,9 @@ export function StoryList() {
                     </p>
                   )}
                   <div className="flex items-center gap-3 mt-2 text-xs text-muted-foreground">
-                    <span>{story.item_count} {story.item_count === 1 ? 'item' : 'items'}</span>
+                    <span>
+                      {story.item_count} {story.item_count === 1 ? 'item' : 'items'}
+                    </span>
                     <span>•</span>
                     <span>{formatDate(story.updated_at)}</span>
                   </div>
@@ -300,9 +301,7 @@ export function StoryList() {
         <DialogContent>
           <DialogHeader>
             <DialogTitle>Edit Story</DialogTitle>
-            <DialogDescription>
+            <DialogDescription>Update the story name and description.</DialogDescription>
-              Update the story name and description.
-            </DialogDescription>
           </DialogHeader>
           <div className="space-y-4 py-4">
             <div className="space-y-2">
@@ -347,7 +346,8 @@ export function StoryList() {
           <AlertDialogHeader>
             <AlertDialogTitle>Are you sure?</AlertDialogTitle>
             <AlertDialogDescription>
-              This will permanently delete the story and all its items. This action cannot be undone.
+              This will permanently delete the story and all its items. This action cannot be
+              undone.
             </AlertDialogDescription>
           </AlertDialogHeader>
           <AlertDialogFooter>

app/src/components/StoriesTab/StoryTrackEditor.tsx

@@ -111,6 +111,7 @@ interface StoryTrackEditorProps {
 }
 
 const TRACK_HEIGHT = 48;
+const TIME_RULER_HEIGHT = 24; // h-6 = 1.5rem = 24px
 const MIN_PIXELS_PER_SECOND = 10;
 const MAX_PIXELS_PER_SECOND = 200;
 const DEFAULT_PIXELS_PER_SECOND = 50;
@@ -539,7 +540,10 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
         return;
       }
 
-      if (e.key === 'Escape') {
+      if (e.key === ' ') {
+        e.preventDefault();
+        handlePlayPause();
+      } else if (e.key === 'Escape') {
         setSelectedClipId(null);
       } else if (e.key === 's' || e.key === 'S') {
         if (selectedClipId) {
@@ -561,7 +565,14 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
 
     window.addEventListener('keydown', handleKeyDown);
     return () => window.removeEventListener('keydown', handleKeyDown);
-  }, [selectedClipId, handleSplit, handleDuplicate, handleDelete, setSelectedClipId]);
+  }, [
+    selectedClipId,
+    handleSplit,
+    handleDuplicate,
+    handleDelete,
+    setSelectedClipId,
+    handlePlayPause,
+  ]);
 
   // Add global mouse listeners for trimming
   useEffect(() => {
@@ -586,7 +597,8 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
     });
     setDragPosition({
       x: rect.left - tracksRef.current.getBoundingClientRect().left + tracksRef.current.scrollLeft,
-      y: rect.top - tracksRef.current.getBoundingClientRect().top,
+      // Subtract ruler height since clips are positioned relative to tracks area, not the scrollable container
+      y: rect.top - tracksRef.current.getBoundingClientRect().top - TIME_RULER_HEIGHT,
     });
     setDraggingItem(item.id);
   };
@@ -597,7 +609,8 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
 
       const rect = tracksRef.current.getBoundingClientRect();
       const x = e.clientX - rect.left + tracksRef.current.scrollLeft - dragOffset.x;
-      const y = e.clientY - rect.top - dragOffset.y;
+      // Subtract ruler height since clips are positioned relative to tracks area
+      const y = e.clientY - rect.top - dragOffset.y - TIME_RULER_HEIGHT;
 
       setDragPosition({ x: Math.max(0, x), y });
     },
@@ -673,6 +686,22 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
   // Playhead position
   const playheadLeft = msToPixels(currentTimeMs);
 
+  // Auto-scroll timeline to follow playhead during playback
+  useEffect(() => {
+    if (!isCurrentlyPlaying || !tracksRef.current) return;
+
+    const container = tracksRef.current;
+    const containerWidth = container.clientWidth;
+    const scrollLeft = container.scrollLeft;
+    const halfwayPoint = scrollLeft + containerWidth / 2;
+
+    // If playhead is past the halfway point, scroll to keep it centered
+    if (playheadLeft > halfwayPoint) {
+      const targetScroll = playheadLeft - containerWidth / 2;
+      container.scrollLeft = targetScroll;
+    }
+  }, [isCurrentlyPlaying, playheadLeft]);
+
   // Calculate tracks area height
   const tracksAreaHeight = tracks.length * TRACK_HEIGHT;
   const timelineContainerHeight = editorHeight - 40; // Subtract toolbar height
@@ -701,7 +730,13 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
         <div className="flex items-center justify-between px-3 py-2 border-b bg-muted/30 mt-2">
           {/* Play controls - left side */}
           <div className="flex items-center gap-2">
-            <Button variant="ghost" size="icon" className="h-7 w-7" onClick={handlePlayPause}>
+            <Button
+              variant="ghost"
+              size="icon"
+              className="h-7 w-7"
+              onClick={handlePlayPause}
+              title="Play/Pause (Space)"
+            >
               {isCurrentlyPlaying ? <Pause className="h-4 w-4" /> : <Play className="h-4 w-4" />}
             </Button>
             <Button

app/src/components/VoiceProfiles/AudioSampleRecording.tsx

@@ -1,8 +1,31 @@
 import { Mic, Pause, Play, Square } from 'lucide-react';
+import { memo, useEffect, useState } from 'react';
+import { Visualizer } from 'react-sound-visualizer';
 import { Button } from '@/components/ui/button';
-import { FormControl, FormItem, FormLabel, FormMessage } from '@/components/ui/form';
+import { FormControl, FormItem, FormMessage } from '@/components/ui/form';
 import { formatAudioDuration } from '@/lib/utils/audio';
 
+const MemoizedWaveform = memo(function MemoizedWaveform({
+  audioStream,
+}: {
+  audioStream: MediaStream;
+}) {
+  return (
+    <div className="absolute inset-0 pointer-events-none flex items-center justify-center opacity-30">
+      <Visualizer audio={audioStream} autoStart strokeColor="#b39a3d">
+        {({ canvasRef }) => (
+          <canvas
+            ref={canvasRef}
+            width={500}
+            height={150}
+            className="w-full h-full"
+          />
+        )}
+      </Visualizer>
+    </div>
+  );
+});
+
 interface AudioSampleRecordingProps {
   file: File | null | undefined;
   isRecording: boolean;
@@ -14,6 +37,7 @@ interface AudioSampleRecordingProps {
   onPlayPause: () => void;
   isPlaying: boolean;
   isTranscribing?: boolean;
+  showWaveform?: boolean;
 }
 
 export function AudioSampleRecording({
@@ -27,29 +51,67 @@ export function AudioSampleRecording({
   onPlayPause,
   isPlaying,
   isTranscribing = false,
+  showWaveform = true,
 }: AudioSampleRecordingProps) {
+  const [audioStream, setAudioStream] = useState<MediaStream | null>(null);
+
+  // Request microphone access when component mounts
+  useEffect(() => {
+    if (!showWaveform) return;
+
+    let stream: MediaStream | null = null;
+
+    navigator.mediaDevices
+      .getUserMedia({ audio: true, video: false })
+      .then((s) => {
+        stream = s;
+        setAudioStream(s);
+      })
+      .catch((err) => {
+        console.warn('Could not access microphone for visualization:', err);
+      });
+
+    return () => {
+      if (stream) {
+        stream.getTracks().forEach((track) => {
+          track.stop();
+        });
+      }
+    };
+  }, [showWaveform]);
+
   return (
     <FormItem>
-      <FormLabel>Record Audio</FormLabel>
       <FormControl>
         <div className="space-y-4">
           {!isRecording && !file && (
-            <div className="flex flex-col items-center justify-center gap-4 p-4 border-2 border-dashed rounded-lg min-h-[180px]">
+            <div className="relative flex flex-col items-center justify-center gap-4 p-4 border-2 border-dashed rounded-lg min-h-[180px] overflow-hidden">
-              <Button type="button" onClick={onStart} size="lg" className="flex items-center gap-2">
+              {showWaveform && audioStream && (
+                <MemoizedWaveform audioStream={audioStream} />
+              )}
+              <Button
+                type="button"
+                onClick={onStart}
+                size="lg"
+                className="relative z-10 flex items-center gap-2"
+              >
                 <Mic className="h-5 w-5" />
                 Start Recording
               </Button>
-              <p className="text-sm text-muted-foreground text-center">
+              <p className="relative z-10 text-sm text-muted-foreground text-center">
                 Click to start recording. Maximum duration: 30 seconds.
               </p>
             </div>
           )}
 
           {isRecording && (
-            <div className="flex flex-col items-center justify-center gap-4 p-4 border-2 border-destructive rounded-lg bg-destructive/5 min-h-[180px]">
+            <div className="relative flex flex-col items-center justify-center gap-4 p-4 border-2 border-accent rounded-lg bg-accent/5 min-h-[180px] overflow-hidden">
-              <div className="flex items-center gap-4">
+              {showWaveform && audioStream && (
+                <MemoizedWaveform audioStream={audioStream} />
+              )}
+              <div className="relative z-10 flex items-center gap-4">
                 <div className="flex items-center gap-2">
-                  <div className="h-3 w-3 rounded-full bg-destructive animate-pulse" />
+                  <div className="h-3 w-3 rounded-full bg-accent animate-pulse" />
                   <span className="text-lg font-mono font-semibold">
                     {formatAudioDuration(duration)}
                   </span>
@@ -58,13 +120,12 @@ export function AudioSampleRecording({
               <Button
                 type="button"
                 onClick={onStop}
-                variant="destructive"
+                className="relative z-10 flex items-center gap-2 bg-accent text-accent-foreground hover:bg-accent/90"
-                className="flex items-center gap-2"
               >
                 <Square className="h-4 w-4" />
                 Stop Recording
               </Button>
-              <p className="text-sm text-muted-foreground text-center">
+              <p className="relative z-10 text-sm text-muted-foreground text-center">
                 {formatAudioDuration(30 - duration)} remaining
               </p>
             </div>

app/src/components/VoiceProfiles/AudioSampleSystem.tsx

@@ -1,6 +1,6 @@
 import { Mic, Monitor, Pause, Play, Square } from 'lucide-react';
 import { Button } from '@/components/ui/button';
-import { FormControl, FormItem, FormLabel, FormMessage } from '@/components/ui/form';
+import { FormControl, FormItem, FormMessage } from '@/components/ui/form';
 import { formatAudioDuration } from '@/lib/utils/audio';
 
 interface AudioSampleSystemProps {
@@ -30,7 +30,6 @@ export function AudioSampleSystem({
 }: AudioSampleSystemProps) {
   return (
     <FormItem>
-      <FormLabel>Capture System Audio</FormLabel>
       <FormControl>
         <div className="space-y-4">
           {!isRecording && !file && (

app/src/components/VoiceProfiles/AudioSampleUpload.tsx

@@ -1,7 +1,7 @@
 import { Mic, Pause, Play, Upload } from 'lucide-react';
 import { useRef, useState } from 'react';
 import { Button } from '@/components/ui/button';
-import { FormControl, FormItem, FormLabel, FormMessage } from '@/components/ui/form';
+import { FormControl, FormItem, FormMessage } from '@/components/ui/form';
 
 interface AudioSampleUploadProps {
   file: File | null | undefined;
@@ -31,7 +31,6 @@ export function AudioSampleUpload({
 
   return (
     <FormItem>
-      <FormLabel>Audio File</FormLabel>
       <FormControl>
         <div className="flex flex-col gap-2">
           <input

app/src/components/VoiceProfiles/ProfileCard.tsx

@@ -15,6 +15,7 @@ import {
 import type { VoiceProfileResponse } from '@/lib/api/types';
 import { useDeleteProfile, useExportProfile } from '@/lib/hooks/useProfiles';
 import { cn } from '@/lib/utils/cn';
+import { useServerStore } from '@/stores/serverStore';
 import { useUIStore } from '@/stores/uiStore';
 
 interface ProfileCardProps {
@@ -23,15 +24,19 @@ interface ProfileCardProps {
 
 export function ProfileCard({ profile }: ProfileCardProps) {
   const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
+  const [avatarError, setAvatarError] = useState(false);
   const deleteProfile = useDeleteProfile();
   const exportProfile = useExportProfile();
   const setEditingProfileId = useUIStore((state) => state.setEditingProfileId);
   const setProfileDialogOpen = useUIStore((state) => state.setProfileDialogOpen);
   const selectedProfileId = useUIStore((state) => state.selectedProfileId);
   const setSelectedProfileId = useUIStore((state) => state.setSelectedProfileId);
+  const serverUrl = useServerStore((state) => state.serverUrl);
 
   const isSelected = selectedProfileId === profile.id;
 
+  const avatarUrl = profile.avatar_path ? `${serverUrl}/profiles/${profile.id}/avatar` : null;
+
   const handleSelect = () => {
     setSelectedProfileId(isSelected ? null : profile.id);
   };
@@ -67,8 +72,20 @@ export function ProfileCard({ profile }: ProfileCardProps) {
       >
         <CardHeader className="p-3 pb-2">
           <CardTitle className="flex items-center gap-1.5 text-base font-medium">
-            <div className="h-6 w-6 rounded-full bg-muted flex items-center justify-center shrink-0">
+            <div className="h-6 w-6 rounded-full bg-muted flex items-center justify-center shrink-0 overflow-hidden">
-              <Mic className="h-3.5 w-3.5 text-muted-foreground" />
+              {avatarUrl && !avatarError ? (
+                <img
+                  src={avatarUrl}
+                  alt={`${profile.name} avatar`}
+                  className={cn(
+                    'h-full w-full object-cover transition-all duration-200',
+                    !isSelected && 'grayscale',
+                  )}
+                  onError={() => setAvatarError(true)}
+                />
+              ) : (
+                <Mic className="h-3.5 w-3.5 text-muted-foreground" />
+              )}
             </div>
             <span className="break-words">{profile.name}</span>
           </CardTitle>

app/src/components/VoiceProfiles/ProfileForm.tsx

@@ -1,6 +1,6 @@
 import { zodResolver } from '@hookform/resolvers/zod';
-import { Mic, Monitor, Upload } from 'lucide-react';
+import { Edit2, Mic, Monitor, Upload, X } from 'lucide-react';
-import { useEffect, useState } from 'react';
+import { useEffect, useRef, useState } from 'react';
 import { useForm } from 'react-hook-form';
 import * as z from 'zod';
 import { Button } from '@/components/ui/button';
@@ -36,14 +36,17 @@ import { useAudioRecording } from '@/lib/hooks/useAudioRecording';
 import {
   useAddSample,
   useCreateProfile,
+  useDeleteAvatar,
   useProfile,
   useUpdateProfile,
+  useUploadAvatar,
 } from '@/lib/hooks/useProfiles';
 import { useSystemAudioCapture } from '@/lib/hooks/useSystemAudioCapture';
 import { useTranscription } from '@/lib/hooks/useTranscription';
-import { isTauri } from '@/lib/tauri';
 import { formatAudioDuration, getAudioDuration } from '@/lib/utils/audio';
-import { useUIStore } from '@/stores/uiStore';
+import { usePlatform } from '@/platform/PlatformContext';
+import { useServerStore } from '@/stores/serverStore';
+import { type ProfileFormDraft, useUIStore } from '@/stores/uiStore';
 import { AudioSampleRecording } from './AudioSampleRecording';
 import { AudioSampleSystem } from './AudioSampleSystem';
 import { AudioSampleUpload } from './AudioSampleUpload';
@@ -57,6 +60,7 @@ const baseProfileSchema = z.object({
   language: z.enum(LANGUAGE_CODES as [LanguageCode, ...LanguageCode[]]),
   sampleFile: z.instanceof(File).optional(),
   referenceText: z.string().max(1000).optional(),
+  avatarFile: z.instanceof(File).optional(),
 });
 
 const profileSchema = baseProfileSchema.refine(
@@ -75,22 +79,52 @@ const profileSchema = baseProfileSchema.refine(
 
 type ProfileFormValues = z.infer<typeof profileSchema>;
 
+// Helper to convert File to base64
+async function fileToBase64(file: File): Promise<string> {
+  return new Promise((resolve, reject) => {
+    const reader = new FileReader();
+    reader.onload = () => resolve(reader.result as string);
+    reader.onerror = reject;
+    reader.readAsDataURL(file);
+  });
+}
+
+// Helper to convert base64 to File
+function base64ToFile(base64: string, fileName: string, fileType: string): File {
+  const arr = base64.split(',');
+  const bstr = atob(arr[1]);
+  let n = bstr.length;
+  const u8arr = new Uint8Array(n);
+  while (n--) {
+    u8arr[n] = bstr.charCodeAt(n);
+  }
+  return new File([u8arr], fileName, { type: fileType });
+}
+
 export function ProfileForm() {
+  const platform = usePlatform();
   const open = useUIStore((state) => state.profileDialogOpen);
   const setOpen = useUIStore((state) => state.setProfileDialogOpen);
   const editingProfileId = useUIStore((state) => state.editingProfileId);
   const setEditingProfileId = useUIStore((state) => state.setEditingProfileId);
+  const profileFormDraft = useUIStore((state) => state.profileFormDraft);
+  const setProfileFormDraft = useUIStore((state) => state.setProfileFormDraft);
   const { data: editingProfile } = useProfile(editingProfileId || '');
   const createProfile = useCreateProfile();
   const updateProfile = useUpdateProfile();
   const addSample = useAddSample();
+  const uploadAvatar = useUploadAvatar();
+  const deleteAvatar = useDeleteAvatar();
   const transcribe = useTranscription();
   const { toast } = useToast();
-  const [sampleMode, setSampleMode] = useState<'upload' | 'record' | 'system'>('upload');
+  const [sampleMode, setSampleMode] = useState<'upload' | 'record' | 'system'>('record');
   const [audioDuration, setAudioDuration] = useState<number | null>(null);
   const [isValidatingAudio, setIsValidatingAudio] = useState(false);
+  const [avatarPreview, setAvatarPreview] = useState<string | null>(null);
+  const avatarInputRef = useRef<HTMLInputElement>(null);
   const { isPlaying, playPause, cleanup: cleanupAudio } = useAudioPlayer();
   const isCreating = !editingProfileId;
+  const serverUrl = useServerStore((state) => state.serverUrl);
 
   const form = useForm<ProfileFormValues>({
     resolver: zodResolver(profileSchema),
@@ -100,10 +134,12 @@ export function ProfileForm() {
       language: 'en',
       sampleFile: undefined,
       referenceText: '',
+      avatarFile: undefined,
     },
   });
 
   const selectedFile = form.watch('sampleFile');
+  const selectedAvatarFile = form.watch('avatarFile');
 
   // Validate audio duration when file is selected
   useEffect(() => {
@@ -220,6 +256,20 @@ export function ProfileForm() {
     }
   }, [systemRecordingError, toast]);
 
+  // Handle avatar preview
+  useEffect(() => {
+    if (selectedAvatarFile instanceof File) {
+      const url = URL.createObjectURL(selectedAvatarFile);
+      setAvatarPreview(url);
+      return () => URL.revokeObjectURL(url);
+    } else if (editingProfile?.avatar_path) {
+      setAvatarPreview(`${serverUrl}/profiles/${editingProfile.id}/avatar`);
+    } else {
+      setAvatarPreview(null);
+    }
+  }, [selectedAvatarFile, editingProfile, serverUrl]);
+
+  // Restore form state from draft or editing profile
   useEffect(() => {
     if (editingProfile) {
       form.reset({
@@ -228,18 +278,46 @@ export function ProfileForm() {
         language: editingProfile.language as LanguageCode,
         sampleFile: undefined,
         referenceText: undefined,
+        avatarFile: undefined,
       });
-    } else {
+    } else if (profileFormDraft && open) {
+      // Restore from draft when opening in create mode
+      form.reset({
+        name: profileFormDraft.name,
+        description: profileFormDraft.description,
+        language: profileFormDraft.language as LanguageCode,
+        referenceText: profileFormDraft.referenceText,
+        sampleFile: undefined,
+        avatarFile: undefined,
+      });
+      setSampleMode(profileFormDraft.sampleMode);
+      // Restore the file if we have it saved
+      if (
+        profileFormDraft.sampleFileData &&
+        profileFormDraft.sampleFileName &&
+        profileFormDraft.sampleFileType
+      ) {
+        const file = base64ToFile(
+          profileFormDraft.sampleFileData,
+          profileFormDraft.sampleFileName,
+          profileFormDraft.sampleFileType,
+        );
+        form.setValue('sampleFile', file);
+      }
+    } else if (!open) {
+      // Only reset to defaults when modal is closed and no draft
       form.reset({
         name: '',
         description: '',
         language: 'en',
         sampleFile: undefined,
         referenceText: undefined,
+        avatarFile: undefined,
       });
-      setSampleMode('upload');
+      setSampleMode('record');
+      setAvatarPreview(null);
     }
-  }, [editingProfile, form]);
+  }, [editingProfile, profileFormDraft, open, form]);
 
   async function handleTranscribe() {
     const file = form.getValues('sampleFile');
@@ -281,6 +359,52 @@ export function ProfileForm() {
     playPause(file);
   }
 
+  function handleAvatarFileChange(e: React.ChangeEvent<HTMLInputElement>) {
+    const file = e.target.files?.[0];
+    if (file) {
+      if (!file.type.startsWith('image/')) {
+        toast({
+          title: 'Invalid file type',
+          description: 'Please select an image file (PNG, JPG, or WebP)',
+          variant: 'destructive',
+        });
+        return;
+      }
+      if (file.size > 5 * 1024 * 1024) {
+        toast({
+          title: 'File too large',
+          description: 'Image must be less than 5MB',
+          variant: 'destructive',
+        });
+        return;
+      }
+      form.setValue('avatarFile', file);
+    }
+  }
+
+  async function handleRemoveAvatar() {
+    if (editingProfileId && editingProfile?.avatar_path) {
+      try {
+        await deleteAvatar.mutateAsync(editingProfileId);
+        toast({
+          title: 'Avatar removed',
+          description: 'Avatar image has been removed successfully.',
+        });
+      } catch (error) {
+        toast({
+          title: 'Failed to remove avatar',
+          description: error instanceof Error ? error.message : 'Unknown error',
+          variant: 'destructive',
+        });
+      }
+    }
+    form.setValue('avatarFile', undefined);
+    setAvatarPreview(null);
+    if (avatarInputRef.current) {
+      avatarInputRef.current.value = '';
+    }
+  }
+
   async function onSubmit(data: ProfileFormValues) {
     try {
       if (editingProfileId) {
@@ -293,6 +417,24 @@ export function ProfileForm() {
             language: data.language,
           },
         });
+
+        // Handle avatar upload/update if file changed
+        if (data.avatarFile) {
+          try {
+            await uploadAvatar.mutateAsync({
+              profileId: editingProfileId,
+              file: data.avatarFile,
+            });
+          } catch (avatarError) {
+            toast({
+              title: 'Avatar upload failed',
+              description:
+                avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
+              variant: 'destructive',
+            });
+          }
+        }
+
         toast({
           title: 'Voice updated',
           description: `"${data.name}" has been updated successfully.`,
@@ -369,6 +511,24 @@ export function ProfileForm() {
             file: sampleFile,
             referenceText: referenceText,
           });
+
+          // Handle avatar upload if provided
+          if (data.avatarFile) {
+            try {
+              await uploadAvatar.mutateAsync({
+                profileId: profile.id,
+                file: data.avatarFile,
+              });
+            } catch (avatarError) {
+              toast({
+                title: 'Avatar upload failed',
+                description:
+                  avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
+                variant: 'destructive',
+              });
+            }
+          }
+
           toast({
             title: 'Profile created',
             description: `"${data.name}" has been created with a sample.`,
@@ -383,6 +543,8 @@ export function ProfileForm() {
         }
       }
 
+      // Clear draft and reset form on success
+      setProfileFormDraft(null);
       form.reset();
       setEditingProfileId(null);
       setOpen(false);
@@ -395,12 +557,41 @@ export function ProfileForm() {
     }
   }
 
-  function handleOpenChange(open: boolean) {
+  async function handleOpenChange(newOpen: boolean) {
-    setOpen(open);
+    if (!newOpen && isCreating) {
-    if (!open) {
+      // Save draft when closing the create modal
+      const values = form.getValues();
+      const hasContent =
+        values.name || values.description || values.referenceText || values.sampleFile;
+
+      if (hasContent) {
+        const draft: ProfileFormDraft = {
+          name: values.name || '',
+          description: values.description || '',
+          language: values.language || 'en',
+          referenceText: values.referenceText || '',
+          sampleMode,
+        };
+
+        // Save file as base64 if present
+        if (values.sampleFile) {
+          try {
+            draft.sampleFileName = values.sampleFile.name;
+            draft.sampleFileType = values.sampleFile.type;
+            draft.sampleFileData = await fileToBase64(values.sampleFile);
+          } catch {
+            // If file conversion fails, just don't save the file
+          }
+        }
+
+        setProfileFormDraft(draft);
+      }
+    }
+
+    setOpen(newOpen);
+    if (!newOpen) {
       setEditingProfileId(null);
-      form.reset();
+      // Don't reset form here - let the effect handle it based on draft state
-      setSampleMode('upload');
       if (isRecording) {
         cancelRecording();
       }
@@ -413,174 +604,119 @@ export function ProfileForm() {
 
   return (
     <Dialog open={open} onOpenChange={handleOpenChange}>
-      <DialogContent className="max-w-4xl">
+      <DialogContent className="max-w-none w-screen h-screen left-0 top-0 translate-x-0 translate-y-0 rounded-none p-6 overflow-y-auto">
-        <DialogHeader>
+        <div className="max-w-5xl max-h-[85vh] mx-auto my-auto w-full flex flex-col">
-          <DialogTitle>{editingProfileId ? 'Edit Voice' : 'Create Voice Profile'}</DialogTitle>
+          <DialogHeader>
-          <DialogDescription>
+            <DialogTitle className="text-2xl">
-            {editingProfileId
+              {editingProfileId ? 'Edit Voice' : 'Clone voice'}
-              ? 'Update your voice profile details and manage samples.'
+            </DialogTitle>
-              : 'Create a new voice profile with an audio sample to clone the voice.'}
+            <DialogDescription>
-          </DialogDescription>
+              {editingProfileId
-        </DialogHeader>
+                ? 'Update your voice profile details and manage samples.'
-
+                : 'Create a new voice profile with an audio sample to clone the voice.'}
-        <Form {...form}>
+            </DialogDescription>
-          <form onSubmit={form.handleSubmit(onSubmit)}>
+            {isCreating && profileFormDraft && (
-            <div className="grid gap-6 grid-cols-2">
+              <div className="flex items-center gap-2 pt-2">
-              {/* Left column: Profile info */}
+                <span className="text-xs text-muted-foreground">Draft restored</span>
-              <div className="space-y-4">
+                <Button
-                <FormField
+                  type="button"
-                  control={form.control}
+                  variant="ghost"
-                  name="name"
+                  size="sm"
-                  render={({ field }) => (
+                  className="h-6 px-2 text-xs text-muted-foreground"
-                    <FormItem>
+                  onClick={() => {
-                      <FormLabel>Name</FormLabel>
+                    setProfileFormDraft(null);
-                      <FormControl>
+                    form.reset({
-                        <Input placeholder="My Voice" {...field} />
+                      name: '',
-                      </FormControl>
+                      description: '',
-                      <FormMessage />
+                      language: 'en',
-                    </FormItem>
+                      sampleFile: undefined,
-                  )}
+                      referenceText: '',
-                />
+                    });
-
+                    setSampleMode('record');
-                <FormField
+                  }}
-                  control={form.control}
+                >
-                  name="description"
+                  <X className="h-3 w-3 mr-1" />
-                  render={({ field }) => (
+                  Discard
-                    <FormItem>
+                </Button>
-                      <FormLabel>Description (Optional)</FormLabel>
-                      <FormControl>
-                        <Textarea placeholder="Describe this voice..." {...field} />
-                      </FormControl>
-                      <FormMessage />
-                    </FormItem>
-                  )}
-                />
-
-                <FormField
-                  control={form.control}
-                  name="language"
-                  render={({ field }) => (
-                    <FormItem>
-                      <FormLabel>Language</FormLabel>
-                      <Select onValueChange={field.onChange} defaultValue={field.value}>
-                        <FormControl>
-                          <SelectTrigger>
-                            <SelectValue />
-                          </SelectTrigger>
-                        </FormControl>
-                        <SelectContent>
-                          {LANGUAGE_OPTIONS.map((lang) => (
-                            <SelectItem key={lang.value} value={lang.value}>
-                              {lang.label}
-                            </SelectItem>
-                          ))}
-                        </SelectContent>
-                      </Select>
-                      <FormMessage />
-                    </FormItem>
-                  )}
-                />
               </div>
+            )}
+          </DialogHeader>
 
-              {/* Right column: Sample management */}
+          <Form {...form}>
-              <div className="space-y-4 border-l pl-6">
+            <form onSubmit={form.handleSubmit(onSubmit)} className="flex-1 min-h-0 flex flex-col">
-                {isCreating ? (
+              <div className="grid gap-6 grid-cols-2 flex-1 overflow-y-auto min-h-0">
-                  <>
+                {/* Left column: Sample management */}
-                    <div>
+                <div className="space-y-4 border-r pr-6">
-                      <h3 className="text-sm font-medium mb-2">Add Sample</h3>
+                  {isCreating ? (
-                      <p className="text-sm text-muted-foreground mb-4">
+                    <>
-                        Provide an audio sample to clone the voice. You can add more samples later.
+                      <Tabs
-                      </p>
+                        className="pt-4"
-                    </div>
+                        value={sampleMode}
-
+                        onValueChange={(v) => {
-                    <Tabs
+                          const newMode = v as 'upload' | 'record' | 'system';
-                      value={sampleMode}
+                          // Cancel any active recordings when switching modes
-                      onValueChange={(v) => {
+                          if (isRecording && newMode !== 'record') {
-                        const newMode = v as 'upload' | 'record' | 'system';
+                            cancelRecording();
-                        // Cancel any active recordings when switching modes
+                          }
-                        if (isRecording && newMode !== 'record') {
+                          if (isSystemRecording && newMode !== 'system') {
-                          cancelRecording();
+                            cancelSystemRecording();
-                        }
+                          }
-                        if (isSystemRecording && newMode !== 'system') {
+                          setSampleMode(newMode);
-                          cancelSystemRecording();
+                        }}
-                        }
-                        setSampleMode(newMode);
-                      }}
-                    >
-                      <TabsList
-                        className={`grid w-full ${isTauri() && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
                       >
-                        <TabsTrigger value="upload" className="flex items-center gap-2">
+                        <TabsList
-                          <Upload className="h-4 w-4 shrink-0" />
+                          className={`grid w-full ${platform.metadata.isTauri && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
-                          Upload
+                        >
-                        </TabsTrigger>
+                          <TabsTrigger value="upload" className="flex items-center gap-2">
-                        <TabsTrigger value="record" className="flex items-center gap-2">
+                            <Upload className="h-4 w-4 shrink-0" />
-                          <Mic className="h-4 w-4 shrink-0" />
+                            Upload
-                          Record
-                        </TabsTrigger>
-                        {isTauri() && isSystemAudioSupported && (
-                          <TabsTrigger value="system" className="flex items-center gap-2">
-                            <Monitor className="h-4 w-4 shrink-0" />
-                            System Audio
                           </TabsTrigger>
-                        )}
+                          <TabsTrigger value="record" className="flex items-center gap-2">
-                      </TabsList>
+                            <Mic className="h-4 w-4 shrink-0" />
-
+                            Record
-                      <TabsContent value="upload" className="space-y-4">
+                          </TabsTrigger>
-                        <FormField
+                          {platform.metadata.isTauri && isSystemAudioSupported && (
-                          control={form.control}
+                            <TabsTrigger value="system" className="flex items-center gap-2">
-                          name="sampleFile"
+                              <Monitor className="h-4 w-4 shrink-0" />
-                          render={({ field: { onChange, name } }) => (
+                              System Audio
-                            <AudioSampleUpload
+                            </TabsTrigger>
-                              file={selectedFile}
-                              onFileChange={onChange}
-                              onTranscribe={handleTranscribe}
-                              onPlayPause={handlePlayPause}
-                              isPlaying={isPlaying}
-                              isValidating={isValidatingAudio}
-                              isTranscribing={transcribe.isPending}
-                              isDisabled={
-                                audioDuration !== null && audioDuration > MAX_AUDIO_DURATION_SECONDS
-                              }
-                              fieldName={name}
-                            />
                           )}
-                        />
+                        </TabsList>
-                      </TabsContent>
 
-                      <TabsContent value="record" className="space-y-4">
+                        <TabsContent value="upload" className="space-y-4">
-                        <FormField
+                          <FormField
-                          control={form.control}
+                            control={form.control}
-                          name="sampleFile"
+                            name="sampleFile"
-                          render={() => (
+                            render={({ field: { onChange, name } }) => (
-                            <AudioSampleRecording
+                              <AudioSampleUpload
-                              file={selectedFile}
+                                file={selectedFile}
-                              isRecording={isRecording}
+                                onFileChange={onChange}
-                              duration={duration}
+                                onTranscribe={handleTranscribe}
-                              onStart={startRecording}
+                                onPlayPause={handlePlayPause}
-                              onStop={stopRecording}
+                                isPlaying={isPlaying}
-                              onCancel={handleCancelRecording}
+                                isValidating={isValidatingAudio}
-                              onTranscribe={handleTranscribe}
+                                isTranscribing={transcribe.isPending}
-                              onPlayPause={handlePlayPause}
+                                isDisabled={
-                              isPlaying={isPlaying}
+                                  audioDuration !== null &&
-                              isTranscribing={transcribe.isPending}
+                                  audioDuration > MAX_AUDIO_DURATION_SECONDS
-                            />
+                                }
-                          )}
+                                fieldName={name}
-                        />
+                              />
-                      </TabsContent>
+                            )}
+                          />
+                        </TabsContent>
 
-                      {isTauri() && isSystemAudioSupported && (
+                        <TabsContent value="record" className="space-y-4">
-                        <TabsContent value="system" className="space-y-4">
                           <FormField
                             control={form.control}
                             name="sampleFile"
                             render={() => (
-                              <AudioSampleSystem
+                              <AudioSampleRecording
                                 file={selectedFile}
-                                isRecording={isSystemRecording}
+                                isRecording={isRecording}
-                                duration={systemDuration}
+                                duration={duration}
-                                onStart={startSystemRecording}
+                                onStart={startRecording}
-                                onStop={stopSystemRecording}
+                                onStop={stopRecording}
                                 onCancel={handleCancelRecording}
                                 onTranscribe={handleTranscribe}
                                 onPlayPause={handlePlayPause}
@@ -590,55 +726,188 @@ export function ProfileForm() {
                             )}
                           />
                         </TabsContent>
-                      )}
-                    </Tabs>
 
-                    <FormField
+                        {platform.metadata.isTauri && isSystemAudioSupported && (
-                      control={form.control}
+                          <TabsContent value="system" className="space-y-4">
-                      name="referenceText"
+                            <FormField
-                      render={({ field }) => (
+                              control={form.control}
-                        <FormItem>
+                              name="sampleFile"
-                          <FormLabel>Reference Text</FormLabel>
+                              render={() => (
-                          <FormControl>
+                                <AudioSampleSystem
-                            <Textarea
+                                  file={selectedFile}
-                              placeholder="Enter the exact text spoken in the audio..."
+                                  isRecording={isSystemRecording}
-                              className="min-h-[100px]"
+                                  duration={systemDuration}
-                              {...field}
+                                  onStart={startSystemRecording}
+                                  onStop={stopSystemRecording}
+                                  onCancel={handleCancelRecording}
+                                  onTranscribe={handleTranscribe}
+                                  onPlayPause={handlePlayPause}
+                                  isPlaying={isPlaying}
+                                  isTranscribing={transcribe.isPending}
+                                />
+                              )}
                             />
-                          </FormControl>
+                          </TabsContent>
-                          <FormMessage />
+                        )}
-                        </FormItem>
+                      </Tabs>
-                      )}
-                    />
-                  </>
-                ) : (
-                  // Show sample list when editing
-                  editingProfileId && (
-                    <div>
-                      <SampleList profileId={editingProfileId} />
-                    </div>
-                  )
-                )}
-              </div>
-            </div>
 
-            <div className="flex gap-2 justify-end mt-6 pt-4 border-t">
+                      <FormField
-              <Button type="button" variant="outline" onClick={() => handleOpenChange(false)}>
+                        control={form.control}
-                Cancel
+                        name="referenceText"
-              </Button>
+                        render={({ field }) => (
-              <Button
+                          <FormItem>
-                type="submit"
+                            <FormLabel>Reference Text</FormLabel>
-                disabled={createProfile.isPending || updateProfile.isPending || addSample.isPending}
+                            <FormControl>
-              >
+                              <Textarea
-                {createProfile.isPending || updateProfile.isPending || addSample.isPending
+                                placeholder="Enter the exact text spoken in the audio..."
-                  ? 'Saving...'
+                                className="min-h-[100px]"
-                  : editingProfileId
+                                {...field}
-                    ? 'Save Changes'
+                              />
-                    : 'Create Profile'}
+                            </FormControl>
-              </Button>
+                            <FormMessage />
-            </div>
+                          </FormItem>
-          </form>
+                        )}
-        </Form>
+                      />
+                    </>
+                  ) : (
+                    // Show sample list when editing
+                    editingProfileId && (
+                      <div>
+                        <SampleList profileId={editingProfileId} />
+                      </div>
+                    )
+                  )}
+                </div>
+
+                {/* Right column: Profile info */}
+                <div className="space-y-4">
+                  {/* Avatar Upload */}
+                  <FormField
+                    control={form.control}
+                    name="avatarFile"
+                    render={() => (
+                      <FormItem>
+                        <FormControl>
+                          <div className="flex justify-center pt-4 pb-2">
+                            <div className="relative group">
+                              <div className="h-24 w-24 rounded-full bg-muted flex items-center justify-center shrink-0 overflow-hidden border-2 border-border">
+                                {avatarPreview ? (
+                                  <img
+                                    src={avatarPreview}
+                                    alt="Avatar preview"
+                                    className="h-full w-full object-cover"
+                                  />
+                                ) : (
+                                  <Mic className="h-10 w-10 text-muted-foreground" />
+                                )}
+                              </div>
+                              <button
+                                type="button"
+                                onClick={() => avatarInputRef.current?.click()}
+                                className="absolute inset-0 rounded-full bg-accent/60 opacity-0 group-hover:opacity-100 transition-opacity flex items-center justify-center cursor-pointer"
+                              >
+                                <Edit2 className="h-6 w-6 text-accent-foreground" />
+                              </button>
+                              {(avatarPreview || editingProfile?.avatar_path) && (
+                                <button
+                                  type="button"
+                                  onClick={handleRemoveAvatar}
+                                  disabled={deleteAvatar.isPending}
+                                  className="absolute bottom-0 right-0 h-6 w-6 rounded-full bg-background/60 backdrop-blur-sm text-muted-foreground flex items-center justify-center hover:bg-background/80 hover:text-foreground transition-colors shadow-sm border border-border/50"
+                                >
+                                  <X className="h-3.5 w-3.5" />
+                                </button>
+                              )}
+                            </div>
+                            <input
+                              ref={avatarInputRef}
+                              type="file"
+                              accept="image/png,image/jpeg,image/webp"
+                              onChange={handleAvatarFileChange}
+                              className="hidden"
+                            />
+                          </div>
+                        </FormControl>
+                        <FormMessage />
+                      </FormItem>
+                    )}
+                  />
+
+                  <FormField
+                    control={form.control}
+                    name="name"
+                    render={({ field }) => (
+                      <FormItem>
+                        <FormLabel>Name</FormLabel>
+                        <FormControl>
+                          <Input placeholder="My Voice" {...field} />
+                        </FormControl>
+                        <FormMessage />
+                      </FormItem>
+                    )}
+                  />
+
+                  <FormField
+                    control={form.control}
+                    name="description"
+                    render={({ field }) => (
+                      <FormItem>
+                        <FormLabel>Description (Optional)</FormLabel>
+                        <FormControl>
+                          <Textarea placeholder="Describe this voice..." {...field} />
+                        </FormControl>
+                        <FormMessage />
+                      </FormItem>
+                    )}
+                  />
+
+                  <FormField
+                    control={form.control}
+                    name="language"
+                    render={({ field }) => (
+                      <FormItem>
+                        <FormLabel>Language</FormLabel>
+                        <Select onValueChange={field.onChange} defaultValue={field.value}>
+                          <FormControl>
+                            <SelectTrigger>
+                              <SelectValue />
+                            </SelectTrigger>
+                          </FormControl>
+                          <SelectContent>
+                            {LANGUAGE_OPTIONS.map((lang) => (
+                              <SelectItem key={lang.value} value={lang.value}>
+                                {lang.label}
+                              </SelectItem>
+                            ))}
+                          </SelectContent>
+                        </Select>
+                        <FormMessage />
+                      </FormItem>
+                    )}
+                  />
+                </div>
+              </div>
+
+              <div className="flex gap-2 justify-end mt-6 pt-4 border-t">
+                <Button type="button" variant="outline" onClick={() => handleOpenChange(false)}>
+                  Cancel
+                </Button>
+                <Button
+                  type="submit"
+                  disabled={
+                    createProfile.isPending || updateProfile.isPending || addSample.isPending
+                  }
+                >
+                  {createProfile.isPending || updateProfile.isPending || addSample.isPending
+                    ? 'Saving...'
+                    : editingProfileId
+                      ? 'Save Changes'
+                      : 'Create Profile'}
+                </Button>
+              </div>
+            </form>
+          </Form>
+        </div>
       </DialogContent>
     </Dialog>
   );

app/src/components/VoiceProfiles/SampleList.tsx

@@ -1,11 +1,141 @@
-import { Plus, Trash2, Play } from 'lucide-react';
+import { Check, Edit, Pause, Play, Plus, Trash2, Volume2, X } from 'lucide-react';
-import { useState } from 'react';
+import { useEffect, useRef, useState } from 'react';
 import { Button } from '@/components/ui/button';
+import { CircleButton } from '@/components/ui/circle-button';
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogFooter,
+  DialogHeader,
+  DialogTitle,
+} from '@/components/ui/dialog';
+import { Slider } from '@/components/ui/slider';
+import { Textarea } from '@/components/ui/textarea';
+import { useToast } from '@/components/ui/use-toast';
 import { apiClient } from '@/lib/api/client';
-import { useDeleteSample, useProfileSamples } from '@/lib/hooks/useProfiles';
+import { useDeleteSample, useProfileSamples, useUpdateSample } from '@/lib/hooks/useProfiles';
-import { usePlayerStore } from '@/stores/playerStore';
+import { formatAudioDuration } from '@/lib/utils/audio';
+import { cn } from '@/lib/utils/cn';
 import { SampleUpload } from './SampleUpload';
 
+interface MiniSamplePlayerProps {
+  audioUrl: string;
+}
+
+function MiniSamplePlayer({ audioUrl }: MiniSamplePlayerProps) {
+  const audioRef = useRef<HTMLAudioElement | null>(null);
+  const [isPlaying, setIsPlaying] = useState(false);
+  const [currentTime, setCurrentTime] = useState(0);
+  const [duration, setDuration] = useState(0);
+  const [isLoading, setIsLoading] = useState(true);
+
+  useEffect(() => {
+    const audio = new Audio(audioUrl);
+    audioRef.current = audio;
+
+    const handleLoadedMetadata = () => {
+      setDuration(audio.duration);
+      setIsLoading(false);
+    };
+
+    const handleTimeUpdate = () => {
+      setCurrentTime(audio.currentTime);
+    };
+
+    const handleEnded = () => {
+      setIsPlaying(false);
+      setCurrentTime(0);
+    };
+
+    const handlePlay = () => setIsPlaying(true);
+    const handlePause = () => setIsPlaying(false);
+
+    audio.addEventListener('loadedmetadata', handleLoadedMetadata);
+    audio.addEventListener('timeupdate', handleTimeUpdate);
+    audio.addEventListener('ended', handleEnded);
+    audio.addEventListener('play', handlePlay);
+    audio.addEventListener('pause', handlePause);
+
+    return () => {
+      audio.pause();
+      audio.removeEventListener('loadedmetadata', handleLoadedMetadata);
+      audio.removeEventListener('timeupdate', handleTimeUpdate);
+      audio.removeEventListener('ended', handleEnded);
+      audio.removeEventListener('play', handlePlay);
+      audio.removeEventListener('pause', handlePause);
+      audio.src = '';
+    };
+  }, [audioUrl]);
+
+  const handlePlayPause = () => {
+    if (!audioRef.current) return;
+    if (isPlaying) {
+      audioRef.current.pause();
+    } else {
+      audioRef.current.play();
+    }
+  };
+
+  const handleSeek = (value: number[]) => {
+    if (!audioRef.current || duration === 0) return;
+    const progress = value[0] / 100;
+    audioRef.current.currentTime = progress * duration;
+  };
+
+  const handleStop = () => {
+    if (audioRef.current) {
+      audioRef.current.pause();
+      audioRef.current.currentTime = 0;
+    }
+    setIsPlaying(false);
+    setCurrentTime(0);
+  };
+
+  return (
+    <div className="border-t bg-muted/30 px-3 py-2 mt-2">
+      <div className="flex items-center gap-2">
+        <Button
+          type="button"
+          variant="ghost"
+          size="icon"
+          className="h-7 w-7 shrink-0"
+          onClick={handlePlayPause}
+          disabled={isLoading}
+        >
+          {isPlaying ? <Pause className="h-3.5 w-3.5" /> : <Play className="h-3.5 w-3.5 ml-0.5" />}
+        </Button>
+
+        <div className="flex-1 min-w-0 flex items-center gap-2">
+          <Slider
+            value={duration > 0 ? [(currentTime / duration) * 100] : [0]}
+            onValueChange={handleSeek}
+            max={100}
+            step={0.1}
+            className="flex-1"
+          />
+          <div className="flex items-center gap-1 text-xs text-muted-foreground shrink-0 min-w-[70px]">
+            <span className="font-mono">{formatAudioDuration(currentTime)}</span>
+            <span>/</span>
+            <span className="font-mono">{formatAudioDuration(duration)}</span>
+          </div>
+        </div>
+
+        <Button
+          type="button"
+          variant="ghost"
+          size="icon"
+          className="h-7 w-7 shrink-0"
+          onClick={handleStop}
+          title="Stop"
+        >
+          <X className="h-3.5 w-3.5" />
+        </Button>
+      </div>
+    </div>
+  );
+}
+
 interface SampleListProps {
   profileId: string;
 }
@@ -13,20 +143,62 @@ interface SampleListProps {
 export function SampleList({ profileId }: SampleListProps) {
   const { data: samples, isLoading } = useProfileSamples(profileId);
   const deleteSample = useDeleteSample();
+  const updateSample = useUpdateSample();
+  const { toast } = useToast();
   const [uploadOpen, setUploadOpen] = useState(false);
-  const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
+  const [editingSampleId, setEditingSampleId] = useState<string | null>(null);
-  const currentAudioId = usePlayerStore((state) => state.audioId);
+  const [editedText, setEditedText] = useState<string>('');
-  const isPlaying = usePlayerStore((state) => state.isPlaying);
+  const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
+  const [sampleToDelete, setSampleToDelete] = useState<string | null>(null);
 
-  const handleDelete = (sampleId: string) => {
+  const handleDeleteClick = (sampleId: string) => {
-    if (confirm('Are you sure you want to delete this sample?')) {
+    setSampleToDelete(sampleId);
-      deleteSample.mutate(sampleId);
+    setDeleteDialogOpen(true);
+  };
+
+  const handleDeleteConfirm = () => {
+    if (sampleToDelete) {
+      deleteSample.mutate(sampleToDelete);
+      setDeleteDialogOpen(false);
+      setSampleToDelete(null);
     }
   };
 
-  const handlePlay = (referenceText: string, sampleId: string) => {
+  const handleStartEdit = (sampleId: string, currentText: string) => {
-    const audioUrl = apiClient.getSampleUrl(sampleId);
+    setEditingSampleId(sampleId);
-    setAudioWithAutoPlay(audioUrl, sampleId, null, referenceText.substring(0, 50));
+    setEditedText(currentText);
+  };
+
+  const handleCancelEdit = () => {
+    setEditingSampleId(null);
+    setEditedText('');
+  };
+
+  const handleSaveEdit = async (sampleId: string) => {
+    if (!editedText.trim()) {
+      toast({
+        title: 'Invalid text',
+        description: 'Reference text cannot be empty.',
+        variant: 'destructive',
+      });
+      return;
+    }
+
+    try {
+      await updateSample.mutateAsync({ sampleId, referenceText: editedText.trim() });
+      toast({
+        title: 'Sample updated',
+        description: 'Reference text has been updated successfully.',
+      });
+      setEditingSampleId(null);
+      setEditedText('');
+    } catch (error) {
+      toast({
+        title: 'Update failed',
+        description: error instanceof Error ? error.message : 'Failed to update sample',
+        variant: 'destructive',
+      });
+    }
   };
 
   if (isLoading) {
@@ -34,57 +206,152 @@ export function SampleList({ profileId }: SampleListProps) {
   }
 
   return (
-    <div className="space-y-4">
+    <div className="space-y-4 pt-4">
-      <div className="flex items-center justify-between">
-        <h3 className="text-lg font-semibold">Audio Samples</h3>
-        <Button type="button" size="sm" onClick={() => setUploadOpen(true)}>
-          <Plus className="mr-2 h-4 w-4" />
-          Add Sample
-        </Button>
-      </div>
-
       {samples && samples.length === 0 ? (
-        <div className="text-sm text-muted-foreground py-4">
+        <div className="flex flex-col items-center justify-center py-8 text-center border border-dashed rounded-lg">
-          No samples yet. Add your first audio sample.
+          <Volume2 className="h-8 w-8 text-muted-foreground/50 mb-2" />
+          <p className="text-sm text-muted-foreground">No samples yet</p>
+          <p className="text-xs text-muted-foreground/70 mt-1">
+            Add your first audio sample to get started
+          </p>
         </div>
       ) : (
         <div className="space-y-2">
-          {samples?.map((sample) => (
+          {samples?.map((sample, index) => {
-            <div
+            const isEditing = editingSampleId === sample.id;
-              key={sample.id}
+
-              className="flex items-center justify-between p-3 border rounded-lg"
+            return (
-            >
+              <div
-              <div className="flex-1">
+                key={sample.id}
-                <p className="text-sm font-medium">{sample.reference_text}</p>
+                className={cn(
-                <p className="text-xs text-muted-foreground mt-1">{sample.audio_path}</p>
+                  'group relative rounded-lg border bg-card transition-all duration-200',
+                  isEditing ? 'ring-2 ring-primary/20' : 'hover:border-primary/30',
+                )}
+              >
+                {isEditing ? (
+                  /* Edit Mode */
+                  <div className="p-4 space-y-3">
+                    <div className="flex items-center gap-2 text-xs text-muted-foreground mb-2">
+                      <Edit className="h-3 w-3" />
+                      <span>Editing transcription</span>
+                    </div>
+                    <Textarea
+                      value={editedText}
+                      onChange={(e) => setEditedText(e.target.value)}
+                      className="min-h-[100px] text-sm resize-none"
+                      placeholder="Enter reference text..."
+                      autoFocus
+                    />
+                    <div className="flex items-center justify-end gap-2 pt-1">
+                      <Button
+                        type="button"
+                        size="sm"
+                        variant="ghost"
+                        onClick={handleCancelEdit}
+                        disabled={updateSample.isPending}
+                      >
+                        <X className="h-4 w-4 mr-1" />
+                        Cancel
+                      </Button>
+                      <Button
+                        type="button"
+                        size="sm"
+                        onClick={() => handleSaveEdit(sample.id)}
+                        disabled={updateSample.isPending}
+                      >
+                        <Check className="h-4 w-4 mr-1" />
+                        {updateSample.isPending ? 'Saving...' : 'Save'}
+                      </Button>
+                    </div>
+                  </div>
+                ) : (
+                  <>
+                    {/* View Mode */}
+                    <div className="flex items-center gap-3 p-3 h-[72px]">
+                      {/* Text Content */}
+                      <div className="flex-1 min-w-0 py-0.5">
+                        <p className="text-sm font-medium line-clamp-2 leading-snug">
+                          {sample.reference_text}
+                        </p>
+                      </div>
+
+                      {/* Action Buttons */}
+                      <div className="shrink-0 flex items-center gap-0.5 opacity-0 group-hover:opacity-100 transition-opacity">
+                        <CircleButton
+                          icon={Edit}
+                          title="Edit transcription"
+                          onClick={() => handleStartEdit(sample.id, sample.reference_text)}
+                        />
+                        <CircleButton
+                          icon={Trash2}
+                          title="Delete sample"
+                          onClick={() => handleDeleteClick(sample.id)}
+                          disabled={deleteSample.isPending}
+                        />
+                      </div>
+
+                      {/* Sample Number Badge */}
+                      <div className="absolute top-1 right-2 text-[10px] text-muted-foreground/50 font-medium">
+                        #{index + 1}
+                      </div>
+                    </div>
+
+                    {/* Mini Player - Always visible */}
+                    <MiniSamplePlayer audioUrl={apiClient.getSampleUrl(sample.id)} />
+                  </>
+                )}
               </div>
-              <div className="flex gap-2">
+            );
-                <Button
+          })}
-                  type="button"
-                  variant="ghost"
-                  size="sm"
-                  onClick={() => handlePlay(sample.reference_text, sample.id)}
-                  className={currentAudioId === sample.id && isPlaying ? 'text-primary' : ''}
-                >
-                  <Play className="h-4 w-4 mr-1" />
-                  Play
-                </Button>
-                <Button
-                  type="button"
-                  variant="ghost"
-                  size="sm"
-                  onClick={() => handleDelete(sample.id)}
-                  disabled={deleteSample.isPending}
-                >
-                  <Trash2 className="h-4 w-4 text-destructive" />
-                </Button>
-              </div>
-            </div>
-          ))}
         </div>
       )}
 
+      <Button
+        type="button"
+        variant="outline"
+        className="w-full"
+        onClick={() => setUploadOpen(true)}
+      >
+        <Plus className="mr-2 h-4 w-4" />
+        Add Sample
+      </Button>
+
+      <p className="text-xs text-muted-foreground text-center px-2">
+        Note: A single 30-second sample is the sweet spot. Quality may decrease with multiple
+        samples. In a future update samples might be interchangeable and tagged for varying styles
+        of the same voice.
+      </p>
+
       <SampleUpload profileId={profileId} open={uploadOpen} onOpenChange={setUploadOpen} />
+
+      <Dialog open={deleteDialogOpen} onOpenChange={setDeleteDialogOpen}>
+        <DialogContent>
+          <DialogHeader>
+            <DialogTitle>Delete Sample</DialogTitle>
+            <DialogDescription>
+              Are you sure you want to delete this audio sample? This action cannot be undone.
+            </DialogDescription>
+          </DialogHeader>
+          <DialogFooter>
+            <Button
+              variant="outline"
+              onClick={() => {
+                setDeleteDialogOpen(false);
+                setSampleToDelete(null);
+              }}
+            >
+              Cancel
+            </Button>
+            <Button
+              variant="destructive"
+              onClick={handleDeleteConfirm}
+              disabled={deleteSample.isPending}
+            >
+              {deleteSample.isPending ? 'Deleting...' : 'Delete'}
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
     </div>
   );
 }

app/src/components/VoiceProfiles/SampleUpload.tsx

@@ -27,7 +27,7 @@ import { useAudioRecording } from '@/lib/hooks/useAudioRecording';
 import { useAddSample, useProfile } from '@/lib/hooks/useProfiles';
 import { useSystemAudioCapture } from '@/lib/hooks/useSystemAudioCapture';
 import { useTranscription } from '@/lib/hooks/useTranscription';
-import { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 import { AudioSampleRecording } from './AudioSampleRecording';
 import { AudioSampleSystem } from './AudioSampleSystem';
 import { AudioSampleUpload } from './AudioSampleUpload';
@@ -49,6 +49,7 @@ interface SampleUploadProps {
 }
 
 export function SampleUpload({ profileId, open, onOpenChange }: SampleUploadProps) {
+  const platform = usePlatform();
   const addSample = useAddSample();
   const transcribe = useTranscription();
   const { data: profile } = useProfile(profileId);
@@ -232,7 +233,7 @@ export function SampleUpload({ profileId, open, onOpenChange }: SampleUploadProp
           <form onSubmit={form.handleSubmit(onSubmit)} className="space-y-4">
             <Tabs value={mode} onValueChange={(v) => setMode(v as 'upload' | 'record' | 'system')}>
               <TabsList
-                className={`grid w-full ${isTauri() && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
+                className={`grid w-full ${platform.metadata.isTauri && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
               >
                 <TabsTrigger value="upload" className="flex items-center gap-2">
                   <Upload className="h-4 w-4 shrink-0" />
@@ -242,7 +243,7 @@ export function SampleUpload({ profileId, open, onOpenChange }: SampleUploadProp
                   <Mic className="h-4 w-4 shrink-0" />
                   Record
                 </TabsTrigger>
-                {isTauri() && isSystemAudioSupported && (
+                {platform.metadata.isTauri && isSystemAudioSupported && (
                   <TabsTrigger value="system" className="flex items-center gap-2">
                     <Monitor className="h-4 w-4 shrink-0" />
                     System Audio
@@ -289,7 +290,7 @@ export function SampleUpload({ profileId, open, onOpenChange }: SampleUploadProp
                 />
               </TabsContent>
 
-              {isTauri() && isSystemAudioSupported && (
+              {platform.metadata.isTauri && isSystemAudioSupported && (
                 <TabsContent value="system" className="space-y-4">
                   <FormField
                     control={form.control}

app/src/components/ui/circle-button.tsx

@@ -6,10 +6,11 @@ export interface CircleButtonProps extends React.ButtonHTMLAttributes<HTMLButton
 }
 
 const CircleButton = React.forwardRef<HTMLButtonElement, CircleButtonProps>(
-  ({ className, icon: Icon, ...props }, ref) => {
+  ({ className, icon: Icon, type = 'button', ...props }, ref) => {
     return (
       <button
         ref={ref}
+        type={type}
         className={cn(
           'h-7 w-7 rounded-full flex items-center justify-center flex-shrink-0',
           'hover:bg-muted transition-colors',

app/src/hooks/useAutoUpdater.ts

@@ -1,172 +1,41 @@
-import { relaunch } from '@tauri-apps/plugin-process';
-import { check, type Update } from '@tauri-apps/plugin-updater';
 import { useCallback, useEffect, useState } from 'react';
+import { usePlatform } from '@/platform/PlatformContext';
+import type { UpdateStatus } from '@/platform/types';
 
-export interface UpdateStatus {
+// Re-export UpdateStatus for backwards compatibility
-  checking: boolean;
+export type { UpdateStatus };
-  available: boolean;
-  version?: string;
-  downloading: boolean;
-  installing: boolean;
-  readyToInstall: boolean;
-  error?: string;
-  downloadProgress?: number; // 0-100 percentage
-  downloadedBytes?: number;
-  totalBytes?: number;
-}
-
-// Check if we're on Windows (NSIS installer handles restart automatically)
-const isWindows = () => {
-  return navigator.userAgent.includes('Windows');
-};
-
-const isTauri = () => {
-  return '__TAURI_INTERNALS__' in window;
-};
 
 export function useAutoUpdater(checkOnMount = false) {
-  const [status, setStatus] = useState<UpdateStatus>({
+  const platform = usePlatform();
-    checking: false,
+  const [status, setStatus] = useState<UpdateStatus>(
-    available: false,
+    platform.updater.getStatus(),
-    downloading: false,
+  );
-    installing: false,
-    readyToInstall: false,
-  });
 
-  const [update, setUpdate] = useState<Update | null>(null);
+  // Subscribe to updater status changes
+  useEffect(() => {
+    const unsubscribe = platform.updater.subscribe((newStatus) => {
+      setStatus(newStatus);
+    });
+    return unsubscribe;
+  }, [platform]);
 
   const checkForUpdates = useCallback(async () => {
-    if (!isTauri()) {
+    await platform.updater.checkForUpdates();
-      return;
+  }, [platform]);
-    }
 
-    try {
+  const downloadAndInstall = useCallback(async () => {
-      setStatus((prev) => ({ ...prev, checking: true, error: undefined }));
+    await platform.updater.downloadAndInstall();
+  }, [platform]);
 
-      const foundUpdate = await check();
+  const restartAndInstall = useCallback(async () => {
-
+    await platform.updater.restartAndInstall();
-      if (foundUpdate?.available) {
+  }, [platform]);
-        setUpdate(foundUpdate);
-        setStatus({
-          checking: false,
-          available: true,
-          version: foundUpdate.version,
-          downloading: false,
-          installing: false,
-          readyToInstall: false,
-        });
-      } else {
-        setStatus({
-          checking: false,
-          available: false,
-          downloading: false,
-          installing: false,
-          readyToInstall: false,
-        });
-      }
-    } catch (error) {
-      setStatus({
-        checking: false,
-        available: false,
-        downloading: false,
-        installing: false,
-        readyToInstall: false,
-        error: error instanceof Error ? error.message : 'Failed to check for updates',
-      });
-    }
-  }, []);
-
-  // Download the update (but don't install yet)
-  const downloadAndInstall = async () => {
-    if (!update || !isTauri()) return;
-
-    try {
-      setStatus((prev) => ({ ...prev, downloading: true, error: undefined }));
-
-      let downloadedBytes = 0;
-      let totalBytes = 0;
-
-      // Just download the update
-      await update.download((event) => {
-        switch (event.event) {
-          case 'Started':
-            totalBytes = event.data.contentLength || 0;
-            downloadedBytes = 0;
-            setStatus((prev) => ({
-              ...prev,
-              downloading: true,
-              totalBytes,
-              downloadedBytes: 0,
-              downloadProgress: 0,
-            }));
-            break;
-          case 'Progress': {
-            downloadedBytes += event.data.chunkLength;
-            const progress =
-              totalBytes > 0 ? Math.round((downloadedBytes / totalBytes) * 100) : undefined;
-            setStatus((prev) => ({
-              ...prev,
-              downloadedBytes,
-              downloadProgress: progress,
-            }));
-            break;
-          }
-          case 'Finished':
-            setStatus((prev) => ({
-              ...prev,
-              downloading: false,
-              readyToInstall: true,
-              downloadProgress: 100,
-            }));
-            break;
-        }
-      });
-    } catch (error) {
-      setStatus((prev) => ({
-        ...prev,
-        downloading: false,
-        installing: false,
-        readyToInstall: false,
-        downloadProgress: undefined,
-        downloadedBytes: undefined,
-        totalBytes: undefined,
-        error: error instanceof Error ? error.message : 'Failed to download update',
-      }));
-    }
-  };
-
-  // Install the downloaded update and restart the app
-  const restartAndInstall = async () => {
-    if (!update || !isTauri()) return;
-
-    try {
-      setStatus((prev) => ({ ...prev, installing: true, error: undefined }));
-
-      // Install the update
-      await update.install();
-
-      // On Windows with NSIS, the installer handles the restart automatically.
-      // The process will be killed by the NSIS installer, so we won't reach here.
-      // On macOS/Linux, we need to manually relaunch.
-      if (!isWindows()) {
-        await relaunch();
-      }
-      // If we're on Windows and somehow still running, the NSIS installer
-      // should have already handled everything. Just wait for the process to end.
-    } catch (error) {
-      setStatus((prev) => ({
-        ...prev,
-        installing: false,
-        error: error instanceof Error ? error.message : 'Failed to install update',
-      }));
-    }
-  };
 
   useEffect(() => {
-    if (checkOnMount && isTauri()) {
+    if (checkOnMount && platform.metadata.isTauri) {
       checkForUpdates();
     }
-  }, [checkOnMount, checkForUpdates]);
+  }, [checkOnMount, checkForUpdates, platform.metadata.isTauri]);
 
   return {
     status,

app/src/lib/api/client.ts

@@ -1,4 +1,5 @@
 import { useServerStore } from '@/stores/serverStore';
+import type { LanguageCode } from '@/lib/constants/languages';
 import type {
   VoiceProfileCreate,
   VoiceProfileResponse,
@@ -120,6 +121,16 @@ class ApiClient {
     });
   }
 
+  async updateProfileSample(
+    sampleId: string,
+    referenceText: string,
+  ): Promise<ProfileSampleResponse> {
+    return this.request<ProfileSampleResponse>(`/profiles/samples/${sampleId}`, {
+      method: 'PUT',
+      body: JSON.stringify({ reference_text: referenceText }),
+    });
+  }
+
   async exportProfile(profileId: string): Promise<Blob> {
     const url = `${this.getBaseUrl()}/profiles/${profileId}/export`;
     const response = await fetch(url);
@@ -154,6 +165,32 @@ class ApiClient {
     return response.json();
   }
 
+  async uploadAvatar(profileId: string, file: File): Promise<VoiceProfileResponse> {
+    const url = `${this.getBaseUrl()}/profiles/${profileId}/avatar`;
+    const formData = new FormData();
+    formData.append('file', file);
+
+    const response = await fetch(url, {
+      method: 'POST',
+      body: formData,
+    });
+
+    if (!response.ok) {
+      const error = await response.json().catch(() => ({
+        detail: response.statusText,
+      }));
+      throw new Error(error.detail || `HTTP error! status: ${response.status}`);
+    }
+
+    return response.json();
+  }
+
+  async deleteAvatar(profileId: string): Promise<void> {
+    await this.request<void>(`/profiles/${profileId}/avatar`, {
+      method: 'DELETE',
+    });
+  }
+
   // Generation
   async generateSpeech(data: GenerationRequest): Promise<GenerationResponse> {
     return this.request<GenerationResponse>('/generate', {
@@ -244,7 +281,7 @@ class ApiClient {
   }
 
   // Transcription
-  async transcribeAudio(file: File, language?: 'en' | 'zh'): Promise<TranscriptionResponse> {
+  async transcribeAudio(file: File, language?: LanguageCode): Promise<TranscriptionResponse> {
     const formData = new FormData();
     formData.append('file', file);
     if (language) {

app/src/lib/api/types.ts

@@ -1,9 +1,10 @@
 // API Types matching backend Pydantic models
+import type { LanguageCode } from '@/lib/constants/languages';
 
 export interface VoiceProfileCreate {
   name: string;
   description?: string;
-  language: 'en' | 'zh';
+  language: LanguageCode;
 }
 
 export interface VoiceProfileResponse {
@@ -11,6 +12,7 @@ export interface VoiceProfileResponse {
   name: string;
   description?: string;
   language: string;
+  avatar_path?: string;
   created_at: string;
   updated_at: string;
 }
@@ -29,7 +31,7 @@ export interface ProfileSampleResponse {
 export interface GenerationRequest {
   profile_id: string;
   text: string;
-  language: 'en' | 'zh';
+  language: LanguageCode;
   seed?: number;
   model_size?: '1.7B' | '0.6B';
 }
@@ -62,7 +64,7 @@ export interface HistoryListResponse {
 }
 
 export interface TranscriptionRequest {
-  language?: 'en' | 'zh';
+  language?: LanguageCode;
 }
 
 export interface TranscriptionResponse {

app/src/lib/hooks/useAudioRecording.ts

@@ -1,5 +1,5 @@
 import { useCallback, useEffect, useRef, useState } from 'react';
-import { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 import { convertToWav } from '@/lib/utils/audio';
 
 interface UseAudioRecordingOptions {
@@ -11,6 +11,7 @@ export function useAudioRecording({
   maxDurationSeconds = 29,
   onRecordingComplete,
 }: UseAudioRecordingOptions = {}) {
+  const platform = usePlatform();
   const [isRecording, setIsRecording] = useState(false);
   const [duration, setDuration] = useState(0);
   const [error, setError] = useState<string | null>(null);
@@ -40,15 +41,14 @@ export function useAudioRecording({
         await new Promise((resolve) => setTimeout(resolve, 100));
 
         if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
-          const isTauriEnv = isTauri();
           console.error('MediaDevices check:', {
             hasNavigator: typeof navigator !== 'undefined',
             hasMediaDevices: !!navigator?.mediaDevices,
             hasGetUserMedia: !!navigator?.mediaDevices?.getUserMedia,
-            isTauri: isTauriEnv,
+            isTauri: platform.metadata.isTauri,
           });
 
-          const errorMsg = isTauriEnv
+          const errorMsg = platform.metadata.isTauri
             ? 'Microphone access is not available. Please ensure:\n1. The app has microphone permissions in System Settings (macOS: System Settings > Privacy & Security > Microphone)\n2. You restart the app after granting permissions\n3. You are using Tauri v2 with a webview that supports getUserMedia'
             : 'Microphone access is not available. Please ensure you are using a secure context (HTTPS or localhost) and that your browser has microphone permissions enabled.';
           setError(errorMsg);

app/src/lib/hooks/useHistory.ts

@@ -1,7 +1,7 @@
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
 import { apiClient } from '@/lib/api/client';
 import type { HistoryQuery } from '@/lib/api/types';
-import { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function useHistory(query?: HistoryQuery) {
   return useQuery({
@@ -30,116 +30,52 @@ export function useDeleteGeneration() {
 }
 
 export function useExportGeneration() {
+  const platform = usePlatform();
+
   return useMutation({
     mutationFn: async ({ generationId, text }: { generationId: string; text: string }) => {
       const blob = await apiClient.exportGeneration(generationId);
-      
+
       // Create safe filename from text
-      const safeText = text.substring(0, 30).replace(/[^a-z0-9]/gi, '-').toLowerCase();
+      const safeText = text
+        .substring(0, 30)
+        .replace(/[^a-z0-9]/gi, '-')
+        .toLowerCase();
       const filename = `generation-${safeText}.voicebox.zip`;
-      
+
-      if (isTauri()) {
+      await platform.filesystem.saveFile(filename, blob, [
-        // Use Tauri's native save dialog
+        {
-        try {
+          name: 'Voicebox Generation',
-          const { save } = await import('@tauri-apps/plugin-dialog');
+          extensions: ['zip'],
-          const filePath = await save({
+        },
-            defaultPath: filename,
+      ]);
-            filters: [
+
-              {
-                name: 'Voicebox Generation',
-                extensions: ['voicebox.zip', 'zip'],
-              },
-            ],
-          });
-          
-          if (filePath) {
-            // Write file using Tauri's filesystem API
-            const { writeBinaryFile } = await import('@tauri-apps/plugin-fs');
-            const arrayBuffer = await blob.arrayBuffer();
-            await writeBinaryFile(filePath, new Uint8Array(arrayBuffer));
-          }
-        } catch (error) {
-          console.error('Failed to use Tauri dialog, falling back to browser download:', error);
-          // Fall back to browser download if Tauri dialog fails
-          const url = window.URL.createObjectURL(blob);
-          const a = document.createElement('a');
-          a.href = url;
-          a.download = filename;
-          document.body.appendChild(a);
-          a.click();
-          window.URL.revokeObjectURL(url);
-          document.body.removeChild(a);
-        }
-      } else {
-        // Browser: trigger download
-        const url = window.URL.createObjectURL(blob);
-        const a = document.createElement('a');
-        a.href = url;
-        a.download = filename;
-        document.body.appendChild(a);
-        a.click();
-        window.URL.revokeObjectURL(url);
-        document.body.removeChild(a);
-      }
-      
       return blob;
     },
   });
 }
 
 export function useExportGenerationAudio() {
+  const platform = usePlatform();
+
   return useMutation({
     mutationFn: async ({ generationId, text }: { generationId: string; text: string }) => {
       const blob = await apiClient.exportGenerationAudio(generationId);
-      
+
       // Create safe filename from text
-      const safeText = text.substring(0, 30).replace(/[^a-z0-9]/gi, '-').toLowerCase();
+      const safeText = text
+        .substring(0, 30)
+        .replace(/[^a-z0-9]/gi, '-')
+        .toLowerCase();
       const filename = `${safeText}.wav`;
-      
+
-      if (isTauri()) {
+      await platform.filesystem.saveFile(filename, blob, [
-        // Use Tauri's native save dialog
+        {
-        try {
+          name: 'Audio File',
-          const { save } = await import('@tauri-apps/plugin-dialog');
+          extensions: ['wav'],
-          const filePath = await save({
+        },
-            defaultPath: filename,
+      ]);
-            filters: [
+
-              {
-                name: 'Audio File',
-                extensions: ['wav'],
-              },
-            ],
-          });
-          
-          if (filePath) {
-            // Write file using Tauri's filesystem API
-            const { writeBinaryFile } = await import('@tauri-apps/plugin-fs');
-            const arrayBuffer = await blob.arrayBuffer();
-            await writeBinaryFile(filePath, new Uint8Array(arrayBuffer));
-          }
-        } catch (error) {
-          console.error('Failed to use Tauri dialog, falling back to browser download:', error);
-          // Fall back to browser download if Tauri dialog fails
-          const url = window.URL.createObjectURL(blob);
-          const a = document.createElement('a');
-          a.href = url;
-          a.download = filename;
-          document.body.appendChild(a);
-          a.click();
-          window.URL.revokeObjectURL(url);
-          document.body.removeChild(a);
-        }
-      } else {
-        // Browser: trigger download
-        const url = window.URL.createObjectURL(blob);
-        const a = document.createElement('a');
-        a.href = url;
-        a.download = filename;
-        document.body.appendChild(a);
-        a.click();
-        window.URL.revokeObjectURL(url);
-        document.body.removeChild(a);
-      }
-      
       return blob;
     },
   });

app/src/lib/hooks/useModelDownloadToast.tsx

@@ -140,8 +140,8 @@ export function useModelDownloadToast({
       }
     };
 
-    eventSource.onerror = () => {
+    eventSource.onerror = (error) => {
-      console.error('SSE error');
+      console.error('SSE error:', error);
       eventSource.close();
       eventSourceRef.current = null;
 

app/src/lib/hooks/useProfiles.ts

@@ -1,7 +1,7 @@
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
 import { apiClient } from '@/lib/api/client';
 import type { VoiceProfileCreate } from '@/lib/api/types';
-import { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function useProfiles() {
   return useQuery({
@@ -98,60 +98,43 @@ export function useDeleteSample() {
   });
 }
 
+export function useUpdateSample() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: ({ sampleId, referenceText }: { sampleId: string; referenceText: string }) =>
+      apiClient.updateProfileSample(sampleId, referenceText),
+    onSuccess: (data) => {
+      queryClient.invalidateQueries({
+        queryKey: ['profiles', data.profile_id, 'samples'],
+      });
+      queryClient.invalidateQueries({
+        queryKey: ['profiles', data.profile_id],
+      });
+      queryClient.invalidateQueries({ queryKey: ['profiles'] });
+    },
+  });
+}
+
 export function useExportProfile() {
+  const platform = usePlatform();
+
   return useMutation({
     mutationFn: async (profileId: string) => {
       const blob = await apiClient.exportProfile(profileId);
-      
+
       // Get profile name for filename
       const profile = await apiClient.getProfile(profileId);
       const safeName = profile.name.replace(/[^a-z0-9]/gi, '-').toLowerCase();
       const filename = `profile-${safeName}.voicebox.zip`;
-      
+
-      if (isTauri()) {
+      await platform.filesystem.saveFile(filename, blob, [
-        // Use Tauri's native save dialog
+        {
-        try {
+          name: 'Voicebox Profile',
-          const { save } = await import('@tauri-apps/plugin-dialog');
+          extensions: ['zip'],
-          const filePath = await save({
+        },
-            defaultPath: filename,
+      ]);
-            filters: [
+
-              {
-                name: 'Voicebox Profile',
-                extensions: ['voicebox.zip', 'zip'],
-              },
-            ],
-          });
-          
-          if (filePath) {
-            // Write file using Tauri's filesystem API
-            const { writeBinaryFile } = await import('@tauri-apps/plugin-fs');
-            const arrayBuffer = await blob.arrayBuffer();
-            await writeBinaryFile(filePath, new Uint8Array(arrayBuffer));
-          }
-        } catch (error) {
-          console.error('Failed to use Tauri dialog, falling back to browser download:', error);
-          // Fall back to browser download if Tauri dialog fails
-          const url = window.URL.createObjectURL(blob);
-          const a = document.createElement('a');
-          a.href = url;
-          a.download = filename;
-          document.body.appendChild(a);
-          a.click();
-          window.URL.revokeObjectURL(url);
-          document.body.removeChild(a);
-        }
-      } else {
-        // Browser: trigger download
-        const url = window.URL.createObjectURL(blob);
-        const a = document.createElement('a');
-        a.href = url;
-        a.download = filename;
-        document.body.appendChild(a);
-        a.click();
-        window.URL.revokeObjectURL(url);
-        document.body.removeChild(a);
-      }
-      
       return blob;
     },
   });
@@ -167,3 +150,32 @@ export function useImportProfile() {
     },
   });
 }
+
+export function useUploadAvatar() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: ({ profileId, file }: { profileId: string; file: File }) =>
+      apiClient.uploadAvatar(profileId, file),
+    onSuccess: (_, variables) => {
+      queryClient.invalidateQueries({ queryKey: ['profiles'] });
+      queryClient.invalidateQueries({
+        queryKey: ['profiles', variables.profileId],
+      });
+    },
+  });
+}
+
+export function useDeleteAvatar() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: (profileId: string) => apiClient.deleteAvatar(profileId),
+    onSuccess: (_, profileId) => {
+      queryClient.invalidateQueries({ queryKey: ['profiles'] });
+      queryClient.invalidateQueries({
+        queryKey: ['profiles', profileId],
+      });
+    },
+  });
+}

app/src/lib/hooks/useStories.ts

@@ -1,7 +1,7 @@
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
 import { apiClient } from '@/lib/api/client';
 import type { StoryCreate, StoryItemCreate, StoryItemBatchUpdate, StoryItemReorder, StoryItemMove, StoryItemTrim, StoryItemSplit } from '@/lib/api/types';
-import { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function useStories() {
   return useQuery({
@@ -158,6 +158,8 @@ export function useDuplicateStoryItem() {
 }
 
 export function useExportStoryAudio() {
+  const platform = usePlatform();
+
   return useMutation({
     mutationFn: async ({ storyId, storyName }: { storyId: string; storyName: string }) => {
       const blob = await apiClient.exportStoryAudio(storyId);
@@ -166,49 +168,12 @@ export function useExportStoryAudio() {
       const safeName = storyName.substring(0, 50).replace(/[^a-z0-9]/gi, '-').toLowerCase();
       const filename = `${safeName || 'story'}.wav`;
 
-      if (isTauri()) {
+      await platform.filesystem.saveFile(filename, blob, [
-        // Use Tauri's native save dialog
+        {
-        try {
+          name: 'Audio File',
-          const { save } = await import('@tauri-apps/plugin-dialog');
+          extensions: ['wav'],
-          const filePath = await save({
+        },
-            defaultPath: filename,
+      ]);
-            filters: [
-              {
-                name: 'Audio File',
-                extensions: ['wav'],
-              },
-            ],
-          });
-
-          if (filePath) {
-            // Write file using Tauri's filesystem API
-            const { writeBinaryFile } = await import('@tauri-apps/plugin-fs');
-            const arrayBuffer = await blob.arrayBuffer();
-            await writeBinaryFile(filePath, new Uint8Array(arrayBuffer));
-          }
-        } catch (error) {
-          console.error('Failed to use Tauri dialog, falling back to browser download:', error);
-          // Fall back to browser download if Tauri dialog fails
-          const url = window.URL.createObjectURL(blob);
-          const a = document.createElement('a');
-          a.href = url;
-          a.download = filename;
-          document.body.appendChild(a);
-          a.click();
-          window.URL.revokeObjectURL(url);
-          document.body.removeChild(a);
-        }
-      } else {
-        // Browser: trigger download
-        const url = window.URL.createObjectURL(blob);
-        const a = document.createElement('a');
-        a.href = url;
-        a.download = filename;
-        document.body.appendChild(a);
-        a.click();
-        window.URL.revokeObjectURL(url);
-        document.body.removeChild(a);
-      }
 
       return blob;
     },

app/src/lib/hooks/useSystemAudioCapture.ts

@@ -1,6 +1,5 @@
 import { useState, useRef, useCallback, useEffect } from 'react';
-import { invoke } from '@tauri-apps/api/core';
+import { usePlatform } from '@/platform/PlatformContext';
-import { isTauri } from '@/lib/tauri';
 
 interface UseSystemAudioCaptureOptions {
   maxDurationSeconds?: number;
@@ -15,6 +14,7 @@ export function useSystemAudioCapture({
   maxDurationSeconds = 29,
   onRecordingComplete,
 }: UseSystemAudioCaptureOptions = {}) {
+  const platform = usePlatform();
   const [isRecording, setIsRecording] = useState(false);
   const [duration, setDuration] = useState(0);
   const [error, setError] = useState<string | null>(null);
@@ -26,22 +26,12 @@ export function useSystemAudioCapture({
 
   // Check if system audio capture is supported
   useEffect(() => {
-    if (!isTauri()) {
+    const supported = platform.audio.isSystemAudioSupported();
-      setIsSupported(false);
+    setIsSupported(supported);
-      return;
+  }, [platform]);
-    }
-
-    invoke<boolean>('is_system_audio_supported')
-      .then((supported) => {
-        setIsSupported(supported);
-      })
-      .catch(() => {
-        setIsSupported(false);
-      });
-  }, []);
 
   const startRecording = useCallback(async () => {
-    if (!isTauri()) {
+    if (!platform.metadata.isTauri) {
       const errorMsg = 'System audio capture is only available in the desktop app.';
       setError(errorMsg);
       return;
@@ -58,9 +48,7 @@ export function useSystemAudioCapture({
       setDuration(0);
 
       // Start native capture
-      await invoke('start_system_audio_capture', {
+      await platform.audio.startSystemAudioCapture(maxDurationSeconds);
-        maxDurationSecs: maxDurationSeconds,
-      });
 
       setIsRecording(true);
       isRecordingRef.current = true;
@@ -86,10 +74,10 @@ export function useSystemAudioCapture({
       setError(errorMessage);
       setIsRecording(false);
     }
-  }, [maxDurationSeconds, isSupported]);
+  }, [maxDurationSeconds, isSupported, platform]);
 
   const stopRecording = useCallback(async () => {
-    if (!isRecording || !isTauri()) {
+    if (!isRecording || !platform.metadata.isTauri) {
       return;
     }
 
@@ -102,17 +90,9 @@ export function useSystemAudioCapture({
         timerRef.current = null;
       }
 
-      // Stop capture and get base64 WAV data
+      // Stop capture and get Blob
-      const base64Data = await invoke<string>('stop_system_audio_capture');
+      const blob = await platform.audio.stopSystemAudioCapture();
 
-      // Convert base64 to Blob
-      const binaryString = atob(base64Data);
-      const bytes = new Uint8Array(binaryString.length);
-      for (let i = 0; i < binaryString.length; i++) {
-        bytes[i] = binaryString.charCodeAt(i);
-      }
-
-      const blob = new Blob([bytes], { type: 'audio/wav' });
       // Pass the actual recorded duration
       const recordedDuration = startTimeRef.current 
         ? (Date.now() - startTimeRef.current) / 1000 
@@ -125,7 +105,7 @@ export function useSystemAudioCapture({
           : 'Failed to stop system audio capture.';
       setError(errorMessage);
     }
-  }, [isRecording, onRecordingComplete]);
+  }, [isRecording, onRecordingComplete, platform]);
 
   // Store stopRecording in ref for use in timer
   useEffect(() => {
@@ -155,15 +135,15 @@ export function useSystemAudioCapture({
         timerRef.current = null;
       }
       // Cancel recording on unmount if still recording
-      if (isRecordingRef.current && isTauri()) {
+      if (isRecordingRef.current && platform.metadata.isTauri) {
         // Call stop directly without the callback to avoid stale closure
-        invoke('stop_system_audio_capture').catch((err) => {
+        platform.audio.stopSystemAudioCapture().catch((err) => {
           console.error('Error stopping audio capture on unmount:', err);
         });
       }
     };
     // biome-ignore lint/correctness/useExhaustiveDependencies: Only run on unmount
-  }, []);
+  }, [platform]);
 
   return {
     isRecording,

app/src/lib/hooks/useTranscription.ts

@@ -1,9 +1,10 @@
 import { useMutation } from '@tanstack/react-query';
 import { apiClient } from '@/lib/api/client';
+import type { LanguageCode } from '@/lib/constants/languages';
 
 export function useTranscription() {
   return useMutation({
-    mutationFn: ({ file, language }: { file: File; language?: 'en' | 'zh' }) =>
+    mutationFn: ({ file, language }: { file: File; language?: LanguageCode }) =>
       apiClient.transcribeAudio(file, language),
   });
 }

app/src/lib/tauri.ts

@@ -1,108 +0,0 @@
-/**
- * Tauri integration utilities
- */
-
-import { invoke } from '@tauri-apps/api/core';
-import { listen, emit } from '@tauri-apps/api/event';
-
-/**
- * Check if running in Tauri environment
- */
-export function isTauri(): boolean {
-  return '__TAURI_INTERNALS__' in window;
-}
-
-/**
- * Check if running on macOS
- */
-export function isMacOS(): boolean {
-  return navigator.platform.toLowerCase().includes('mac');
-}
-
-/**
- * Start the bundled Python server (Tauri only)
- */
-export async function startServer(remote = false): Promise<string> {
-  if (!isTauri()) {
-    throw new Error('Not running in Tauri environment');
-  }
-
-  try {
-    const result = await invoke<string>('start_server', { remote });
-    console.log('Server started:', result);
-    return result;
-  } catch (error) {
-    console.error('Failed to start server:', error);
-    throw error;
-  }
-}
-
-/**
- * Stop the bundled Python server (Tauri only)
- */
-export async function stopServer(): Promise<void> {
-  if (!isTauri()) {
-    throw new Error('Not running in Tauri environment');
-  }
-
-  try {
-    await invoke('stop_server');
-    console.log('Server stopped');
-  } catch (error) {
-    console.error('Failed to stop server:', error);
-    throw error;
-  }
-}
-
-/**
- * Set whether the server should keep running when the app closes (Tauri only)
- */
-export async function setKeepServerRunning(keepRunning: boolean): Promise<void> {
-  if (!isTauri()) {
-    return;
-  }
-
-  try {
-    await invoke('set_keep_server_running', { keepRunning });
-  } catch (error) {
-    console.error('Failed to set keep server running setting:', error);
-  }
-}
-
-/**
- * Setup window close handler to check setting and stop server if needed
- */
-export async function setupWindowCloseHandler(): Promise<void> {
-  if (!isTauri()) {
-    return;
-  }
-
-  try {
-    // Listen for window close request from Rust
-    await listen<null>('window-close-requested', async () => {
-      // Import store here to avoid circular dependency
-      const { useServerStore } = await import('@/stores/serverStore');
-      const keepRunning = useServerStore.getState().keepServerRunningOnClose;
-
-      // Check if server was started by this app instance
-      // In dev mode, serverStartedByApp will be false, so we won't try to stop a separately-run server
-      // We need to access the module-level variable - this is a bit hacky but works
-      // @ts-expect-error - accessing module-level variable from another module
-      const serverStartedByApp = window.__voiceboxServerStartedByApp ?? false;
-
-      if (!keepRunning && serverStartedByApp) {
-        // Stop server before closing (only if we started it)
-        try {
-          await stopServer();
-        } catch (error) {
-          console.error('Failed to stop server on close:', error);
-        }
-      }
-
-      // Emit event back to Rust to allow close
-      await emit('window-close-allowed');
-    });
-  } catch (error) {
-    console.error('Failed to setup window close handler:', error);
-  }
-}

app/src/platform/PlatformContext.tsx

@@ -0,0 +1,25 @@
+import { createContext, useContext, type ReactNode } from 'react';
+import type { Platform } from './types';
+
+const PlatformContext = createContext<Platform | null>(null);
+
+export interface PlatformProviderProps {
+  platform: Platform;
+  children: ReactNode;
+}
+
+export function PlatformProvider({ platform, children }: PlatformProviderProps) {
+  return (
+    <PlatformContext.Provider value={platform}>
+      {children}
+    </PlatformContext.Provider>
+  );
+}
+
+export function usePlatform(): Platform {
+  const platform = useContext(PlatformContext);
+  if (!platform) {
+    throw new Error('usePlatform must be used within PlatformProvider');
+  }
+  return platform;
+}

app/src/platform/types.ts

@@ -0,0 +1,70 @@
+/**
+ * Platform abstraction types
+ * These interfaces define the contract that platform implementations must fulfill
+ */
+
+export interface FileFilter {
+  name: string;
+  extensions: string[];
+}
+
+export interface PlatformFilesystem {
+  saveFile(filename: string, blob: Blob, filters?: FileFilter[]): Promise<void>;
+}
+
+export interface UpdateStatus {
+  checking: boolean;
+  available: boolean;
+  version?: string;
+  downloading: boolean;
+  installing: boolean;
+  readyToInstall: boolean;
+  error?: string;
+  downloadProgress?: number; // 0-100 percentage
+  downloadedBytes?: number;
+  totalBytes?: number;
+}
+
+export interface PlatformUpdater {
+  checkForUpdates(): Promise<void>;
+  downloadAndInstall(): Promise<void>;
+  restartAndInstall(): Promise<void>;
+  getStatus(): UpdateStatus;
+  subscribe(callback: (status: UpdateStatus) => void): () => void;
+}
+
+export interface AudioDevice {
+  id: string;
+  name: string;
+  is_default: boolean;
+}
+
+export interface PlatformAudio {
+  isSystemAudioSupported(): boolean;
+  startSystemAudioCapture(maxDurationSecs: number): Promise<void>;
+  stopSystemAudioCapture(): Promise<Blob>;
+  listOutputDevices(): Promise<AudioDevice[]>;
+  playToDevices(audioData: Uint8Array, deviceIds: string[]): Promise<void>;
+  stopPlayback(): void;
+}
+
+export interface PlatformLifecycle {
+  startServer(remote?: boolean): Promise<string>;
+  stopServer(): Promise<void>;
+  setKeepServerRunning(keep: boolean): Promise<void>;
+  setupWindowCloseHandler(): Promise<void>;
+  onServerReady?: () => void;
+}
+
+export interface PlatformMetadata {
+  getVersion(): Promise<string>;
+  isTauri: boolean;
+}
+
+export interface Platform {
+  filesystem: PlatformFilesystem;
+  updater: PlatformUpdater;
+  audio: PlatformAudio;
+  lifecycle: PlatformLifecycle;
+  metadata: PlatformMetadata;
+}

app/src/router.tsx

@@ -10,7 +10,8 @@ import { Toaster } from '@/components/ui/toaster';
 import { VoicesTab } from '@/components/VoicesTab/VoicesTab';
 import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
 import { MODEL_DISPLAY_NAMES, useRestoreActiveTasks } from '@/lib/hooks/useRestoreActiveTasks';
-import { isMacOS } from '@/lib/tauri';
+// Simple platform check that works in both web and Tauri
+const isMacOS = () => navigator.platform.toLowerCase().includes('mac');
 
 // Root layout component
 function RootLayout() {

app/src/stores/storyStore.ts

@@ -89,6 +89,9 @@ export const useStoryStore = create<StoryPlaybackState>((set, get) => ({
       playbackItems: items,
       totalDurationMs: maxEndTimeMs,
       currentTimeMs: startTimeMs,
+      // Reset timing anchors - will be set fresh by the playback hook
+      playbackStartContextTime: null,
+      playbackStartStoryTime: null,
     });
   },
 

app/src/stores/uiStore.ts

@@ -1,5 +1,18 @@
 import { create } from 'zustand';
 
+// Draft state for the create voice profile form
+export interface ProfileFormDraft {
+  name: string;
+  description: string;
+  language: string;
+  referenceText: string;
+  sampleMode: 'upload' | 'record' | 'system';
+  // Note: File objects can't be persisted, so we store metadata
+  sampleFileName?: string;
+  sampleFileType?: string;
+  sampleFileData?: string; // Base64 encoded
+}
+
 interface UIStore {
   // Sidebar
   sidebarOpen: boolean;
@@ -18,6 +31,10 @@ interface UIStore {
   selectedProfileId: string | null;
   setSelectedProfileId: (id: string | null) => void;
 
+  // Profile form draft (for persisting create voice modal state)
+  profileFormDraft: ProfileFormDraft | null;
+  setProfileFormDraft: (draft: ProfileFormDraft | null) => void;
+
   // Theme
   theme: 'light' | 'dark';
   setTheme: (theme: 'light' | 'dark') => void;
@@ -38,6 +55,9 @@ export const useUIStore = create<UIStore>((set) => ({
   selectedProfileId: null,
   setSelectedProfileId: (id) => set({ selectedProfileId: id }),
 
+  profileFormDraft: null,
+  setProfileFormDraft: (draft) => set({ profileFormDraft: draft }),
+
   theme: 'light',
   setTheme: (theme) => {
     set({ theme });

backend/README.md

@@ -19,8 +19,13 @@ Production-quality FastAPI backend for Qwen3-TTS voice cloning.
 backend/
 ├── main.py              # FastAPI app with all routes
 ├── models.py            # Pydantic request/response models
-├── tts.py              # Qwen3-TTS inference
+├── platform_detect.py   # Platform detection for backend selection
-├── transcribe.py       # Whisper ASR
+├── tts.py              # TTS backend abstraction (delegates to MLX or PyTorch)
+├── transcribe.py       # STT backend abstraction (delegates to MLX or PyTorch)
+├── backends/           # Backend implementations
+│   ├── __init__.py     # Backend factory and protocols
+│   ├── mlx_backend.py  # MLX backend (Apple Silicon)
+│   └── pytorch_backend.py  # PyTorch backend (Windows/Linux/Intel)
 ├── profiles.py         # Voice profile CRUD
 ├── history.py          # Generation history
 ├── studio.py           # Audio editing (TODO)
@@ -31,6 +36,15 @@ backend/
     └── validation.py   # Input validation
 ```
 
+### Backend Selection
+
+Voicebox automatically selects the best backend based on platform:
+
+- **Apple Silicon (M1/M2/M3)**: Uses MLX backend with native Metal acceleration (4-5x faster)
+- **Windows/Linux/Intel Mac**: Uses PyTorch backend (CUDA GPU if available, CPU fallback)
+
+The backend is detected at runtime via `platform_detect.py`. Both backends implement the same interface, so the API remains consistent across platforms.
+
 ## API Endpoints
 
 ### Health & Info
@@ -47,12 +61,20 @@ Health check with model status.
   "status": "healthy",
   "model_loaded": true,
   "gpu_available": true,
-  "vram_used_mb": 1024.5
+  "gpu_type": "Metal (Apple Silicon via MLX)",
+  "backend_type": "mlx",
+  "vram_used_mb": null
 }
 ```
 
+**Backend Types:**
+- `"mlx"` - MLX backend (Apple Silicon with Metal acceleration)
+- `"pytorch"` - PyTorch backend (Windows/Linux/Intel Mac)
+
 ### Voice Profiles
 
+**Note:** The database is automatically initialized when the server starts. No manual setup required.
+
 #### `POST /profiles`
 Create a new voice profile.
 
@@ -266,13 +288,12 @@ data/
 pip install -r requirements.txt
 ```
 
-### 2. Initialize Database
+**Note:** On Apple Silicon, also install MLX dependencies for faster inference:
-
 ```bash
-python -c "from database import init_db; init_db()"
+pip install -r requirements-mlx.txt
 ```
 
-### 3. Download Models (Automatic)
+### 2. Download Models (Automatic)
 
 The Qwen3-TTS models are automatically downloaded from HuggingFace Hub on first use, similar to how Whisper models work.
 

backend/__init__.py

@@ -1 +1,3 @@
 # Backend package
+
+__version__ = "0.1.11"

backend/backends/__init__.py

@@ -0,0 +1,166 @@
+"""
+Backend abstraction layer for TTS and STT.
+
+Provides a unified interface for MLX and PyTorch backends.
+"""
+
+from typing import Protocol, Optional, Tuple, List
+from typing_extensions import runtime_checkable
+import numpy as np
+
+from ..platform_detect import get_backend_type
+
+
+@runtime_checkable
+class TTSBackend(Protocol):
+    """Protocol for TTS backend implementations."""
+    
+    async def load_model(self, model_size: str) -> None:
+        """Load TTS model."""
+        ...
+    
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+        
+        Returns:
+            Tuple of (voice_prompt_dict, was_cached)
+        """
+        ...
+    
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """
+        Combine multiple voice prompts.
+        
+        Returns:
+            Tuple of (combined_audio_array, combined_text)
+        """
+        ...
+    
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text.
+        
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        ...
+    
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        ...
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        ...
+    
+    def _get_model_path(self, model_size: str) -> str:
+        """
+        Get model path for a given size.
+        
+        Returns:
+            Model path or HuggingFace Hub ID
+        """
+        ...
+
+
+@runtime_checkable
+class STTBackend(Protocol):
+    """Protocol for STT (Speech-to-Text) backend implementations."""
+    
+    async def load_model(self, model_size: str) -> None:
+        """Load STT model."""
+        ...
+    
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None,
+    ) -> str:
+        """
+        Transcribe audio to text.
+        
+        Returns:
+            Transcribed text
+        """
+        ...
+    
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        ...
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        ...
+
+
+# Global backend instances
+_tts_backend: Optional[TTSBackend] = None
+_stt_backend: Optional[STTBackend] = None
+
+
+def get_tts_backend() -> TTSBackend:
+    """
+    Get or create TTS backend instance based on platform.
+    
+    Returns:
+        TTS backend instance (MLX or PyTorch)
+    """
+    global _tts_backend
+    
+    if _tts_backend is None:
+        backend_type = get_backend_type()
+        
+        if backend_type == "mlx":
+            from .mlx_backend import MLXTTSBackend
+            _tts_backend = MLXTTSBackend()
+        else:
+            from .pytorch_backend import PyTorchTTSBackend
+            _tts_backend = PyTorchTTSBackend()
+    
+    return _tts_backend
+
+
+def get_stt_backend() -> STTBackend:
+    """
+    Get or create STT backend instance based on platform.
+    
+    Returns:
+        STT backend instance (MLX or PyTorch)
+    """
+    global _stt_backend
+    
+    if _stt_backend is None:
+        backend_type = get_backend_type()
+        
+        if backend_type == "mlx":
+            from .mlx_backend import MLXSTTBackend
+            _stt_backend = MLXSTTBackend()
+        else:
+            from .pytorch_backend import PyTorchSTTBackend
+            _stt_backend = PyTorchSTTBackend()
+    
+    return _stt_backend
+
+
+def reset_backends():
+    """Reset backend instances (useful for testing)."""
+    global _tts_backend, _stt_backend
+    _tts_backend = None
+    _stt_backend = None

backend/backends/mlx_backend.py

@@ -0,0 +1,471 @@
+"""
+MLX backend implementation for TTS and STT using mlx-audio.
+"""
+
+from typing import Optional, List, Tuple
+import asyncio
+import numpy as np
+from pathlib import Path
+
+from . import TTSBackend, STTBackend
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+from ..utils.audio import normalize_audio, load_audio
+from ..utils.progress import get_progress_manager
+from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+from ..utils.tasks import get_task_manager
+
+
+class MLXTTSBackend:
+    """MLX-based TTS backend using mlx-audio."""
+    
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self._current_model_size = None
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+    
+    def _get_model_path(self, model_size: str) -> str:
+        """
+        Get the MLX model path.
+        
+        Args:
+            model_size: Model size (1.7B or 0.6B)
+            
+        Returns:
+            HuggingFace Hub model ID for MLX
+        """
+        # MLX model mapping
+        mlx_model_map = {
+            "1.7B": "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16",
+            # 0.6B not yet converted to MLX format
+            "0.6B": "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16",  # Fallback to 1.7B
+        }
+        
+        if model_size not in mlx_model_map:
+            raise ValueError(f"Unknown model size: {model_size}")
+        
+        hf_model_id = mlx_model_map[model_size]
+        print(f"Will download MLX model from HuggingFace Hub: {hf_model_id}")
+        
+        return hf_model_id
+    
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the MLX TTS model.
+        
+        Args:
+            model_size: Model size to load (1.7B or 0.6B)
+        """
+        if model_size is None:
+            model_size = self.model_size
+            
+        # If already loaded with correct size, return
+        if self.model is not None and self._current_model_size == model_size:
+            return
+        
+        # Unload existing model if different size requested
+        if self.model is not None and self._current_model_size != model_size:
+            self.unload_model()
+        
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+    
+    # Alias for compatibility
+    load_model = load_model_async
+    
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        try:
+            from mlx_audio.tts import load
+            
+            # Get model path
+            model_path = self._get_model_path(model_size)
+            
+            # Set up progress tracking
+            progress_manager = get_progress_manager()
+            model_name = f"qwen-tts-{model_size}"
+            
+            # Start tracking download task
+            task_manager = get_task_manager()
+            task_manager.start_download(model_name)
+            
+            print(f"Loading MLX TTS model {model_size}...")
+            
+            # Initialize progress state
+            progress_manager.update_progress(
+                model_name=model_name,
+                current=0,
+                total=1,
+                filename="",
+                status="downloading",
+            )
+            
+            # Set up progress callback
+            progress_callback = create_hf_progress_callback(model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+            
+            # Use progress tracker during download
+            with tracker.patch_download():
+                # Load MLX model (downloads automatically)
+                self.model = load(model_path)
+            
+            self._current_model_size = model_size
+            self.model_size = model_size
+            
+            # Mark as complete
+            progress_manager.mark_complete(model_name)
+            task_manager.complete_download(model_name)
+            
+            print(f"MLX TTS model {model_size} loaded successfully")
+            
+        except ImportError as e:
+            print(f"Error: mlx_audio package not found. Install with: pip install mlx-audio")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            model_name = f"qwen-tts-{model_size}"
+            progress_manager.mark_error(model_name, str(e))
+            task_manager.error_download(model_name, str(e))
+            raise
+        except Exception as e:
+            print(f"Error loading MLX TTS model: {e}")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            model_name = f"qwen-tts-{model_size}"
+            progress_manager.mark_error(model_name, str(e))
+            task_manager.error_download(model_name, str(e))
+            raise
+    
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+            self._current_model_size = None
+            print("MLX TTS model unloaded")
+    
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+        
+        MLX backend stores voice prompt as a dict with audio path and text.
+        The actual voice prompt processing happens during generation.
+        
+        Args:
+            audio_path: Path to reference audio file
+            reference_text: Transcript of reference audio
+            use_cache: Whether to use cached prompt if available
+            
+        Returns:
+            Tuple of (voice_prompt_dict, was_cached)
+        """
+        await self.load_model_async(None)
+        
+        # Check cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cached_prompt = get_cached_voice_prompt(cache_key)
+            if cached_prompt is not None:
+                # Return cached prompt (should be dict format)
+                if isinstance(cached_prompt, dict):
+                    # Validate that the cached audio file still exists
+                    cached_audio_path = cached_prompt.get("ref_audio") or cached_prompt.get("ref_audio_path")
+                    if cached_audio_path and Path(cached_audio_path).exists():
+                        return cached_prompt, True
+                    else:
+                        # Cached file no longer exists, invalidate cache
+                        print(f"Cached audio file not found: {cached_audio_path}, regenerating prompt")
+        
+        # MLX voice prompt format - store audio path and text
+        # The model will process this during generation
+        voice_prompt_items = {
+            "ref_audio": str(audio_path),
+            "ref_text": reference_text,
+        }
+        
+        # Cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cache_voice_prompt(cache_key, voice_prompt_items)
+        
+        return voice_prompt_items, False
+    
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """
+        Combine multiple reference samples for better quality.
+        
+        Args:
+            audio_paths: List of audio file paths
+            reference_texts: List of reference texts
+            
+        Returns:
+            Tuple of (combined_audio, combined_text)
+        """
+        combined_audio = []
+        
+        for audio_path in audio_paths:
+            audio, sr = load_audio(audio_path)
+            audio = normalize_audio(audio)
+            combined_audio.append(audio)
+        
+        # Concatenate audio
+        mixed = np.concatenate(combined_audio)
+        mixed = normalize_audio(mixed)
+        
+        # Combine texts
+        combined_text = " ".join(reference_texts)
+        
+        return mixed, combined_text
+    
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using voice prompt.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Voice prompt dictionary with ref_audio and ref_text
+            language: Language code (en or zh) - may not be fully supported by MLX
+            seed: Random seed for reproducibility
+            instruct: Natural language instruction (may not be supported by MLX)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model_async(None)
+
+        print(f"Generating audio for text: {text}")
+
+        def _generate_sync():
+            """Run synchronous generation in thread pool."""
+            # MLX generate() returns a generator yielding GenerationResult objects
+            audio_chunks = []
+            sample_rate = 24000
+            
+            # Set seed if provided (MLX uses numpy random)
+            if seed is not None:
+                import mlx.core as mx
+                np.random.seed(seed)
+                mx.random.seed(seed)
+            
+            # Extract voice prompt info
+            ref_audio = voice_prompt.get("ref_audio") or voice_prompt.get("ref_audio_path")
+            ref_text = voice_prompt.get("ref_text", "")
+            
+            # Validate that the audio file exists
+            if ref_audio and not Path(ref_audio).exists():
+                print(f"Warning: Audio file not found: {ref_audio}")
+                print("This may be due to a cached voice prompt referencing a deleted temp file.")
+                print("Regenerating without voice prompt.")
+                ref_audio = None
+            
+            # Check if model supports voice cloning via generate method
+            # MLX API may support ref_audio parameter directly
+            try:
+                # Try with voice cloning parameters if supported
+                if ref_audio:
+                    # Check if generate accepts ref_audio parameter
+                    import inspect
+                    sig = inspect.signature(self.model.generate)
+                    if "ref_audio" in sig.parameters:
+                        # Generate with voice cloning
+                        for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text):
+                            audio_chunks.append(np.array(result.audio))
+                            sample_rate = result.sample_rate
+                    else:
+                        # Fallback: generate without voice cloning
+                        for result in self.model.generate(text):
+                            audio_chunks.append(np.array(result.audio))
+                            sample_rate = result.sample_rate
+                else:
+                    # No voice prompt, generate normally
+                    for result in self.model.generate(text):
+                        audio_chunks.append(np.array(result.audio))
+                        sample_rate = result.sample_rate
+            except Exception as e:
+                # If voice cloning fails, try without it
+                print(f"Warning: Voice cloning failed, generating without voice prompt: {e}")
+                for result in self.model.generate(text):
+                    audio_chunks.append(np.array(result.audio))
+                    sample_rate = result.sample_rate
+            
+            # Concatenate all chunks
+            if audio_chunks:
+                audio = np.concatenate([np.asarray(chunk, dtype=np.float32) for chunk in audio_chunks])
+            else:
+                # Fallback: empty audio
+                audio = np.array([], dtype=np.float32)
+            
+            return audio, sample_rate
+
+        # Run blocking inference in thread pool
+        audio, sample_rate = await asyncio.to_thread(_generate_sync)
+
+        return audio, sample_rate
+
+
+class MLXSTTBackend:
+    """MLX-based STT backend using mlx-audio Whisper."""
+    
+    def __init__(self, model_size: str = "base"):
+        self.model = None
+        self.model_size = model_size
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+    
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the MLX Whisper model.
+        
+        Args:
+            model_size: Model size (tiny, base, small, medium, large)
+        """
+        if model_size is None:
+            model_size = self.model_size
+        
+        if self.model is not None and self.model_size == model_size:
+            return
+        
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+    
+    # Alias for compatibility
+    load_model = load_model_async
+    
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        try:
+            # IMPORTANT: Set up progress tracking BEFORE importing mlx_audio
+            # This ensures tqdm is patched before any HuggingFace Hub imports
+            progress_manager = get_progress_manager()
+            progress_model_name = f"whisper-{model_size}"
+
+            # Set up progress callback and tracker
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+
+            # Patch tqdm BEFORE importing mlx_audio
+            # This is critical because mlx_audio imports huggingface_hub which imports tqdm
+            print("[DEBUG] Starting tqdm patch BEFORE mlx_audio import")
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+            print("[DEBUG] tqdm patched, now importing mlx_audio")
+
+            # NOW import mlx_audio - it will use our patched tqdm
+            from mlx_audio.stt import load
+
+            # MLX Whisper uses the standard OpenAI models
+            model_name = f"openai/whisper-{model_size}"
+            
+            # Start tracking download task
+            task_manager = get_task_manager()
+            task_manager.start_download(progress_model_name)
+
+            print(f"Loading MLX Whisper model {model_size}...")
+
+            # Initialize progress state
+            progress_manager.update_progress(
+                model_name=progress_model_name,
+                current=0,
+                total=1,
+                filename="",
+                status="downloading",
+            )
+
+            # Load the model (tqdm is already patched from above)
+            try:
+                self.model = load(model_name)
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
+            
+            self.model_size = model_size
+            
+            # Mark as complete
+            progress_manager.mark_complete(progress_model_name)
+            task_manager.complete_download(progress_model_name)
+            
+            print(f"MLX Whisper model {model_size} loaded successfully")
+            
+        except ImportError as e:
+            print(f"Error: mlx_audio package not found. Install with: pip install mlx-audio")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            progress_model_name = f"whisper-{model_size}"
+            progress_manager.mark_error(progress_model_name, str(e))
+            task_manager.error_download(progress_model_name, str(e))
+            raise
+        except Exception as e:
+            print(f"Error loading MLX Whisper model: {e}")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            progress_model_name = f"whisper-{model_size}"
+            progress_manager.mark_error(progress_model_name, str(e))
+            task_manager.error_download(progress_model_name, str(e))
+            raise
+    
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+            print("MLX Whisper model unloaded")
+    
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None,
+    ) -> str:
+        """
+        Transcribe audio to text.
+
+        Args:
+            audio_path: Path to audio file
+            language: Optional language hint (en or zh)
+
+        Returns:
+            Transcribed text
+        """
+        await self.load_model_async(None)
+
+        def _transcribe_sync():
+            """Run synchronous transcription in thread pool."""
+            # MLX Whisper transcription using generate method
+            # The generate method accepts audio path directly
+            decode_options = {}
+            if language:
+                decode_options["language"] = language
+
+            result = self.model.generate(str(audio_path), **decode_options)
+
+            # Extract text from result
+            if isinstance(result, str):
+                return result.strip()
+            elif isinstance(result, dict):
+                return result.get("text", "").strip()
+            elif hasattr(result, "text"):
+                return result.text.strip()
+            else:
+                return str(result).strip()
+
+        # Run blocking transcription in thread pool
+        return await asyncio.to_thread(_transcribe_sync)

backend/backends/pytorch_backend.py

@@ -0,0 +1,486 @@
+"""
+PyTorch backend implementation for TTS and STT.
+"""
+
+from typing import Optional, List, Tuple
+import asyncio
+import torch
+import numpy as np
+from pathlib import Path
+
+from . import TTSBackend, STTBackend
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+from ..utils.audio import normalize_audio, load_audio
+from ..utils.progress import get_progress_manager
+from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+from ..utils.tasks import get_task_manager
+
+
+class PyTorchTTSBackend:
+    """PyTorch-based TTS backend using Qwen3-TTS."""
+    
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self.device = self._get_device()
+        self._current_model_size = None
+    
+    def _get_device(self) -> str:
+        """Get the best available device."""
+        if torch.cuda.is_available():
+            return "cuda"
+        elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            # MPS can have issues, use CPU for stability
+            return "cpu"
+        return "cpu"
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+    
+    def _get_model_path(self, model_size: str) -> str:
+        """
+        Get the HuggingFace Hub model ID.
+        
+        Args:
+            model_size: Model size (1.7B or 0.6B)
+            
+        Returns:
+            HuggingFace Hub model ID
+        """
+        hf_model_map = {
+            "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
+            "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
+        }
+        
+        if model_size not in hf_model_map:
+            raise ValueError(f"Unknown model size: {model_size}")
+        
+        return hf_model_map[model_size]
+    
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the TTS model with automatic downloading from HuggingFace Hub.
+        
+        Args:
+            model_size: Model size to load (1.7B or 0.6B)
+        """
+        if model_size is None:
+            model_size = self.model_size
+            
+        # If already loaded with correct size, return
+        if self.model is not None and self._current_model_size == model_size:
+            return
+        
+        # Unload existing model if different size requested
+        if self.model is not None and self._current_model_size != model_size:
+            self.unload_model()
+        
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+    
+    # Alias for compatibility
+    load_model = load_model_async
+    
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        try:
+            # IMPORTANT: Set up progress tracking BEFORE importing qwen_tts
+            # This ensures tqdm is patched before any HuggingFace Hub imports
+            progress_manager = get_progress_manager()
+            model_name = f"qwen-tts-{model_size}"
+
+            # Set up progress callback and tracker
+            progress_callback = create_hf_progress_callback(model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+
+            # Patch tqdm BEFORE importing qwen_tts
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+
+            # NOW import qwen_tts - it will use our patched tqdm
+            from qwen_tts import Qwen3TTSModel
+
+            # Get model path (local or HuggingFace Hub ID)
+            model_path = self._get_model_path(model_size)
+
+            print(f"Loading TTS model {model_size} on {self.device}...")
+
+            # Start tracking download task
+            task_manager = get_task_manager()
+            task_manager.start_download(model_name)
+
+            # Initialize progress state to show download has started
+            progress_manager.update_progress(
+                model_name=model_name,
+                current=0,
+                total=1,  # Set to 1 initially, will be updated by callback
+                filename="",
+                status="downloading",
+            )
+
+            # Load the model (tqdm is already patched from above)
+            try:
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    device_map=self.device,
+                    torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
+                )
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
+            
+            # Mark as complete
+            progress_manager.mark_complete(model_name)
+            task_manager.complete_download(model_name)
+            
+            self._current_model_size = model_size
+            self.model_size = model_size
+            
+            print(f"TTS model {model_size} loaded successfully")
+            
+        except ImportError as e:
+            print(f"Error: qwen_tts package not found. Install with: pip install git+https://github.com/QwenLM/Qwen3-TTS.git")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            model_name = f"qwen-tts-{model_size}"
+            progress_manager.mark_error(model_name, str(e))
+            task_manager.error_download(model_name, str(e))
+            raise
+        except Exception as e:
+            print(f"Error loading TTS model: {e}")
+            print(f"Tip: The model will be automatically downloaded from HuggingFace Hub on first use.")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            model_name = f"qwen-tts-{model_size}"
+            progress_manager.mark_error(model_name, str(e))
+            task_manager.error_download(model_name, str(e))
+            raise
+    
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+            self._current_model_size = None
+            
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            
+            print("TTS model unloaded")
+    
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+        
+        Args:
+            audio_path: Path to reference audio file
+            reference_text: Transcript of reference audio
+            use_cache: Whether to use cached prompt if available
+            
+        Returns:
+            Tuple of (voice_prompt_dict, was_cached)
+        """
+        await self.load_model_async(None)
+        
+        # Check cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cached_prompt = get_cached_voice_prompt(cache_key)
+            if cached_prompt is not None:
+                # Cache stores as torch.Tensor but actual prompt is dict
+                # Convert if needed
+                if isinstance(cached_prompt, dict):
+                    # For PyTorch backend, the dict should contain tensors, not file paths
+                    # So we can safely return it
+                    return cached_prompt, True
+                elif isinstance(cached_prompt, torch.Tensor):
+                    # Legacy cache format - convert to dict
+                    # This shouldn't happen in practice, but handle it
+                    return {"prompt": cached_prompt}, True
+        
+        def _create_prompt_sync():
+            """Run synchronous voice prompt creation in thread pool."""
+            return self.model.create_voice_clone_prompt(
+                ref_audio=str(audio_path),
+                ref_text=reference_text,
+                x_vector_only_mode=False,
+            )
+        
+        # Run blocking operation in thread pool
+        voice_prompt_items = await asyncio.to_thread(_create_prompt_sync)
+        
+        # Cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cache_voice_prompt(cache_key, voice_prompt_items)
+        
+        return voice_prompt_items, False
+    
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """
+        Combine multiple reference samples for better quality.
+        
+        Args:
+            audio_paths: List of audio file paths
+            reference_texts: List of reference texts
+            
+        Returns:
+            Tuple of (combined_audio, combined_text)
+        """
+        combined_audio = []
+        
+        for audio_path in audio_paths:
+            audio, sr = load_audio(audio_path)
+            audio = normalize_audio(audio)
+            combined_audio.append(audio)
+        
+        # Concatenate audio
+        mixed = np.concatenate(combined_audio)
+        mixed = normalize_audio(mixed)
+        
+        # Combine texts
+        combined_text = " ".join(reference_texts)
+        
+        return mixed, combined_text
+    
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using voice prompt.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Voice prompt dictionary from create_voice_prompt
+            language: Language code (en or zh)
+            seed: Random seed for reproducibility
+            instruct: Natural language instruction for speech delivery control
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        # Load model
+        await self.load_model_async(None)
+
+        def _generate_sync():
+            """Run synchronous generation in thread pool."""
+            # Set seed if provided
+            if seed is not None:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+
+            # Generate audio - this is the blocking operation
+            wavs, sample_rate = self.model.generate_voice_clone(
+                text=text,
+                voice_clone_prompt=voice_prompt,
+                instruct=instruct,
+            )
+            return wavs[0], sample_rate
+
+        # Run blocking inference in thread pool to avoid blocking event loop
+        audio, sample_rate = await asyncio.to_thread(_generate_sync)
+
+        return audio, sample_rate
+
+
+class PyTorchSTTBackend:
+    """PyTorch-based STT backend using Whisper."""
+    
+    def __init__(self, model_size: str = "base"):
+        self.model = None
+        self.processor = None
+        self.model_size = model_size
+        self.device = self._get_device()
+    
+    def _get_device(self) -> str:
+        """Get the best available device."""
+        if torch.cuda.is_available():
+            return "cuda"
+        elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            # MPS support for Whisper
+            return "cpu"  # Use CPU for stability
+        return "cpu"
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+    
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the Whisper model.
+
+        Args:
+            model_size: Model size (tiny, base, small, medium, large)
+        """
+        print(f"[DEBUG] load_model_async called with size: {model_size}")
+        if model_size is None:
+            model_size = self.model_size
+
+        print(f"[DEBUG] Model already loaded? {self.model is not None}, current size: {self.model_size}, requested: {model_size}")
+        if self.model is not None and self.model_size == model_size:
+            print(f"[DEBUG] Early return - model already loaded")
+            return
+
+        print(f"[DEBUG] Calling asyncio.to_thread for _load_model_sync")
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+        print(f"[DEBUG] asyncio.to_thread completed")
+    
+    # Alias for compatibility
+    load_model = load_model_async
+    
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        print(f"[DEBUG] _load_model_sync called for Whisper {model_size}")
+        try:
+            # IMPORTANT: Set up progress tracking BEFORE importing transformers
+            # This ensures tqdm is patched before any HuggingFace Hub imports
+            progress_manager = get_progress_manager()
+            progress_model_name = f"whisper-{model_size}"
+
+            # Set up progress callback and tracker
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+
+            # Patch tqdm BEFORE importing transformers
+            print("[DEBUG] Starting tqdm patch BEFORE transformers import")
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+            print("[DEBUG] tqdm patched, now importing transformers")
+
+            # NOW import transformers - it will use our patched tqdm
+            from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+            model_name = f"openai/whisper-{model_size}"
+            print(f"[DEBUG] Model name: {model_name}")
+
+            # Start tracking download task
+            task_manager = get_task_manager()
+            task_manager.start_download(progress_model_name)
+            print(f"[DEBUG] Task manager started download")
+
+            print(f"Loading Whisper model {model_size} on {self.device}...")
+
+            # Initialize progress state to show download has started
+            print(f"[DEBUG] Calling update_progress...")
+            progress_manager.update_progress(
+                model_name=progress_model_name,
+                current=0,
+                total=1,  # Set to 1 initially, will be updated by callback
+                filename="",
+                status="downloading",
+            )
+            print(f"[DEBUG] update_progress called, listeners: {len(progress_manager._listeners.get(progress_model_name, []))}")
+
+            # Load models (tqdm is already patched from above)
+            try:
+                self.processor = WhisperProcessor.from_pretrained(model_name)
+                self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
+            
+            self.model.to(self.device)
+            self.model_size = model_size
+            
+            # Mark as complete
+            progress_manager.mark_complete(progress_model_name)
+            task_manager.complete_download(progress_model_name)
+            
+            print(f"Whisper model {model_size} loaded successfully")
+            
+        except Exception as e:
+            print(f"Error loading Whisper model: {e}")
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            progress_model_name = f"whisper-{model_size}"
+            progress_manager.mark_error(progress_model_name, str(e))
+            task_manager.error_download(progress_model_name, str(e))
+            raise
+    
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            del self.processor
+            self.model = None
+            self.processor = None
+            
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            
+            print("Whisper model unloaded")
+    
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None,
+    ) -> str:
+        """
+        Transcribe audio to text.
+        
+        Args:
+            audio_path: Path to audio file
+            language: Optional language hint (en or zh)
+            
+        Returns:
+            Transcribed text
+        """
+        await self.load_model_async(None)
+        
+        def _transcribe_sync():
+            """Run synchronous transcription in thread pool."""
+            # Load audio
+            audio, sr = load_audio(audio_path, sample_rate=16000)
+            
+            # Process audio
+            inputs = self.processor(
+                audio,
+                sampling_rate=16000,
+                return_tensors="pt",
+            )
+            inputs = inputs.to(self.device)
+            
+            # Set language if provided
+            forced_decoder_ids = None
+            if language:
+                # Support all languages from frontend: en, zh, ja, ko, de, fr, ru, pt, es, it
+                # Whisper supports these and many more
+                forced_decoder_ids = self.processor.get_decoder_prompt_ids(
+                    language=language,
+                    task="transcribe",
+                )
+            
+            # Generate transcription
+            with torch.no_grad():
+                predicted_ids = self.model.generate(
+                    inputs["input_features"],
+                    forced_decoder_ids=forced_decoder_ids,
+                )
+            
+            # Decode
+            transcription = self.processor.batch_decode(
+                predicted_ids,
+                skip_special_tokens=True,
+            )[0]
+            
+            return transcription.strip()
+        
+        # Run blocking transcription in thread pool
+        return await asyncio.to_thread(_transcribe_sync)

backend/build_binary.py

@@ -4,16 +4,19 @@ PyInstaller build script for creating standalone Python server binary.
 
 import PyInstaller.__main__
 import os
+import platform
 from pathlib import Path
 
 
+def is_apple_silicon():
+    """Check if running on Apple Silicon."""
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
 def build_server():
     """Build Python server as standalone binary."""
     backend_dir = Path(__file__).parent
 
-    # Check for local editable qwen_tts install
-    local_qwen_path = Path.home() / 'Projects' / 'voice' / 'Qwen3-TTS'
-
     # PyInstaller arguments
     args = [
         'server.py',  # Use server.py as entry point instead of main.py
@@ -21,12 +24,13 @@ def build_server():
         '--name', 'voicebox-server',
     ]
 
-    # Add local qwen_tts path if it exists (for editable installs)
+    # Add local qwen_tts path if specified (for editable installs)
-    if local_qwen_path.exists():
+    qwen_tts_path = os.getenv('QWEN_TTS_PATH')
-        args.extend(['--paths', str(local_qwen_path)])
+    if qwen_tts_path and Path(qwen_tts_path).exists():
-        print(f"Using local qwen_tts source from: {local_qwen_path}")
+        args.extend(['--paths', str(qwen_tts_path)])
+        print(f"Using local qwen_tts source from: {qwen_tts_path}")
 
-    # Add hidden imports
+    # Add common hidden imports
     args.extend([
         '--hidden-import', 'backend',
         '--hidden-import', 'backend.main',
@@ -37,6 +41,9 @@ def build_server():
         '--hidden-import', 'backend.history',
         '--hidden-import', 'backend.tts',
         '--hidden-import', 'backend.transcribe',
+        '--hidden-import', 'backend.platform_detect',
+        '--hidden-import', 'backend.backends',
+        '--hidden-import', 'backend.backends.pytorch_backend',
         '--hidden-import', 'backend.utils.audio',
         '--hidden-import', 'backend.utils.cache',
         '--hidden-import', 'backend.utils.progress',
@@ -61,6 +68,29 @@ def build_server():
         # Fix for pkg_resources and jaraco namespace packages
         '--hidden-import', 'pkg_resources.extern',
         '--collect-submodules', 'jaraco',
+    ])
+
+    # Add MLX-specific imports if building on Apple Silicon
+    if is_apple_silicon():
+        print("Building for Apple Silicon - including MLX dependencies")
+        args.extend([
+            '--hidden-import', 'backend.backends.mlx_backend',
+            '--hidden-import', 'mlx',
+            '--hidden-import', 'mlx.core',
+            '--hidden-import', 'mlx.nn',
+            '--hidden-import', 'mlx_audio',
+            '--hidden-import', 'mlx_audio.tts',
+            '--hidden-import', 'mlx_audio.stt',
+            '--collect-submodules', 'mlx',
+            '--collect-submodules', 'mlx_audio',
+            # Collect MLX data files including Metal shader libraries (.metallib)
+            '--collect-data', 'mlx',
+            '--collect-data', 'mlx_audio',
+        ])
+    else:
+        print("Building for non-Apple Silicon platform - PyTorch only")
+
+    args.extend([
         '--noconfirm',
         '--clean',
     ])

backend/database.py

@@ -17,11 +17,12 @@ Base = declarative_base()
 class VoiceProfile(Base):
     """Voice profile database model."""
     __tablename__ = "profiles"
-    
+
     id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
     name = Column(String, unique=True, nullable=False)
     description = Column(Text)
     language = Column(String, default="en")
+    avatar_path = Column(String, nullable=True)
     created_at = Column(DateTime, default=datetime.utcnow)
     updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
 
@@ -277,6 +278,16 @@ def _run_migrations(engine):
             conn.commit()
             print("Added trim_end_ms column to story_items")
 
+    # Migration: Add avatar_path to profiles table
+    if 'profiles' in inspector.get_table_names():
+        columns = {col['name'] for col in inspector.get_columns('profiles')}
+        if 'avatar_path' not in columns:
+            print("Migrating profiles: adding avatar_path column")
+            with engine.connect() as conn:
+                conn.execute(text("ALTER TABLE profiles ADD COLUMN avatar_path VARCHAR"))
+                conn.commit()
+                print("Added avatar_path column to profiles")
+
 
 def get_db():
     """Get database session (generator for dependency injection)."""

backend/export_import.py

@@ -75,6 +75,16 @@ def export_profile_to_zip(profile_id: str, db: Session) -> bytes:
     zip_buffer = io.BytesIO()
     
     with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+        # Check if profile has avatar
+        has_avatar = False
+        if profile.avatar_path:
+            avatar_path = Path(profile.avatar_path)
+            if avatar_path.exists():
+                has_avatar = True
+                # Add avatar to ZIP root with original extension
+                avatar_ext = avatar_path.suffix
+                zip_file.write(avatar_path, f"avatar{avatar_ext}")
+
         # Create manifest.json
         manifest = {
             "version": "1.0",
@@ -82,30 +92,31 @@ def export_profile_to_zip(profile_id: str, db: Session) -> bytes:
                 "name": profile.name,
                 "description": profile.description,
                 "language": profile.language,
-            }
+            },
+            "has_avatar": has_avatar,
         }
         zip_file.writestr("manifest.json", json.dumps(manifest, indent=2))
-        
+
         # Create samples.json mapping
         samples_data = {}
         profile_dir = _get_profiles_dir() / profile_id
-        
+
         for sample in samples:
             # Get filename from audio_path (should be {sample_id}.wav)
             audio_path = Path(sample.audio_path)
             filename = audio_path.name
-            
+
             # Read audio file
             if not audio_path.exists():
                 raise ValueError(f"Audio file not found: {audio_path}")
-            
+
             # Add to samples directory in ZIP
             zip_path = f"samples/{filename}"
             zip_file.write(audio_path, zip_path)
-            
+
             # Map filename to reference text
             samples_data[filename] = sample.reference_text
-        
+
         zip_file.writestr("samples.json", json.dumps(samples_data, indent=2))
     
     zip_buffer.seek(0)
@@ -168,11 +179,31 @@ async def import_profile_from_zip(file_bytes: bytes, db: Session) -> VoiceProfil
             )
             
             profile = await create_profile(profile_create, db)
-            
+
             # Extract and add samples
             profile_dir = _get_profiles_dir() / profile.id
             profile_dir.mkdir(parents=True, exist_ok=True)
-            
+
+            # Handle avatar if present
+            avatar_files = [f for f in namelist if f.startswith("avatar.")]
+            if avatar_files:
+                try:
+                    avatar_file = avatar_files[0]
+                    # Extract to temporary file
+                    import tempfile
+                    with tempfile.NamedTemporaryFile(suffix=Path(avatar_file).suffix, delete=False) as tmp:
+                        tmp.write(zip_file.read(avatar_file))
+                        tmp_path = tmp.name
+
+                    try:
+                        from .profiles import upload_avatar
+                        await upload_avatar(profile.id, tmp_path, db)
+                    finally:
+                        Path(tmp_path).unlink(missing_ok=True)
+                except Exception as e:
+                    # Avatar import is optional - continue even if it fails
+                    pass
+
             for filename, reference_text in samples_data.items():
                 # Validate filename
                 if not filename.endswith('.wav'):

backend/main.py

@@ -11,6 +11,7 @@ from fastapi.staticfiles import StaticFiles
 from sqlalchemy.orm import Session
 from typing import List, Optional
 from datetime import datetime
+import asyncio
 import uvicorn
 import argparse
 import torch
@@ -18,16 +19,21 @@ import tempfile
 import io
 from pathlib import Path
 import uuid
+import asyncio
+import signal
+import os
 
-from . import database, models, profiles, history, tts, transcribe, config, export_import, channels, stories
+from . import database, models, profiles, history, tts, transcribe, config, export_import, channels, stories, __version__
 from .database import get_db, Generation as DBGeneration, VoiceProfile as DBVoiceProfile
 from .utils.progress import get_progress_manager
 from .utils.tasks import get_task_manager
+from .utils.cache import clear_voice_prompt_cache
+from .platform_detect import get_backend_type
 
 app = FastAPI(
     title="voicebox API",
     description="Production-quality Qwen3-TTS voice cloning API",
-    version="0.1.0",
+    version=__version__,
 )
 
 # CORS middleware
@@ -47,23 +53,43 @@ app.add_middleware(
 @app.get("/")
 async def root():
     """Root endpoint."""
-    return {"message": "voicebox API", "version": "0.1.7"}
+    return {"message": "voicebox API", "version": __version__}
+
+
+@app.post("/shutdown")
+async def shutdown():
+    """Gracefully shutdown the server."""
+    async def shutdown_async():
+        await asyncio.sleep(0.1)  # Give response time to send
+        os.kill(os.getpid(), signal.SIGTERM)
+
+    asyncio.create_task(shutdown_async())
+    return {"message": "Shutting down..."}
 
 
 @app.get("/health", response_model=models.HealthResponse)
 async def health():
     """Health check endpoint."""
-    from huggingface_hub import hf_hub_download
+    from huggingface_hub import hf_hub_download, constants as hf_constants
     from pathlib import Path
     import os
-    
+
     tts_model = tts.get_tts_model()
+    backend_type = get_backend_type()
 
     # Check for GPU availability (CUDA or MPS)
     has_cuda = torch.cuda.is_available()
     has_mps = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
     gpu_available = has_cuda or has_mps
 
+    gpu_type = None
+    if has_cuda:
+        gpu_type = f"CUDA ({torch.cuda.get_device_name(0)})"
+    elif has_mps:
+        gpu_type = "MPS (Apple Silicon)"
+    elif backend_type == "mlx":
+        gpu_type = "Metal (Apple Silicon via MLX)"
+
     vram_used = None
     if has_cuda:
         vram_used = torch.cuda.memory_allocated() / 1024 / 1024  # MB
@@ -90,7 +116,11 @@ async def health():
     model_downloaded = None
     try:
         # Check if the default model (1.7B) is cached
-        default_model_id = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+        # Use different model IDs based on backend
+        if backend_type == "mlx":
+            default_model_id = "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16"
+        else:
+            default_model_id = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
         
         # Method 1: Try scan_cache_dir if available
         try:
@@ -101,15 +131,16 @@ async def health():
                     model_downloaded = True
                     break
         except (ImportError, Exception):
-            # Method 2: Check cache directory
+            # Method 2: Check cache directory (using HuggingFace's OS-specific cache location)
-            cache_dir = os.path.expanduser("~/.cache/huggingface/hub")
+            cache_dir = hf_constants.HF_HUB_CACHE
-            repo_cache = Path(cache_dir) / "models--" + default_model_id.replace("/", "--")
+            repo_cache = Path(cache_dir) / ("models--" + default_model_id.replace("/", "--"))
             if repo_cache.exists():
                 has_model_files = (
                     any(repo_cache.rglob("*.bin")) or
                     any(repo_cache.rglob("*.safetensors")) or
                     any(repo_cache.rglob("*.pt")) or
-                    any(repo_cache.rglob("*.pth"))
+                    any(repo_cache.rglob("*.pth")) or
+                    any(repo_cache.rglob("*.npz"))  # MLX models may use npz
                 )
                 model_downloaded = has_model_files
     except Exception:
@@ -121,7 +152,9 @@ async def health():
         model_downloaded=model_downloaded,
         model_size=model_size,
         gpu_available=gpu_available,
+        gpu_type=gpu_type,
         vram_used_mb=vram_used,
+        backend_type=backend_type,
     )
 
 
@@ -261,6 +294,74 @@ async def delete_profile_sample(
     return {"message": "Sample deleted successfully"}
 
 
+@app.put("/profiles/samples/{sample_id}", response_model=models.ProfileSampleResponse)
+async def update_profile_sample(
+    sample_id: str,
+    data: models.ProfileSampleUpdate,
+    db: Session = Depends(get_db),
+):
+    """Update a profile sample's reference text."""
+    sample = await profiles.update_profile_sample(sample_id, data.reference_text, db)
+    if not sample:
+        raise HTTPException(status_code=404, detail="Sample not found")
+    return sample
+
+
+@app.post("/profiles/{profile_id}/avatar", response_model=models.VoiceProfileResponse)
+async def upload_profile_avatar(
+    profile_id: str,
+    file: UploadFile = File(...),
+    db: Session = Depends(get_db),
+):
+    """Upload or update avatar image for a profile."""
+    # Save uploaded file to temp location
+    with tempfile.NamedTemporaryFile(delete=False, suffix=Path(file.filename).suffix) as tmp:
+        content = await file.read()
+        tmp.write(content)
+        tmp_path = tmp.name
+
+    try:
+        profile = await profiles.upload_avatar(profile_id, tmp_path, db)
+        return profile
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    finally:
+        # Clean up temp file
+        Path(tmp_path).unlink(missing_ok=True)
+
+
+@app.get("/profiles/{profile_id}/avatar")
+async def get_profile_avatar(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get avatar image for a profile."""
+    profile = await profiles.get_profile(profile_id, db)
+    if not profile:
+        raise HTTPException(status_code=404, detail="Profile not found")
+
+    if not profile.avatar_path:
+        raise HTTPException(status_code=404, detail="No avatar found for this profile")
+
+    avatar_path = Path(profile.avatar_path)
+    if not avatar_path.exists():
+        raise HTTPException(status_code=404, detail="Avatar file not found")
+
+    return FileResponse(avatar_path)
+
+
+@app.delete("/profiles/{profile_id}/avatar")
+async def delete_profile_avatar(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete avatar image for a profile."""
+    success = await profiles.delete_avatar(profile_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Profile not found or no avatar to delete")
+    return {"message": "Avatar deleted successfully"}
+
+
 @app.get("/profiles/{profile_id}/export")
 async def export_profile(
     profile_id: str,
@@ -451,6 +552,36 @@ async def generate_speech(
         tts_model = tts.get_tts_model()
         # Load the requested model size if different from current (async to not block)
         model_size = data.model_size or "1.7B"
+
+        # Check if model needs to be downloaded first
+        model_path = tts_model._get_model_path(model_size)
+        if model_path.startswith("Qwen/"):
+            # Model not cached - check if it exists remotely or needs download
+            from huggingface_hub import constants as hf_constants
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_path.replace("/", "--"))
+            if not repo_cache.exists():
+                # Start download in background
+                model_name = f"qwen-tts-{model_size}"
+
+                async def download_model_background():
+                    try:
+                        await tts_model.load_model_async(model_size)
+                    except Exception as e:
+                        task_manager.error_download(model_name, str(e))
+
+                task_manager.start_download(model_name)
+                asyncio.create_task(download_model_background())
+
+                # Return 202 Accepted with download info
+                raise HTTPException(
+                    status_code=202,
+                    detail={
+                        "message": f"Model {model_size} is being downloaded. Please wait and try again.",
+                        "model_name": model_name,
+                        "downloading": True
+                    }
+                )
+
         await tts_model.load_model_async(model_size)
         audio, sample_rate = await tts_model.generate(
             data.text,
@@ -684,6 +815,37 @@ async def transcribe_audio(
         
         # Transcribe
         whisper_model = transcribe.get_whisper_model()
+
+        # Check if Whisper model is downloaded (uses default size "base")
+        model_size = whisper_model.model_size
+        model_name = f"openai/whisper-{model_size}"
+
+        # Check if model is cached
+        from huggingface_hub import constants as hf_constants
+        repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_name.replace("/", "--"))
+        if not repo_cache.exists():
+            # Start download in background
+            progress_model_name = f"whisper-{model_size}"
+
+            async def download_whisper_background():
+                try:
+                    await whisper_model.load_model_async(model_size)
+                except Exception as e:
+                    get_task_manager().error_download(progress_model_name, str(e))
+
+            get_task_manager().start_download(progress_model_name)
+            asyncio.create_task(download_whisper_background())
+
+            # Return 202 Accepted
+            raise HTTPException(
+                status_code=202,
+                detail={
+                    "message": f"Whisper model {model_size} is being downloaded. Please wait and try again.",
+                    "model_name": progress_model_name,
+                    "downloading": True
+                }
+            )
+
         text = await whisper_model.transcribe(tmp_path, language)
         
         return models.TranscriptionResponse(
@@ -994,10 +1156,12 @@ async def get_model_progress(model_name: str):
 @app.get("/models/status", response_model=models.ModelStatusListResponse)
 async def get_model_status():
     """Get status of all available models."""
-    from huggingface_hub import hf_hub_download
+    from huggingface_hub import hf_hub_download, constants as hf_constants
     from pathlib import Path
     import os
     
+    backend_type = get_backend_type()
+    
     # Try to import scan_cache_dir (might not be available in older versions)
     try:
         from huggingface_hub import scan_cache_dir
@@ -1009,7 +1173,7 @@ async def get_model_status():
         """Check if TTS model is loaded with specific size."""
         try:
             tts_model = tts.get_tts_model()
-            return tts_model.is_loaded() and tts_model.model_size == model_size
+            return tts_model.is_loaded() and getattr(tts_model, 'model_size', None) == model_size
         except Exception:
             return False
     
@@ -1017,50 +1181,66 @@ async def get_model_status():
         """Check if Whisper model is loaded with specific size."""
         try:
             whisper_model = transcribe.get_whisper_model()
-            return whisper_model.is_loaded() and whisper_model.model_size == model_size
+            return whisper_model.is_loaded() and getattr(whisper_model, 'model_size', None) == model_size
         except Exception:
             return False
     
+    # Use backend-specific model IDs
+    if backend_type == "mlx":
+        tts_1_7b_id = "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16"
+        tts_0_6b_id = "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16"  # Fallback to 1.7B
+        whisper_base_id = "mlx-community/whisper-base"
+        whisper_small_id = "mlx-community/whisper-small"
+        whisper_medium_id = "mlx-community/whisper-medium"
+        whisper_large_id = "mlx-community/whisper-large"
+    else:
+        tts_1_7b_id = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+        tts_0_6b_id = "Qwen/Qwen3-TTS-12Hz-0.6B-Base"
+        whisper_base_id = "openai/whisper-base"
+        whisper_small_id = "openai/whisper-small"
+        whisper_medium_id = "openai/whisper-medium"
+        whisper_large_id = "openai/whisper-large"
+    
     model_configs = [
         {
             "model_name": "qwen-tts-1.7B",
             "display_name": "Qwen TTS 1.7B",
-            "hf_repo_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
+            "hf_repo_id": tts_1_7b_id,
             "model_size": "1.7B",
             "check_loaded": lambda: check_tts_loaded("1.7B"),
         },
         {
             "model_name": "qwen-tts-0.6B",
             "display_name": "Qwen TTS 0.6B",
-            "hf_repo_id": "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
+            "hf_repo_id": tts_0_6b_id,
             "model_size": "0.6B",
             "check_loaded": lambda: check_tts_loaded("0.6B"),
         },
         {
             "model_name": "whisper-base",
             "display_name": "Whisper Base",
-            "hf_repo_id": "openai/whisper-base",
+            "hf_repo_id": whisper_base_id,
             "model_size": "base",
             "check_loaded": lambda: check_whisper_loaded("base"),
         },
         {
             "model_name": "whisper-small",
             "display_name": "Whisper Small",
-            "hf_repo_id": "openai/whisper-small",
+            "hf_repo_id": whisper_small_id,
             "model_size": "small",
             "check_loaded": lambda: check_whisper_loaded("small"),
         },
         {
             "model_name": "whisper-medium",
             "display_name": "Whisper Medium",
-            "hf_repo_id": "openai/whisper-medium",
+            "hf_repo_id": whisper_medium_id,
             "model_size": "medium",
             "check_loaded": lambda: check_whisper_loaded("medium"),
         },
         {
             "model_name": "whisper-large",
             "display_name": "Whisper Large",
-            "hf_repo_id": "openai/whisper-large",
+            "hf_repo_id": whisper_large_id,
             "model_size": "large",
             "check_loaded": lambda: check_whisper_loaded("large"),
         },
@@ -1097,19 +1277,21 @@ async def get_model_status():
                             pass
                         break
             
-            # Method 2: Fallback to checking cache directory directly
+            # Method 2: Fallback to checking cache directory directly (using HuggingFace's OS-specific cache location)
             if not downloaded:
                 try:
-                    cache_dir = os.path.expanduser("~/.cache/huggingface/hub")
+                    cache_dir = hf_constants.HF_HUB_CACHE
-                    repo_cache = Path(cache_dir) / "models--" + config["hf_repo_id"].replace("/", "--")
+                    repo_cache = Path(cache_dir) / ("models--" + config["hf_repo_id"].replace("/", "--"))
                     
                     if repo_cache.exists():
                         # Check for model files (bin, safetensors, or other common model files)
+                        # MLX models may use .npz or .safetensors
                         has_model_files = (
                             any(repo_cache.rglob("*.bin")) or
                             any(repo_cache.rglob("*.safetensors")) or
                             any(repo_cache.rglob("*.pt")) or
                             any(repo_cache.rglob("*.pth")) or
+                            any(repo_cache.rglob("*.npz")) or
                             any(repo_cache.rglob("model.safetensors.index.json")) or
                             any(repo_cache.rglob("pytorch_model.bin.index.json"))
                         )
@@ -1209,22 +1391,26 @@ async def trigger_model_download(request: models.ModelDownloadRequest):
     
     config = model_configs[request.model_name]
     
-    try:
+    async def download_in_background():
-        # Start tracking download
+        """Download model in background without blocking the HTTP request."""
-        task_manager.start_download(request.model_name)
+        try:
-        
+            # Call the load function (which may be async)
-        # Trigger download by loading the model (which will download if not cached)
+            result = config["load_func"]()
-        # Run in background to avoid blocking
+            # If it's a coroutine, await it
-        await asyncio.to_thread(config["load_func"])
+            if asyncio.iscoroutine(result):
-        
+                await result
-        # Mark download as complete
+            task_manager.complete_download(request.model_name)
-        task_manager.complete_download(request.model_name)
+        except Exception as e:
-        
+            task_manager.error_download(request.model_name, str(e))
-        return {"message": f"Model {request.model_name} download started"}
+
-    except Exception as e:
+    # Start tracking download
-        # Mark download as failed
+    task_manager.start_download(request.model_name)
-        task_manager.error_download(request.model_name, str(e))
+
-        raise HTTPException(status_code=500, detail=str(e))
+    # Start download in background task (don't await)
+    asyncio.create_task(download_in_background())
+
+    # Return immediately - frontend should poll progress endpoint
+    return {"message": f"Model {request.model_name} download started"}
 
 
 @app.delete("/models/{model_name}")
@@ -1232,6 +1418,7 @@ async def delete_model(model_name: str):
     """Delete a downloaded model from the HuggingFace cache."""
     import shutil
     import os
+    from huggingface_hub import constants as hf_constants
     
     # Map model names to HuggingFace repo IDs
     model_configs = {
@@ -1284,8 +1471,8 @@ async def delete_model(model_name: str):
             if whisper_model.is_loaded() and whisper_model.model_size == config["model_size"]:
                 transcribe.unload_whisper_model()
         
-        # Find and delete the cache directory
+        # Find and delete the cache directory (using HuggingFace's OS-specific cache location)
-        cache_dir = os.path.expanduser("~/.cache/huggingface/hub")
+        cache_dir = hf_constants.HF_HUB_CACHE
         repo_cache_dir = Path(cache_dir) / ("models--" + hf_repo_id.replace("/", "--"))
         
         # Check if the cache directory exists
@@ -1309,6 +1496,19 @@ async def delete_model(model_name: str):
         raise HTTPException(status_code=500, detail=f"Failed to delete model: {str(e)}")
 
 
+@app.post("/cache/clear")
+async def clear_cache():
+    """Clear all voice prompt caches (memory and disk)."""
+    try:
+        deleted_count = clear_voice_prompt_cache()
+        return {
+            "message": f"Voice prompt cache cleared successfully",
+            "files_deleted": deleted_count,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to clear cache: {str(e)}")
+
+
 # ============================================
 # TASK MANAGEMENT
 # ============================================
@@ -1381,10 +1581,13 @@ async def get_active_tasks():
 
 def _get_gpu_status() -> str:
     """Get GPU availability status."""
+    backend_type = get_backend_type()
     if torch.cuda.is_available():
         return f"CUDA ({torch.cuda.get_device_name(0)})"
     elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
         return "MPS (Apple Silicon)"
+    elif backend_type == "mlx":
+        return "Metal (Apple Silicon via MLX)"
     return "None (CPU only)"
 
 
@@ -1394,8 +1597,28 @@ async def startup_event():
     print("voicebox API starting up...")
     database.init_db()
     print(f"Database initialized at {database._db_path}")
+    backend_type = get_backend_type()
+    print(f"Backend: {backend_type.upper()}")
     print(f"GPU available: {_get_gpu_status()}")
 
+    # Initialize progress manager with main event loop for thread-safe operations
+    try:
+        progress_manager = get_progress_manager()
+        progress_manager._set_main_loop(asyncio.get_running_loop())
+        print("Progress manager initialized with event loop")
+    except Exception as e:
+        print(f"Warning: Could not initialize progress manager event loop: {e}")
+
+    # Ensure HuggingFace cache directory exists
+    try:
+        from huggingface_hub import constants as hf_constants
+        cache_dir = Path(hf_constants.HF_HUB_CACHE)
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        print(f"HuggingFace cache directory: {cache_dir}")
+    except Exception as e:
+        print(f"Warning: Could not create HuggingFace cache directory: {e}")
+        print("Model downloads may fail. Please ensure the directory exists and has write permissions.")
+
 
 @app.on_event("shutdown")
 async def shutdown_event():

backend/models.py

@@ -20,6 +20,7 @@ class VoiceProfileResponse(BaseModel):
     name: str
     description: Optional[str]
     language: str
+    avatar_path: Optional[str] = None
     created_at: datetime
     updated_at: datetime
 
@@ -32,6 +33,11 @@ class ProfileSampleCreate(BaseModel):
     reference_text: str = Field(..., min_length=1, max_length=1000)
 
 
+class ProfileSampleUpdate(BaseModel):
+    """Request model for updating a profile sample."""
+    reference_text: str = Field(..., min_length=1, max_length=1000)
+
+
 class ProfileSampleResponse(BaseModel):
     """Response model for profile sample."""
     id: str
@@ -118,7 +124,9 @@ class HealthResponse(BaseModel):
     model_downloaded: Optional[bool] = None  # Whether model is cached/downloaded
     model_size: Optional[str] = None  # Current model size if loaded
     gpu_available: bool
+    gpu_type: Optional[str] = None  # GPU type (CUDA, MPS, or None)
     vram_used_mb: Optional[float] = None
+    backend_type: Optional[str] = None  # Backend type (mlx or pytorch)
 
 
 class ModelStatus(BaseModel):

backend/platform_detect.py

@@ -0,0 +1,33 @@
+"""
+Platform detection for backend selection.
+"""
+
+import platform
+from typing import Literal
+
+
+def is_apple_silicon() -> bool:
+    """
+    Check if running on Apple Silicon (arm64 macOS).
+    
+    Returns:
+        True if on Apple Silicon, False otherwise
+    """
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def get_backend_type() -> Literal["mlx", "pytorch"]:
+    """
+    Detect the best backend for the current platform.
+    
+    Returns:
+        "mlx" on Apple Silicon (if MLX is available), "pytorch" otherwise
+    """
+    if is_apple_silicon():
+        try:
+            import mlx
+            return "mlx"
+        except ImportError:
+            # MLX not installed, fallback to PyTorch
+            return "pytorch"
+    return "pytorch"

backend/profiles.py

@@ -21,6 +21,8 @@ from .database import (
     ProfileSample as DBProfileSample,
 )
 from .utils.audio import validate_reference_audio, load_audio, save_audio
+from .utils.images import validate_image, process_avatar
+from .utils.cache import _get_cache_dir, clear_profile_cache
 from .tts import get_tts_model
 from . import config
 
@@ -119,6 +121,10 @@ async def add_profile_sample(
     db.commit()
     db.refresh(db_sample)
     
+    # Invalidate combined audio cache for this profile
+    # Since a new sample was added, any cached combined audio is now stale
+    clear_profile_cache(profile_id)
+    
     return ProfileSampleResponse.model_validate(db_sample)
 
 
@@ -240,6 +246,9 @@ async def delete_profile(
     if profile_dir.exists():
         shutil.rmtree(profile_dir)
     
+    # Clean up combined audio cache files for this profile
+    clear_profile_cache(profile_id)
+    
     return True
 
 
@@ -261,6 +270,9 @@ async def delete_profile_sample(
     if not sample:
         return False
     
+    # Store profile_id before deleting
+    profile_id = sample.profile_id
+    
     # Delete audio file
     audio_path = Path(sample.audio_path)
     if audio_path.exists():
@@ -270,9 +282,47 @@ async def delete_profile_sample(
     db.delete(sample)
     db.commit()
     
+    # Invalidate combined audio cache for this profile
+    # Since the sample set changed, any cached combined audio is now stale
+    clear_profile_cache(profile_id)
+    
     return True
 
 
+async def update_profile_sample(
+    sample_id: str,
+    reference_text: str,
+    db: Session,
+) -> Optional[ProfileSampleResponse]:
+    """
+    Update a profile sample's reference text.
+    
+    Args:
+        sample_id: Sample ID
+        reference_text: Updated reference text
+        db: Database session
+        
+    Returns:
+        Updated sample or None if not found
+    """
+    sample = db.query(DBProfileSample).filter_by(id=sample_id).first()
+    if not sample:
+        return None
+    
+    # Store profile_id before updating
+    profile_id = sample.profile_id
+    
+    sample.reference_text = reference_text
+    db.commit()
+    db.refresh(sample)
+    
+    # Invalidate combined audio cache for this profile
+    # Since the reference text changed, cache keys and combined text are now stale
+    clear_profile_cache(profile_id)
+    
+    return ProfileSampleResponse.model_validate(sample)
+
+
 async def create_voice_prompt_for_profile(
     profile_id: str,
     db: Session,
@@ -280,23 +330,23 @@ async def create_voice_prompt_for_profile(
 ) -> dict:
     """
     Create a combined voice prompt from all samples in a profile.
-    
+
     Args:
         profile_id: Profile ID
         db: Database session
         use_cache: Whether to use cached prompts
-        
+
     Returns:
         Voice prompt dictionary
     """
     # Get all samples for profile
     samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
-    
+
     if not samples:
         raise ValueError(f"No samples found for profile {profile_id}")
-    
+
     tts_model = get_tts_model()
-    
+
     if len(samples) == 1:
         # Single sample - use directly
         sample = samples[0]
@@ -310,27 +360,127 @@ async def create_voice_prompt_for_profile(
         # Multiple samples - combine them
         audio_paths = [s.audio_path for s in samples]
         reference_texts = [s.reference_text for s in samples]
-        
+
         # Combine audio
         combined_audio, combined_text = await tts_model.combine_voice_prompts(
             audio_paths,
             reference_texts,
         )
+
+        # Save combined audio to cache directory (persistent)
+        # Create a hash of sample IDs to identify this specific combination
+        import hashlib
+        sample_ids_str = "-".join(sorted([s.id for s in samples]))
+        combination_hash = hashlib.md5(sample_ids_str.encode()).hexdigest()[:12]
         
-        # Save combined audio temporarily
+        # Store in cache directory
-        import tempfile
+        cache_dir = _get_cache_dir()
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        cache_dir.mkdir(parents=True, exist_ok=True)
-            save_audio(combined_audio, tmp.name, 24000)
+        combined_path = cache_dir / f"combined_{profile_id}_{combination_hash}.wav"
-            tmp_path = tmp.name
         
-        try:
+        # Save combined audio
-            # Create prompt from combined audio
+        save_audio(combined_audio, str(combined_path), 24000)
-            voice_prompt, _ = await tts_model.create_voice_prompt(
+
-                tmp_path,
+        # Create prompt from combined audio
-                combined_text,
+        voice_prompt, _ = await tts_model.create_voice_prompt(
-                use_cache=use_cache,
+            str(combined_path),
-            )
+            combined_text,
-            return voice_prompt
+            use_cache=use_cache,
-        finally:
+        )
-            # Clean up temp file
+        return voice_prompt
-            Path(tmp_path).unlink(missing_ok=True)
+
+
+async def upload_avatar(
+    profile_id: str,
+    image_path: str,
+    db: Session,
+) -> VoiceProfileResponse:
+    """
+    Upload and process avatar image for a profile.
+
+    Args:
+        profile_id: Profile ID
+        image_path: Path to uploaded image file
+        db: Database session
+
+    Returns:
+        Updated profile
+    """
+    # Validate profile exists
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {profile_id} not found")
+
+    # Validate image
+    is_valid, error_msg = validate_image(image_path)
+    if not is_valid:
+        raise ValueError(error_msg)
+
+    # Delete existing avatar if present
+    if profile.avatar_path:
+        old_avatar = Path(profile.avatar_path)
+        if old_avatar.exists():
+            old_avatar.unlink()
+
+    # Determine file extension from uploaded file
+    from PIL import Image
+    with Image.open(image_path) as img:
+        # Normalize JPEG variants (MPO is multi-picture format from some cameras)
+        img_format = img.format
+        if img_format in ('MPO', 'JPG'):
+            img_format = 'JPEG'
+        
+        ext_map = {
+            'PNG': '.png',
+            'JPEG': '.jpg',
+            'WEBP': '.webp'
+        }
+        ext = ext_map.get(img_format, '.png')
+
+    # Save processed image to profile directory
+    profile_dir = _get_profiles_dir() / profile_id
+    profile_dir.mkdir(parents=True, exist_ok=True)
+    output_path = profile_dir / f"avatar{ext}"
+
+    process_avatar(image_path, str(output_path))
+
+    # Update database
+    profile.avatar_path = str(output_path)
+    profile.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(profile)
+
+    return VoiceProfileResponse.model_validate(profile)
+
+
+async def delete_avatar(
+    profile_id: str,
+    db: Session,
+) -> bool:
+    """
+    Delete avatar image for a profile.
+
+    Args:
+        profile_id: Profile ID
+        db: Database session
+
+    Returns:
+        True if deleted, False if not found or no avatar
+    """
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile or not profile.avatar_path:
+        return False
+
+    # Delete avatar file
+    avatar_path = Path(profile.avatar_path)
+    if avatar_path.exists():
+        avatar_path.unlink()
+
+    # Update database
+    profile.avatar_path = None
+    profile.updated_at = datetime.utcnow()
+
+    db.commit()
+
+    return True

backend/requirements-mlx.txt

@@ -0,0 +1,5 @@
+# MLX-specific dependencies (Apple Silicon only)
+# These should only be installed on aarch64-apple-darwin platforms
+
+mlx>=0.30.0
+mlx-audio>=0.3.1

backend/requirements.txt

@@ -21,3 +21,4 @@ numpy>=1.24.0
 
 # Utilities
 python-multipart>=0.0.6
+Pillow>=10.0.0

backend/transcribe.py

@@ -1,274 +1,22 @@
 """
-Whisper ASR module for transcription.
+STT (Speech-to-Text) module - delegates to backend abstraction layer.
 """
 
-from typing import Optional, List, Dict
+from typing import Optional
-import asyncio
+from .backends import get_stt_backend, STTBackend
-import torch
-import numpy as np
-from pathlib import Path
-from .utils.progress import get_progress_manager
-from .utils.hf_progress import HFProgressTracker, create_hf_progress_callback
-from .utils.tasks import get_task_manager
 
 
-class WhisperModel:
+def get_whisper_model() -> STTBackend:
-    """Manages Whisper model loading and transcription."""
+    """
+    Get STT backend instance (MLX or PyTorch based on platform).
     
-    def __init__(self, model_size: str = "base"):
+    Returns:
-        self.model = None
+        STT backend instance
-        self.processor = None
+    """
-        self.model_size = model_size
+    return get_stt_backend()
-        self.device = self._get_device()
-    
-    def _get_device(self) -> str:
-        """Get the best available device."""
-        if torch.cuda.is_available():
-            return "cuda"
-        elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-            # MPS support for Whisper
-            return "cpu"  # Use CPU for stability
-        return "cpu"
-    
-    def is_loaded(self) -> bool:
-        """Check if model is loaded."""
-        return self.model is not None
-    
-    def load_model(self, model_size: Optional[str] = None):
-        """
-        Lazy load the Whisper model.
-        
-        Args:
-            model_size: Model size (tiny, base, small, medium, large)
-        """
-        if model_size is None:
-            model_size = self.model_size
-        
-        if self.model is not None and self.model_size == model_size:
-            return
-        
-        try:
-            from transformers import WhisperProcessor, WhisperForConditionalGeneration
-            
-            model_name = f"openai/whisper-{model_size}"
-            
-            # Set up progress tracking
-            progress_manager = get_progress_manager()
-            progress_model_name = f"whisper-{model_size}"
-            
-            # Start tracking download task
-            task_manager = get_task_manager()
-            task_manager.start_download(progress_model_name)
-            
-            print(f"Loading Whisper model {model_size} on {self.device}...")
-            
-            # Initialize progress state to show download has started
-            progress_manager.update_progress(
-                model_name=progress_model_name,
-                current=0,
-                total=1,  # Set to 1 initially, will be updated by callback
-                filename="",
-                status="downloading",
-            )
-            
-            # Set up progress callback
-            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
-            tracker = HFProgressTracker(progress_callback)
-            
-            # Use progress tracker during download
-            with tracker.patch_download():
-                self.processor = WhisperProcessor.from_pretrained(model_name)
-                self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
-            
-            self.model.to(self.device)
-            self.model_size = model_size
-            
-            # Mark as complete
-            progress_manager.mark_complete(progress_model_name)
-            task_manager.complete_download(progress_model_name)
-            
-            print(f"Whisper model {model_size} loaded successfully")
-            
-        except Exception as e:
-            print(f"Error loading Whisper model: {e}")
-            progress_manager = get_progress_manager()
-            task_manager = get_task_manager()
-            progress_model_name = f"whisper-{model_size}"
-            progress_manager.mark_error(progress_model_name, str(e))
-            task_manager.error_download(progress_model_name, str(e))
-            raise
-    
-    async def load_model_async(self, model_size: Optional[str] = None):
-        """
-        Async version of load_model that runs in thread pool.
-        
-        This prevents blocking the event loop during model loading.
-        """
-        if model_size is None:
-            model_size = self.model_size
-            
-        # If already loaded with correct size, return immediately
-        if self.model is not None and self.model_size == model_size:
-            return
-        
-        # Run the blocking load operation in a thread pool
-        await asyncio.to_thread(self.load_model, model_size)
-    
-    def unload_model(self):
-        """Unload the model to free memory."""
-        if self.model is not None:
-            del self.model
-            del self.processor
-            self.model = None
-            self.processor = None
-            
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-            
-            print("Whisper model unloaded")
-    
-    async def transcribe(
-        self,
-        audio_path: str,
-        language: Optional[str] = None,
-    ) -> str:
-        """
-        Transcribe audio to text.
-        
-        Args:
-            audio_path: Path to audio file
-            language: Optional language hint (en or zh)
-            
-        Returns:
-            Transcribed text
-        """
-        await self.load_model_async()
-        
-        from .utils.audio import load_audio
-        
-        def _transcribe_sync():
-            """Run synchronous transcription in thread pool."""
-            # Load audio
-            audio, sr = load_audio(audio_path, sample_rate=16000)
-            
-            # Process audio
-            inputs = self.processor(
-                audio,
-                sampling_rate=16000,
-                return_tensors="pt",
-            )
-            inputs = inputs.to(self.device)
-            
-            # Set language if provided
-            forced_decoder_ids = None
-            if language:
-                lang_code = "en" if language == "en" else "zh"
-                forced_decoder_ids = self.processor.get_decoder_prompt_ids(
-                    language=lang_code,
-                    task="transcribe",
-                )
-            
-            # Generate transcription
-            with torch.no_grad():
-                predicted_ids = self.model.generate(
-                    inputs["input_features"],
-                    forced_decoder_ids=forced_decoder_ids,
-                )
-            
-            # Decode
-            transcription = self.processor.batch_decode(
-                predicted_ids,
-                skip_special_tokens=True,
-            )[0]
-            
-            return transcription.strip()
-        
-        # Run blocking transcription in thread pool
-        return await asyncio.to_thread(_transcribe_sync)
-    
-    async def transcribe_with_timestamps(
-        self,
-        audio_path: str,
-        language: Optional[str] = None,
-    ) -> List[Dict[str, any]]:
-        """
-        Transcribe audio with word-level timestamps.
-        
-        Args:
-            audio_path: Path to audio file
-            language: Optional language hint
-            
-        Returns:
-            List of word segments with timestamps
-        """
-        await self.load_model_async()
-        
-        from .utils.audio import load_audio
-        
-        def _transcribe_timestamps_sync():
-            """Run synchronous transcription with timestamps in thread pool."""
-            # Load audio
-            audio, sr = load_audio(audio_path, sample_rate=16000)
-            
-            # Process audio
-            inputs = self.processor(
-                audio,
-                sampling_rate=16000,
-                return_tensors="pt",
-            )
-            inputs = inputs.to(self.device)
-            
-            # Set language if provided
-            forced_decoder_ids = None
-            if language:
-                lang_code = "en" if language == "en" else "zh"
-                forced_decoder_ids = self.processor.get_decoder_prompt_ids(
-                    language=lang_code,
-                    task="transcribe",
-                )
-            
-            # Generate with timestamps
-            with torch.no_grad():
-                predicted_ids = self.model.generate(
-                    inputs["input_features"],
-                    forced_decoder_ids=forced_decoder_ids,
-                    return_timestamps=True,
-                )
-            
-            # Parse timestamps (simplified - would need more robust parsing)
-            # For now, return basic transcription
-            # TODO: Implement proper timestamp parsing
-            transcription = self.processor.batch_decode(
-                predicted_ids,
-                skip_special_tokens=True,
-            )[0]
-            
-            return [
-                {
-                    "text": transcription,
-                    "start": 0.0,
-                    "end": len(audio) / sr,
-                }
-            ]
-        
-        # Run blocking transcription in thread pool
-        return await asyncio.to_thread(_transcribe_timestamps_sync)
-
-
-# Global model instance
-_whisper_model: Optional[WhisperModel] = None
-
-
-def get_whisper_model() -> WhisperModel:
-    """Get or create Whisper model instance."""
-    global _whisper_model
-    if _whisper_model is None:
-        _whisper_model = WhisperModel()
-    return _whisper_model
 
 
 def unload_whisper_model():
     """Unload Whisper model to free memory."""
-    global _whisper_model
+    backend = get_stt_backend()
-    if _whisper_model is not None:
+    backend.unload_model()
-        _whisper_model.unload_model()

backend/tts.py

@@ -1,372 +1,37 @@
 """
-TTS inference module using Qwen3-TTS.
+TTS inference module - delegates to backend abstraction layer.
 """
 
-from typing import Optional, List, Tuple
+from typing import Optional
-import asyncio
-import torch
 import numpy as np
 import io
 import soundfile as sf
-from pathlib import Path
 
-from .utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+from .backends import get_tts_backend, TTSBackend
-from .utils.audio import normalize_audio
-from .utils.progress import get_progress_manager
-from .utils.hf_progress import HFProgressTracker, create_hf_progress_callback
-from .utils.tasks import get_task_manager
-from . import config
 
 
-class TTSModel:
+def get_tts_model() -> TTSBackend:
-    """Manages Qwen3-TTS model loading and inference."""
+    """
+    Get TTS backend instance (MLX or PyTorch based on platform).
     
-    def __init__(self, model_size: str = "1.7B"):
+    Returns:
-        self.model = None
+        TTS backend instance
-        self.model_size = model_size
+    """
-        self.device = self._get_device()
+    return get_tts_backend()
-        self._current_model_size = None
-    
-    def _get_device(self) -> str:
-        """Get the best available device."""
-        if torch.cuda.is_available():
-            return "cuda"
-        elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-            # MPS can have issues, use CPU for stability
-            return "cpu"
-        return "cpu"
-    
-    def is_loaded(self) -> bool:
-        """Check if model is loaded."""
-        return self.model is not None
-    
-    def _get_model_path(self, model_size: str) -> str:
-        """
-        Get the model path, downloading from HuggingFace Hub if needed.
-        
-        Args:
-            model_size: Model size (1.7B or 0.6B)
-            
-        Returns:
-            Path to model (either local or HuggingFace Hub ID)
-        """
-        # HuggingFace Hub model IDs
-        hf_model_map = {
-            "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
-            "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
-        }
-        
-        # Local directory names (for backwards compatibility)
-        local_model_map = {
-            "1.7B": "Qwen--Qwen3-TTS-12Hz-1.7B-Base",
-            "0.6B": "Qwen--Qwen3-TTS-12Hz-0.6B-Base",
-        }
-        
-        if model_size not in hf_model_map:
-            raise ValueError(f"Unknown model size: {model_size}")
-        
-        # Check if model exists locally (backwards compatibility)
-        local_path = config.get_models_dir() / local_model_map[model_size]
-        if local_path.exists():
-            print(f"Found local model at {local_path}")
-            return str(local_path)
-        
-        # Use HuggingFace Hub model ID (will auto-download)
-        hf_model_id = hf_model_map[model_size]
-        print(f"Will download model from HuggingFace Hub: {hf_model_id}")
-        
-        return hf_model_id
-    
-    def load_model(self, model_size: Optional[str] = None):
-        """
-        Lazy load the TTS model with automatic downloading from HuggingFace Hub.
-        
-        The model will be automatically downloaded on first use and cached locally.
-        This works similar to how Whisper models are loaded.
-        
-        Args:
-            model_size: Model size to load (1.7B or 0.6B)
-        """
-        if model_size is None:
-            model_size = self.model_size
-            
-        # If already loaded with correct size, return
-        if self.model is not None and self._current_model_size == model_size:
-            return
-        
-        # Unload existing model if different size requested
-        if self.model is not None and self._current_model_size != model_size:
-            self.unload_model()
-        
-        try:
-            from qwen_tts import Qwen3TTSModel
-            
-            # Get model path (local or HuggingFace Hub ID)
-            model_path = self._get_model_path(model_size)
-            
-            # Set up progress tracking
-            progress_manager = get_progress_manager()
-            model_name = f"qwen-tts-{model_size}"
-            
-            # Check if model is being downloaded from HuggingFace Hub
-            if model_path.startswith("Qwen/"):
-                print(f"Loading TTS model {model_size} on {self.device}...")
-                
-                # Start tracking download task
-                task_manager = get_task_manager()
-                task_manager.start_download(model_name)
-                
-                # Initialize progress state to show download has started
-                progress_manager.update_progress(
-                    model_name=model_name,
-                    current=0,
-                    total=1,  # Set to 1 initially, will be updated by callback
-                    filename="",
-                    status="downloading",
-                )
-                
-                # Set up progress callback
-                progress_callback = create_hf_progress_callback(model_name, progress_manager)
-                tracker = HFProgressTracker(progress_callback)
-                
-                # Use progress tracker during download
-                with tracker.patch_download():
-                    # Load the model - downloads will happen automatically with progress tracking
-                    self.model = Qwen3TTSModel.from_pretrained(
-                        model_path,
-                        device_map=self.device,
-                        torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
-                    )
-                
-                # Mark as complete
-                progress_manager.mark_complete(model_name)
-                task_manager.complete_download(model_name)
-            else:
-                # Local model, no download needed
-                print(f"Loading TTS model {model_size} on {self.device}...")
-                self.model = Qwen3TTSModel.from_pretrained(
-                    model_path,
-                    device_map=self.device,
-                    torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
-                )
-            
-            self._current_model_size = model_size
-            self.model_size = model_size
-            
-            print(f"TTS model {model_size} loaded successfully")
-            
-        except ImportError as e:
-            print(f"Error: qwen_tts package not found. Install with: pip install git+https://github.com/QwenLM/Qwen3-TTS.git")
-            progress_manager = get_progress_manager()
-            task_manager = get_task_manager()
-            model_name = f"qwen-tts-{model_size}"
-            progress_manager.mark_error(model_name, str(e))
-            task_manager.error_download(model_name, str(e))
-            raise
-        except Exception as e:
-            print(f"Error loading TTS model: {e}")
-            print(f"Tip: The model will be automatically downloaded from HuggingFace Hub on first use.")
-            progress_manager = get_progress_manager()
-            task_manager = get_task_manager()
-            model_name = f"qwen-tts-{model_size}"
-            progress_manager.mark_error(model_name, str(e))
-            task_manager.error_download(model_name, str(e))
-            raise
-    
-    async def load_model_async(self, model_size: Optional[str] = None):
-        """
-        Async version of load_model that runs in thread pool.
-        
-        This prevents blocking the event loop during model loading.
-        """
-        if model_size is None:
-            model_size = self.model_size
-            
-        # If already loaded with correct size, return immediately
-        if self.model is not None and self._current_model_size == model_size:
-            return
-        
-        # Run the blocking load operation in a thread pool
-        await asyncio.to_thread(self.load_model, model_size)
-    
-    def unload_model(self):
-        """Unload the model to free memory."""
-        if self.model is not None:
-            del self.model
-            self.model = None
-            self._current_model_size = None
-            
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-            
-            print("TTS model unloaded")
-    
-    async def create_voice_prompt(
-        self,
-        audio_path: str,
-        reference_text: str,
-        use_cache: bool = True,
-    ) -> Tuple[dict, bool]:
-        """
-        Create voice prompt from reference audio.
-        
-        Args:
-            audio_path: Path to reference audio file
-            reference_text: Transcript of reference audio
-            use_cache: Whether to use cached prompt if available
-            
-        Returns:
-            Tuple of (voice_prompt_dict, was_cached)
-        """
-        await self.load_model_async()
-        
-        # Check cache if enabled
-        if use_cache:
-            cache_key = get_cache_key(audio_path, reference_text)
-            cached_prompt = get_cached_voice_prompt(cache_key)
-            if cached_prompt is not None:
-                return cached_prompt, True
-        
-        def _create_prompt_sync():
-            """Run synchronous voice prompt creation in thread pool."""
-            return self.model.create_voice_clone_prompt(
-                ref_audio=str(audio_path),
-                ref_text=reference_text,
-                x_vector_only_mode=False,
-            )
-        
-        # Run blocking operation in thread pool
-        voice_prompt_items = await asyncio.to_thread(_create_prompt_sync)
-        
-        # Cache if enabled
-        if use_cache:
-            cache_voice_prompt(cache_key, voice_prompt_items)
-        
-        return voice_prompt_items, False
-    
-    async def combine_voice_prompts(
-        self,
-        audio_paths: List[str],
-        reference_texts: List[str],
-    ) -> Tuple[np.ndarray, str]:
-        """
-        Combine multiple reference samples for better quality.
-        
-        Args:
-            audio_paths: List of audio file paths
-            reference_texts: List of reference texts
-            
-        Returns:
-            Tuple of (combined_audio, combined_text)
-        """
-        from .utils.audio import load_audio
-        
-        combined_audio = []
-        
-        for audio_path in audio_paths:
-            audio, sr = load_audio(audio_path)
-            audio = normalize_audio(audio)
-            combined_audio.append(audio)
-        
-        # Concatenate audio
-        mixed = np.concatenate(combined_audio)
-        mixed = normalize_audio(mixed)
-        
-        # Combine texts
-        combined_text = " ".join(reference_texts)
-        
-        return mixed, combined_text
-    
-    async def generate(
-        self,
-        text: str,
-        voice_prompt: dict,
-        language: str = "en",
-        seed: Optional[int] = None,
-        instruct: Optional[str] = None,
-    ) -> Tuple[np.ndarray, int]:
-        """
-        Generate audio from text using voice prompt.
-
-        Args:
-            text: Text to synthesize
-            voice_prompt: Voice prompt dictionary from create_voice_prompt
-            language: Language code (en or zh)
-            seed: Random seed for reproducibility
-            instruct: Natural language instruction for speech delivery control
-
-        Returns:
-            Tuple of (audio_array, sample_rate)
-        """
-        # Load model (already handles async via to_thread if needed)
-        await self.load_model_async()
-
-        def _generate_sync():
-            """Run synchronous generation in thread pool."""
-            # Set seed if provided
-            if seed is not None:
-                torch.manual_seed(seed)
-                if torch.cuda.is_available():
-                    torch.cuda.manual_seed(seed)
-
-            # Generate audio - this is the blocking operation
-            wavs, sample_rate = self.model.generate_voice_clone(
-                text=text,
-                voice_clone_prompt=voice_prompt,
-                instruct=instruct,
-            )
-            return wavs[0], sample_rate
-
-        # Run blocking inference in thread pool to avoid blocking event loop
-        audio, sample_rate = await asyncio.to_thread(_generate_sync)
-
-        return audio, sample_rate
-    
-    async def generate_from_reference(
-        self,
-        text: str,
-        audio_path: str,
-        reference_text: str,
-        language: str = "en",
-        seed: Optional[int] = None,
-    ) -> Tuple[np.ndarray, int]:
-        """
-        Generate audio directly from reference (convenience method).
-        
-        Args:
-            text: Text to synthesize
-            audio_path: Path to reference audio
-            reference_text: Transcript of reference audio
-            language: Language code
-            seed: Random seed
-            
-        Returns:
-            Tuple of (audio_array, sample_rate)
-        """
-        # Create voice prompt (with caching)
-        voice_prompt, _ = await self.create_voice_prompt(audio_path, reference_text)
-        
-        # Generate
-        return await self.generate(text, voice_prompt, language, seed)
-
-
-# Global model instance
-_tts_model: Optional[TTSModel] = None
-
-
-def get_tts_model() -> TTSModel:
-    """Get or create TTS model instance."""
-    global _tts_model
-    if _tts_model is None:
-        _tts_model = TTSModel()
-    return _tts_model
 
 
 def unload_tts_model():
     """Unload TTS model to free memory."""
-    global _tts_model
+    backend = get_tts_backend()
-    if _tts_model is not None:
+    backend.unload_model()
-        _tts_model.unload_model()
+
+
+def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:
+    """Convert audio array to WAV bytes."""
+    buffer = io.BytesIO()
+    sf.write(buffer, audio, sample_rate, format="WAV")
+    buffer.seek(0)
+    return buffer.read()
 
 
 def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:

backend/utils/cache.py

@@ -5,7 +5,7 @@ Voice prompt caching utilities.
 import hashlib
 import torch
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Union, Dict, Any
 
 from .. import config
 
@@ -15,8 +15,8 @@ def _get_cache_dir() -> Path:
     return config.get_cache_dir()
 
 
-# In-memory cache
+# In-memory cache - can store dict (voice prompt) or tensor (legacy)
-_memory_cache: dict[str, torch.Tensor] = {}
+_memory_cache: dict[str, Union[torch.Tensor, Dict[str, Any]]] = {}
 
 
 def get_cache_key(audio_path: str, reference_text: str) -> str:
@@ -43,7 +43,7 @@ def get_cache_key(audio_path: str, reference_text: str) -> str:
 
 def get_cached_voice_prompt(
     cache_key: str,
-) -> Optional[torch.Tensor]:
+) -> Optional[Union[torch.Tensor, Dict[str, Any]]]:
     """
     Get cached voice prompt if available.
 
@@ -51,7 +51,7 @@ def get_cached_voice_prompt(
         cache_key: Cache key
 
     Returns:
-        Cached voice prompt tensor or None
+        Cached voice prompt (dict or tensor) or None
     """
     # Check in-memory cache
     if cache_key in _memory_cache:
@@ -73,18 +73,78 @@ def get_cached_voice_prompt(
 
 def cache_voice_prompt(
     cache_key: str,
-    voice_prompt: torch.Tensor,
+    voice_prompt: Union[torch.Tensor, Dict[str, Any]],
 ) -> None:
     """
     Cache voice prompt to memory and disk.
 
     Args:
         cache_key: Cache key
-        voice_prompt: Voice prompt tensor
+        voice_prompt: Voice prompt (dict or tensor)
     """
     # Store in memory
     _memory_cache[cache_key] = voice_prompt
 
-    # Store on disk
+    # Store on disk (torch.save can handle both dicts and tensors)
     cache_file = _get_cache_dir() / f"{cache_key}.prompt"
     torch.save(voice_prompt, cache_file)
+
+
+def clear_voice_prompt_cache() -> int:
+    """
+    Clear all voice prompt caches (memory and disk).
+    
+    Returns:
+        Number of cache files deleted
+    """
+    # Clear memory cache
+    _memory_cache.clear()
+    
+    # Clear disk cache
+    cache_dir = _get_cache_dir()
+    deleted_count = 0
+    
+    if cache_dir.exists():
+        # Delete prompt cache files
+        for cache_file in cache_dir.glob("*.prompt"):
+            try:
+                cache_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                print(f"Failed to delete cache file {cache_file}: {e}")
+        
+        # Delete combined audio files
+        for audio_file in cache_dir.glob("combined_*.wav"):
+            try:
+                audio_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                print(f"Failed to delete combined audio file {audio_file}: {e}")
+    
+    return deleted_count
+
+
+def clear_profile_cache(profile_id: str) -> int:
+    """
+    Clear cache files for a specific profile.
+    
+    Args:
+        profile_id: Profile ID
+    
+    Returns:
+        Number of cache files deleted
+    """
+    cache_dir = _get_cache_dir()
+    deleted_count = 0
+    
+    if cache_dir.exists():
+        # Delete combined audio files for this profile
+        pattern = f"combined_{profile_id}_*.wav"
+        for audio_file in cache_dir.glob(pattern):
+            try:
+                audio_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                print(f"Failed to delete combined audio file {audio_file}: {e}")
+    
+    return deleted_count

backend/utils/hf_progress.py

@@ -29,8 +29,9 @@ class HFProgressTracker:
         
         class TrackedTqdm(original_tqdm):
             """A tqdm subclass that reports progress to our tracker."""
-            
+
             def __init__(self, *args, **kwargs):
+                print(f"[DEBUG TrackedTqdm] __init__ called with desc: {kwargs.get('desc', '')}")
                 # Extract filename from desc before passing to parent
                 desc = kwargs.get("desc", "")
                 if not desc and args:
@@ -79,8 +80,9 @@ class HFProgressTracker:
                     }
             
             def update(self, n=1):
+                print(f"[DEBUG TrackedTqdm] update called with n={n}")
                 result = super().update(n)
-                
+
                 # Report progress
                 with tracker._lock:
                     if id(self) in tracker._active_tqdms:
@@ -118,11 +120,13 @@ class HFProgressTracker:
     @contextmanager
     def patch_download(self):
         """Context manager to patch tqdm for progress tracking."""
+        print("[DEBUG HFProgressTracker] patch_download called")
         try:
             import tqdm as tqdm_module
-            
+
             # Store original tqdm class
             self._original_tqdm_class = tqdm_module.tqdm
+            print(f"[DEBUG HFProgressTracker] Original tqdm class: {self._original_tqdm_class}")
             
             # Reset totals
             with self._lock:
@@ -135,18 +139,22 @@ class HFProgressTracker:
             
             # Create our tracked tqdm class
             tracked_tqdm = self._create_tracked_tqdm_class()
-            
+            print(f"[DEBUG HFProgressTracker] Created TrackedTqdm class: {tracked_tqdm}")
+
             # Patch tqdm.tqdm
             tqdm_module.tqdm = tracked_tqdm
-            
+            print(f"[DEBUG HFProgressTracker] Patched tqdm.tqdm")
+
             # Also patch tqdm.auto.tqdm if it exists (used by huggingface_hub)
             self._original_tqdm_auto = None
             if hasattr(tqdm_module, "auto") and hasattr(tqdm_module.auto, "tqdm"):
                 self._original_tqdm_auto = tqdm_module.auto.tqdm
                 tqdm_module.auto.tqdm = tracked_tqdm
+                print(f"[DEBUG HFProgressTracker] Patched tqdm.auto.tqdm")
             
             # Patch in sys.modules to catch already-imported references
             self._patched_modules = {}
+            patched_count = 0
             for module_name in list(sys.modules.keys()):
                 if "huggingface" in module_name or module_name.startswith("tqdm"):
                     try:
@@ -159,8 +167,11 @@ class HFProgressTracker:
                             ):
                                 self._patched_modules[module_name] = attr
                                 setattr(module, "tqdm", tracked_tqdm)
+                                patched_count += 1
+                                print(f"[DEBUG HFProgressTracker] Patched {module_name}.tqdm")
                     except (AttributeError, TypeError):
                         pass
+            print(f"[DEBUG HFProgressTracker] Patched {patched_count} modules in sys.modules")
             
             yield
             

backend/utils/images.py

@@ -0,0 +1,114 @@
+"""Image processing utilities for avatar uploads."""
+
+from pathlib import Path
+from typing import Optional, Tuple
+from PIL import Image
+
+# JPEG can be reported as 'JPEG' or 'MPO' (for multi-picture format from some cameras)
+ALLOWED_FORMATS = {'PNG', 'JPEG', 'WEBP', 'MPO', 'JPG'}
+MAX_SIZE = 512
+MAX_FILE_SIZE = 5 * 1024 * 1024  # 5MB
+
+
+def validate_image(file_path: str) -> Tuple[bool, Optional[str]]:
+    """
+    Validate image format and file size.
+    
+    Args:
+        file_path: Path to image file
+        
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    path = Path(file_path)
+    
+    # Check file size
+    if path.stat().st_size > MAX_FILE_SIZE:
+        return False, f"File size exceeds maximum of {MAX_FILE_SIZE // (1024 * 1024)}MB"
+    
+    try:
+        with Image.open(file_path) as img:
+            # Verify the image can be loaded
+            img.load()
+            
+            # Check format (normalize JPEG variants)
+            img_format = img.format
+            if img_format in ('MPO', 'JPG'):
+                img_format = 'JPEG'
+            
+            if img_format not in {'PNG', 'JPEG', 'WEBP'}:
+                return False, f"Invalid format '{img_format}'. Allowed formats: PNG, JPEG, WEBP"
+            
+            return True, None
+    except Exception as e:
+        return False, f"Invalid image file: {str(e)}"
+
+
+def process_avatar(input_path: str, output_path: str, max_size: int = MAX_SIZE) -> None:
+    """
+    Process avatar image: resize and optimize.
+    
+    Resizes image to fit within max_size x max_size while maintaining aspect ratio.
+    
+    Args:
+        input_path: Path to input image
+        output_path: Path to save processed image
+        max_size: Maximum width or height in pixels
+    """
+    with Image.open(input_path) as img:
+        # Handle EXIF orientation for JPEG images
+        try:
+            from PIL import ExifTags
+            for orientation in ExifTags.TAGS.keys():
+                if ExifTags.TAGS[orientation] == 'Orientation':
+                    break
+            exif = img._getexif()
+            if exif is not None:
+                orientation_value = exif.get(orientation)
+                if orientation_value == 3:
+                    img = img.rotate(180, expand=True)
+                elif orientation_value == 6:
+                    img = img.rotate(270, expand=True)
+                elif orientation_value == 8:
+                    img = img.rotate(90, expand=True)
+        except (AttributeError, KeyError, IndexError, TypeError):
+            # No EXIF data or orientation tag
+            pass
+        
+        # Convert to RGB if necessary (handles RGBA, P, CMYK, etc.)
+        if img.mode not in ('RGB', 'L'):
+            if img.mode == 'RGBA':
+                # Create white background for RGBA images
+                background = Image.new('RGB', img.size, (255, 255, 255))
+                background.paste(img, mask=img.split()[3])  # Use alpha channel as mask
+                img = background
+            elif img.mode == 'CMYK':
+                # Convert CMYK to RGB
+                img = img.convert('RGB')
+            elif img.mode == 'P':
+                # Convert palette mode to RGB
+                img = img.convert('RGB')
+            else:
+                img = img.convert('RGB')
+        
+        # Calculate new size maintaining aspect ratio
+        img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
+        
+        # Determine output format from extension
+        output_ext = Path(output_path).suffix.lower()
+        
+        format_map = {
+            '.png': 'PNG',
+            '.jpeg': 'JPEG',
+            '.jpg': 'JPEG',
+            '.webp': 'WEBP'
+        }
+        
+        output_format = format_map.get(output_ext, 'PNG')
+        
+        # Save with optimization
+        save_kwargs = {'optimize': True}
+        if output_format == 'JPEG':
+            save_kwargs['quality'] = 90
+        
+        img.save(output_path, format=output_format, **save_kwargs)

backend/utils/progress.py

@@ -6,16 +6,55 @@ from typing import Optional, Callable, Dict, List
 from fastapi.responses import StreamingResponse
 import asyncio
 import json
+import threading
 from datetime import datetime
 
 
 class ProgressManager:
-    """Manages download progress for multiple models."""
+    """Manages download progress for multiple models.
+    
+    Thread-safe: can be called from background threads (e.g., via asyncio.to_thread).
+    """
     
     def __init__(self):
         self._progress: Dict[str, Dict] = {}
         self._listeners: Dict[str, list] = {}
+        self._lock = threading.Lock()  # Thread-safe lock for progress dict
+        self._main_loop: Optional[asyncio.AbstractEventLoop] = None
     
+    def _set_main_loop(self, loop: asyncio.AbstractEventLoop):
+        """Set the main event loop for thread-safe operations."""
+        self._main_loop = loop
+    
+    def _notify_listeners_threadsafe(self, model_name: str, progress_data: Dict):
+        """Notify listeners in a thread-safe manner."""
+        import logging
+        logger = logging.getLogger(__name__)
+        
+        if model_name not in self._listeners:
+            return
+            
+        for queue in self._listeners[model_name]:
+            try:
+                # Check if we're in the main event loop thread
+                try:
+                    running_loop = asyncio.get_running_loop()
+                    # We're in an async context, can use put_nowait directly
+                    queue.put_nowait(progress_data.copy())
+                except RuntimeError:
+                    # Not in async context (running in background thread)
+                    # Use call_soon_threadsafe to safely put on queue
+                    if self._main_loop and self._main_loop.is_running():
+                        self._main_loop.call_soon_threadsafe(
+                            lambda q=queue, d=progress_data.copy(): q.put_nowait(d) if not q.full() else None
+                        )
+                    else:
+                        logger.debug(f"No main loop available for {model_name}, skipping notification")
+            except asyncio.QueueFull:
+                logger.warning(f"Queue full for {model_name}, dropping update")
+            except Exception as e:
+                logger.warning(f"Error notifying listener for {model_name}: {e}")
+
     def update_progress(
         self,
         model_name: str,
@@ -26,7 +65,9 @@ class ProgressManager:
     ):
         """
         Update progress for a model download.
-        
+
+        Thread-safe: can be called from background threads.
+
         Args:
             model_name: Name of the model (e.g., "qwen-tts-1.7B", "whisper-base")
             current: Current bytes downloaded
@@ -34,9 +75,12 @@ class ProgressManager:
             filename: Current file being downloaded
             status: Status string (downloading, extracting, complete, error)
         """
+        import logging
+        logger = logging.getLogger(__name__)
+
         progress_pct = (current / total * 100) if total > 0 else 0
-        
+
-        self._progress[model_name] = {
+        progress_data = {
             "model_name": model_name,
             "current": current,
             "total": total,
@@ -45,26 +89,43 @@ class ProgressManager:
             "status": status,
             "timestamp": datetime.now().isoformat(),
         }
-        
+
-        # Notify all listeners
+        print(f"[DEBUG] update_progress called: {model_name}, {progress_pct:.1f}%")
-        if model_name in self._listeners:
+
-            for queue in self._listeners[model_name]:
+        # Thread-safe update of progress dict
-                try:
+        with self._lock:
-                    queue.put_nowait(self._progress[model_name].copy())
+            self._progress[model_name] = progress_data
-                except asyncio.QueueFull:
+
-                    pass
+        # Notify all listeners (thread-safe)
+        listener_count = len(self._listeners.get(model_name, []))
+        print(f"[DEBUG] Listener count for {model_name}: {listener_count}")
+        print(f"[DEBUG] All listeners: {list(self._listeners.keys())}")
+        print(f"[DEBUG] Main loop set: {self._main_loop is not None}")
+        if self._main_loop:
+            print(f"[DEBUG] Main loop running: {self._main_loop.is_running()}")
+
+        if listener_count > 0:
+            logger.debug(f"Notifying {listener_count} listeners for {model_name}: {progress_pct:.1f}% ({filename})")
+            print(f"[DEBUG] About to notify listeners...")
+            self._notify_listeners_threadsafe(model_name, progress_data)
+            print(f"[DEBUG] Notified listeners")
+        else:
+            logger.debug(f"No listeners for {model_name}, progress update stored: {progress_pct:.1f}%")
     
     def get_progress(self, model_name: str) -> Optional[Dict]:
-        """Get current progress for a model."""
+        """Get current progress for a model. Thread-safe."""
-        return self._progress.get(model_name)
+        with self._lock:
+            progress = self._progress.get(model_name)
+            return progress.copy() if progress else None
     
     def get_all_active(self) -> List[Dict]:
-        """Get all active downloads (status is 'downloading' or 'extracting')."""
+        """Get all active downloads (status is 'downloading' or 'extracting'). Thread-safe."""
         active = []
-        for model_name, progress in self._progress.items():
+        with self._lock:
-            status = progress.get("status", "")
+            for model_name, progress in self._progress.items():
-            if status in ("downloading", "extracting"):
+                status = progress.get("status", "")
-                active.append(progress.copy())
+                if status in ("downloading", "extracting"):
+                    active.append(progress.copy())
         return active
     
     def create_progress_callback(self, model_name: str, filename: Optional[str] = None):
@@ -98,30 +159,57 @@ class ProgressManager:
     async def subscribe(self, model_name: str):
         """
         Subscribe to progress updates for a model.
-        
+
         Yields progress updates as Server-Sent Events.
         """
-        queue = asyncio.Queue(maxsize=10)
+        import logging
+        logger = logging.getLogger(__name__)
         
+        # Store the main event loop for thread-safe operations
+        try:
+            self._main_loop = asyncio.get_running_loop()
+        except RuntimeError:
+            pass
+
+        queue = asyncio.Queue(maxsize=10)
+
         # Add to listeners
         if model_name not in self._listeners:
             self._listeners[model_name] = []
         self._listeners[model_name].append(queue)
-        
+
+        logger.info(f"SSE client subscribed to {model_name}, total listeners: {len(self._listeners[model_name])}")
+
         try:
-            # Send initial progress if available
+            # Send initial progress if available and still in progress (thread-safe read)
-            if model_name in self._progress:
+            with self._lock:
-                yield f"data: {json.dumps(self._progress[model_name])}\n\n"
+                initial_progress = self._progress.get(model_name)
+                if initial_progress:
+                    initial_progress = initial_progress.copy()
             
+            if initial_progress:
+                status = initial_progress.get('status')
+                # Only send initial progress if download is actually in progress
+                # Don't send old 'complete' or 'error' status from previous downloads
+                if status in ('downloading', 'extracting'):
+                    logger.info(f"Sending initial progress for {model_name}: {status}")
+                    yield f"data: {json.dumps(initial_progress)}\n\n"
+                else:
+                    logger.info(f"Skipping initial progress for {model_name} (status: {status})")
+            else:
+                logger.info(f"No initial progress available for {model_name}")
+
             # Stream updates
             while True:
                 try:
                     # Wait for update with timeout
                     progress = await asyncio.wait_for(queue.get(), timeout=1.0)
+                    logger.debug(f"Sending progress update for {model_name}: {progress.get('status')} - {progress.get('progress', 0):.1f}%")
                     yield f"data: {json.dumps(progress)}\n\n"
-                    
+
                     # Stop if complete or error
                     if progress.get("status") in ("complete", "error"):
+                        logger.info(f"Download {progress.get('status')} for {model_name}, closing SSE connection")
                         break
                 except asyncio.TimeoutError:
                     # Send heartbeat
@@ -133,32 +221,53 @@ class ProgressManager:
                 self._listeners[model_name].remove(queue)
                 if not self._listeners[model_name]:
                     del self._listeners[model_name]
+                logger.info(f"SSE client unsubscribed from {model_name}, remaining listeners: {len(self._listeners.get(model_name, []))}")
     
     def mark_complete(self, model_name: str):
-        """Mark a model download as complete."""
+        """Mark a model download as complete. Thread-safe."""
-        if model_name in self._progress:
+        import logging
-            self._progress[model_name]["status"] = "complete"
+        logger = logging.getLogger(__name__)
-            self._progress[model_name]["progress"] = 100.0
+
-            # Notify listeners
+        with self._lock:
-            if model_name in self._listeners:
+            if model_name in self._progress:
-                for queue in self._listeners[model_name]:
+                self._progress[model_name]["status"] = "complete"
-                    try:
+                self._progress[model_name]["progress"] = 100.0
-                        queue.put_nowait(self._progress[model_name].copy())
+                progress_data = self._progress[model_name].copy()
-                    except asyncio.QueueFull:
+            else:
-                        pass
+                logger.warning(f"Cannot mark {model_name} as complete: not found in progress")
+                return
+        
+        logger.info(f"Marked {model_name} as complete")
+        # Notify listeners (thread-safe)
+        self._notify_listeners_threadsafe(model_name, progress_data)
     
     def mark_error(self, model_name: str, error: str):
-        """Mark a model download as failed."""
+        """Mark a model download as failed. Thread-safe."""
-        if model_name in self._progress:
+        import logging
-            self._progress[model_name]["status"] = "error"
+        logger = logging.getLogger(__name__)
-            self._progress[model_name]["error"] = error
+
-            # Notify listeners
+        with self._lock:
-            if model_name in self._listeners:
+            if model_name in self._progress:
-                for queue in self._listeners[model_name]:
+                self._progress[model_name]["status"] = "error"
-                    try:
+                self._progress[model_name]["error"] = error
-                        queue.put_nowait(self._progress[model_name].copy())
+                progress_data = self._progress[model_name].copy()
-                    except asyncio.QueueFull:
+            else:
-                        pass
+                # Create new progress entry for error
+                progress_data = {
+                    "model_name": model_name,
+                    "current": 0,
+                    "total": 0,
+                    "progress": 0,
+                    "filename": None,
+                    "status": "error",
+                    "error": error,
+                    "timestamp": datetime.now().isoformat(),
+                }
+                self._progress[model_name] = progress_data
+        
+        logger.error(f"Marked {model_name} as error: {error}")
+        # Notify listeners (thread-safe)
+        self._notify_listeners_threadsafe(model_name, progress_data)
 
 
 # Global progress manager instance

backend/voicebox-server.spec

@@ -4,16 +4,20 @@ from PyInstaller.utils.hooks import collect_submodules
 from PyInstaller.utils.hooks import copy_metadata
 
 datas = []
-hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern']
+hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
 datas += collect_data_files('qwen_tts')
+datas += collect_data_files('mlx')
+datas += collect_data_files('mlx_audio')
 datas += copy_metadata('qwen-tts')
 hiddenimports += collect_submodules('qwen_tts')
 hiddenimports += collect_submodules('jaraco')
+hiddenimports += collect_submodules('mlx')
+hiddenimports += collect_submodules('mlx_audio')
 
 
 a = Analysis(
     ['server.py'],
-    pathex=['C:\\Users\\ijame\\Projects\\voice\\Qwen3-TTS'],
+    pathex=[],
     binaries=[],
     datas=datas,
     hiddenimports=hiddenimports,

bun.lock

@@ -13,7 +13,7 @@
     },
     "app": {
       "name": "@voicebox/app",
-      "version": "0.1.5",
+      "version": "0.1.9",
       "dependencies": {
         "@dnd-kit/core": "^6.3.1",
         "@dnd-kit/sortable": "^10.0.0",
@@ -50,6 +50,7 @@
         "react": "^18.3.0",
         "react-dom": "^18.3.0",
         "react-hook-form": "^7.53.0",
+        "react-sound-visualizer": "^1.4.0",
         "tailwind-merge": "^2.5.4",
         "wavesurfer.js": "^7.0.0",
         "zod": "^3.23.8",
@@ -67,7 +68,7 @@
     },
     "landing": {
       "name": "@voicebox/landing",
-      "version": "0.1.5",
+      "version": "0.1.9",
       "dependencies": {
         "@radix-ui/react-separator": "^1.1.8",
         "@radix-ui/react-slot": "^1.2.4",
@@ -92,7 +93,7 @@
     },
     "tauri": {
       "name": "@voicebox/tauri",
-      "version": "0.1.5",
+      "version": "0.1.9",
       "dependencies": {
         "@tauri-apps/api": "^2.0.0",
         "@tauri-apps/plugin-shell": "^2.0.0",
@@ -111,7 +112,7 @@
     },
     "web": {
       "name": "@voicebox/web",
-      "version": "0.1.5",
+      "version": "0.1.9",
       "dependencies": {
         "@tanstack/react-query": "^5.0.0",
         "react": "^18.3.0",
@@ -970,6 +971,8 @@
 
     "react-remove-scroll-bar": ["[email protected]", "", { "dependencies": { "react-style-singleton": "^2.2.2", "tslib": "^2.0.0" }, "peerDependencies": { "@types/react": "*", "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" }, "optionalPeers": ["@types/react"] }, "sha512-9r+yi9+mgU33AKcj6IbT9oRCO78WriSj6t/cF8DWBZJ9aOGPOTEDvdUDz1FwKim7QXWwmHqtdHnRJfhAxEG46Q=="],
 
+    "react-sound-visualizer": ["[email protected]", "", { "dependencies": { "sound-visualizer": "^1.2.0" }, "peerDependencies": { "react": ">= 16" } }, "sha512-Qe7tFTd1owtQ8nYrUYXg7QLt8mw7iUy86mqj/+IwmXzSw+NlhnMnAGPuisb1Lk3ncliFnM+AQbZb3C4RQN9uMQ=="],
+
     "react-style-singleton": ["[email protected]", "", { "dependencies": { "get-nonce": "^1.0.0", "tslib": "^2.0.0" }, "peerDependencies": { "@types/react": "*", "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-b6jSvxvVnyptAiLjbkWLE/lOnR4lfTtDAl+eUC7RZy+QQWc6wRzIV2CE6xBuMmDxc2qIihtDCZD5NPOFl7fRBQ=="],
 
     "read-cache": ["[email protected]", "", { "dependencies": { "pify": "^2.3.0" } }, "sha512-Owdv/Ft7IjOgm/i0xvNDZ1LrRANRfew4b2prF3OWMQLxLfu3bS8FVhCsrSCMK4lR56Y9ya+AThoTpDCTxCmpRA=="],
@@ -1004,6 +1007,8 @@
 
     "slash": ["[email protected]", "", {}, "sha512-g9Q1haeby36OSStwb4ntCGGGaKsaVSjQ68fBxoQcutl5fS1vuY18H3wSt3jFyFtrkx+Kz0V1G85A4MyAdDMi2Q=="],
 
+    "sound-visualizer": ["[email protected]", "", {}, "sha512-2+Un0PrrBgXylnCjrVYUoRW7KEDH29h7O8/MGzeDOgFGBPb9oX/2n/RGBxJXvVv2U3KFwX5olUWeJKf0Rr5TLQ=="],
+
     "source-map-js": ["[email protected]", "", {}, "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA=="],
 
     "strip-ansi": ["[email protected]", "", { "dependencies": { "ansi-regex": "^5.0.1" } }, "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A=="],

docs/.gitignore

@@ -0,0 +1,3 @@
+node_modules
+.mintlify
+.DS_Store

docs/README.md

@@ -0,0 +1,64 @@
+# Voicebox Documentation
+
+This directory contains the documentation for Voicebox, built with [Mintlify](https://mintlify.com).
+
+## Development
+
+### Prerequisites
+
+Install Mintlify globally using bun:
+
+```bash
+bun add -g mintlify
+```
+
+Or use the helper script:
+
+```bash
+bun run install:mintlify
+```
+
+### Running Locally
+
+```bash
+bun run dev
+```
+
+This will start the Mintlify dev server.
+
+The docs will be available at `http://localhost:3000`
+
+### Structure
+
+```
+docs/
+├── mint.json           # Mintlify configuration
+├── custom.css          # Custom styles
+├── overview/           # Getting started & feature docs
+├── guides/             # User guides
+├── api/                # API reference
+├── development/        # Developer documentation
+├── logo/               # Logo assets
+└── public/             # Static assets
+```
+
+### Writing Docs
+
+- Use `.mdx` files for all documentation pages
+- Follow the existing structure in `mint.json` for navigation
+- Use Mintlify components for enhanced formatting (Card, CardGroup, Accordion, etc.)
+- Reference the [Mintlify documentation](https://mintlify.com/docs) for available components
+
+## Deployment
+
+Docs are automatically deployed when changes are pushed to the main branch.
+
+To manually deploy:
+
+```bash
+mintlify deploy
+```
+
+## Contributing
+
+See [CONTRIBUTING.md](../CONTRIBUTING.md) for contribution guidelines.

docs/TROUBLESHOOTING.md

@@ -90,6 +90,26 @@ chmod +x voicebox-*.AppImage
    - Slower but works without GPU
    - Backend automatically falls back to CPU
 
+### MLX "Failed to load the default metallib" error (Apple Silicon)
+
+**Symptoms:** Generation fails with "library not found" or "metallib" errors
+
+**Solutions:**
+1. **Rebuild server binary**
+   ```bash
+   bun run build:server
+   ```
+   The build script should automatically include MLX Metal shader libraries.
+
+2. **Check MLX installation**
+   ```bash
+   pip install -r backend/requirements-mlx.txt
+   ```
+
+3. **Verify backend detection**
+   - Check server logs for "Backend: MLX"
+   - If showing "Backend: PYTORCH", MLX may not be installed correctly
+
 ### Audio playback issues
 
 **Symptoms:** Generated audio won't play
@@ -111,19 +131,27 @@ chmod +x voicebox-*.AppImage
 **Symptoms:** Generation takes >30 seconds
 
 **Solutions:**
-1. **Use GPU** (if available)
+1. **Check backend type** (Apple Silicon)
+   - Check Settings → Server Status
+   - Should show "Backend: MLX" on Apple Silicon
+   - If showing "Backend: PYTORCH", install MLX: `pip install -r backend/requirements-mlx.txt`
+   - MLX provides 4-5x faster inference on Apple Silicon
+
+2. **Use GPU** (if available)
    - Check Settings → Server Status
    - Should show "GPU available: true"
+   - Apple Silicon: Should show "Metal (Apple Silicon via MLX)"
+   - Windows/Linux: Should show "CUDA" if GPU available
 
-2. **Enable caching**
+3. **Enable caching**
    - Voice prompts are cached automatically
    - Second generation with same voice should be faster
 
-3. **Use smaller model**
+4. **Use smaller model**
    - 0.6B model is faster than 1.7B
    - Quality difference is minimal for most voices
 
-4. **Check system resources**
+5. **Check system resources**
    - Close other CPU/GPU intensive apps
    - Ensure adequate RAM (8GB+ recommended)
 

docs/api/authentication.mdx

@@ -0,0 +1,55 @@
+---
+title: "Authentication"
+description: "API authentication and security"
+---
+
+## Current Status
+
+<Warning>
+  Authentication is not currently implemented in Voicebox. The API is intended for local use only.
+</Warning>
+
+## Local Usage
+
+For local development and usage:
+- API runs on `localhost:17493`
+- No authentication required
+- Access restricted to local machine
+
+## Future Implementation
+
+Authentication will be added in a future release for:
+- Remote deployments
+- Multi-user access
+- Production environments
+
+Planned authentication methods:
+- API keys
+- OAuth 2.0
+- JWT tokens
+
+## Security Best Practices
+
+Until authentication is implemented:
+
+<CardGroup cols={2}>
+  <Card title="Use VPN" icon="shield">
+    Use WireGuard or Tailscale for remote access
+  </Card>
+  <Card title="Reverse Proxy" icon="server">
+    Run behind nginx with basic auth
+  </Card>
+  <Card title="Firewall" icon="fire">
+    Restrict access to trusted IPs only
+  </Card>
+  <Card title="Local Only" icon="laptop">
+    Don't expose to public internet
+  </Card>
+</CardGroup>
+
+## Coming Soon
+
+- API key management
+- User accounts
+- Rate limiting
+- Access control

docs/api/generation.mdx

@@ -0,0 +1,119 @@
+---
+title: "Generation API"
+description: "Generate speech from text"
+---
+
+## Generate Speech
+
+```http
+POST /generate
+```
+
+**Request:**
+```json
+{
+  "text": "Hello world",
+  "profile_id": "abc123",
+  "language": "en"
+}
+```
+
+**Response:**
+```json
+{
+  "id": "gen123",
+  "text": "Hello world",
+  "profile_id": "abc123",
+  "language": "en",
+  "audio_url": "/audio/gen123.wav",
+  "duration": 2.3,
+  "created_at": "2024-01-29T12:00:00Z"
+}
+```
+
+## List History
+
+```http
+GET /history
+```
+
+**Query Parameters:**
+- `profile_id` (optional) - Filter by voice profile
+- `limit` (optional) - Number of results (default: 50)
+- `offset` (optional) - Pagination offset
+
+**Response:**
+```json
+{
+  "generations": [
+    {
+      "id": "gen123",
+      "text": "Hello world",
+      "profile_id": "abc123",
+      "duration": 2.3,
+      "created_at": "2024-01-29T12:00:00Z"
+    }
+  ],
+  "total": 100
+}
+```
+
+## Get Generation
+
+```http
+GET /history/{id}
+```
+
+**Response:**
+```json
+{
+  "id": "gen123",
+  "text": "Hello world",
+  "profile_id": "abc123",
+  "language": "en",
+  "audio_url": "/audio/gen123.wav",
+  "duration": 2.3,
+  "created_at": "2024-01-29T12:00:00Z"
+}
+```
+
+## Delete Generation
+
+```http
+DELETE /history/{id}
+```
+
+**Response:**
+```json
+{
+  "success": true
+}
+```
+
+## TypeScript Example
+
+```typescript
+import { VoiceboxClient } from '@/lib/api'
+
+const client = new VoiceboxClient({
+  baseUrl: 'http://localhost:17493'
+})
+
+// Generate speech
+const generation = await client.generate({
+  text: 'Hello world',
+  profile_id: 'abc123',
+  language: 'en'
+})
+
+// Get audio URL
+const audioUrl = generation.audio_url
+
+// List history
+const history = await client.listHistory({
+  profile_id: 'abc123',
+  limit: 20
+})
+```
+
+For full API documentation, visit `http://localhost:17493/docs` when the server is running.

docs/api/overview.mdx

@@ -0,0 +1,219 @@
+---
+title: "API Overview"
+description: "Integrate voice synthesis into your applications with the Voicebox REST API"
+---
+
+## Introduction
+
+Voicebox exposes a full REST API that allows you to integrate voice synthesis into your own applications. The API runs on `http://localhost:17493` by default.
+
+<Card title="Interactive API Docs" icon="book" href="http://localhost:17493/docs">
+  When Voicebox is running, visit the auto-generated API documentation at `http://localhost:17493/docs`
+</Card>
+
+## Base URL
+
+```
+http://localhost:17493
+```
+
+For remote deployments, replace `localhost` with your server's IP or hostname.
+
+## Authentication
+
+<Note>
+  Currently, the API does not require authentication for local development. Authentication will be added in a future release for production deployments.
+</Note>
+
+## Quick Example
+
+Here's a simple example of generating speech:
+
+```bash
+# Generate speech
+curl -X POST http://localhost:17493/generate \
+  -H "Content-Type: application/json" \
+  -d '{
+    "text": "Hello world",
+    "profile_id": "abc123",
+    "language": "en"
+  }'
+```
+
+## API Endpoints
+
+The Voicebox API is organized into several categories:
+
+<CardGroup cols={2}>
+  <Card title="Voice Profiles" icon="user" href="/api/voice-profiles">
+    Create, list, update, and delete voice profiles
+  </Card>
+  <Card title="Generation" icon="waveform" href="/api/generation">
+    Generate speech from text using voice profiles
+  </Card>
+  <Card title="Recordings" icon="microphone" href="/api/recordings">
+    Record and transcribe audio
+  </Card>
+  <Card title="Stories" icon="film">
+    Create and manage multi-voice stories (coming soon)
+  </Card>
+</CardGroup>
+
+## Core Endpoints
+
+### Voice Profiles
+
+```http
+GET    /profiles              # List all profiles
+POST   /profiles              # Create a new profile
+GET    /profiles/{id}         # Get profile details
+PUT    /profiles/{id}         # Update a profile
+DELETE /profiles/{id}         # Delete a profile
+POST   /profiles/{id}/samples # Add voice sample
+```
+
+### Generation
+
+```http
+POST   /generate              # Generate speech
+GET    /history               # List generation history
+GET    /history/{id}          # Get generation details
+DELETE /history/{id}          # Delete from history
+```
+
+### Recordings
+
+```http
+POST   /recordings            # Start recording
+POST   /recordings/stop       # Stop recording
+POST   /transcribe            # Transcribe audio
+```
+
+## Response Format
+
+All API responses follow a consistent JSON format:
+
+```json
+{
+  "success": true,
+  "data": {
+    // Response data
+  },
+  "error": null
+}
+```
+
+Error responses:
+
+```json
+{
+  "success": false,
+  "data": null,
+  "error": {
+    "message": "Error description",
+    "code": "ERROR_CODE"
+  }
+}
+```
+
+## Data Models
+
+### Voice Profile
+
+```json
+{
+  "id": "abc123",
+  "name": "John Smith",
+  "language": "en",
+  "description": "Professional narrator voice",
+  "created_at": "2024-01-29T12:00:00Z",
+  "samples": [
+    {
+      "id": "sample123",
+      "audio_path": "/path/to/sample.wav",
+      "duration": 15.5
+    }
+  ]
+}
+```
+
+### Generation
+
+```json
+{
+  "id": "gen123",
+  "text": "Hello world",
+  "profile_id": "abc123",
+  "language": "en",
+  "audio_path": "/path/to/output.wav",
+  "duration": 2.3,
+  "created_at": "2024-01-29T12:00:00Z"
+}
+```
+
+## TypeScript Client
+
+Voicebox provides an auto-generated TypeScript client with full type safety:
+
+```typescript
+import { VoiceboxClient } from '@/lib/api'
+
+const client = new VoiceboxClient({
+  baseUrl: 'http://localhost:17493'
+})
+
+// Create a profile
+const profile = await client.createProfile({
+  name: 'John Smith',
+  language: 'en'
+})
+
+// Generate speech
+const generation = await client.generate({
+  text: 'Hello world',
+  profile_id: profile.id,
+  language: 'en'
+})
+```
+
+The client is automatically generated from the OpenAPI schema. See [Development Setup](/development/setup#generate-openapi-client) for details.
+
+## Rate Limiting
+
+<Info>
+  Currently, there are no rate limits for local usage. Rate limiting will be added in a future release for production deployments.
+</Info>
+
+## WebSocket Support
+
+<Note>
+  Real-time streaming generation via WebSockets is planned for a future release.
+</Note>
+
+## Use Cases
+
+<CardGroup cols={2}>
+  <Card title="Game Development" icon="gamepad">
+    Generate dynamic dialogue for NPCs and characters
+  </Card>
+  <Card title="Content Creation" icon="video">
+    Automate voiceovers for videos and podcasts
+  </Card>
+  <Card title="Accessibility" icon="universal-access">
+    Build text-to-speech tools for visually impaired users
+  </Card>
+  <Card title="Voice Assistants" icon="robot">
+    Create custom voice interfaces
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Voice Profiles API" icon="user" href="/api/voice-profiles">
+    Learn how to manage voice profiles
+  </Card>
+  <Card title="Generation API" icon="waveform" href="/api/generation">
+    Generate speech from text
+  </Card>
+</CardGroup>

docs/api/recordings.mdx

@@ -0,0 +1,95 @@
+---
+title: "Recordings API"
+description: "Record and transcribe audio"
+---
+
+## Start Recording
+
+```http
+POST /recordings/start
+```
+
+**Request:**
+```json
+{
+  "source": "microphone"
+}
+```
+
+**Response:**
+```json
+{
+  "recording_id": "rec123",
+  "status": "recording"
+}
+```
+
+## Stop Recording
+
+```http
+POST /recordings/stop
+```
+
+**Request:**
+```json
+{
+  "recording_id": "rec123"
+}
+```
+
+**Response:**
+```json
+{
+  "recording_id": "rec123",
+  "audio_url": "/audio/rec123.wav",
+  "duration": 15.5
+}
+```
+
+## Transcribe Audio
+
+```http
+POST /transcribe
+```
+
+**Request:** (multipart/form-data)
+```
+audio: <file>
+language: "en" (optional)
+```
+
+**Response:**
+```json
+{
+  "text": "Transcribed speech text here",
+  "language": "en",
+  "duration": 15.5,
+  "confidence": 0.95
+}
+```
+
+## TypeScript Example
+
+```typescript
+import { VoiceboxClient } from '@/lib/api'
+
+const client = new VoiceboxClient({
+  baseUrl: 'http://localhost:17493'
+})
+
+// Start recording
+const recording = await client.startRecording({
+  source: 'microphone'
+})
+
+// ... record audio ...
+
+// Stop recording
+const result = await client.stopRecording(recording.id)
+
+// Transcribe
+const transcription = await client.transcribe(audioFile, 'en')
+console.log(transcription.text)
+```
+
+For full API documentation, visit `http://localhost:17493/docs` when the server is running.

docs/api/voice-profiles.mdx

@@ -0,0 +1,149 @@
+---
+title: "Voice Profiles API"
+description: "Manage voice profiles programmatically"
+---
+
+## Endpoints
+
+### List Profiles
+
+```http
+GET /profiles
+```
+
+**Response:**
+```json
+{
+  "profiles": [
+    {
+      "id": "abc123",
+      "name": "John Smith",
+      "language": "en",
+      "description": "Professional narrator",
+      "created_at": "2024-01-29T12:00:00Z",
+      "sample_count": 2
+    }
+  ]
+}
+```
+
+### Get Profile
+
+```http
+GET /profiles/{id}
+```
+
+**Response:**
+```json
+{
+  "id": "abc123",
+  "name": "John Smith",
+  "language": "en",
+  "description": "Professional narrator",
+  "created_at": "2024-01-29T12:00:00Z",
+  "samples": [
+    {
+      "id": "sample123",
+      "duration": 15.5,
+      "created_at": "2024-01-29T12:00:00Z"
+    }
+  ]
+}
+```
+
+### Create Profile
+
+```http
+POST /profiles
+```
+
+**Request:**
+```json
+{
+  "name": "John Smith",
+  "language": "en",
+  "description": "Professional narrator"
+}
+```
+
+**Response:**
+```json
+{
+  "id": "abc123",
+  "name": "John Smith",
+  "language": "en",
+  "description": "Professional narrator",
+  "created_at": "2024-01-29T12:00:00Z"
+}
+```
+
+### Update Profile
+
+```http
+PUT /profiles/{id}
+```
+
+**Request:**
+```json
+{
+  "name": "Updated Name",
+  "description": "Updated description"
+}
+```
+
+### Delete Profile
+
+```http
+DELETE /profiles/{id}
+```
+
+**Response:**
+```json
+{
+  "success": true
+}
+```
+
+### Add Voice Sample
+
+```http
+POST /profiles/{id}/samples
+```
+
+**Request:** (multipart/form-data)
+```
+audio: <file>
+```
+
+**Response:**
+```json
+{
+  "sample_id": "sample123",
+  "duration": 15.5
+}
+```
+
+## TypeScript Example
+
+```typescript
+import { VoiceboxClient } from '@/lib/api'
+
+const client = new VoiceboxClient({
+  baseUrl: 'http://localhost:17493'
+})
+
+// Create profile
+const profile = await client.createProfile({
+  name: 'John Smith',
+  language: 'en',
+  description: 'Professional narrator'
+})
+
+// Add sample
+await client.addSample(profile.id, audioFile)
+
+// List all profiles
+const profiles = await client.listProfiles()
+```
+
+For full API documentation, visit `http://localhost:17493/docs` when the server is running.

docs/custom.css

@@ -0,0 +1,15 @@
+/* Anchor hover styles */
+.nav-anchor:hover {
+	@apply text-[#BF9E40];
+}
+
+/* Icon wrapper on hover */
+.nav-anchor:hover div {
+	background: #BF9E40 !important;
+	filter: brightness(1) !important;
+}
+
+/* Icon SVG on hover */
+.nav-anchor:hover svg {
+	@apply bg-white !important;
+}

docs/developer/architecture.mdx

@@ -0,0 +1,206 @@
+---
+title: "Architecture"
+description: "Understanding Voicebox's technical architecture"
+---
+
+## System Overview
+
+Voicebox uses a client-server architecture with a React frontend and Python backend. The desktop app is built with Tauri and contains two main layers:
+
+**Frontend Layer:** A React application that handles the UI components, state management with Zustand, and data fetching with React Query (TanStack Query).
+
+**Backend Layer:** A Python FastAPI server that provides the REST API, runs the TTS engine (Qwen3-TTS), manages the SQLite database, and handles audio processing.
+
+These two layers communicate via HTTP, with the frontend making API requests to the backend.
+
+## Frontend Architecture
+
+### Tech Stack
+
+- **Framework**: React 18 with TypeScript
+- **State Management**: Zustand stores
+- **Data Fetching**: React Query (TanStack Query)
+- **Styling**: Tailwind CSS
+- **Audio**: WaveSurfer.js
+- **Desktop**: Tauri (Rust)
+
+### Component Structure
+
+```
+app/src/
+├── components/       # React components
+│   ├── profiles/    # Voice profile UI
+│   ├── generation/  # Speech generation UI
+│   ├── stories/     # Timeline editor
+│   └── shared/      # Reusable components
+├── lib/             # Utilities
+│   ├── api/         # Generated API client
+│   └── utils/       # Helper functions
+├── hooks/           # React hooks
+└── stores/          # Zustand state stores
+```
+
+### State Management
+
+```typescript
+// Example: Profile store
+const useProfileStore = create((set) => ({
+  profiles: [],
+  selectedProfile: null,
+  setProfiles: (profiles) => set({ profiles }),
+  selectProfile: (id) => set({ selectedProfile: id })
+}))
+```
+
+## Backend Architecture
+
+### Tech Stack
+
+- **Framework**: FastAPI (Python 3.11+)
+- **TTS Model**: Qwen3-TTS
+- **Transcription**: Whisper
+- **Database**: SQLite
+- **Audio**: librosa, soundfile
+
+### API Structure
+
+```python
+# main.py - API routes
+@app.post("/generate")
+async def generate_speech(request: GenerateRequest):
+    # 1. Validate request
+    # 2. Load voice profile
+    # 3. Generate audio with TTS
+    # 4. Save to database
+    # 5. Return response
+```
+
+### Data Model
+
+The database uses three main tables:
+
+**Profile Table:** Stores voice profiles with fields for id, name, and language.
+
+**Sample Table:** Stores audio samples linked to profiles via profile_id, with fields for audio_path and duration.
+
+**Generation Table:** Stores generated audio with fields for id, profile_id, text, and audio_path.
+
+## Desktop App (Tauri)
+
+### Rust Backend
+
+```rust
+// Sidecar process management
+// File system access
+// Native integrations
+```
+
+### Responsibilities
+
+- Launch Python backend as sidecar process
+- Native file dialogs
+- System tray integration
+- Auto-updates
+- OS-specific features
+
+## Build Process
+
+### Development
+
+```bash
+# Frontend (Vite dev server)
+cd app && bun run dev
+
+# Backend (manual start)
+cd backend && uvicorn main:app --reload
+
+# Desktop app (connects to manual backend)
+bun run dev
+```
+
+### Production
+
+```bash
+# Build everything (server binary + Tauri app)
+bun run build
+
+# Or build separately:
+# 1. Build server binary (PyInstaller)
+bun run build:server
+
+# 2. Build Tauri app (includes server)
+cd tauri && bun run tauri build
+```
+
+## Data Flow
+
+### Generation Flow
+
+When a user generates speech, the data flows through the following stages:
+
+1. **User Input** - User enters text in a React component
+2. **State Update** - Text is stored in Zustand state
+3. **API Request** - React Query mutation triggers an API call via fetch
+4. **Backend Processing** - FastAPI endpoint receives the request
+5. **TTS Generation** - Qwen3-TTS model generates the audio
+6. **Storage** - Audio file is saved to disk and a database record is created
+7. **Response** - Backend returns the audio URL
+8. **Cache Update** - React Query updates its cache with the response
+9. **UI Update** - Component re-renders with new data
+10. **Playback** - User can play the generated audio
+
+## Performance Considerations
+
+### Frontend
+
+- **Code splitting** - Lazy load routes
+- **Memoization** - React.memo for heavy components
+- **Virtual scrolling** - For large lists
+- **Debouncing** - Search and input handling
+
+### Backend
+
+- **Async operations** - All I/O is async
+- **Model caching** - Keep TTS model in memory
+- **Voice prompt caching** - Reuse embeddings
+- **Connection pooling** - Database connections
+
+## Security
+
+### Current
+
+- Local-only by default
+- No authentication (localhost trust)
+- File system sandboxing via Tauri
+
+### Planned
+
+- API key authentication
+- User accounts
+- Rate limiting
+- HTTPS support
+
+## Deployment Modes
+
+### Local Mode
+
+- Backend runs as sidecar
+- All data stays on device
+- No network required
+
+### Remote Mode
+
+- Backend on separate machine
+- Frontend connects via HTTP
+- Shared infrastructure possible
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Development Setup" icon="code" href="/development/setup">
+    Set up your dev environment
+  </Card>
+  <Card title="Contributing" icon="code-pull-request" href="/development/contributing">
+    Contribute to Voicebox
+  </Card>
+</CardGroup>

docs/developer/audio-channels.mdx

@@ -0,0 +1,310 @@
+---
+title: "Audio Channels"
+description: "How audio output routing works in Voicebox"
+---
+
+## Overview
+
+Audio channels allow routing voice output to different audio devices. This is useful for multi-output setups where different voices should play through different speakers or applications.
+
+## Architecture
+
+**Channel:** A named audio bus that can be assigned to output devices.
+
+**Device Mapping:** Links channels to OS audio device identifiers.
+
+**Profile Mapping:** Links voice profiles to channels (many-to-many).
+
+## Data Model
+
+### AudioChannel Table
+
+```python
+class AudioChannel(Base):
+    __tablename__ = "audio_channels"
+    
+    id = Column(String, primary_key=True)
+    name = Column(String, nullable=False)
+    is_default = Column(Boolean, default=False)
+    created_at = Column(DateTime)
+```
+
+### ChannelDeviceMapping Table
+
+```python
+class ChannelDeviceMapping(Base):
+    __tablename__ = "channel_device_mappings"
+    
+    id = Column(String, primary_key=True)
+    channel_id = Column(String, ForeignKey("audio_channels.id"))
+    device_id = Column(String)  # OS device identifier
+```
+
+### ProfileChannelMapping Table
+
+```python
+class ProfileChannelMapping(Base):
+    __tablename__ = "profile_channel_mappings"
+    
+    profile_id = Column(String, ForeignKey("profiles.id"), primary_key=True)
+    channel_id = Column(String, ForeignKey("audio_channels.id"), primary_key=True)
+```
+
+## Default Channel
+
+A default channel is created on database initialization:
+
+```python
+def init_db():
+    # Create default channel if it doesn't exist
+    default_channel = db.query(AudioChannel).filter(
+        AudioChannel.is_default == True
+    ).first()
+    
+    if not default_channel:
+        default_channel = AudioChannel(
+            id=str(uuid.uuid4()),
+            name="Default",
+            is_default=True
+        )
+        db.add(default_channel)
+        
+        # Assign all existing profiles to default channel
+        profiles = db.query(VoiceProfile).all()
+        for profile in profiles:
+            mapping = ProfileChannelMapping(
+                profile_id=profile.id,
+                channel_id=default_channel.id
+            )
+            db.add(mapping)
+```
+
+## Core Operations
+
+### Creating a Channel
+
+```python
+async def create_channel(
+    data: AudioChannelCreate,
+    db: Session,
+) -> AudioChannelResponse:
+    # Check name uniqueness
+    existing = db.query(DBAudioChannel).filter_by(name=data.name).first()
+    if existing:
+        raise ValueError(f"Channel with name '{data.name}' already exists")
+    
+    # Create channel
+    channel = DBAudioChannel(
+        id=str(uuid.uuid4()),
+        name=data.name,
+        is_default=False,
+    )
+    db.add(channel)
+    
+    # Add device mappings
+    for device_id in data.device_ids:
+        mapping = DBChannelDeviceMapping(
+            id=str(uuid.uuid4()),
+            channel_id=channel.id,
+            device_id=device_id,
+        )
+        db.add(mapping)
+    
+    db.commit()
+```
+
+### Updating a Channel
+
+```python
+async def update_channel(
+    channel_id: str,
+    data: AudioChannelUpdate,
+    db: Session,
+) -> AudioChannelResponse:
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    
+    # Cannot modify default channel
+    if channel.is_default:
+        raise ValueError("Cannot modify the default channel")
+    
+    # Update name
+    if data.name is not None:
+        channel.name = data.name
+    
+    # Update device mappings
+    if data.device_ids is not None:
+        # Delete existing
+        db.query(DBChannelDeviceMapping).filter_by(channel_id=channel_id).delete()
+        
+        # Add new
+        for device_id in data.device_ids:
+            mapping = DBChannelDeviceMapping(
+                channel_id=channel.id,
+                device_id=device_id,
+            )
+            db.add(mapping)
+    
+    db.commit()
+```
+
+### Deleting a Channel
+
+```python
+async def delete_channel(channel_id: str, db: Session) -> bool:
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    
+    # Cannot delete default channel
+    if channel.is_default:
+        raise ValueError("Cannot delete the default channel")
+    
+    # Delete device mappings
+    db.query(DBChannelDeviceMapping).filter_by(channel_id=channel_id).delete()
+    
+    # Delete profile-channel mappings
+    db.query(DBProfileChannelMapping).filter_by(channel_id=channel_id).delete()
+    
+    # Delete channel
+    db.delete(channel)
+    db.commit()
+```
+
+## Voice Assignment
+
+### Assigning Voices to Channel
+
+```python
+async def set_channel_voices(
+    channel_id: str,
+    data: ChannelVoiceAssignment,
+    db: Session,
+) -> None:
+    # Verify channel exists
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    if not channel:
+        raise ValueError(f"Channel {channel_id} not found")
+    
+    # Verify all profiles exist
+    for profile_id in data.profile_ids:
+        profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+        if not profile:
+            raise ValueError(f"Profile {profile_id} not found")
+    
+    # Delete existing mappings
+    db.query(DBProfileChannelMapping).filter_by(channel_id=channel_id).delete()
+    
+    # Add new mappings
+    for profile_id in data.profile_ids:
+        mapping = DBProfileChannelMapping(
+            profile_id=profile_id,
+            channel_id=channel_id,
+        )
+        db.add(mapping)
+    
+    db.commit()
+```
+
+### Assigning Channels to Voice
+
+```python
+async def set_profile_channels(
+    profile_id: str,
+    data: ProfileChannelAssignment,
+    db: Session,
+) -> None:
+    # Verify profile exists
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {profile_id} not found")
+    
+    # Delete existing mappings
+    db.query(DBProfileChannelMapping).filter_by(profile_id=profile_id).delete()
+    
+    # Add new mappings
+    for channel_id in data.channel_ids:
+        mapping = DBProfileChannelMapping(
+            profile_id=profile_id,
+            channel_id=channel_id,
+        )
+        db.add(mapping)
+    
+    db.commit()
+```
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/channels` | List all channels |
+| POST | `/channels` | Create a channel |
+| GET | `/channels/{id}` | Get channel by ID |
+| PUT | `/channels/{id}` | Update channel |
+| DELETE | `/channels/{id}` | Delete channel |
+| GET | `/channels/{id}/voices` | Get assigned voices |
+| PUT | `/channels/{id}/voices` | Set assigned voices |
+| GET | `/profiles/{id}/channels` | Get profile's channels |
+| PUT | `/profiles/{id}/channels` | Set profile's channels |
+
+## Request/Response Schemas
+
+### AudioChannelCreate
+
+```json
+{
+  "name": "Speakers",
+  "device_ids": ["device_uuid_1", "device_uuid_2"]
+}
+```
+
+### AudioChannelResponse
+
+```json
+{
+  "id": "channel_uuid",
+  "name": "Speakers",
+  "is_default": false,
+  "device_ids": ["device_uuid_1", "device_uuid_2"],
+  "created_at": "2024-01-15T10:30:00Z"
+}
+```
+
+### ChannelVoiceAssignment
+
+```json
+{
+  "profile_ids": ["profile_1", "profile_2"]
+}
+```
+
+## Use Cases
+
+### Multi-Output Setup
+
+**Scenario:** Stream with different voice characters
+
+1. Create "Stream" channel → OBS virtual audio
+2. Create "Monitor" channel → Headphones
+3. Assign "Narrator" profile → Both channels
+4. Assign "Character 1" profile → Stream only
+
+### Virtual Audio Cables
+
+Common device IDs for virtual audio:
+- VB-Audio Virtual Cable
+- BlackHole (macOS)
+- Soundflower (macOS)
+
+## Frontend Integration
+
+The frontend needs to:
+
+1. **Enumerate devices** using Web Audio API or Tauri
+2. **Display channel list** with device assignments
+3. **Allow profile assignment** via drag/drop or dropdown
+4. **Route playback** to correct device based on profile's channel
+
+## Limitations
+
+- Device IDs are OS-specific
+- Hot-plugging may invalidate device IDs
+- Default channel cannot be modified/deleted
+- Frontend handles actual audio routing (backend just stores config)

docs/developer/autoupdater.mdx

@@ -0,0 +1,84 @@
+---
+title: "Auto-Updater"
+description: "Configure and use the Tauri auto-updater"
+---
+
+## Overview
+
+Voicebox uses Tauri's built-in auto-updater to deliver updates to users automatically.
+
+## Quick Reference
+
+For detailed setup instructions, see the existing documentation:
+
+- [AUTOUPDATER_QUICKSTART.md](https://github.com/jamiepine/voicebox/blob/main/docs/AUTOUPDATER_QUICKSTART.md)
+- [AUTOUPDATER.md](https://github.com/jamiepine/voicebox/blob/main/docs/AUTOUPDATER.md)
+
+## How It Works
+
+The auto-updater follows a secure update process:
+
+1. **Check for Updates** - The Voicebox app periodically checks GitHub Releases for new versions
+2. **Download Update** - If a new version is found, the update package is downloaded
+3. **Verify Signature** - The downloaded package is cryptographically verified using the public key
+4. **Install** - After verification, the update is installed
+5. **Restart** - The app restarts with the new version
+
+## Configuration
+
+Updates are configured in `tauri/src-tauri/tauri.conf.json`:
+
+```json
+{
+  "updater": {
+    "active": true,
+    "endpoints": [
+      "https://github.com/jamiepine/voicebox/releases/latest/download/latest.json"
+    ],
+    "dialog": true,
+    "pubkey": "YOUR_PUBLIC_KEY"
+  }
+}
+```
+
+## Generating Keys
+
+```bash
+# Generate signing keys
+bun run generate:keys
+
+# Keys saved to ~/.tauri/voicebox.key
+```
+
+<Warning>
+  Keep your private key secure! Never commit it to the repository.
+</Warning>
+
+## Release Process
+
+1. **Bump version** using bumpversion
+2. **Push tag** to trigger CI/CD
+3. **GitHub Actions** builds and signs releases
+4. **Users** receive update notification
+
+## User Experience
+
+When an update is available:
+
+1. User sees a notification dialog
+2. User clicks "Update"
+3. Update downloads in background
+4. App restarts with new version
+
+## For Developers
+
+See the full documentation files for:
+
+- Setting up signing keys
+- Configuring GitHub releases
+- Testing updates locally
+- Troubleshooting update failures
+
+<Card title="View Full Docs" href="https://github.com/jamiepine/voicebox/tree/main/docs">
+  Access AUTOUPDATER.md and AUTOUPDATER_QUICKSTART.md in the repository
+</Card>

docs/developer/building.mdx

@@ -0,0 +1,270 @@
+---
+title: "Building"
+description: "Build Voicebox for production"
+---
+
+## Overview
+
+Voicebox uses a multi-step build process to create platform-specific installers.
+
+## Quick Build
+
+```bash
+# Build for your current platform (automatically builds server binary first)
+make build
+
+# Or manually
+bun run build
+```
+
+This automatically:
+1. Builds the Python server binary (`bun run build:server`)
+2. Builds the Tauri app (`cd tauri && bun run tauri build`)
+
+## Build Process
+
+The build process consists of two steps, but `bun run build` handles both automatically:
+
+### 1. Server Binary Build (Automatic)
+
+The Python backend is compiled into a standalone executable using PyInstaller. This happens automatically when you run `bun run build`.
+
+**Platform-specific binaries:**
+- macOS (Apple Silicon): `voicebox-server-aarch64-apple-darwin` (includes MLX backend)
+- macOS (Intel): `voicebox-server-x86_64-apple-darwin` (PyTorch backend)
+- Windows: `voicebox-server-x86_64-pc-windows-msvc.exe` (PyTorch backend)
+- Linux: `voicebox-server-x86_64-unknown-linux-gnu` (PyTorch backend)
+
+<Note>
+  The build script automatically detects your platform and includes the appropriate backend (MLX for Apple Silicon, PyTorch for others).
+</Note>
+
+**Manual build (if needed):**
+```bash
+bun run build:server
+```
+
+### 2. Tauri App Build (Automatic)
+
+The Tauri app build is also handled automatically, which:
+1. Builds the React frontend (Vite)
+2. Compiles the Rust backend
+3. Bundles the server binary as a sidecar
+4. Creates platform-specific installers
+
+**Manual build (if needed):**
+```bash
+cd tauri && bun run tauri build
+```
+
+### 3. Output
+
+Installers are created in `tauri/src-tauri/target/release/bundle/`:
+
+**macOS:**
+- `dmg/` - Disk image installer
+- `macos/` - App bundle
+
+**Windows:**
+- `msi/` - MSI installer
+- `nsis/` - NSIS installer
+
+**Linux:**
+- `deb/` - Debian package
+- `appimage/` - AppImage
+
+## Advanced Options
+
+### Building for Specific Platform
+
+```bash
+# Build for macOS (Apple Silicon)
+bun run tauri build -- --target aarch64-apple-darwin
+
+# Build for macOS (Intel)
+bun run tauri build -- --target x86_64-apple-darwin
+
+# Build for Windows
+bun run tauri build -- --target x86_64-pc-windows-msvc
+
+# Build for Linux
+bun run tauri build -- --target x86_64-unknown-linux-gnu
+```
+
+### Using Local Qwen3-TTS
+
+If you're developing Qwen3-TTS locally:
+
+```bash
+export QWEN_TTS_PATH=~/path/to/Qwen3-TTS
+bun run build:server  # Build server binary only
+# or
+bun run build  # Build everything
+```
+
+This makes PyInstaller use your local version instead of the pip package.
+
+### Debug Build
+
+```bash
+cd tauri
+bun run tauri build --debug
+```
+
+Creates a debug build with symbols and logging.
+
+## Build Configuration
+
+### Tauri Config
+
+Edit `tauri/src-tauri/tauri.conf.json`:
+
+```json
+{
+  "bundle": {
+    "identifier": "com.voicebox.app",
+    "icon": [
+      "icons/32x32.png",
+      "icons/128x128.png",
+      "icons/icon.icns",
+      "icons/icon.ico"
+    ]
+  }
+}
+```
+
+### Sidecar Configuration
+
+The Python server is bundled as a sidecar:
+
+```json
+{
+  "tauri": {
+    "bundle": {
+      "externalBin": [
+        "binaries/voicebox-server"
+      ]
+    }
+  }
+}
+```
+
+## Code Signing
+
+### macOS
+
+To sign the app for distribution:
+
+```bash
+# Set signing identity
+export APPLE_SIGNING_IDENTITY="Developer ID Application: Your Name"
+
+# Build with signing
+bun run tauri build
+```
+
+For notarization:
+
+```bash
+# Set credentials
+export APPLE_ID="[email protected]"
+export APPLE_PASSWORD="app-specific-password"
+
+# Build and notarize
+bun run tauri build
+```
+
+### Windows
+
+For Windows code signing:
+
+```bash
+# Set certificate
+export WINDOWS_CERTIFICATE_PATH="/path/to/cert.pfx"
+export WINDOWS_CERTIFICATE_PASSWORD="password"
+
+# Build with signing
+bun run tauri build
+```
+
+## Release Process
+
+The full release process is automated:
+
+```bash
+# 1. Bump version
+bumpversion patch  # or minor/major
+
+# 2. Build all platforms (CI/CD handles this)
+git push --tags
+
+# 3. GitHub Actions creates releases
+```
+
+See [CONTRIBUTING.md](/development/contributing) for the full release workflow.
+
+## Troubleshooting
+
+<AccordionGroup>
+  <Accordion title="Server Binary Build Fails">
+    **Common issues:**
+    - Missing Python dependencies: `pip install -r requirements.txt`
+    - PyInstaller not found: `pip install pyinstaller`
+    - Qwen3-TTS not installed: `pip install git+https://github.com/QwenLM/Qwen3-TTS.git`
+
+    **Solution:**
+    ```bash
+    cd backend
+    source venv/bin/activate
+    pip install -r requirements.txt
+    pip install pyinstaller
+    ```
+  </Accordion>
+
+  <Accordion title="Tauri Build Fails">
+    **Common issues:**
+    - Rust not installed: `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh`
+    - Server binary missing: Usually auto-built, but can run manually: `./scripts/build-server.sh`
+    - Node modules outdated: `bun install`
+
+    **Solution:**
+    ```bash
+    # Clean and rebuild
+    cd tauri/src-tauri
+    cargo clean
+    cd ../..
+    bun run build  # Automatically builds server binary first
+    ```
+  </Accordion>
+
+  <Accordion title="App Won't Launch After Build">
+    **Check:**
+    - Server binary has execute permissions
+    - All dependencies are bundled
+    - Check logs in the app's data directory
+
+    **macOS:**
+    ```bash
+    tail -f ~/Library/Application\ Support/com.voicebox.app/logs/server.log
+    ```
+
+    **Windows:**
+    ```bash
+    type %APPDATA%\com.voicebox.app\logs\server.log
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## CI/CD
+
+GitHub Actions automatically builds releases when tags are pushed:
+
+```yaml
+# .github/workflows/release.yml
+on:
+  push:
+    tags:
+      - 'v*'
+```
+
+See the [repository](https://github.com/jamiepine/voicebox) for the full CI/CD configuration.

docs/developer/contributing.mdx

@@ -0,0 +1,326 @@
+---
+title: "Contributing"
+description: "How to contribute to Voicebox"
+---
+
+Thank you for your interest in contributing to Voicebox! This guide will help you get started.
+
+## Code of Conduct
+
+- Be respectful and inclusive
+- Welcome newcomers and help them learn
+- Focus on constructive feedback
+- Respect different viewpoints and experiences
+
+## Getting Started
+
+Before you start contributing, make sure you have:
+
+1. **Read the documentation** to understand how Voicebox works
+2. **Set up your development environment** - see [Development Setup](/development/setup)
+3. **Explored the codebase** to understand the project structure
+4. **Checked existing issues** to see if someone else is working on something similar
+
+## Ways to Contribute
+
+<CardGroup cols={2}>
+  <Card title="Report Bugs" icon="bug">
+    Found a bug? Open an issue with reproduction steps
+  </Card>
+  <Card title="Request Features" icon="lightbulb">
+    Have an idea? Start a discussion or open an issue
+  </Card>
+  <Card title="Improve Docs" icon="book">
+    Fix typos, add examples, or clarify instructions
+  </Card>
+  <Card title="Write Code" icon="code">
+    Fix bugs, add features, or optimize performance
+  </Card>
+</CardGroup>
+
+## Development Workflow
+
+### 1. Fork & Clone
+
+```bash
+# Fork the repository on GitHub
+# Then clone your fork
+git clone https://github.com/YOUR_USERNAME/voicebox.git
+cd voicebox
+```
+
+### 2. Create a Branch
+
+Use descriptive branch names:
+
+```bash
+# For features
+git checkout -b feature/voice-effects
+
+# For bug fixes
+git checkout -b fix/audio-playback-issue
+
+# For documentation
+git checkout -b docs/api-examples
+```
+
+### 3. Make Your Changes
+
+Follow these guidelines:
+
+<AccordionGroup>
+  <Accordion title="Code Style">
+    **TypeScript/React:**
+    - Use TypeScript strict mode
+    - Prefer functional components with hooks
+    - Use named exports
+    - Format with Biome (runs automatically)
+
+    **Python:**
+    - Follow PEP 8
+    - Use type hints
+    - Use async/await for I/O
+    - Document functions with docstrings
+
+    **Rust:**
+    - Follow Rust conventions
+    - Use meaningful names
+    - Handle errors explicitly
+    - Run `rustfmt`
+  </Accordion>
+
+  <Accordion title="Commit Messages">
+    Write clear, descriptive commit messages:
+
+    ```bash
+    # Good
+    git commit -m "Add voice profile export feature"
+    git commit -m "Fix audio playback stopping after 30 seconds"
+
+    # Avoid
+    git commit -m "Update code"
+    git commit -m "Fix bug"
+    ```
+
+    Format:
+    - Use imperative mood ("Add feature" not "Added feature")
+    - Keep first line under 50 characters
+    - Add detailed description if needed
+  </Accordion>
+
+  <Accordion title="Testing">
+    - Test your changes manually in the app
+    - Ensure backend API endpoints work
+    - Check for TypeScript/Python errors
+    - Verify UI components render correctly
+    - Add automated tests when possible
+  </Accordion>
+</AccordionGroup>
+
+### 4. Push & Create PR
+
+```bash
+# Push your branch
+git push origin feature/your-feature-name
+
+# Then create a pull request on GitHub
+```
+
+## Pull Request Guidelines
+
+When creating a pull request:
+
+<Steps>
+  <Step title="Use a Clear Title">
+    Examples:
+    - "Add voice profile export functionality"
+    - "Fix audio playback stopping after 30 seconds"
+    - "Improve generation speed with caching"
+  </Step>
+
+  <Step title="Provide Description">
+    Include:
+    - What changes you made
+    - Why you made them
+    - How to test them
+    - Screenshots (for UI changes)
+    - Reference related issues
+  </Step>
+
+  <Step title="Update Documentation">
+    - Update relevant docs if behavior changes
+    - Add API documentation for new endpoints
+    - Update README if needed
+  </Step>
+
+  <Step title="Check the Checklist">
+    - [ ] Code follows style guidelines
+    - [ ] Documentation updated
+    - [ ] Changes tested
+    - [ ] No breaking changes (or documented)
+    - [ ] CHANGELOG.md updated
+  </Step>
+</Steps>
+
+## Project Structure
+
+Understanding the codebase:
+
+```
+voicebox/
+├── app/              # Shared React frontend
+│   ├── src/
+│   │   ├── components/   # UI components
+│   │   ├── lib/          # Utilities and API client
+│   │   ├── hooks/        # React hooks
+│   │   └── stores/       # Zustand state stores
+├── backend/          # Python FastAPI server
+│   ├── main.py       # API routes
+│   ├── tts.py        # Voice synthesis logic
+│   ├── database.py   # SQLite operations
+│   └── models.py     # Pydantic models
+├── tauri/            # Desktop app wrapper
+│   └── src-tauri/    # Rust backend
+├── web/              # Web deployment
+├── landing/          # Marketing website
+└── scripts/          # Build & release scripts
+```
+
+## Areas for Contribution
+
+### Bug Fixes
+
+- Check [existing issues](https://github.com/jamiepine/voicebox/issues) for bugs
+- Test your fix thoroughly
+- Add regression tests if possible
+
+### New Features
+
+- Check the [roadmap](https://github.com/jamiepine/voicebox#roadmap) for planned features
+- Discuss major features in an issue first
+- Keep features focused and well-scoped
+
+### Documentation
+
+- Improve clarity and fix typos
+- Add code examples
+- Create tutorials or guides
+- Document API endpoints
+
+### UI/UX Improvements
+
+- Improve accessibility
+- Enhance visual design
+- Optimize performance
+- Add animations/transitions
+
+### Infrastructure
+
+- Improve build process
+- Add CI/CD improvements
+- Optimize bundle size
+- Add testing infrastructure
+
+## API Development
+
+When adding new API endpoints:
+
+<Steps>
+  <Step title="Add Route">
+    In `backend/main.py`:
+
+    ```python
+    @app.post("/api/new-endpoint")
+    async def new_endpoint(data: RequestModel) -> ResponseModel:
+        """Endpoint description."""
+        # Implementation
+        return response
+    ```
+  </Step>
+
+  <Step title="Create Models">
+    In `backend/models.py`:
+
+    ```python
+    class RequestModel(BaseModel):
+        field: str
+
+    class ResponseModel(BaseModel):
+        result: str
+    ```
+  </Step>
+
+  <Step title="Regenerate Client">
+    ```bash
+    bun run generate:api
+    ```
+
+    This updates the TypeScript client with type-safe bindings.
+  </Step>
+
+  <Step title="Update Docs">
+    Add documentation in `/docs/api/`
+  </Step>
+</Steps>
+
+## Testing
+
+Currently testing is primarily manual. When adding tests:
+
+**Backend:**
+```bash
+cd backend
+pytest
+```
+
+**Frontend:**
+```bash
+bun run test
+```
+
+**E2E (future):**
+```bash
+bun run test:e2e
+```
+
+## Release Process
+
+Releases are managed by maintainers using `bumpversion`:
+
+```bash
+# Bump version (patch, minor, or major)
+bumpversion patch
+
+# Push with tags
+git push && git push --tags
+```
+
+GitHub Actions automatically builds and publishes releases when tags are pushed.
+
+## Community
+
+- **GitHub Issues:** Bug reports and feature requests
+- **GitHub Discussions:** General questions and ideas
+- **Discord:** Real-time chat (coming soon)
+
+## Recognition
+
+Contributors are recognized in:
+- [CHANGELOG.md](https://github.com/jamiepine/voicebox/blob/main/CHANGELOG.md)
+- GitHub contributor list
+- Release notes
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the MIT License.
+
+## Questions?
+
+If you have questions:
+
+1. Check the [documentation](/overview/introduction)
+2. Search [existing issues](https://github.com/jamiepine/voicebox/issues)
+3. Open a new issue or discussion
+4. See [CONTRIBUTING.md](https://github.com/jamiepine/voicebox/blob/main/CONTRIBUTING.md) in the repo
+
+Thank you for contributing to Voicebox! 🎉

docs/developer/history.mdx

@@ -0,0 +1,260 @@
+---
+title: "Generation History"
+description: "How generation history tracking works in Voicebox"
+---
+
+## Overview
+
+The history module tracks all generated audio, providing a searchable record of past generations. Each generation stores the text, settings, and a reference to the audio file.
+
+## Data Model
+
+### Generation Table
+
+```python
+class Generation(Base):
+    __tablename__ = "generations"
+
+    id = Column(String, primary_key=True)
+    profile_id = Column(String, ForeignKey("profiles.id"))
+    text = Column(Text, nullable=False)
+    language = Column(String, default="en")
+    audio_path = Column(String, nullable=False)
+    duration = Column(Float, nullable=False)
+    seed = Column(Integer)
+    instruct = Column(Text)
+    created_at = Column(DateTime)
+```
+
+## File Storage
+
+Generated audio is stored in:
+
+```
+data/
+└── generations/
+    └── {generation_id}.wav
+```
+
+## Core Functions
+
+### Creating a Generation Record
+
+After TTS generates audio, a history entry is created:
+
+```python
+async def create_generation(
+    profile_id: str,
+    text: str,
+    language: str,
+    audio_path: str,
+    duration: float,
+    seed: Optional[int],
+    db: Session,
+    instruct: Optional[str] = None,
+) -> GenerationResponse:
+    db_generation = DBGeneration(
+        id=str(uuid.uuid4()),
+        profile_id=profile_id,
+        text=text,
+        language=language,
+        audio_path=audio_path,
+        duration=duration,
+        seed=seed,
+        instruct=instruct,
+        created_at=datetime.utcnow(),
+    )
+    
+    db.add(db_generation)
+    db.commit()
+    
+    return GenerationResponse.model_validate(db_generation)
+```
+
+### Listing Generations
+
+Supports filtering and pagination:
+
+```python
+async def list_generations(
+    query: HistoryQuery,
+    db: Session,
+) -> HistoryListResponse:
+    # Build query with profile name join
+    q = db.query(
+        DBGeneration,
+        DBVoiceProfile.name.label('profile_name')
+    ).join(
+        DBVoiceProfile,
+        DBGeneration.profile_id == DBVoiceProfile.id
+    )
+    
+    # Apply filters
+    if query.profile_id:
+        q = q.filter(DBGeneration.profile_id == query.profile_id)
+    
+    if query.search:
+        q = q.filter(DBGeneration.text.like(f"%{query.search}%"))
+    
+    # Order and paginate
+    total = q.count()
+    q = q.order_by(DBGeneration.created_at.desc())
+    q = q.offset(query.offset).limit(query.limit)
+    
+    return HistoryListResponse(items=results, total=total)
+```
+
+### Getting Statistics
+
+Aggregate statistics for the dashboard:
+
+```python
+async def get_generation_stats(db: Session) -> dict:
+    total = db.query(func.count(DBGeneration.id)).scalar()
+    total_duration = db.query(func.sum(DBGeneration.duration)).scalar()
+    
+    by_profile = db.query(
+        DBGeneration.profile_id,
+        func.count(DBGeneration.id).label('count')
+    ).group_by(DBGeneration.profile_id).all()
+    
+    return {
+        "total_generations": total,
+        "total_duration_seconds": total_duration,
+        "generations_by_profile": {
+            profile_id: count for profile_id, count in by_profile
+        },
+    }
+```
+
+## Deletion
+
+Deleting a generation removes both the database record and audio file:
+
+```python
+async def delete_generation(generation_id: str, db: Session) -> bool:
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        return False
+    
+    # Delete audio file
+    audio_path = Path(generation.audio_path)
+    if audio_path.exists():
+        audio_path.unlink()
+    
+    # Delete database record
+    db.delete(generation)
+    db.commit()
+    
+    return True
+```
+
+### Cascade Delete
+
+When deleting a profile, all its generations are also deleted:
+
+```python
+async def delete_generations_by_profile(profile_id: str, db: Session) -> int:
+    generations = db.query(DBGeneration).filter_by(profile_id=profile_id).all()
+    
+    for generation in generations:
+        Path(generation.audio_path).unlink(missing_ok=True)
+        db.delete(generation)
+    
+    db.commit()
+    return len(generations)
+```
+
+## Export/Import
+
+### Exporting a Generation
+
+Generations can be exported as ZIP archives:
+
+```
+generation_export.zip
+├── generation.json   # Metadata
+└── audio.wav         # Audio file
+```
+
+### Importing a Generation
+
+The import process:
+
+1. Extract ZIP archive
+2. Validate metadata and audio
+3. Create new generation ID
+4. Copy audio to generations directory
+5. Create database record
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/history` | List generations with filters |
+| GET | `/history/stats` | Get aggregate statistics |
+| GET | `/history/{id}` | Get generation by ID |
+| DELETE | `/history/{id}` | Delete generation |
+| GET | `/history/{id}/export` | Export as ZIP |
+| GET | `/history/{id}/export-audio` | Export audio only |
+| POST | `/history/import` | Import from ZIP |
+
+### Query Parameters
+
+```
+GET /history?profile_id=uuid&search=hello&limit=50&offset=0
+```
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `profile_id` | string | null | Filter by profile |
+| `search` | string | null | Search in text |
+| `limit` | int | 50 | Results per page |
+| `offset` | int | 0 | Pagination offset |
+
+### Response Schema
+
+```json
+{
+  "items": [
+    {
+      "id": "uuid",
+      "profile_id": "uuid",
+      "profile_name": "My Voice",
+      "text": "Hello world",
+      "language": "en",
+      "audio_path": "/path/to/audio.wav",
+      "duration": 1.5,
+      "seed": 42,
+      "instruct": null,
+      "created_at": "2024-01-15T10:30:00Z"
+    }
+  ],
+  "total": 150
+}
+```
+
+## Usage in Stories
+
+Generations can be added to stories for multi-voice narratives. The story system references generations by ID:
+
+```python
+class StoryItem(Base):
+    generation_id = Column(String, ForeignKey("generations.id"))
+```
+
+This allows the same generation to be reused across multiple stories without duplicating audio files.
+
+## Storage Considerations
+
+### Disk Usage
+
+Each generation creates a WAV file. For a 10-second clip at 24kHz:
+- ~480KB per file (mono, 16-bit)
+
+### Cleanup Strategy
+
+Consider implementing:
+- Automatic cleanup of old generations
+- Storage quota per profile
+- Compression for archival

docs/developer/model-management.mdx

@@ -0,0 +1,341 @@
+---
+title: "Model Management"
+description: "How model downloading, loading, and status tracking works in Voicebox"
+---
+
+## Overview
+
+Voicebox manages two types of models:
+
+**TTS Models:** Qwen3-TTS for voice cloning (0.6B and 1.7B variants).
+
+**ASR Models:** Whisper for transcription (tiny through large).
+
+Models are downloaded from HuggingFace Hub on first use and cached locally.
+
+## Available Models
+
+### TTS Models
+
+| Model | HuggingFace ID | Size | VRAM |
+|-------|----------------|------|------|
+| 0.6B | `Qwen/Qwen3-TTS-12Hz-0.6B-Base` | ~1.2GB | ~2GB |
+| 1.7B | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ~3.4GB | ~6GB |
+
+### Whisper Models
+
+| Model | HuggingFace ID | Size | VRAM |
+|-------|----------------|------|------|
+| tiny | `openai/whisper-tiny` | ~150MB | ~1GB |
+| base | `openai/whisper-base` | ~300MB | ~1GB |
+| small | `openai/whisper-small` | ~500MB | ~2GB |
+| medium | `openai/whisper-medium` | ~1.5GB | ~5GB |
+| large | `openai/whisper-large` | ~3GB | ~10GB |
+
+## Model Storage
+
+Models are cached in the HuggingFace cache directory:
+
+```
+~/.cache/huggingface/hub/
+├── models--Qwen--Qwen3-TTS-12Hz-1.7B-Base/
+├── models--Qwen--Qwen3-TTS-12Hz-0.6B-Base/
+├── models--openai--whisper-base/
+└── ...
+```
+
+## Progress Tracking
+
+### Progress Manager
+
+Tracks download progress across all models:
+
+```python
+class ProgressManager:
+    def __init__(self):
+        self._progress = {}  # model_name -> progress_info
+    
+    def update_progress(
+        self,
+        model_name: str,
+        current: int,
+        total: int,
+        filename: str,
+        status: str,
+    ):
+        self._progress[model_name] = {
+            "current": current,
+            "total": total,
+            "filename": filename,
+            "status": status,  # downloading, complete, error
+            "updated_at": datetime.utcnow(),
+        }
+    
+    def get_progress(self, model_name: str) -> Optional[dict]:
+        return self._progress.get(model_name)
+```
+
+### HuggingFace Progress Callback
+
+Hooks into HuggingFace's download system:
+
+```python
+class HFProgressTracker:
+    def __init__(self, callback):
+        self.callback = callback
+    
+    @contextmanager
+    def patch_download(self):
+        """Context manager to intercept HF downloads."""
+        original_download = hf_hub_download
+        
+        def patched_download(*args, **kwargs):
+            # Intercept progress
+            result = original_download(*args, **kwargs)
+            self.callback(progress_info)
+            return result
+        
+        # Apply patch
+        with patch('huggingface_hub.hf_hub_download', patched_download):
+            yield
+```
+
+### Server-Sent Events (SSE)
+
+Progress is streamed to the frontend:
+
+```python
+@app.get("/models/progress/{model_name}")
+async def get_model_progress(model_name: str):
+    async def event_generator():
+        while True:
+            progress = progress_manager.get_progress(model_name)
+            if progress:
+                yield f"data: {json.dumps(progress)}\n\n"
+            
+            if progress and progress["status"] in ["complete", "error"]:
+                break
+            
+            await asyncio.sleep(0.5)
+    
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream"
+    )
+```
+
+## Task Manager
+
+Tracks active downloads and generations:
+
+```python
+class TaskManager:
+    def __init__(self):
+        self._active_downloads = {}
+        self._active_generations = {}
+    
+    def start_download(self, model_name: str):
+        self._active_downloads[model_name] = {
+            "status": "downloading",
+            "started_at": datetime.utcnow(),
+        }
+    
+    def complete_download(self, model_name: str):
+        if model_name in self._active_downloads:
+            del self._active_downloads[model_name]
+    
+    def get_active_tasks(self) -> dict:
+        return {
+            "downloads": list(self._active_downloads.values()),
+            "generations": list(self._active_generations.values()),
+        }
+```
+
+## Model Status
+
+Check which models are downloaded and loaded:
+
+```python
+@app.get("/models/status")
+async def get_model_status() -> ModelStatusListResponse:
+    models = []
+    
+    # Check TTS models
+    for size, hf_id in [("1.7B", "Qwen/Qwen3-TTS-12Hz-1.7B-Base"), ...]:
+        downloaded = is_model_downloaded(hf_id)
+        loaded = tts_model._current_model_size == size
+        
+        models.append(ModelStatus(
+            model_name=f"qwen-tts-{size}",
+            display_name=f"Qwen3-TTS {size}",
+            downloaded=downloaded,
+            size_mb=get_model_size_mb(hf_id),
+            loaded=loaded,
+        ))
+    
+    # Check Whisper models
+    for size in ["tiny", "base", "small", "medium", "large"]:
+        hf_id = f"openai/whisper-{size}"
+        downloaded = is_model_downloaded(hf_id)
+        
+        models.append(ModelStatus(
+            model_name=f"whisper-{size}",
+            display_name=f"Whisper {size}",
+            downloaded=downloaded,
+            size_mb=get_model_size_mb(hf_id),
+            loaded=False,  # Whisper is loaded on-demand
+        ))
+    
+    return ModelStatusListResponse(models=models)
+```
+
+## Manual Model Operations
+
+### Load Model
+
+```python
+@app.post("/models/load")
+async def load_model(model_size: str = "1.7B"):
+    tts_model = get_tts_model()
+    await tts_model.load_model_async(model_size)
+    return {"status": "loaded", "model_size": model_size}
+```
+
+### Unload Model
+
+```python
+@app.post("/models/unload")
+async def unload_model():
+    tts_model = get_tts_model()
+    tts_model.unload_model()
+    return {"status": "unloaded"}
+```
+
+### Trigger Download
+
+```python
+@app.post("/models/download")
+async def trigger_model_download(request: ModelDownloadRequest):
+    # This triggers the download in background
+    # Progress is tracked via /models/progress/{model_name}
+    
+    if request.model_name.startswith("qwen-tts"):
+        size = request.model_name.split("-")[-1]
+        asyncio.create_task(download_tts_model(size))
+    elif request.model_name.startswith("whisper"):
+        size = request.model_name.split("-")[-1]
+        asyncio.create_task(download_whisper_model(size))
+    
+    return {"status": "downloading"}
+```
+
+### Delete Model
+
+```python
+@app.delete("/models/{model_name}")
+async def delete_model(model_name: str):
+    # Find and delete from HuggingFace cache
+    cache_dir = Path.home() / ".cache" / "huggingface" / "hub"
+    
+    model_dirs = list(cache_dir.glob(f"models--*--{model_name}*"))
+    for model_dir in model_dirs:
+        shutil.rmtree(model_dir)
+    
+    return {"status": "deleted"}
+```
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/models/status` | Get status of all models |
+| POST | `/models/load` | Load TTS model |
+| POST | `/models/unload` | Unload TTS model |
+| POST | `/models/download` | Trigger model download |
+| GET | `/models/progress/{name}` | Stream download progress (SSE) |
+| DELETE | `/models/{name}` | Delete downloaded model |
+| GET | `/tasks/active` | Get active downloads/generations |
+
+## Response Schemas
+
+### ModelStatus
+
+```json
+{
+  "model_name": "qwen-tts-1.7B",
+  "display_name": "Qwen3-TTS 1.7B",
+  "downloaded": true,
+  "size_mb": 3400,
+  "loaded": true
+}
+```
+
+### ActiveTasksResponse
+
+```json
+{
+  "downloads": [
+    {
+      "model_name": "whisper-medium",
+      "status": "downloading",
+      "started_at": "2024-01-15T10:30:00Z"
+    }
+  ],
+  "generations": [
+    {
+      "task_id": "uuid",
+      "profile_id": "uuid",
+      "text_preview": "Hello world...",
+      "started_at": "2024-01-15T10:30:00Z"
+    }
+  ]
+}
+```
+
+## Frontend Integration
+
+### Progress Display
+
+```typescript
+// Subscribe to download progress via SSE
+const eventSource = new EventSource(`/models/progress/${modelName}`);
+
+eventSource.onmessage = (event) => {
+  const progress = JSON.parse(event.data);
+  updateProgressBar(progress.current / progress.total);
+  
+  if (progress.status === 'complete') {
+    eventSource.close();
+  }
+};
+```
+
+### Model Status UI
+
+```typescript
+// Fetch model status
+const { data: models } = useQuery({
+  queryKey: ['models', 'status'],
+  queryFn: () => api.getModelStatus(),
+});
+
+// Display download/load buttons based on status
+models.map(model => (
+  <ModelCard
+    name={model.display_name}
+    downloaded={model.downloaded}
+    loaded={model.loaded}
+    onDownload={() => triggerDownload(model.model_name)}
+    onLoad={() => loadModel(model.model_name)}
+  />
+));
+```
+
+## Error Handling
+
+| Error | Cause | Solution |
+|-------|-------|----------|
+| Download failed | Network issue | Retry download |
+| OOM on load | Model too large | Use smaller model |
+| Model not found | Cache corrupted | Re-download |
+| Slow download | HF rate limit | Wait and retry |

docs/developer/setup.mdx

@@ -0,0 +1,239 @@
+---
+title: "Development Setup"
+description: "Set up your local development environment for Voicebox"
+---
+
+## Prerequisites
+
+Before you begin, ensure you have the following installed:
+
+<CardGroup cols={3}>
+  <Card title="Bun" icon="package">
+    [Download Bun](https://bun.sh)
+    ```bash
+    curl -fsSL https://bun.sh/install | bash
+    ```
+  </Card>
+  <Card title="Python 3.11+" icon="python">
+    [Download Python](https://python.org)
+    ```bash
+    python --version
+    ```
+  </Card>
+  <Card title="Rust" icon="rust">
+    [Install Rust](https://rustup.rs)
+    ```bash
+    rustc --version
+    ```
+  </Card>
+</CardGroup>
+
+## Clone the Repository
+
+```bash
+git clone https://github.com/jamiepine/voicebox.git
+cd voicebox
+```
+
+## Quick Setup (Recommended)
+
+The easiest way to get started is using the Makefile:
+
+```bash
+# Setup everything
+make setup
+
+# Start development
+make dev
+```
+
+<Note>
+  The Makefile is available on macOS and Linux. Windows users should follow the manual setup below.
+</Note>
+
+## Manual Setup
+
+### 1. Install JavaScript Dependencies
+
+```bash
+bun install
+```
+
+This installs dependencies for:
+- `app/` - Shared React frontend
+- `tauri/` - Tauri desktop wrapper
+- `web/` - Web deployment wrapper
+
+### 2. Set Up Python Backend
+
+```bash
+cd backend
+
+# Create virtual environment
+python -m venv venv
+
+# Activate virtual environment
+source venv/bin/activate  # macOS/Linux
+# or
+venv\Scripts\activate  # Windows
+
+# Install Python dependencies
+pip install -r requirements.txt
+
+# Install MLX dependencies (Apple Silicon only - for faster inference)
+# On Apple Silicon, this enables native Metal acceleration
+if [[ $(uname -m) == "arm64" ]]; then
+  pip install -r requirements-mlx.txt
+fi
+
+# Install Qwen3-TTS
+pip install git+https://github.com/QwenLM/Qwen3-TTS.git
+```
+
+## Running in Development
+
+Development requires **two terminals**: one for the Python backend, one for the Tauri app.
+
+<Tabs>
+  <Tab title="Terminal 1: Backend">
+    Start the Python server first:
+
+    ```bash
+    cd backend
+    source venv/bin/activate  # Activate venv
+    bun run dev:server
+    ```
+
+    Or manually:
+    ```bash
+    uvicorn main:app --reload --port 17493
+    ```
+
+    Backend will be available at `http://localhost:17493`
+  </Tab>
+
+  <Tab title="Terminal 2: Desktop App">
+    Then start the Tauri app:
+
+    ```bash
+    bun run dev
+    ```
+
+    This will:
+    - Create a placeholder sidecar binary
+    - Start Vite dev server on port 5173
+    - Launch Tauri window
+    - Enable hot reload
+  </Tab>
+</Tabs>
+
+<Info>
+  In dev mode, the app connects to your manually-started Python server. The bundled server binary is only used in production builds.
+</Info>
+
+### Optional: Web App
+
+```bash
+bun run dev:web
+```
+
+Web app will be available at `http://localhost:5174`
+
+## Model Downloads
+
+Models are automatically downloaded from HuggingFace Hub on first use:
+
+- **Whisper** (transcription): Auto-downloads on first transcription
+- **Qwen3-TTS** (voice cloning): Auto-downloads on first generation (~2-4GB)
+
+<Warning>
+  First-time usage will be slower due to model downloads, but subsequent runs will use cached models.
+</Warning>
+
+## Project Structure
+
+```
+voicebox/
+├── app/              # Shared React frontend
+│   └── src/
+│       ├── components/   # UI components
+│       ├── lib/          # Utilities and API client
+│       └── hooks/        # React hooks
+├── backend/          # Python FastAPI server
+│   ├── main.py       # API routes
+│   ├── tts.py        # Voice synthesis
+│   └── database.py   # SQLite operations
+├── tauri/            # Desktop app wrapper
+│   └── src-tauri/    # Rust backend
+├── web/              # Web deployment
+├── landing/          # Marketing website
+└── scripts/          # Build & release scripts
+```
+
+## Available Make Commands
+
+Run `make help` to see all available commands:
+
+```bash
+make setup          # Install all dependencies
+make dev            # Start development servers
+make dev-web        # Start web development server
+make build          # Build desktop app
+make build-web      # Build web app
+make clean          # Clean build artifacts
+make test           # Run tests
+```
+
+## Generate OpenAPI Client
+
+After starting the backend server, generate the TypeScript API client:
+
+```bash
+./scripts/generate-api.sh
+# or
+bun run generate:api
+```
+
+This downloads the OpenAPI schema and generates the TypeScript client in `app/src/lib/api/`
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Architecture" icon="diagram-project" href="/development/architecture">
+    Understand the system architecture
+  </Card>
+  <Card title="Contributing" icon="code-pull-request" href="/development/contributing">
+    Read the contribution guidelines
+  </Card>
+  <Card title="Building" icon="hammer" href="/development/building">
+    Learn how to build production releases
+  </Card>
+  <Card title="API Reference" icon="code" href="/api/overview">
+    Explore the REST API
+  </Card>
+</CardGroup>
+
+## Troubleshooting
+
+<AccordionGroup>
+  <Accordion title="Backend won't start">
+    - Check Python version (must be 3.11+)
+    - Ensure virtual environment is activated
+    - Verify all dependencies are installed: `pip install -r requirements.txt`
+    - Check if port 17493 is available
+  </Accordion>
+
+  <Accordion title="Tauri build fails">
+    - Ensure Rust is installed: `rustc --version`
+    - Clean the build: `cd tauri/src-tauri && cargo clean`
+    - Try rebuilding: `bun run dev`
+  </Accordion>
+
+  <Accordion title="OpenAPI client generation fails">
+    - Ensure backend is running: `curl http://localhost:17493/openapi.json`
+    - Check network connectivity
+    - Verify the backend is accessible at localhost:17493
+  </Accordion>
+</AccordionGroup>
+
+See the full [Troubleshooting Guide](/guides/troubleshooting) for more issues and solutions.

docs/developer/stories.mdx

@@ -0,0 +1,320 @@
+---
+title: "Stories & Timeline"
+description: "How the multi-voice timeline editor works in Voicebox"
+---
+
+## Overview
+
+Stories allow users to arrange multiple voice generations on a timeline to create multi-voice narratives. The system supports tracks, trimming, splitting, and audio mixing.
+
+## Architecture
+
+**Story:** A container that holds story items with metadata.
+
+**Story Item:** Links a generation to a story with timeline position, track, and trim data.
+
+**Export:** Combines all items into a single mixed audio file.
+
+## Data Model
+
+### Story Table
+
+```python
+class Story(Base):
+    __tablename__ = "stories"
+    
+    id = Column(String, primary_key=True)
+    name = Column(String, nullable=False)
+    description = Column(Text)
+    created_at = Column(DateTime)
+    updated_at = Column(DateTime)
+```
+
+### StoryItem Table
+
+```python
+class StoryItem(Base):
+    __tablename__ = "story_items"
+    
+    id = Column(String, primary_key=True)
+    story_id = Column(String, ForeignKey("stories.id"))
+    generation_id = Column(String, ForeignKey("generations.id"))
+    start_time_ms = Column(Integer, default=0)  # Timeline position
+    track = Column(Integer, default=0)          # Track number
+    trim_start_ms = Column(Integer, default=0)  # Trim from start
+    trim_end_ms = Column(Integer, default=0)    # Trim from end
+    created_at = Column(DateTime)
+```
+
+## Timeline Concepts
+
+### Start Time
+
+`start_time_ms` defines when an item begins on the timeline:
+
+```
+Timeline (ms):  0----1000----2000----3000----4000
+Item 1:         [======]
+Item 2:                      [==========]
+Item 3:                [====]
+```
+
+### Tracks
+
+Multiple tracks allow overlapping audio:
+
+```
+Track 0:  [Item 1]        [Item 3]
+Track 1:            [Item 2]
+```
+
+### Trimming
+
+Trim values cut audio from the start or end without destroying the original:
+
+```
+Original:    [=========AUDIO=========]
+trim_start:   ^^
+trim_end:                           ^^
+Result:        [=====AUDIO=====]
+```
+
+## Core Operations
+
+### Adding Items
+
+When adding a generation to a story:
+
+```python
+async def add_item_to_story(
+    story_id: str,
+    data: StoryItemCreate,
+    db: Session,
+) -> StoryItemDetail:
+    # Calculate start time if not provided
+    if data.start_time_ms is None:
+        # Find the end of all existing items
+        existing_items = get_items_with_durations(story_id, db)
+        max_end_time_ms = max(
+            item.start_time_ms + int(gen.duration * 1000)
+            for item, gen in existing_items
+        )
+        start_time_ms = max_end_time_ms + 200  # 200ms gap
+    
+    # Create the item
+    item = DBStoryItem(
+        id=str(uuid.uuid4()),
+        story_id=story_id,
+        generation_id=data.generation_id,
+        start_time_ms=start_time_ms,
+        track=data.track or 0,
+    )
+    db.add(item)
+    db.commit()
+```
+
+### Moving Items
+
+Update position and/or track:
+
+```python
+async def move_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemMove,
+    db: Session,
+) -> StoryItemDetail:
+    item = get_item(story_id, item_id, db)
+    
+    item.start_time_ms = data.start_time_ms
+    item.track = data.track
+    
+    db.commit()
+```
+
+### Trimming Items
+
+Non-destructive trimming:
+
+```python
+async def trim_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemTrim,
+    db: Session,
+) -> StoryItemDetail:
+    item = get_item(story_id, item_id, db)
+    generation = get_generation(item.generation_id, db)
+    
+    # Validate trim doesn't exceed duration
+    max_duration_ms = int(generation.duration * 1000)
+    if data.trim_start_ms + data.trim_end_ms >= max_duration_ms:
+        return None  # Invalid trim
+    
+    item.trim_start_ms = data.trim_start_ms
+    item.trim_end_ms = data.trim_end_ms
+    
+    db.commit()
+```
+
+### Splitting Items
+
+Split one item into two at a specific time:
+
+```python
+async def split_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemSplit,
+    db: Session,
+) -> List[StoryItemDetail]:
+    item = get_item(story_id, item_id, db)
+    generation = get_generation(item.generation_id, db)
+    
+    # Calculate split point
+    current_trim_start = item.trim_start_ms
+    current_trim_end = item.trim_end_ms
+    original_duration_ms = int(generation.duration * 1000)
+    absolute_split_ms = current_trim_start + data.split_time_ms
+    
+    # Update original: trim from end
+    item.trim_end_ms = original_duration_ms - absolute_split_ms
+    
+    # Create new item: trim from start
+    new_item = DBStoryItem(
+        generation_id=item.generation_id,  # Same generation
+        start_time_ms=item.start_time_ms + data.split_time_ms,
+        track=item.track,
+        trim_start_ms=absolute_split_ms,
+        trim_end_ms=current_trim_end,
+    )
+    
+    db.add(new_item)
+    db.commit()
+    
+    return [item, new_item]
+```
+
+### Duplicating Items
+
+Create a copy with all properties:
+
+```python
+async def duplicate_story_item(
+    story_id: str,
+    item_id: str,
+    db: Session,
+) -> StoryItemDetail:
+    original = get_item(story_id, item_id, db)
+    generation = get_generation(original.generation_id, db)
+    
+    # Calculate effective duration for positioning
+    effective_duration_ms = (
+        int(generation.duration * 1000) 
+        - original.trim_start_ms 
+        - original.trim_end_ms
+    )
+    
+    # Place copy after original with 200ms gap
+    new_item = DBStoryItem(
+        generation_id=original.generation_id,
+        start_time_ms=original.start_time_ms + effective_duration_ms + 200,
+        track=original.track,
+        trim_start_ms=original.trim_start_ms,
+        trim_end_ms=original.trim_end_ms,
+    )
+    
+    db.add(new_item)
+    db.commit()
+```
+
+## Audio Export
+
+### Mixing Algorithm
+
+The export function mixes all items into a single audio file:
+
+```python
+async def export_story_audio(story_id: str, db: Session) -> bytes:
+    items = get_all_items_with_generations(story_id, db)
+    
+    # Calculate total duration
+    max_end_time_ms = max(
+        data['start_time_ms'] + data['duration_ms']
+        for data in audio_data
+    )
+    
+    # Create output buffer
+    total_samples = int((max_end_time_ms / 1000.0) * sample_rate)
+    final_audio = np.zeros(total_samples, dtype=np.float32)
+    
+    # Mix each item at its position
+    for data in audio_data:
+        audio = data['audio']
+        start_sample = int((data['start_time_ms'] / 1000.0) * sample_rate)
+        
+        # Apply trim
+        trimmed_audio = audio[trim_start_sample:len(audio) - trim_end_sample]
+        
+        # Add to buffer (overlapping items sum together)
+        final_audio[start_sample:start_sample + len(trimmed_audio)] += trimmed_audio
+    
+    # Normalize to prevent clipping
+    max_val = np.abs(final_audio).max()
+    if max_val > 1.0:
+        final_audio = final_audio / max_val
+    
+    return audio_to_bytes(final_audio, sample_rate)
+```
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/stories` | List all stories |
+| POST | `/stories` | Create a story |
+| GET | `/stories/{id}` | Get story with items |
+| PUT | `/stories/{id}` | Update story metadata |
+| DELETE | `/stories/{id}` | Delete story |
+| POST | `/stories/{id}/items` | Add item to story |
+| DELETE | `/stories/{id}/items/{item_id}` | Remove item |
+| PUT | `/stories/{id}/items/{item_id}/move` | Move item |
+| PUT | `/stories/{id}/items/{item_id}/trim` | Trim item |
+| POST | `/stories/{id}/items/{item_id}/split` | Split item |
+| POST | `/stories/{id}/items/{item_id}/duplicate` | Duplicate item |
+| PUT | `/stories/{id}/items/times` | Batch update times |
+| PUT | `/stories/{id}/items/reorder` | Reorder items |
+| GET | `/stories/{id}/export-audio` | Export mixed audio |
+
+## Response Schemas
+
+### StoryItemDetail
+
+```json
+{
+  "id": "item_uuid",
+  "story_id": "story_uuid",
+  "generation_id": "generation_uuid",
+  "start_time_ms": 1500,
+  "track": 0,
+  "trim_start_ms": 200,
+  "trim_end_ms": 100,
+  "profile_id": "profile_uuid",
+  "profile_name": "Narrator",
+  "text": "Hello world",
+  "audio_path": "/path/to/audio.wav",
+  "duration": 2.5,
+  "created_at": "2024-01-15T10:30:00Z"
+}
+```
+
+## Frontend Integration
+
+The timeline UI needs to:
+
+1. **Fetch story** with all items
+2. **Render waveforms** for each item
+3. **Handle drag/drop** to move items
+4. **Handle edge drag** for trimming
+5. **Sync playhead** across all tracks
+6. **Export** when user clicks download

docs/developer/transcription.mdx

@@ -0,0 +1,299 @@
+---
+title: "Transcription"
+description: "How Whisper-based audio transcription works in Voicebox"
+---
+
+## Overview
+
+Voicebox uses OpenAI's Whisper model for automatic speech recognition (ASR). This powers the transcription feature for creating reference text from audio recordings.
+
+## Architecture
+
+The transcription system is built around the `WhisperModel` class:
+
+**Model Loading:** Lazy loading with HuggingFace Hub download.
+
+**Audio Processing:** Resampling and preprocessing for Whisper.
+
+**Inference:** Running transcription with optional language hints.
+
+## WhisperModel Class
+
+```python
+class WhisperModel:
+    def __init__(self, model_size: str = "base"):
+        self.model = None
+        self.processor = None
+        self.model_size = model_size
+        self.device = self._get_device()
+```
+
+### Model Sizes
+
+| Size | Parameters | VRAM | Speed | Quality |
+|------|------------|------|-------|---------|
+| tiny | 39M | ~1GB | Fastest | Basic |
+| base | 74M | ~1GB | Fast | Good |
+| small | 244M | ~2GB | Medium | Better |
+| medium | 769M | ~5GB | Slow | High |
+| large | 1550M | ~10GB | Slowest | Best |
+
+Default is `base` for balance of speed and quality.
+
+## Model Loading
+
+Models are downloaded from HuggingFace Hub:
+
+```python
+def load_model(self, model_size: Optional[str] = None):
+    from transformers import WhisperProcessor, WhisperForConditionalGeneration
+    
+    model_name = f"openai/whisper-{model_size}"
+    
+    # Track download progress
+    progress_manager = get_progress_manager()
+    task_manager = get_task_manager()
+    task_manager.start_download(f"whisper-{model_size}")
+    
+    # Load processor and model
+    with tracker.patch_download():
+        self.processor = WhisperProcessor.from_pretrained(model_name)
+        self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
+    
+    self.model.to(self.device)
+    
+    # Mark complete
+    progress_manager.mark_complete(f"whisper-{model_size}")
+    task_manager.complete_download(f"whisper-{model_size}")
+```
+
+### Async Loading
+
+Like TTS, loading runs in a thread pool:
+
+```python
+async def load_model_async(self, model_size: Optional[str] = None):
+    if self.model is not None and self.model_size == model_size:
+        return
+    await asyncio.to_thread(self.load_model, model_size)
+```
+
+## Transcription
+
+### Basic Transcription
+
+```python
+async def transcribe(
+    self,
+    audio_path: str,
+    language: Optional[str] = None,
+) -> str:
+    await self.load_model_async()
+    
+    def _transcribe_sync():
+        # Load and resample to 16kHz (Whisper requirement)
+        audio, sr = load_audio(audio_path, sample_rate=16000)
+        
+        # Process audio
+        inputs = self.processor(
+            audio,
+            sampling_rate=16000,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(self.device)
+        
+        # Set language hint if provided
+        forced_decoder_ids = None
+        if language:
+            forced_decoder_ids = self.processor.get_decoder_prompt_ids(
+                language=language,
+                task="transcribe",
+            )
+        
+        # Generate
+        with torch.no_grad():
+            predicted_ids = self.model.generate(
+                inputs["input_features"],
+                forced_decoder_ids=forced_decoder_ids,
+            )
+        
+        # Decode
+        transcription = self.processor.batch_decode(
+            predicted_ids,
+            skip_special_tokens=True,
+        )[0]
+        
+        return transcription.strip()
+    
+    return await asyncio.to_thread(_transcribe_sync)
+```
+
+### Supported Languages
+
+Whisper supports 99+ languages. Common ones in Voicebox:
+
+| Code | Language |
+|------|----------|
+| en | English |
+| zh | Chinese |
+| ja | Japanese |
+| ko | Korean |
+| de | German |
+| fr | French |
+| ru | Russian |
+| pt | Portuguese |
+| es | Spanish |
+| it | Italian |
+
+### Language Detection
+
+When no language is specified, Whisper auto-detects:
+
+```python
+# Without language hint - auto-detect
+transcription = await whisper.transcribe(audio_path)
+
+# With language hint - more accurate for short clips
+transcription = await whisper.transcribe(audio_path, language="en")
+```
+
+## Transcription with Timestamps
+
+For advanced use cases, word-level timestamps are available:
+
+```python
+async def transcribe_with_timestamps(
+    self,
+    audio_path: str,
+    language: Optional[str] = None,
+) -> List[Dict[str, any]]:
+    await self.load_model_async()
+    
+    def _transcribe_timestamps_sync():
+        audio, sr = load_audio(audio_path, sample_rate=16000)
+        inputs = self.processor(audio, sampling_rate=16000, return_tensors="pt")
+        
+        with torch.no_grad():
+            predicted_ids = self.model.generate(
+                inputs["input_features"],
+                return_timestamps=True,
+            )
+        
+        # Parse timestamps
+        return [
+            {
+                "text": transcription,
+                "start": 0.0,
+                "end": len(audio) / sr,
+            }
+        ]
+    
+    return await asyncio.to_thread(_transcribe_timestamps_sync)
+```
+
+## Memory Management
+
+### Unloading
+
+Free memory when not needed:
+
+```python
+def unload_model(self):
+    if self.model is not None:
+        del self.model
+        del self.processor
+        self.model = None
+        self.processor = None
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+```
+
+### Global Instance
+
+A singleton pattern manages the model:
+
+```python
+_whisper_model: Optional[WhisperModel] = None
+
+def get_whisper_model() -> WhisperModel:
+    global _whisper_model
+    if _whisper_model is None:
+        _whisper_model = WhisperModel()
+    return _whisper_model
+```
+
+## Audio Preprocessing
+
+### Resampling
+
+Whisper requires 16kHz audio:
+
+```python
+audio, sr = load_audio(audio_path, sample_rate=16000)
+```
+
+### Format Support
+
+The `load_audio` utility handles:
+- WAV
+- MP3
+- FLAC
+- OGG
+- M4A
+
+All formats are converted to mono 16kHz.
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/transcribe` | Transcribe audio file |
+
+### Request
+
+Multipart form data:
+
+```
+POST /transcribe
+Content-Type: multipart/form-data
+
+file: <audio_file>
+language: en  (optional)
+```
+
+### Response
+
+```json
+{
+  "text": "Hello, this is a test transcription.",
+  "duration": 3.5
+}
+```
+
+## Use Cases
+
+### Reference Text for Voice Cloning
+
+1. User records audio sample
+2. Audio is sent to `/transcribe`
+3. Transcription becomes `reference_text`
+4. Both are added to voice profile
+
+### Quality Tips
+
+- Provide language hint for short audio
+- Use clean audio with minimal noise
+- Longer audio (>5s) improves accuracy
+- Consider `small` or `medium` model for better quality
+
+## Error Handling
+
+Common issues:
+
+| Error | Cause | Solution |
+|-------|-------|----------|
+| Model not found | First run, download failed | Retry with network |
+| OOM | Model too large | Use smaller model |
+| Empty result | No speech detected | Check audio has speech |
+| Wrong language | Auto-detect failed | Provide language hint |

docs/developer/tts-generation.mdx

@@ -0,0 +1,283 @@
+---
+title: "TTS Generation"
+description: "How text-to-speech generation works in Voicebox"
+---
+
+## Overview
+
+Voicebox uses Qwen3-TTS for voice cloning and text-to-speech generation. The TTS module handles model loading, voice prompt creation, and audio synthesis.
+
+## Architecture
+
+The TTS system is built around the `TTSModel` class which manages:
+
+**Model Loading:** Lazy loading with automatic HuggingFace Hub download.
+
+**Voice Prompts:** Converting reference audio into embeddings.
+
+**Generation:** Synthesizing speech from text using voice prompts.
+
+## TTSModel Class
+
+```python
+class TTSModel:
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self.device = self._get_device()  # cuda, mps, or cpu
+```
+
+### Device Selection
+
+The model automatically selects the best available device:
+
+```python
+def _get_device(self) -> str:
+    if torch.cuda.is_available():
+        return "cuda"
+    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        return "cpu"  # MPS can have issues, use CPU for stability
+    return "cpu"
+```
+
+## Model Loading
+
+Models are downloaded from HuggingFace Hub on first use:
+
+```python
+def load_model(self, model_size: Optional[str] = None):
+    # Model IDs on HuggingFace Hub
+    hf_model_map = {
+        "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
+        "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
+    }
+    
+    # Load with progress tracking
+    with tracker.patch_download():
+        self.model = Qwen3TTSModel.from_pretrained(
+            model_path,
+            device_map=self.device,
+            torch_dtype=torch.bfloat16,  # float32 on CPU
+        )
+```
+
+### Async Loading
+
+Loading runs in a thread pool to avoid blocking the event loop:
+
+```python
+async def load_model_async(self, model_size: Optional[str] = None):
+    if self.model is not None and self._current_model_size == model_size:
+        return
+    await asyncio.to_thread(self.load_model, model_size)
+```
+
+## Voice Prompt Creation
+
+Voice prompts are created from reference audio and cached for reuse:
+
+```python
+async def create_voice_prompt(
+    self,
+    audio_path: str,
+    reference_text: str,
+    use_cache: bool = True,
+) -> Tuple[dict, bool]:
+    await self.load_model_async()
+    
+    # Check cache
+    if use_cache:
+        cache_key = get_cache_key(audio_path, reference_text)
+        cached = get_cached_voice_prompt(cache_key)
+        if cached:
+            return cached, True
+    
+    # Create prompt (blocking, run in thread pool)
+    voice_prompt = await asyncio.to_thread(
+        self.model.create_voice_clone_prompt,
+        ref_audio=audio_path,
+        ref_text=reference_text,
+    )
+    
+    # Cache the result
+    cache_voice_prompt(cache_key, voice_prompt)
+    return voice_prompt, False
+```
+
+### Combining Multiple Samples
+
+When a profile has multiple samples, they're combined:
+
+```python
+async def combine_voice_prompts(
+    self,
+    audio_paths: List[str],
+    reference_texts: List[str],
+) -> Tuple[np.ndarray, str]:
+    combined_audio = []
+    
+    for audio_path in audio_paths:
+        audio, sr = load_audio(audio_path)
+        audio = normalize_audio(audio)
+        combined_audio.append(audio)
+    
+    # Concatenate and normalize
+    mixed = np.concatenate(combined_audio)
+    mixed = normalize_audio(mixed)
+    
+    # Combine texts
+    combined_text = " ".join(reference_texts)
+    
+    return mixed, combined_text
+```
+
+## Speech Generation
+
+The core generation function:
+
+```python
+async def generate(
+    self,
+    text: str,
+    voice_prompt: dict,
+    language: str = "en",
+    seed: Optional[int] = None,
+    instruct: Optional[str] = None,
+) -> Tuple[np.ndarray, int]:
+    await self.load_model_async()
+    
+    def _generate_sync():
+        # Set seed for reproducibility
+        if seed is not None:
+            torch.manual_seed(seed)
+        
+        # Generate audio
+        wavs, sample_rate = self.model.generate_voice_clone(
+            text=text,
+            voice_clone_prompt=voice_prompt,
+            instruct=instruct,  # Natural language delivery control
+        )
+        return wavs[0], sample_rate
+    
+    # Run in thread pool
+    return await asyncio.to_thread(_generate_sync)
+```
+
+### Instruct Feature
+
+The `instruct` parameter allows natural language control over speech delivery:
+
+```python
+# Examples:
+instruct = "Speak slowly and clearly"
+instruct = "Sound excited and enthusiastic"
+instruct = "Whisper softly"
+```
+
+## Caching Strategy
+
+Voice prompts are cached to avoid recomputation:
+
+```python
+def get_cache_key(audio_path: str, reference_text: str) -> str:
+    """Generate cache key from audio hash and text."""
+    audio_hash = hashlib.md5(Path(audio_path).read_bytes()).hexdigest()
+    text_hash = hashlib.md5(reference_text.encode()).hexdigest()
+    return f"{audio_hash}_{text_hash}"
+```
+
+Cache is stored in `data/cache/voice_prompts/`.
+
+## Memory Management
+
+### Unloading Models
+
+Free VRAM/RAM when not needed:
+
+```python
+def unload_model(self):
+    if self.model is not None:
+        del self.model
+        self.model = None
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+```
+
+### Model Switching
+
+When switching between model sizes (1.7B ↔ 0.6B):
+
+```python
+# Unload existing model first
+if self.model is not None and self._current_model_size != model_size:
+    self.unload_model()
+```
+
+## Generation Flow
+
+1. **Request** → Validate text and profile ID
+2. **Profile** → Load profile samples from database
+3. **Voice Prompt** → Create or retrieve cached prompt
+4. **Generate** → Run TTS inference
+5. **Save** → Write audio to generations directory
+6. **Record** → Create history entry in database
+7. **Response** → Return audio path and metadata
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/generate` | Generate speech from text |
+| GET | `/audio/{id}` | Serve generated audio file |
+
+### Request Schema
+
+```json
+{
+  "profile_id": "uuid",
+  "text": "Text to synthesize",
+  "language": "en",
+  "seed": 42,
+  "model_size": "1.7B",
+  "instruct": "Speak clearly"
+}
+```
+
+### Response Schema
+
+```json
+{
+  "id": "generation_uuid",
+  "profile_id": "profile_uuid",
+  "text": "Text to synthesize",
+  "language": "en",
+  "audio_path": "/path/to/audio.wav",
+  "duration": 3.5,
+  "seed": 42,
+  "instruct": "Speak clearly",
+  "created_at": "2024-01-15T10:30:00Z"
+}
+```
+
+## Performance Considerations
+
+### GPU Acceleration
+
+- CUDA provides fastest inference
+- MPS (Apple Silicon) has stability issues, uses CPU fallback
+- CPU inference is slower but always works
+
+### Batch Size
+
+Currently generates one utterance at a time. For long texts, consider:
+- Splitting into sentences
+- Sequential generation
+- Concatenating results
+
+### Memory Usage
+
+| Model | VRAM/RAM Required |
+|-------|-------------------|
+| 0.6B | ~2GB |
+| 1.7B | ~6GB |

docs/developer/voice-profiles.mdx

@@ -0,0 +1,202 @@
+---
+title: "Voice Profiles"
+description: "How voice profile management works in Voicebox"
+---
+
+## Overview
+
+Voice profiles are the foundation of Voicebox's voice cloning capability. Each profile stores reference audio samples and metadata that the TTS model uses to clone a voice.
+
+## Architecture
+
+The voice profile system consists of three main components:
+
+**Database Layer:** SQLite tables store profile metadata and sample references.
+
+**File Storage:** Audio samples are stored on disk in a structured directory format.
+
+**Profile Module:** The `profiles.py` module provides the business logic for CRUD operations.
+
+## Data Model
+
+### VoiceProfile Table
+
+```python
+class VoiceProfile(Base):
+    __tablename__ = "profiles"
+    
+    id = Column(String, primary_key=True)
+    name = Column(String, unique=True, nullable=False)
+    description = Column(Text)
+    language = Column(String, default="en")
+    created_at = Column(DateTime)
+    updated_at = Column(DateTime)
+```
+
+### ProfileSample Table
+
+```python
+class ProfileSample(Base):
+    __tablename__ = "profile_samples"
+    
+    id = Column(String, primary_key=True)
+    profile_id = Column(String, ForeignKey("profiles.id"))
+    audio_path = Column(String, nullable=False)
+    reference_text = Column(Text, nullable=False)
+```
+
+## File Structure
+
+Profiles are stored in the data directory:
+
+```
+data/
+└── profiles/
+    └── {profile_id}/
+        ├── {sample_id_1}.wav
+        ├── {sample_id_2}.wav
+        └── ...
+```
+
+## Core Functions
+
+### Creating a Profile
+
+```python
+async def create_profile(data: VoiceProfileCreate, db: Session) -> VoiceProfileResponse:
+    # 1. Create database record
+    db_profile = DBVoiceProfile(
+        id=str(uuid.uuid4()),
+        name=data.name,
+        description=data.description,
+        language=data.language,
+    )
+    db.add(db_profile)
+    db.commit()
+    
+    # 2. Create profile directory
+    profile_dir = profiles_dir / db_profile.id
+    profile_dir.mkdir(parents=True, exist_ok=True)
+    
+    return VoiceProfileResponse.model_validate(db_profile)
+```
+
+### Adding Samples
+
+When a sample is added, the audio is validated and copied to the profile directory:
+
+```python
+async def add_profile_sample(
+    profile_id: str,
+    audio_path: str,
+    reference_text: str,
+    db: Session,
+) -> ProfileSampleResponse:
+    # 1. Validate audio (duration, format, quality)
+    is_valid, error_msg = validate_reference_audio(audio_path)
+    if not is_valid:
+        raise ValueError(f"Invalid reference audio: {error_msg}")
+    
+    # 2. Copy to profile directory
+    sample_id = str(uuid.uuid4())
+    dest_path = profile_dir / f"{sample_id}.wav"
+    audio, sr = load_audio(audio_path)
+    save_audio(audio, str(dest_path), sr)
+    
+    # 3. Create database record
+    db_sample = DBProfileSample(
+        id=sample_id,
+        profile_id=profile_id,
+        audio_path=str(dest_path),
+        reference_text=reference_text,
+    )
+    db.add(db_sample)
+    db.commit()
+```
+
+### Voice Prompt Creation
+
+When generating speech, samples are combined into a voice prompt:
+
+```python
+async def create_voice_prompt_for_profile(
+    profile_id: str,
+    db: Session,
+) -> dict:
+    samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
+    
+    if len(samples) == 1:
+        # Single sample - use directly
+        voice_prompt, _ = await tts_model.create_voice_prompt(
+            sample.audio_path,
+            sample.reference_text,
+        )
+    else:
+        # Multiple samples - combine them
+        combined_audio, combined_text = await tts_model.combine_voice_prompts(
+            [s.audio_path for s in samples],
+            [s.reference_text for s in samples],
+        )
+        voice_prompt, _ = await tts_model.create_voice_prompt(
+            combined_audio_path,
+            combined_text,
+        )
+    
+    return voice_prompt
+```
+
+## Audio Validation
+
+Reference audio is validated before being accepted:
+
+- **Duration:** 3-30 seconds recommended
+- **Format:** WAV, MP3, FLAC, OGG supported
+- **Sample Rate:** Resampled to 24kHz
+- **Channels:** Converted to mono if stereo
+
+## Export/Import
+
+Profiles can be exported as ZIP archives for sharing:
+
+```
+profile_export.zip
+├── profile.json     # Metadata
+├── samples/
+│   ├── sample_1.wav
+│   └── sample_1.json  # Reference text
+└── ...
+```
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/profiles` | List all profiles |
+| POST | `/profiles` | Create a profile |
+| GET | `/profiles/{id}` | Get profile by ID |
+| PUT | `/profiles/{id}` | Update profile |
+| DELETE | `/profiles/{id}` | Delete profile |
+| GET | `/profiles/{id}/samples` | Get profile samples |
+| POST | `/profiles/{id}/samples` | Add sample to profile |
+| PUT | `/profiles/samples/{id}` | Update sample text |
+| DELETE | `/profiles/samples/{id}` | Delete sample |
+| GET | `/profiles/{id}/export` | Export as ZIP |
+| POST | `/profiles/import` | Import from ZIP |
+
+## Best Practices
+
+### Sample Quality
+
+- Use clean audio with minimal background noise
+- Ensure the reference text exactly matches what is spoken
+- Multiple samples (3-5) improve voice cloning quality
+
+### Language Matching
+
+- Set the profile language to match the reference audio
+- Supported languages: en, zh, ja, ko, de, fr, ru, pt, es, it
+
+### Naming Conventions
+
+- Use descriptive names that identify the voice
+- Avoid special characters that may cause filesystem issues

docs/mint.json

@@ -0,0 +1,102 @@
+{
+  "$schema": "https://mintlify.com/schema.json",
+  "name": "Voicebox",
+  "logo": {
+    "light": "/logo/icon-light.png",
+    "dark": "/logo/icon-dark.png"
+  },
+  "favicon": "/favicon.png",
+  "colors": {
+    "primary": "#BF9E40",
+    "light": "#D4B560",
+    "dark": "#A68A35"
+  },
+  "styles": {
+    "css": ["/custom.css"]
+  },
+  "anchors": [
+    {
+      "name": "Overview",
+      "icon": "book-open",
+      "url": "overview"
+    },
+    {
+      "name": "API",
+      "icon": "code",
+      "url": "api"
+    },
+    {
+      "name": "Developer",
+      "icon": "book-open-cover",
+      "url": "developer"
+    },
+    {
+      "name": "GitHub",
+      "icon": "github",
+      "url": "https://github.com/jamiepine/voicebox"
+    }
+  ],
+  "navigation": [
+    {
+      "group": "Getting Started",
+      "icon": "rocket",
+      "pages": ["overview/introduction", "overview/installation", "overview/quick-start"]
+    },
+    {
+      "group": "Features",
+      "icon": "sparkles",
+      "pages": [
+        "overview/voice-cloning",
+        "overview/stories-editor",
+        "overview/recording-transcription",
+        "overview/generation-history",
+        "overview/remote-mode"
+      ]
+    },
+    {
+      "group": "User Guides",
+      "icon": "compass",
+      "pages": [
+        "overview/creating-voice-profiles",
+        "overview/generating-speech",
+        "overview/building-stories",
+        "overview/troubleshooting"
+      ]
+    },
+    {
+      "group": "Development",
+      "icon": "wrench",
+      "pages": [
+        "developer/setup",
+        "developer/architecture",
+        "developer/contributing",
+        "developer/building",
+        "developer/autoupdater"
+      ]
+    },
+    {
+      "group": "API Reference",
+      "icon": "code",
+      "pages": [
+        "api/overview",
+        "api/authentication",
+        "api/voice-profiles",
+        "api/generation",
+        "api/recordings"
+      ]
+    },
+    {
+      "group": "Architecture",
+      "icon": "book-open-cover",
+      "pages": [
+        "developer/voice-profiles",
+        "developer/tts-generation",
+        "developer/history",
+        "developer/stories",
+        "developer/transcription",
+        "developer/audio-channels",
+        "developer/model-management"
+      ]
+    }
+  ]
+}

docs/nixpacks.toml

@@ -0,0 +1,11 @@
+[phases.setup]
+nixPkgs = ["nodejs_20", "bun"]
+
+[phases.install]
+cmds = ["bun install"]
+
+[phases.build]
+cmds = ["bun run build"]
+
+[start]
+cmd = "bun run start"

docs/overview/building-stories.mdx

@@ -0,0 +1,37 @@
+---
+title: "Building Stories"
+description: "Create multi-voice narratives with the Stories Editor"
+---
+
+## Getting Started
+
+The Stories Editor is perfect for creating podcasts, audiobooks, and multi-speaker content.
+
+<Steps>
+  <Step title="Create Story">
+    **Stories** → **+ New Story**
+  </Step>
+  <Step title="Add Tracks">
+    Create tracks for each speaker
+  </Step>
+  <Step title="Add Clips">
+    Generate or drag audio to tracks
+  </Step>
+  <Step title="Arrange">
+    Position and trim clips on timeline
+  </Step>
+  <Step title="Export">
+    Render final audio
+  </Step>
+</Steps>
+
+## Use Cases
+
+- Multi-host podcasts
+- Audiobook narration with character voices
+- Game dialogue scenes
+- Educational content with multiple speakers
+
+## Coming Soon
+
+Full timeline editor documentation will be added as features are finalized.

docs/overview/creating-voice-profiles.mdx

@@ -0,0 +1,296 @@
+---
+title: "Creating Voice Profiles"
+description: "Advanced guide to creating high-quality voice profiles"
+---
+
+## Overview
+
+Voice profiles are the foundation of voice cloning in Voicebox. This guide covers best practices for creating professional-quality voice profiles.
+
+## Quick Start
+
+<Steps>
+  <Step title="Prepare Audio">
+    10-30 seconds of clear speech
+  </Step>
+  <Step title="Create Profile">
+    **Profiles** → **+ New Profile**
+  </Step>
+  <Step title="Upload Sample">
+    Add your audio file
+  </Step>
+  <Step title="Generate">
+    Use the profile to generate speech
+  </Step>
+</Steps>
+
+## Audio Requirements
+
+### Ideal Sample Characteristics
+
+<CardGroup cols={2}>
+  <Card title="Duration" icon="clock">
+    **10-30 seconds**
+
+    Too short: Poor quality
+    Too long: Unnecessary
+  </Card>
+  <Card title="Clarity" icon="volume">
+    **Clear speech**
+
+    No background noise
+    No music or overlapping voices
+  </Card>
+  <Card title="Quality" icon="sparkles">
+    **High fidelity**
+
+    44.1kHz or 48kHz sample rate
+    Minimal compression
+  </Card>
+  <Card title="Content" icon="microphone">
+    **Natural speech**
+
+    Conversational tone
+    Complete sentences
+  </Card>
+</CardGroup>
+
+### File Formats
+
+Supported formats:
+- **WAV** (recommended) - Lossless quality
+- **MP3** - Acceptable, minimal compression
+- **M4A** - Acceptable
+- **FLAC** - Lossless alternative
+
+<Tip>
+  Use WAV for best results. Avoid heavily compressed formats.
+</Tip>
+
+## Recording Tips
+
+### Environment
+
+<AccordionGroup>
+  <Accordion title="Quiet Space">
+    - Record in a quiet room
+    - Turn off fans, AC, appliances
+    - Close windows to reduce outside noise
+    - Use soft furnishings to reduce echo
+  </Accordion>
+
+  <Accordion title="Microphone Placement">
+    - 6-12 inches from mouth
+    - Slight angle to reduce plosives (p, b, t)
+    - Use a pop filter if available
+    - Maintain consistent distance
+  </Accordion>
+
+  <Accordion title="Recording Settings">
+    - 44.1kHz or 48kHz sample rate
+    - 16-bit or 24-bit depth
+    - Mono is fine (stereo will be converted)
+    - Avoid automatic gain control
+  </Accordion>
+</AccordionGroup>
+
+### Speaking
+
+- **Natural pace** - Don't rush or speak too slowly
+- **Clear articulation** - Pronounce words clearly
+- **Consistent volume** - Maintain steady loudness
+- **Normal tone** - Speak as you normally would
+- **Complete sentences** - Avoid fragments or "ums"
+
+## Multiple Samples
+
+Adding multiple samples can significantly improve quality:
+
+### Why Multiple Samples?
+
+<CardGroup cols={2}>
+  <Card title="Robustness" icon="shield">
+    Model learns a more complete representation
+  </Card>
+  <Card title="Versatility" icon="palette">
+    Handles different speaking styles better
+  </Card>
+  <Card title="Quality" icon="star">
+    Reduces artifacts and improves naturalness
+  </Card>
+  <Card title="Consistency" icon="check">
+    More reliable across different texts
+  </Card>
+</CardGroup>
+
+### Sample Variety
+
+Consider adding samples with:
+
+1. **Different tones**
+   - Casual conversation
+   - Professional/formal
+   - Excited/enthusiastic
+   - Calm/serious
+
+2. **Different content**
+   - Narratives
+   - Questions
+   - Statements
+   - Emotions (happy, sad, neutral)
+
+3. **Different recording conditions**
+   - Studio quality
+   - Phone call quality (if needed)
+   - Room acoustics
+
+<Warning>
+  All samples should be from the **same speaker**. Mixing voices will produce poor results.
+</Warning>
+
+## Processing Existing Audio
+
+If you have existing audio (podcasts, videos, etc.):
+
+### Extracting Clean Segments
+
+<Steps>
+  <Step title="Find Clean Speech">
+    Look for segments with:
+    - Just the target speaker
+    - No background music
+    - Minimal noise
+  </Step>
+
+  <Step title="Use Audio Editor">
+    Tools like Audacity or Adobe Audition:
+    - Cut out clean 10-30s segments
+    - Remove silence at start/end
+    - Normalize volume if needed
+  </Step>
+
+  <Step title="Export as WAV">
+    Save as high-quality WAV file
+  </Step>
+</Steps>
+
+### Noise Reduction
+
+If you have light background noise:
+
+```
+1. Use noise reduction in Audacity:
+   - Select noise-only section
+   - Get Noise Profile
+   - Select full audio
+   - Apply noise reduction (gentle settings)
+
+2. Avoid over-processing:
+   - Can introduce artifacts
+   - May reduce voice quality
+```
+
+## Testing & Iteration
+
+### Test Your Profile
+
+After creating a profile:
+
+<Steps>
+  <Step title="Generate Test">
+    Generate a simple phrase:
+    ```
+    "Hello, this is a test of my voice profile."
+    ```
+  </Step>
+
+  <Step title="Evaluate Quality">
+    Listen for:
+    - Natural tone
+    - Clear pronunciation
+    - Proper prosody
+    - Lack of artifacts
+  </Step>
+
+  <Step title="Iterate">
+    If quality is poor:
+    - Add more samples
+    - Try different source audio
+    - Check sample quality
+  </Step>
+</Steps>
+
+### Common Issues
+
+<AccordionGroup>
+  <Accordion title="Robotic Voice">
+    **Cause**: Poor quality samples or too short
+
+    **Fix**: Use longer, higher quality samples
+  </Accordion>
+
+  <Accordion title="Wrong Tone">
+    **Cause**: Sample tone doesn't match desired output
+
+    **Fix**: Record samples in the style you want to generate
+  </Accordion>
+
+  <Accordion title="Artifacts/Glitches">
+    **Cause**: Background noise or audio issues in samples
+
+    **Fix**: Clean up samples or re-record in quieter environment
+  </Accordion>
+</AccordionGroup>
+
+## Advanced Tips
+
+### Celebrity/Character Voices
+
+For cloning public figures or characters:
+
+1. **Legal considerations** - Ensure you have rights or it's fair use
+2. **Source quality** - Find high-quality interview audio or clean clips
+3. **Consistency** - Use clips where they speak similarly
+4. **Multiple samples** - Very important for recognizable voices
+
+### Accent & Dialect
+
+The model will preserve accent and dialect:
+
+- British English will generate British English
+- Southern accent will produce Southern accent
+- Regional pronunciations will be maintained
+
+### Emotion Transfer
+
+The emotional tone of samples affects generation:
+
+- Energetic samples → Energetic output
+- Calm samples → Calm output
+- Mix samples for versatile profile
+
+## Managing Profiles
+
+### Organization
+
+- **Descriptive names** - "John Smith - Professional Narrator"
+- **Add descriptions** - Note recording conditions, use cases
+- **Language tags** - Mark the primary language
+- **Archive unused** - Keep profile list manageable
+
+### Export/Import
+
+- **Export** profiles to share or backup
+- **Import** from colleagues or teammates
+- Profiles include voice embeddings, not original audio
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Generate Speech" icon="waveform" href="/guides/generating-speech">
+    Use your profile to generate speech
+  </Card>
+  <Card title="Build Stories" icon="film" href="/guides/building-stories">
+    Create multi-voice narratives
+  </Card>
+</CardGroup>

docs/overview/generating-speech.mdx

@@ -0,0 +1,65 @@
+---
+title: "Generating Speech"
+description: "Generate high-quality speech from text"
+---
+
+## Basic Generation
+
+<Steps>
+  <Step title="Select Profile">
+    Choose a voice profile from the dropdown
+  </Step>
+  <Step title="Enter Text">
+    Type or paste your text
+  </Step>
+  <Step title="Generate">
+    Click **Generate** and wait a few seconds
+  </Step>
+  <Step title="Play & Export">
+    Preview and download the result
+  </Step>
+</Steps>
+
+## Text Formatting Tips
+
+The way you format text affects the output quality.
+
+### Punctuation
+
+Use proper punctuation for natural pauses:
+
+```
+Good: "Hello! How are you today? I'm doing great."
+Bad: "Hello how are you today Im doing great"
+```
+
+### Emphasis
+
+Use formatting to suggest emphasis:
+
+```
+- ALL CAPS for louder/emphasized: "That was AMAZING!"
+- Italics for subtle emphasis: "I *really* enjoyed that"
+- Bold for strong emphasis: "This is **very** important"
+```
+
+<Note>
+  The model interprets these hints but results may vary.
+</Note>
+
+## Advanced Features
+
+### Batch Generation
+
+For long-form content, split into smaller chunks for better control and faster processing.
+
+### Voice Caching
+
+Voicebox caches voice prompts for faster re-generation with the same profile.
+
+## Coming Soon
+
+- Real-time streaming
+- Word-level timing control
+- Emotion and style controls
+- SSML support

docs/overview/generation-history.mdx

@@ -0,0 +1,88 @@
+---
+title: "Generation History"
+description: "Track and manage all your generated audio"
+---
+
+## Overview
+
+Voicebox keeps a complete history of all generated audio, making it easy to find, reuse, and manage your creations.
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Full History" icon="clock">
+    Every generation is automatically saved
+  </Card>
+  <Card title="Search & Filter" icon="search">
+    Find by text, voice, or date
+  </Card>
+  <Card title="Re-generate" icon="rotate">
+    Regenerate any past generation with one click
+  </Card>
+  <Card title="Export" icon="download">
+    Download individual or batch exports
+  </Card>
+</CardGroup>
+
+## Viewing History
+
+Navigate to the **History** tab to see all your generations.
+
+Each entry shows:
+- Generated text
+- Voice profile used
+- Timestamp
+- Audio duration
+- Language
+
+## Actions
+
+### Play
+Click any generation to play it immediately.
+
+### Re-generate
+Regenerate with the same settings or modify the text/voice.
+
+### Download
+Export as WAV, MP3, or M4A.
+
+### Delete
+Remove unwanted generations to free up space.
+
+### Add to Story
+Drag generations to the Stories Editor timeline.
+
+## Search & Filter
+
+<Tabs>
+  <Tab title="By Text">
+    Search for specific text content
+    ```
+    "Hello world"
+    ```
+  </Tab>
+  <Tab title="By Voice">
+    Filter by voice profile
+    ```
+    Select from dropdown
+    ```
+  </Tab>
+  <Tab title="By Date">
+    Filter by date range
+    ```
+    Last 7 days, Last 30 days, Custom range
+    ```
+  </Tab>
+</Tabs>
+
+## Storage
+
+History is stored locally:
+
+- **macOS**: `~/Library/Application Support/com.voicebox.app/data/`
+- **Windows**: `%APPDATA%/com.voicebox.app/data/`
+- **Linux**: `~/.config/com.voicebox.app/data/`
+
+<Warning>
+  Deleting the data directory will remove all history. Export important files first.
+</Warning>

docs/overview/installation.mdx

@@ -0,0 +1,119 @@
+---
+title: "Installation"
+description: "Download and install Voicebox on macOS, Windows, or Linux"
+---
+
+## Download
+
+Voicebox is available for macOS and Windows, with Linux builds coming soon.
+
+<CardGroup cols={2}>
+  <Card title="macOS" icon="apple">
+    Download for Apple Silicon or Intel Macs
+  </Card>
+  <Card title="Windows" icon="windows">
+    Download MSI installer or Setup executable
+  </Card>
+</CardGroup>
+
+### macOS
+
+<Tabs>
+  <Tab title="Apple Silicon">
+    Download: [voicebox_aarch64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_aarch64.app.tar.gz)
+
+    ```bash
+    # Extract the archive
+    tar -xzf voicebox_aarch64.app.tar.gz
+
+    # Move to Applications
+    mv Voicebox.app /Applications/
+    ```
+  </Tab>
+  <Tab title="Intel">
+    Download: [voicebox_x64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_x64.app.tar.gz)
+
+    ```bash
+    # Extract the archive
+    tar -xzf voicebox_x64.app.tar.gz
+
+    # Move to Applications
+    mv Voicebox.app /Applications/
+    ```
+  </Tab>
+</Tabs>
+
+### Windows
+
+<Tabs>
+  <Tab title="MSI Installer">
+    Download: [voicebox_x64_en-US.msi](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_x64_en-US.msi)
+
+    Double-click the MSI file and follow the installation wizard.
+  </Tab>
+  <Tab title="Setup Executable">
+    Download: [voicebox_x64-setup.exe](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_x64-setup.exe)
+
+    Run the executable and follow the installation wizard.
+  </Tab>
+</Tabs>
+
+### Linux
+
+<Note>
+  Linux builds are coming soon. Currently blocked by GitHub runner disk space limitations.
+</Note>
+
+## First Launch
+
+When you launch Voicebox for the first time:
+
+1. **Model Download** — Qwen3-TTS model (~2-4GB) will download automatically on first use
+2. **Data Directory** — Voice profiles and generated audio are stored in:
+   - macOS: `~/Library/Application Support/com.voicebox.app/`
+   - Windows: `%APPDATA%/com.voicebox.app/`
+   - Linux: `~/.config/com.voicebox.app/`
+
+3. **Backend Server** — The bundled Python server starts automatically
+
+<Tip>
+  First generation will be slower due to model downloads. Subsequent runs use cached models.
+</Tip>
+
+## System Requirements
+
+### Minimum
+
+- **OS:** macOS 11+, Windows 10+, or Linux
+- **RAM:** 8GB
+- **Storage:** 5GB free space (for models and data)
+- **CPU:** Modern multi-core processor
+
+### Recommended
+
+- **RAM:** 16GB+
+- **GPU:** CUDA-capable NVIDIA GPU (for faster generation)
+- **Storage:** 10GB+ free space
+
+<Note>
+  CPU inference is supported but significantly slower than GPU. A CUDA-capable GPU is highly recommended for real-time workflows.
+</Note>
+
+## Verification
+
+After installation, verify everything works:
+
+1. Launch Voicebox
+2. Check the server status indicator in the bottom-left corner (should be green)
+3. Navigate to **Profiles** and create a test profile
+4. Generate a short audio clip to verify the TTS engine works
+
+<Check>
+  If you see a green status indicator and can generate audio, you're all set!
+</Check>
+
+## Next Steps
+
+<Card title="Quick Start Guide" icon="rocket" href="/overview/quick-start">
+  Create your first voice profile and generate speech
+</Card>