merge: resolve conflicts with latest main

feat: Chatterbox TTS engine with multilingual voice cloning

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.6
+current_version = 0.1.13
 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]
-search = "version": "{current_version}"
-replace = "version": "{new_version}"
+[bumpversion:file:backend/__init__.py]
+search = __version__ = "{current_version}"
+replace = __version__ = "{new_version}"

.github/workflows/build-cuda.yml

@@ -0,0 +1,73 @@
+name: Build CUDA Backend
+
+on:
+  workflow_dispatch:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  build-cuda-windows:
+    runs-on: windows-latest
+    permissions:
+      contents: write
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: "pip"
+
+      - name: Install Python dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pyinstaller
+          pip install -r backend/requirements.txt
+
+      - name: Install PyTorch with CUDA 12.1
+        run: |
+          pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall --no-deps
+          pip install torchaudio --index-url https://download.pytorch.org/whl/cu121
+
+      - name: Verify CUDA support in torch
+        run: |
+          python -c "import torch; print(f'CUDA available in build: {torch.cuda.is_available()}'); print(f'CUDA version: {torch.version.cuda}')"
+
+      - name: Build CUDA server binary
+        shell: bash
+        working-directory: backend
+        run: python build_binary.py --cuda
+
+      - name: Split binary for GitHub Releases
+        shell: bash
+        run: |
+          python scripts/split_binary.py \
+            backend/dist/voicebox-server-cuda.exe \
+            --output release-assets/
+
+      - name: Upload split parts to GitHub Release
+        if: startsWith(github.ref, 'refs/tags/')
+        uses: softprops/action-gh-release@v1
+        with:
+          files: |
+            release-assets/voicebox-server-cuda.part*.exe
+            release-assets/voicebox-server-cuda.sha256
+            release-assets/voicebox-server-cuda.manifest
+          draft: true
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Upload binary as workflow artifact (for testing)
+        uses: actions/upload-artifact@v4
+        with:
+          name: voicebox-server-cuda-windows
+          path: backend/dist/voicebox-server-cuda.exe
+          retention-days: 7
+
+  # Linux CUDA build can be added later with:
+  # build-cuda-linux:
+  #   runs-on: ubuntu-22.04
+  #   ...

.github/workflows/build-windows.yml

@@ -0,0 +1,63 @@
+name: Build Windows
+
+on:
+  workflow_dispatch:
+
+jobs:
+  build-windows:
+    permissions:
+      contents: write
+    runs-on: windows-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: "pip"
+
+      - name: Install Python dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pyinstaller
+          pip install -r backend/requirements.txt
+
+      - name: Build Python server
+        shell: bash
+        run: |
+          cd backend
+          python build_binary.py
+
+          PLATFORM=$(rustc --print host-tuple)
+          mkdir -p ../tauri/src-tauri/binaries
+          cp dist/voicebox-server.exe ../tauri/src-tauri/binaries/voicebox-server-${PLATFORM}.exe
+          echo "Built voicebox-server-${PLATFORM}.exe"
+
+      - name: Setup Bun
+        uses: oven-sh/setup-bun@v2
+
+      - name: Install Rust stable
+        uses: dtolnay/rust-toolchain@stable
+
+      - name: Rust cache
+        uses: swatinem/rust-cache@v2
+        with:
+          workspaces: "./tauri/src-tauri -> target"
+
+      - name: Install dependencies
+        run: bun install
+
+      - uses: tauri-apps/tauri-action@v0
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          projectPath: tauri
+          tagName: v__VERSION__
+          releaseName: "voicebox v__VERSION__ (test build)"
+          releaseBody: "Test build for audio export fix"
+          releaseDraft: true
+          prerelease: true
+          args: ""
+          includeUpdaterJson: false

.github/workflows/release.yml

@@ -4,7 +4,7 @@ on:
   workflow_dispatch:
   push:
     tags:
-      - 'v*'
+      - "v*"
 
 jobs:
   release:
@@ -14,18 +14,22 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - platform: 'macos-latest'
-            args: '--target aarch64-apple-darwin'
-            python-version: '3.12'
-          - platform: 'macos-15-intel'
-            args: '--target x86_64-apple-darwin'
-            python-version: '3.12'
+          - 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'
-          - platform: 'windows-latest'
-            args: ''
-            python-version: '3.12'
+          #   backend: 'pytorch'
+          - platform: "windows-latest"
+            args: ""
+            python-version: "3.12"
+            backend: "pytorch"
 
     runs-on: ${{ matrix.platform }}
 
@@ -49,7 +53,7 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
+          cache: "pip"
 
       - name: Install Python dependencies
         run: |
@@ -57,6 +61,17 @@ 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: Install PyTorch with CUDA (Windows only)
+      #   if: matrix.platform == 'windows-latest'
+      #   run: |
+      #     pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall --no-deps
+      #     pip install torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+
       - name: Build Python server (Linux/macOS)
         if: matrix.platform != 'windows-latest'
         run: |
@@ -91,7 +106,7 @@ jobs:
       - name: Rust cache
         uses: swatinem/rust-cache@v2
         with:
-          workspaces: './tauri/src-tauri -> target'
+          workspaces: "./tauri/src-tauri -> target"
 
       - name: Install dependencies
         run: bun install
@@ -127,13 +142,14 @@ jobs:
         with:
           projectPath: tauri
           tagName: v__VERSION__
-          releaseName: 'voicebox v__VERSION__'
+          releaseName: "voicebox v__VERSION__"
           releaseBody: |
             ## What's Changed
             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

@@ -5,6 +5,14 @@ All notable changes to Voicebox will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Fixed
+- **Profile Name Validation** - Added proper validation to prevent duplicate profile names ([#134](https://github.com/jamiepine/voicebox/issues/134))
+  - Users now receive clear error messages when attempting to create or update profiles with duplicate names
+  - Improved error handling in create and update profile API endpoints
+  - Added comprehensive test suite for duplicate name validation
+
 ## [0.1.0] - 2026-01-25
 
 ### Added
@@ -53,6 +61,33 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Fixed
+- Audio export failing when Tauri save dialog returns object instead of string path
+- OpenAPI client generator script now documents the local backend port and avoids an unused loop variable warning
+
+### 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
+- **Audiobook Tab** - New long-form narration workflow in the app
+  - Import/paste `.txt` book content and review/edit before generation
+  - Generate a quick 5-sentence preview before full run
+  - Chunk long text automatically and process chunk-by-chunk with retry support
+  - Auto-create and update a Story during generation, with export shortcut
+- **Text chunking utility** - Added reusable sentence-aware chunking for large text inputs (`app/src/lib/utils/textChunking.ts`)
+
+### Changed
+- **README** - Added Makefile reference and updated Quick Start with Makefile-based setup instructions alongside manual setup
+- **Navigation** - Added Audiobook route/tab to the app sidebar
+- **Generation API types** - Added optional `instruct` field to `GenerationRequest`
+- **App styling** - Added `scrollbar-visible` utility styles for long-scroll panels/editors
+
+---
+
+## [Unreleased - Planned]
+
 ### Planned
 - Real-time streaming synthesis
 - Conversation mode with multiple speakers

CONTRIBUTING.md

@@ -32,6 +32,29 @@ Thank you for your interest in contributing to Voicebox! This document provides
 
 ### Development Setup
 
+**Using `just` (recommended):**
+
+Install [just](https://github.com/casey/just) (`brew install just` or `cargo install just`), then:
+
+```bash
+just setup   # creates venv, installs Python + JS deps
+just dev     # starts backend + desktop app in one terminal
+```
+
+Other useful commands:
+
+```bash
+just dev-web       # backend + web app (no Tauri/Rust build)
+just dev-backend   # backend only
+just kill          # stop all dev processes
+just clean-all     # nuke everything and start fresh
+just --list        # see all available commands
+```
+
+**Using the Makefile:** Run `make setup` then `make dev`. See `make help` for all commands.
+
+**Manual setup (required for Windows):**
+
 1. **Fork and clone the repository**
    ```bash
    git clone https://github.com/YOUR_USERNAME/voicebox.git
@@ -62,37 +85,43 @@ 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**
-   ```bash
-   cd backend
-   python -c "from database import init_db; init_db()"
-   ```
-   This creates the SQLite database at `data/voicebox.db`.
+4. **Start development servers**
 
-5. **Start development servers**
-   
-   **Terminal 1: Backend server**
+   Development requires two terminals: one for the Python backend, one for the Tauri app.
+
+   **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 +138,36 @@ 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
-bun run tauri build
+export QWEN_TTS_PATH=~/path/to/your/Qwen3-TTS
+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 +184,26 @@ 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
@@ -359,7 +426,7 @@ See [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) for common issues and sol
 
 - **Backend won't start:** Check Python version (3.11+), ensure venv is activated, install dependencies
 - **Tauri build fails:** Ensure Rust is installed, clean build with `cd tauri/src-tauri && cargo clean`
-- **OpenAPI client generation fails:** Ensure backend is running, check `curl http://localhost:8000/openapi.json`
+- **OpenAPI client generation fails:** Ensure backend is running, check `curl http://localhost:17493/openapi.json`
 
 ## Questions?
 

Makefile

@@ -0,0 +1,250 @@
+# 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, $$2}'
+
+# =============================================================================
+# SETUP
+# =============================================================================
+
+.PHONY: setup setup-js setup-python setup-rust
+
+setup: setup-js setup-python ## Full project setup (all dependencies)
+	@echo -e "$(GREEN)✓ Setup complete!$(NC)"
+	@echo -e "  Run $(YELLOW)make dev$(NC) to start development servers"
+
+setup-js: ## Install JavaScript dependencies (bun)
+	@echo -e "$(BLUE)Installing JavaScript dependencies...$(NC)"
+	bun install
+
+setup-python: $(VENV)/bin/activate ## Set up Python virtual environment and dependencies
+	@echo -e "$(BLUE)Installing Python dependencies...$(NC)"
+	$(PIP) install --upgrade pip
+	$(PIP) install -r $(BACKEND_DIR)/requirements.txt
+	$(PIP) install --no-deps chatterbox-tts
+	@if [ "$$(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 && if [ "$$(uname)" = "Linux" ] && lspci 2>/dev/null | grep -qi nvidia; then \
+			WEBKIT_DISABLE_DMABUF_RENDERER=1 $(MAKE) dev-frontend; \
+		else \
+			$(MAKE) dev-frontend; \
+		fi & \
+		wait
+
+dev-backend: ## Start FastAPI backend server
+	@echo -e "$(BLUE)Starting backend server on http://localhost:17493$(NC)"
+	$(VENV_BIN)/uvicorn backend.main:app --reload --port 17493
+
+dev-frontend: ## Start Tauri desktop app
+	@echo -e "$(BLUE)Starting Tauri desktop app...$(NC)"
+	bun run dev
+
+dev-web: ## Start backend + web app (parallel)
+	@echo -e "$(BLUE)Starting web development servers...$(NC)"
+	@trap 'kill 0' EXIT; \
+		$(MAKE) dev-backend & \
+		sleep 2 && cd $(WEB_DIR) && bun run dev & \
+		wait
+
+kill-dev: ## Kill all development processes
+	@echo -e "$(YELLOW)Killing development processes...$(NC)"
+	-pkill -f "uvicorn main:app" 2>/dev/null || true
+	-pkill -f "vite" 2>/dev/null || true
+	@echo -e "$(GREEN)✓ Processes killed$(NC)"
+
+# =============================================================================
+# BUILD
+# =============================================================================
+
+.PHONY: build build-server build-tauri build-web
+
+build: build-server build-tauri ## Build everything (server binary + desktop app)
+	@echo -e "$(GREEN)✓ Build complete!$(NC)"
+
+build-server: ## Build Python server binary
+	@echo -e "$(BLUE)Building server binary...$(NC)"
+	PATH="$(VENV_BIN):$$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
-- **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
+<br/>
 
-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 a **local, free and open-source alternative to ElevenLabs** — 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.
 
 ---
 
@@ -52,10 +80,10 @@ Voicebox is available now for macOS and Windows.
 
 | Platform | Download |
 |----------|----------|
-| macOS (Apple Silicon) | [voicebox_aarch64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_aarch64.app.tar.gz) |
-| macOS (Intel) | [voicebox_x64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_x64.app.tar.gz) |
-| Windows (MSI) | [voicebox_0.1.0_x64_en-US.msi](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_0.1.0_x64_en-US.msi) |
-| Windows (Setup) | [voicebox_0.1.0_x64-setup.exe](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_0.1.0_x64-setup.exe) |
+| macOS (Apple Silicon) | [Voicebox_aarch64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/Voicebox_aarch64.app.tar.gz) |
+| macOS (Intel) | [Voicebox_x64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/Voicebox_x64.app.tar.gz) |
+| Windows (MSI) | [Latest Windows MSI](https://github.com/jamiepine/voicebox/releases/latest) |
+| Windows (Setup) | [Latest Windows Setup](https://github.com/jamiepine/voicebox/releases/latest) |
 
 > **Linux builds coming soon** — Currently blocked by GitHub runner disk space limitations.
 
@@ -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
 
@@ -107,19 +147,22 @@ Powered by Alibaba's **Qwen3-TTS** — a breakthrough model that achieves near-p
 
 Voicebox exposes a full REST API, so you can integrate voice synthesis into your own apps.
 
+For the current local app and development workflow, the backend is typically available at `http://localhost:17493`.
+If you launch the backend manually with a different host or port, use that address instead.
+
 ```bash
 # Generate speech
-curl -X POST http://localhost:8000/api/generate \
+curl -X POST http://localhost:17493/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:17493/profiles
 
-# Create a profile from audio
-curl -X POST http://localhost:8000/api/profiles \
-  -F "[email protected]" \
-  -F "name=My Voice"
+# Create a profile
+curl -X POST http://localhost:17493/profiles \
+  -H "Content-Type: application/json" \
+  -d '{"name": "My Voice", "language": "en"}'
 ```
 
 **Use cases:**
@@ -130,7 +173,7 @@ curl -X POST http://localhost:8000/api/profiles \
 - Voice assistants
 - Content creation automation
 
-Full API documentation available at `http://localhost:8000/docs` when running.
+Full API documentation is available at `http://localhost:17493/docs` in the default local workflow, or at `/docs` on whatever server address you configured.
 
 ---
 
@@ -142,8 +185,9 @@ 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 |
-| Transcription | Whisper |
+| Voice Model | Qwen3-TTS (PyTorch or MLX) |
+| Transcription | Whisper (PyTorch or MLX) |
+| Inference Engine | MLX (Apple Silicon) / PyTorch (Windows/Linux/Intel) |
 | Database | SQLite |
 | Audio | WaveSurfer.js, librosa |
 
@@ -187,21 +231,22 @@ See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed setup and contribution guide
 ### Quick Start
 
 ```bash
-# Clone the repo
-git clone https://github.com/voicebox-sh/voicebox.git
+git clone https://github.com/jamiepine/voicebox.git
 cd voicebox
 
-# Install dependencies
-bun install
-
-# Install Python dependencies
-cd backend && pip install -r requirements.txt && cd ..
-
-# Start development
-bun run dev
+just setup   # creates Python venv, installs all deps
+just dev     # starts backend + desktop app
 ```
 
-**Prerequisites:** [Bun](https://bun.sh), [Rust](https://rustup.rs), [Python 3.11+](https://python.org). CUDA-capable GPU recommended (CPU inference supported but slower).
+Install [just](https://github.com/casey/just): `brew install just` or `cargo install just`. Run `just --list` to see all commands.
+
+Also available via Makefile: `make setup && make dev` (run `make help` for all commands).
+
+**Prerequisites:** [Bun](https://bun.sh), [Rust](https://rustup.rs), [Python 3.11+](https://python.org), [XCode on macOS](https://developer.apple.com/xcode/).
+
+**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": "0.1.6",
+  "version": "0.1.13",
   "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

@@ -1,16 +1,12 @@
-import { useEffect, useRef, useState } from 'react';
 import { RouterProvider } from '@tanstack/react-router';
+import { useEffect, useRef, useState } from 'react';
 import voiceboxLogo from '@/assets/voicebox-logo.png';
 import ShinyText from '@/components/ShinyText';
 import { TitleBarDragRegion } from '@/components/TitleBarDragRegion';
+import { useAutoUpdater } from '@/hooks/useAutoUpdater';
 import { TOP_SAFE_AREA_PADDING } from '@/lib/constants/ui';
-import {
-  isTauri,
-  setKeepServerRunning,
-  setupWindowCloseHandler,
-  startServer,
-} from '@/lib/tauri';
 import { cn } from '@/lib/utils/cn';
+import { usePlatform } from '@/platform/PlatformContext';
 import { router } from '@/router';
 import { useServerStore } from '@/stores/serverStore';
 
@@ -38,29 +34,45 @@ const LOADING_MESSAGES = [
 ];
 
 function App() {
+  const platform = usePlatform();
   const [serverReady, setServerReady] = useState(false);
   const [loadingMessageIndex, setLoadingMessageIndex] = useState(0);
   const serverStartingRef = useRef(false);
 
+  // Automatically check for app updates on startup and show toast notifications
+  useAutoUpdater({ checkOnMount: true, showToast: true });
+
   // 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);
       });
     }
-  }, []);
+    // Empty dependency array - platform is stable from context, only run once
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.metadata.isTauri, platform.lifecycle]);
+
+  // Setup lifecycle callbacks
+  useEffect(() => {
+    platform.lifecycle.onServerReady = () => {
+      setServerReady(true);
+    };
+    // Empty dependency array - platform is stable from context, only run once
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.lifecycle]);
 
   // 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 +95,21 @@ 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 +119,13 @@ function App() {
       // Window close event handles server shutdown based on setting
       serverStartingRef.current = false;
     };
-  }, []);
+    // Empty dependency array - platform is stable from context, only run once
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.metadata.isTauri, platform.lifecycle]);
 
   // Cycle through loading messages every 3 seconds
   useEffect(() => {
-    if (!isTauri() || serverReady) {
+    if (!platform.metadata.isTauri || serverReady) {
       return;
     }
 
@@ -117,10 +134,10 @@ 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', {
-                    audioData: Array.from(audioData),
-                    deviceIds: deviceIds,
-                  });
-                  debug.log('play_audio_to_devices completed successfully, result:', result);
+                  await platform.audio.playToDevices(audioData, deviceIds);
+                  debug.log('play_audio_to_devices completed successfully');
 
                   // Mark that we're using native playback
                   isUsingNativePlaybackRef.current = true;
@@ -357,14 +354,22 @@ export function AudioPlayer() {
           }
         }
 
-        // Standard WaveSurfer auto-play
-        // Use a small delay to ensure audio element is fully ready
-        setTimeout(() => {
-          wavesurfer.play().catch((error) => {
-            debug.error('Failed to autoplay:', error);
-            // Don't show error for autoplay failures (browser restrictions)
-          });
-        }, 100);
+        // Only auto-play if shouldAutoPlay flag is set (user explicitly clicked to play)
+        const shouldAutoPlayNow = usePlayerStore.getState().shouldAutoPlay;
+        if (shouldAutoPlayNow) {
+          // Clear the flag first
+          usePlayerStore.getState().clearAutoPlayFlag();
+          
+          // Use a small delay to ensure audio element is fully ready
+          setTimeout(() => {
+            wavesurfer.play().catch((error) => {
+              debug.error('Failed to autoplay:', error);
+              // Don't show error for autoplay failures (browser restrictions)
+            });
+          }, 100);
+        } else {
+          debug.log('Skipping auto-play - shouldAutoPlay is false');
+        }
       });
 
       // Handle play/pause
@@ -508,15 +513,13 @@ export function AudioPlayer() {
     }
 
     // Stop native playback if it was active
-    if (isUsingNativePlaybackRef.current && isTauri()) {
-      (async () => {
-        try {
-          await invoke('stop_audio_playback');
-          debug.log('Stopped native audio playback');
-        } catch (error) {
-          debug.error('Failed to stop native playback:', error);
-        }
-      })();
+    if (isUsingNativePlaybackRef.current && platform.metadata.isTauri) {
+      try {
+        platform.audio.stopPlayback();
+        debug.log('Stopped native audio playback');
+      } catch (error) {
+        debug.error('Failed to stop native playback:', error);
+      }
     }
 
     // Reset native playback flag when loading new audio
@@ -703,7 +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);
@@ -716,7 +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');
@@ -734,10 +737,7 @@ export function AudioPlayer() {
           const audioData = new Uint8Array(await response.arrayBuffer());
 
           // Play via native audio
-          await invoke('play_audio_to_devices', {
-            audioData: Array.from(audioData),
-            deviceIds: deviceIds,
-          });
+          await platform.audio.playToDevices(audioData, deviceIds);
 
           // Mark that we're using native playback
           isUsingNativePlaybackRef.current = true;
@@ -798,10 +798,12 @@ export function AudioPlayer() {
 
   const handleClose = () => {
     // Stop any native playback
-    if (isUsingNativePlaybackRef.current && isTauri()) {
-      invoke('stop_audio_playback').catch((error) => {
+    if (isUsingNativePlaybackRef.current && platform.metadata.isTauri) {
+      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 result;
+        return await platform.audio.listOutputDevices();
       } catch (error) {
         console.error('Failed to list audio devices:', error);
         return [];
       }
     },
-    enabled: isTauri(),
+    enabled: platform.metadata.isTauri,
   });
 
   const { data: profiles } = useQuery({
@@ -125,6 +124,13 @@ export function AudioTab() {
     );
   }
 
+  const handleChannelDelete = async (e, channelId) => {
+    e.stopPropagation();
+    if (await confirm('Delete this channel?')) {
+      deleteChannel.mutate(channelId);
+    }
+  }
+
   const allChannels = channels || [];
   const allDevices = devices || [];
   const selectedChannel = selectedChannelId
@@ -242,12 +248,7 @@ export function AudioTab() {
                             variant="ghost"
                             size="sm"
                             className="h-8 w-8 p-0"
-                            onClick={(e) => {
-                              e.stopPropagation();
-                              if (confirm('Delete this channel?')) {
-                                deleteChannel.mutate(channel.id);
-                              }
-                            }}
+                            onClick={(e) => handleChannelDelete(e, channel.id)}
                           >
                             <Trash2 className="h-4 w-4" />
                           </Button>
@@ -342,7 +343,7 @@ 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

@@ -1,6 +1,6 @@
 import { useMatchRoute } from '@tanstack/react-router';
 import { AnimatePresence, motion } from 'framer-motion';
-import { Loader2, MessageSquare, Sparkles } from 'lucide-react';
+import { Loader2, SlidersHorizontal, Sparkles } from 'lucide-react';
 import { useEffect, useRef, useState } from 'react';
 import { Button } from '@/components/ui/button';
 import { Form, FormControl, FormField, FormItem, FormMessage } from '@/components/ui/form';
@@ -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) {
@@ -190,82 +187,136 @@ export function FloatingGenerateBox({
       }}
     >
       <motion.div
-        className="bg-background/30 backdrop-blur-2xl border border-accent/20 rounded-[2rem] shadow-2xl hover:bg-background/40 hover:border-accent/20 transition-all duration-300 overflow-hidden p-3"
+        className="bg-background/30 backdrop-blur-2xl border border-accent/20 rounded-[2rem] shadow-2xl hover:bg-background/40 hover:border-accent/20 transition-all duration-300 p-3"
         transition={{ duration: 0.6, ease: 'easeInOut' }}
       >
         <Form {...form}>
           <form onSubmit={form.handleSubmit(onSubmit)}>
             <div className="flex gap-2">
-              <motion.div className="flex-1" transition={{ duration: 0.3, ease: 'easeOut' }}>
-                {isInstructMode && (
-                  <span className="text-xs text-accent font-medium mb-1 block">
-                    Delivery instructions:
-                  </span>
-                )}
-                <FormField
-                  control={form.control}
-                  name={isInstructMode ? 'instruct' : 'text'}
-                  render={({ field }) => (
-                    <FormItem>
-                      <FormControl>
-                        <motion.div
-                          animate={{
-                            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);
-                              }
+              <motion.div
+                className={cn('flex-1', isExpanded && 'mr-12')}
+                transition={{ duration: 0.3, ease: 'easeOut' }}
+              >
+                {/* Text field - hidden when in instruct mode */}
+                <div style={{ display: isInstructMode ? 'none' : 'block' }}>
+                  <FormField
+                    control={form.control}
+                    name="text"
+                    render={({ field }) => (
+                      <FormItem>
+                        <FormControl>
+                          <motion.div
+                            animate={{
+                              height: isExpanded ? 'auto' : '32px',
                             }}
-                            placeholder={
-                              isInstructMode
-                                ? 'Add delivery instructions...'
-                                : isStoriesRoute && currentStory
+                            transition={{ duration: 0.15, ease: 'easeOut' }}
+                            style={{ overflow: 'hidden' }}
+                          >
+                            <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"
-                            style={{
-                              minHeight: isExpanded ? '100px' : '32px',
-                              maxHeight: '300px',
+                              }
+                              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>
+                {/* 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}
-                            onClick={() => setIsExpanded(true)}
-                            onFocus={() => setIsExpanded(true)}
-                          />
-                        </motion.div>
-                      </FormControl>
-                      <FormMessage className="text-xs" />
-                    </FormItem>
-                  )}
-                />
+                            transition={{ duration: 0.15, ease: 'easeOut' }}
+                            style={{ overflow: 'hidden' }}
+                          >
+                            <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="e.g. very happy and excited"
+                              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">
-                <Button
-                  type="submit"
-                  disabled={isPending || !selectedProfileId}
-                  className="h-10 w-10 rounded-full bg-accent hover:bg-accent/90 hover:scale-105 text-accent-foreground shadow-lg hover:shadow-accent/50 transition-all duration-200"
-                  size="icon"
-                >
-                  {isPending ? (
-                    <Loader2 className="h-4 w-4 animate-spin" />
-                  ) : (
-                    <Sparkles className="h-4 w-4" />
-                  )}
-                </Button>
+                <div className="group relative">
+                  <Button
+                    type="submit"
+                    disabled={isPending || !selectedProfileId}
+                    className="h-10 w-10 rounded-full bg-accent hover:bg-accent/90 hover:scale-105 text-accent-foreground shadow-lg hover:shadow-accent/50 transition-all duration-200"
+                    size="icon"
+                  >
+                    {isPending ? (
+                      <Loader2 className="h-4 w-4 animate-spin" />
+                    ) : (
+                      <Sparkles className="h-4 w-4" />
+                    )}
+                  </Button>
+                  <span className="pointer-events-none absolute bottom-full left-1/2 -translate-x-1/2 mb-2 whitespace-nowrap rounded-md bg-popover px-3 py-1.5 text-xs text-popover-foreground border border-border opacity-0 transition-opacity group-hover:opacity-100 z-[9999]">
+                    {isPending
+                      ? 'Generating...'
+                      : !selectedProfileId
+                        ? 'Select a voice profile first'
+                        : 'Generate speech'}
+                  </span>
+                </div>
                 <AnimatePresence>
-                  {isExpanded && (
+                  {isExpanded && form.watch('engine') === 'qwen' && (
                     <motion.div
                       initial={{ opacity: 0, scale: 0.8 }}
                       animate={{ opacity: 1, scale: 1 }}
@@ -273,17 +324,25 @@ export function FloatingGenerateBox({
                       transition={{ duration: 0.2 }}
                       className="absolute top-0 right-[calc(100%+0.5rem)]"
                     >
-                      <Button
-                        type="button"
-                        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 ${
-                          isInstructMode ? 'text-accent' : ''
-                        }`}
-                      >
-                        <MessageSquare className="h-4 w-4" />
-                      </Button>
+                      <div className="group relative">
+                        <Button
+                          type="button"
+                          variant="ghost"
+                          size="icon"
+                          onClick={() => setIsInstructMode(!isInstructMode)}
+                          className={cn(
+                            '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',
+                          )}
+                        >
+                          <SlidersHorizontal className="h-4 w-4" />
+                        </Button>
+                        <span className="pointer-events-none absolute bottom-full left-1/2 -translate-x-1/2 mb-2 whitespace-nowrap rounded-md bg-popover px-3 py-1.5 text-xs text-popover-foreground border border-border opacity-0 transition-opacity group-hover:opacity-100 z-[9999]">
+                          Fine tune instructions
+                        </span>
+                      </div>
                     </motion.div>
                   )}
                 </AnimatePresence>
@@ -343,30 +402,48 @@ export function FloatingGenerateBox({
                     )}
                   />
 
-                  <FormField
-                    control={form.control}
-                    name="modelSize"
-                    render={({ field }) => (
-                      <FormItem className="flex-1 space-y-0">
-                        <Select onValueChange={field.onChange} defaultValue={field.value}>
-                          <FormControl>
-                            <SelectTrigger className="h-8 text-xs bg-card border-border rounded-full hover:bg-background/50 transition-all">
-                              <SelectValue />
-                            </SelectTrigger>
-                          </FormControl>
-                          <SelectContent>
-                            <SelectItem value="1.7B" className="text-xs text-muted-foreground">
-                              Qwen3-TTS 1.7B
-                            </SelectItem>
-                            <SelectItem value="0.6B" className="text-xs text-muted-foreground">
-                              Qwen3-TTS 0.6B
-                            </SelectItem>
-                          </SelectContent>
-                        </Select>
-                        <FormMessage className="text-xs" />
-                      </FormItem>
-                    )}
-                  />
+                  <FormItem className="flex-1 space-y-0">
+                    <Select
+                      value={
+                        form.watch('engine') === 'luxtts'
+                          ? 'luxtts'
+                          : form.watch('engine') === 'chatterbox'
+                            ? 'chatterbox'
+                            : `qwen:${form.watch('modelSize') || '1.7B'}`
+                      }
+                      onValueChange={(value) => {
+                        if (value === 'luxtts') {
+                          form.setValue('engine', 'luxtts');
+                        } else if (value === 'chatterbox') {
+                          form.setValue('engine', 'chatterbox');
+                        } else {
+                          const [, modelSize] = value.split(':');
+                          form.setValue('engine', 'qwen');
+                          form.setValue('modelSize', modelSize as '1.7B' | '0.6B');
+                        }
+                      }}
+                    >
+                      <FormControl>
+                        <SelectTrigger className="h-8 text-xs bg-card border-border rounded-full hover:bg-background/50 transition-all">
+                          <SelectValue />
+                        </SelectTrigger>
+                      </FormControl>
+                      <SelectContent>
+                        <SelectItem value="qwen:1.7B" className="text-xs text-muted-foreground">
+                          Qwen3-TTS 1.7B
+                        </SelectItem>
+                        <SelectItem value="qwen:0.6B" className="text-xs text-muted-foreground">
+                          Qwen3-TTS 0.6B
+                        </SelectItem>
+                        <SelectItem value="luxtts" className="text-xs text-muted-foreground">
+                          LuxTTS
+                        </SelectItem>
+                        <SelectItem value="chatterbox" className="text-xs text-muted-foreground">
+                          Chatterbox
+                        </SelectItem>
+                      </SelectContent>
+                    </Select>
+                  </FormItem>
                 </div>
               </motion.div>
             </AnimatePresence>

app/src/components/Generation/GenerationForm.tsx

@@ -76,29 +76,74 @@ export function GenerationForm() {
               )}
             />
 
-            <FormField
-              control={form.control}
-              name="instruct"
-              render={({ field }) => (
-                <FormItem>
-                  <FormLabel>Delivery Instructions (optional)</FormLabel>
-                  <FormControl>
-                    <Textarea
-                      placeholder="e.g. Speak slowly with emphasis, Warm and friendly tone, Professional and authoritative..."
-                      className="min-h-[80px]"
-                      {...field}
-                    />
-                  </FormControl>
-                  <FormDescription>
-                    Natural language instructions to control speech delivery (tone, emotion, pace).
-                    Max 500 characters
-                  </FormDescription>
-                  <FormMessage />
-                </FormItem>
-              )}
-            />
+            {form.watch('engine') === 'qwen' && (
+              <FormField
+                control={form.control}
+                name="instruct"
+                render={({ field }) => (
+                  <FormItem>
+                    <FormLabel>Delivery Instructions (optional)</FormLabel>
+                    <FormControl>
+                      <Textarea
+                        placeholder="e.g. Speak slowly with emphasis, Warm and friendly tone, Professional and authoritative..."
+                        className="min-h-[80px]"
+                        {...field}
+                      />
+                    </FormControl>
+                    <FormDescription>
+                      Natural language instructions to control speech delivery (tone, emotion,
+                      pace). Max 500 characters
+                    </FormDescription>
+                    <FormMessage />
+                  </FormItem>
+                )}
+              />
+            )}
 
             <div className="grid gap-4 md:grid-cols-3">
+              <FormItem>
+                <FormLabel>Model</FormLabel>
+                <Select
+                  value={
+                    form.watch('engine') === 'luxtts'
+                      ? 'luxtts'
+                      : form.watch('engine') === 'chatterbox'
+                        ? 'chatterbox'
+                        : `qwen:${form.watch('modelSize') || '1.7B'}`
+                  }
+                  onValueChange={(value) => {
+                    if (value === 'luxtts') {
+                      form.setValue('engine', 'luxtts');
+                    } else if (value === 'chatterbox') {
+                      form.setValue('engine', 'chatterbox');
+                    } else {
+                      const [, modelSize] = value.split(':');
+                      form.setValue('engine', 'qwen');
+                      form.setValue('modelSize', modelSize as '1.7B' | '0.6B');
+                    }
+                  }}
+                >
+                  <FormControl>
+                    <SelectTrigger>
+                      <SelectValue />
+                    </SelectTrigger>
+                  </FormControl>
+                  <SelectContent>
+                    <SelectItem value="qwen:1.7B">Qwen3-TTS 1.7B</SelectItem>
+                    <SelectItem value="qwen:0.6B">Qwen3-TTS 0.6B</SelectItem>
+                    <SelectItem value="luxtts">LuxTTS</SelectItem>
+                    <SelectItem value="chatterbox">Chatterbox</SelectItem>
+                  </SelectContent>
+                </Select>
+                <FormDescription>
+                  {form.watch('engine') === 'luxtts'
+                    ? 'Fast, English-focused'
+                    : form.watch('engine') === 'chatterbox'
+                      ? 'Multilingual, incl. Hebrew'
+                      : 'Multi-language, two sizes'}
+                </FormDescription>
+              </FormItem>
+
               <FormField
                 control={form.control}
                 name="language"
@@ -124,29 +169,6 @@ export function GenerationForm() {
                 )}
               />
 
-              <FormField
-                control={form.control}
-                name="modelSize"
-                render={({ field }) => (
-                  <FormItem>
-                    <FormLabel>Model Size</FormLabel>
-                    <Select onValueChange={field.onChange} defaultValue={field.value}>
-                      <FormControl>
-                        <SelectTrigger>
-                          <SelectValue />
-                        </SelectTrigger>
-                      </FormControl>
-                      <SelectContent>
-                        <SelectItem value="1.7B">Qwen TTS 1.7B (Higher Quality)</SelectItem>
-                        <SelectItem value="0.6B">Qwen TTS 0.6B (Faster)</SelectItem>
-                      </SelectContent>
-                    </Select>
-                    <FormDescription>Larger models produce better quality</FormDescription>
-                    <FormMessage />
-                  </FormItem>
-                )}
-              />
-
               <FormField
                 control={form.control}
                 name="seed"
@@ -170,11 +192,7 @@ export function GenerationForm() {
               />
             </div>
 
-            <Button
-              type="submit"
-              className="w-full"
-              disabled={isPending || !selectedProfileId}
-            >
+            <Button type="submit" className="w-full" disabled={isPending || !selectedProfileId}>
               {isPending ? (
                 <>
                   <Loader2 className="mr-2 h-4 w-4 animate-spin" />

app/src/components/History/HistoryTable.tsx

@@ -1,4 +1,12 @@
-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 { Button } from '@/components/ui/button';
 import {
@@ -18,6 +26,7 @@ import {
 import { Textarea } from '@/components/ui/textarea';
 import { useToast } from '@/components/ui/use-toast';
 import { apiClient } from '@/lib/api/client';
+import type { HistoryResponse } from '@/lib/api/types';
 import { BOTTOM_SAFE_AREA_PADDING } from '@/lib/constants/ui';
 import {
   useDeleteGeneration,
@@ -33,18 +42,27 @@ 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,
   });
@@ -53,13 +71,63 @@ export function HistoryTable() {
   const exportGeneration = useExportGeneration();
   const exportGenerationAudio = useExportGenerationAudio();
   const importGeneration = useImportGeneration();
-  const setAudio = usePlayerStore((state) => state.setAudio);
+  const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
   const restartCurrentAudio = usePlayerStore((state) => state.restartCurrentAudio);
   const currentAudioId = usePlayerStore((state) => state.audioId);
   const isPlaying = usePlayerStore((state) => state.isPlaying);
   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;
@@ -77,9 +145,9 @@ export function HistoryTable() {
     if (currentAudioId === audioId) {
       restartCurrentAudio();
     } else {
-      // Otherwise, load the new audio
+      // Otherwise, load the new audio and auto-play it
       const audioUrl = apiClient.getAudioUrl(audioId);
-      setAudio(audioUrl, audioId, profileId, text.substring(0, 50));
+      setAudioWithAutoPlay(audioUrl, audioId, profileId, text.substring(0, 50));
     }
   };
 
@@ -113,24 +181,16 @@ export function HistoryTable() {
     );
   };
 
-  const _handleImportClick = () => {
-    file_handleImportClickk.click();
+  const handleDeleteClick = (generationId: string, profileName: string) => {
+    setGenerationToDelete({ id: generationId, name: profileName });
+    setDeleteDialogOpen(true);
   };
 
-  const _handleFileChange = (_e: React.ChangeEvent<HTMLInputElement>) => {
-    cons_handleFileChangeet.files?.[0];
-    if (file) {
-      // Validate file extension
-      if (!file.name.endsWith('.voicebox.zip')) {
-        toast({
-          title: 'Invalid file type',
-          description: 'Please select a valid .voicebox.zip file',
-          variant: 'destructive',
-        });
-        return;
-      }
-      setSelectedFile(file);
-      setImportDialogOpen(true);
+  const handleDeleteConfirm = () => {
+    if (generationToDelete) {
+      deleteGeneration.mutate(generationToDelete.id);
+      setDeleteDialogOpen(false);
+      setGenerationToDelete(null);
     }
   };
 
@@ -159,13 +219,16 @@ export function HistoryTable() {
     }
   };
 
-  if (isLoading) {
-    return null;
+  if (isLoading && page === 0) {
+    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 total = historyData?.total || 0;
-  const _hasMore = history.length === limit && (page + 1) * limit < total;
+  const history = allHistory;
+  const hasMore = allHistory.length < total;
 
   return (
     <div className="flex flex-col h-full min-h-0 relative">
@@ -229,11 +292,16 @@ export function HistoryTable() {
                     <Textarea
                       value={gen.text}
                       className="flex-1 resize-none text-sm text-muted-foreground select-text"
+                      readOnly
                     />
                   </div>
 
                   {/* Far right - Ellipsis actions */}
-                  <div className="w-10 shrink-0 flex justify-end">
+                  <div
+                    className="w-10 shrink-0 flex justify-end"
+                    onMouseDown={(e) => e.stopPropagation()}
+                    onClick={(e) => e.stopPropagation()}
+                  >
                     <DropdownMenu>
                       <DropdownMenuTrigger asChild>
                         <Button
@@ -241,7 +309,6 @@ export function HistoryTable() {
                           size="icon"
                           className="h-8 w-8"
                           aria-label="Actions"
-                          onClick={(e) => e.stopPropagation()}
                         >
                           <MoreHorizontal className="h-4 w-4" />
                         </Button>
@@ -268,7 +335,7 @@ 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"
                         >
@@ -281,10 +348,53 @@ 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/ModelsTab/ModelsTab.tsx

@@ -2,7 +2,7 @@ import { ModelManagement } from '@/components/ServerSettings/ModelManagement';
 
 export function ModelsTab() {
   return (
-    <div className="space-y-4 overflow-y-auto flex flex-col">
+    <div className="h-full flex flex-col p-4">
       <ModelManagement />
     </div>
   );

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/GpuAcceleration.tsx

@@ -0,0 +1,387 @@
+import { useQuery, useQueryClient } from '@tanstack/react-query';
+import { AlertCircle, Cpu, Download, Loader2, RotateCw, Trash2, Zap } from 'lucide-react';
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { Badge } from '@/components/ui/badge';
+import { Button } from '@/components/ui/button';
+import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
+import { Progress } from '@/components/ui/progress';
+import { apiClient } from '@/lib/api/client';
+import type { CudaDownloadProgress } from '@/lib/api/types';
+import { useServerHealth } from '@/lib/hooks/useServer';
+import { usePlatform } from '@/platform/PlatformContext';
+import { useServerStore } from '@/stores/serverStore';
+
+type RestartPhase = 'idle' | 'stopping' | 'waiting' | 'ready';
+
+export function GpuAcceleration() {
+  const platform = usePlatform();
+  const queryClient = useQueryClient();
+  const serverUrl = useServerStore((state) => state.serverUrl);
+  const { data: health } = useServerHealth();
+
+  const [restartPhase, setRestartPhase] = useState<RestartPhase>('idle');
+  const [error, setError] = useState<string | null>(null);
+  const [downloadProgress, setDownloadProgress] = useState<CudaDownloadProgress | null>(null);
+  const healthPollRef = useRef<ReturnType<typeof setInterval> | null>(null);
+
+  // Query CUDA backend status
+  const {
+    data: cudaStatus,
+    isLoading: cudaStatusLoading,
+    refetch: refetchCudaStatus,
+  } = useQuery({
+    queryKey: ['cuda-status', serverUrl],
+    queryFn: () => apiClient.getCudaStatus(),
+    refetchInterval: (query) => (query.state.status === 'pending' ? false : 10000),
+    retry: 1,
+    enabled: !!health, // Only fetch when backend is reachable
+  });
+
+  // Derived state
+  const isCurrentlyCuda = health?.backend_variant === 'cuda';
+  const cudaAvailable = cudaStatus?.available ?? false;
+  const cudaDownloading = cudaStatus?.downloading ?? false;
+
+  // Clean up health poll on unmount
+  useEffect(() => {
+    return () => {
+      if (healthPollRef.current) {
+        clearInterval(healthPollRef.current);
+        healthPollRef.current = null;
+      }
+    };
+  }, []);
+
+  // SSE progress tracking during download
+  useEffect(() => {
+    if (!cudaDownloading || !serverUrl) {
+      return;
+    }
+
+    const eventSource = new EventSource(`${serverUrl}/backend/cuda-progress`);
+
+    eventSource.onmessage = (event) => {
+      try {
+        const data = JSON.parse(event.data) as CudaDownloadProgress;
+        setDownloadProgress(data);
+
+        if (data.status === 'complete') {
+          eventSource.close();
+          setDownloadProgress(null);
+          refetchCudaStatus();
+        } else if (data.status === 'error') {
+          eventSource.close();
+          setError(data.error || 'Download failed');
+          setDownloadProgress(null);
+          refetchCudaStatus();
+        }
+      } catch (e) {
+        console.error('Error parsing CUDA progress event:', e);
+      }
+    };
+
+    eventSource.onerror = () => {
+      eventSource.close();
+    };
+
+    return () => {
+      eventSource.close();
+    };
+  }, [cudaDownloading, serverUrl, refetchCudaStatus]);
+
+  // Start aggressive health polling during restart
+  const startHealthPolling = useCallback(() => {
+    if (healthPollRef.current) return;
+
+    healthPollRef.current = setInterval(async () => {
+      try {
+        const result = await apiClient.getHealth();
+        if (result.status === 'healthy') {
+          // Server is back up
+          if (healthPollRef.current) {
+            clearInterval(healthPollRef.current);
+            healthPollRef.current = null;
+          }
+          setRestartPhase('ready');
+          // Invalidate all queries to refresh UI
+          queryClient.invalidateQueries();
+          // Reset after a moment
+          setTimeout(() => setRestartPhase('idle'), 2000);
+        }
+      } catch {
+        // Server still down, keep polling
+      }
+    }, 1000);
+  }, [queryClient]);
+
+  const handleDownload = async () => {
+    setError(null);
+    try {
+      await apiClient.downloadCudaBackend();
+      refetchCudaStatus();
+    } catch (e: unknown) {
+      const msg = e instanceof Error ? e.message : 'Failed to start download';
+      if (msg.includes('already downloaded')) {
+        refetchCudaStatus();
+      } else {
+        setError(msg);
+      }
+    }
+  };
+
+  const handleRestart = async () => {
+    setError(null);
+    setRestartPhase('stopping');
+
+    try {
+      setRestartPhase('waiting');
+      startHealthPolling();
+      await platform.lifecycle.restartServer();
+      // Invoke resolved — server is likely ready. Stop polling and refresh.
+      if (healthPollRef.current) {
+        clearInterval(healthPollRef.current);
+        healthPollRef.current = null;
+      }
+      setRestartPhase('ready');
+      queryClient.invalidateQueries();
+      setTimeout(() => setRestartPhase('idle'), 2000);
+    } catch (e: unknown) {
+      setRestartPhase('idle');
+      if (healthPollRef.current) {
+        clearInterval(healthPollRef.current);
+        healthPollRef.current = null;
+      }
+      setError(e instanceof Error ? e.message : 'Restart failed');
+    }
+  };
+
+  const handleSwitchToCpu = async () => {
+    // To switch to CPU: delete the CUDA binary, then restart.
+    // start_server always prefers CUDA if present, so we must remove it first.
+    setError(null);
+    setRestartPhase('stopping');
+
+    try {
+      await apiClient.deleteCudaBackend();
+      setRestartPhase('waiting');
+      startHealthPolling();
+      await platform.lifecycle.restartServer();
+      // Invoke resolved — server is likely ready
+      if (healthPollRef.current) {
+        clearInterval(healthPollRef.current);
+        healthPollRef.current = null;
+      }
+      setRestartPhase('ready');
+      queryClient.invalidateQueries();
+      setTimeout(() => setRestartPhase('idle'), 2000);
+    } catch (e: unknown) {
+      setRestartPhase('idle');
+      if (healthPollRef.current) {
+        clearInterval(healthPollRef.current);
+        healthPollRef.current = null;
+      }
+      setError(e instanceof Error ? e.message : 'Failed to switch to CPU');
+      refetchCudaStatus();
+    }
+  };
+
+  const handleDelete = async () => {
+    setError(null);
+    try {
+      await apiClient.deleteCudaBackend();
+      refetchCudaStatus();
+    } catch (e: unknown) {
+      setError(e instanceof Error ? e.message : 'Failed to delete CUDA backend');
+    }
+  };
+
+  const formatBytes = (bytes: number): string => {
+    if (bytes === 0) return '0 B';
+    const k = 1024;
+    const sizes = ['B', 'KB', 'MB', 'GB'];
+    const i = Math.floor(Math.log(bytes) / Math.log(k));
+    return `${(bytes / k ** i).toFixed(1)} ${sizes[i]}`;
+  };
+
+  // Don't render until health data is available
+  if (!health) return null;
+
+  // If the system already has native GPU (MPS, etc.), only show info - no CUDA needed
+  const hasNativeGpu =
+    health.gpu_available &&
+    !isCurrentlyCuda &&
+    health.gpu_type &&
+    !health.gpu_type.includes('CUDA');
+
+  return (
+    <Card>
+      <CardHeader>
+        <CardTitle className="flex items-center gap-2">
+          <Zap className="h-4 w-4" />
+          GPU Acceleration
+        </CardTitle>
+      </CardHeader>
+      <CardContent className="space-y-4">
+        {/* Current status */}
+        <div className="flex items-center justify-between">
+          <div className="space-y-1">
+            <div className="text-sm font-medium">Backend</div>
+            <div className="text-sm text-muted-foreground">
+              {isCurrentlyCuda ? 'CUDA (GPU accelerated)' : 'CPU'}
+            </div>
+          </div>
+          <Badge variant={isCurrentlyCuda ? 'default' : 'secondary'}>
+            {isCurrentlyCuda ? (
+              <>
+                <Zap className="h-3 w-3 mr-1" /> CUDA
+              </>
+            ) : (
+              <>
+                <Cpu className="h-3 w-3 mr-1" /> CPU
+              </>
+            )}
+          </Badge>
+        </div>
+
+        {/* GPU info from health */}
+        {health.gpu_type && (
+          <div className="space-y-1">
+            <div className="text-sm font-medium">GPU</div>
+            <div className="text-sm text-muted-foreground">{health.gpu_type}</div>
+            {health.vram_used_mb != null && (
+              <div className="text-xs text-muted-foreground">
+                VRAM: {health.vram_used_mb.toFixed(0)} MB used
+              </div>
+            )}
+          </div>
+        )}
+
+        {/* Native GPU detected - no CUDA download needed */}
+        {hasNativeGpu && (
+          <div className="p-3 rounded-lg bg-accent/10 border border-accent/20">
+            <div className="text-sm">
+              Your system uses <strong>{health.gpu_type}</strong> for acceleration. No additional
+              downloads needed.
+            </div>
+          </div>
+        )}
+
+        {/* CUDA download section - only show when native GPU is NOT detected (i.e., Windows/Linux NVIDIA users) */}
+        {!hasNativeGpu && (
+          <>
+            {/* Download progress */}
+            {cudaDownloading && downloadProgress && (
+              <div className="space-y-2">
+                <div className="flex items-center justify-between text-sm">
+                  <div className="flex items-center gap-2">
+                    <Loader2 className="h-4 w-4 animate-spin" />
+                    <span>{downloadProgress.filename || 'Downloading CUDA backend...'}</span>
+                  </div>
+                  {downloadProgress.total > 0 && (
+                    <span className="text-muted-foreground">
+                      {downloadProgress.progress.toFixed(1)}%
+                    </span>
+                  )}
+                </div>
+                {downloadProgress.total > 0 && (
+                  <>
+                    <Progress value={downloadProgress.progress} className="h-2" />
+                    <div className="text-xs text-muted-foreground">
+                      {formatBytes(downloadProgress.current)} /{' '}
+                      {formatBytes(downloadProgress.total)}
+                    </div>
+                  </>
+                )}
+              </div>
+            )}
+
+            {/* Restart in progress */}
+            {restartPhase !== 'idle' && (
+              <div className="flex items-center gap-2 p-3 rounded-lg bg-primary/5 border">
+                <Loader2 className="h-4 w-4 animate-spin" />
+                <span className="text-sm">
+                  {restartPhase === 'stopping' && 'Stopping server...'}
+                  {restartPhase === 'waiting' && 'Restarting server...'}
+                  {restartPhase === 'ready' && 'Server restarted successfully!'}
+                </span>
+              </div>
+            )}
+
+            {/* Error display */}
+            {error && (
+              <div className="flex items-center gap-2 text-sm text-destructive">
+                <AlertCircle className="h-4 w-4 shrink-0" />
+                <span>{error}</span>
+              </div>
+            )}
+
+            {/* Actions */}
+            {restartPhase === 'idle' && !cudaDownloading && (
+              <div className="space-y-2">
+                {/* Not downloaded yet - show download button */}
+                {!cudaAvailable && (
+                  <div className="space-y-3">
+                    <p className="text-sm text-muted-foreground">
+                      Download the CUDA backend (~2.4 GB) for NVIDIA GPU acceleration. Requires an
+                      NVIDIA GPU with CUDA support.
+                    </p>
+                    <Button onClick={handleDownload} className="w-full" size="sm">
+                      <Download className="h-4 w-4 mr-2" />
+                      Download CUDA Backend
+                    </Button>
+                  </div>
+                )}
+
+                {/* Downloaded but not active - show switch button */}
+                {cudaAvailable && !isCurrentlyCuda && platform.metadata.isTauri && (
+                  <div className="space-y-3">
+                    <p className="text-sm text-muted-foreground">
+                      CUDA backend is downloaded and ready. Restart the server to enable GPU
+                      acceleration.
+                    </p>
+                    <Button onClick={handleRestart} className="w-full" size="sm">
+                      <RotateCw className="h-4 w-4 mr-2" />
+                      Switch to CUDA Backend
+                    </Button>
+                  </div>
+                )}
+
+                {/* Currently active - show switch back to CPU */}
+                {isCurrentlyCuda && platform.metadata.isTauri && (
+                  <div className="space-y-3">
+                    <p className="text-sm text-muted-foreground">
+                      Running with CUDA GPU acceleration. Switch back to CPU if needed (you can
+                      re-download later).
+                    </p>
+                    <Button
+                      onClick={handleSwitchToCpu}
+                      variant="outline"
+                      className="w-full"
+                      size="sm"
+                    >
+                      <RotateCw className="h-4 w-4 mr-2" />
+                      Switch to CPU Backend
+                    </Button>
+                  </div>
+                )}
+
+                {/* Delete option when downloaded (and not active) */}
+                {cudaAvailable && !isCurrentlyCuda && (
+                  <Button
+                    onClick={handleDelete}
+                    variant="ghost"
+                    className="w-full text-muted-foreground hover:text-destructive"
+                    size="sm"
+                  >
+                    <Trash2 className="h-4 w-4 mr-2" />
+                    Remove CUDA Backend
+                  </Button>
+                )}
+              </div>
+            )}
+          </>
+        )}
+      </CardContent>
+    </Card>
+  );
+}

app/src/components/ServerSettings/ModelManagement.tsx

@@ -1,6 +1,21 @@
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
-import { Download, Loader2, Trash2 } from 'lucide-react';
-import { useState } from 'react';
+import {
+  ChevronDown,
+  ChevronRight,
+  ChevronUp,
+  CircleCheck,
+  CircleX,
+  Download,
+  ExternalLink,
+  HardDrive,
+  Heart,
+  Loader2,
+  RotateCcw,
+  Scale,
+  Trash2,
+  X,
+} from 'lucide-react';
+import { useCallback, useMemo, useState } from 'react';
 import {
   AlertDialog,
   AlertDialogAction,
@@ -13,29 +28,162 @@ import {
 } from '@/components/ui/alert-dialog';
 import { Badge } from '@/components/ui/badge';
 import { Button } from '@/components/ui/button';
-import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card';
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogHeader,
+  DialogTitle,
+} from '@/components/ui/dialog';
+import { Progress } from '@/components/ui/progress';
 import { useToast } from '@/components/ui/use-toast';
 import { apiClient } from '@/lib/api/client';
+import type { ActiveDownloadTask, HuggingFaceModelInfo, ModelStatus } from '@/lib/api/types';
 import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
-import { ModelProgress } from './ModelProgress';
+
+async function fetchHuggingFaceModelInfo(repoId: string): Promise<HuggingFaceModelInfo> {
+  const response = await fetch(`https://huggingface.co/api/models/${repoId}`);
+  if (!response.ok) throw new Error(`Failed to fetch model info: ${response.status}`);
+  return response.json();
+}
+
+function formatDownloads(n: number): string {
+  if (n >= 1_000_000) return `${(n / 1_000_000).toFixed(1)}M`;
+  if (n >= 1_000) return `${(n / 1_000).toFixed(1)}k`;
+  return n.toString();
+}
+
+function formatLicense(license: string): string {
+  const map: Record<string, string> = {
+    'apache-2.0': 'Apache 2.0',
+    mit: 'MIT',
+    'cc-by-4.0': 'CC BY 4.0',
+    'cc-by-sa-4.0': 'CC BY-SA 4.0',
+    'cc-by-nc-4.0': 'CC BY-NC 4.0',
+    'openrail++': 'OpenRAIL++',
+    openrail: 'OpenRAIL',
+  };
+  return map[license] || license;
+}
+
+function formatPipelineTag(tag: string): string {
+  return tag
+    .split('-')
+    .map((w) => w.charAt(0).toUpperCase() + w.slice(1))
+    .join(' ');
+}
+
+function formatBytes(bytes: number): string {
+  if (bytes === 0) return '0 B';
+  const k = 1024;
+  const sizes = ['B', 'KB', 'MB', 'GB'];
+  const i = Math.floor(Math.log(bytes) / Math.log(k));
+  return `${(bytes / k ** i).toFixed(1)} ${sizes[i]}`;
+}
 
 export function ModelManagement() {
   const { toast } = useToast();
   const queryClient = useQueryClient();
   const [downloadingModel, setDownloadingModel] = useState<string | null>(null);
   const [downloadingDisplayName, setDownloadingDisplayName] = useState<string | null>(null);
+  const [consoleOpen, setConsoleOpen] = useState(false);
+  const [dismissedErrors, setDismissedErrors] = useState<Set<string>>(new Set());
+  const [localErrors, setLocalErrors] = useState<Map<string, string>>(new Map());
+
+  // Modal state
+  const [selectedModel, setSelectedModel] = useState<ModelStatus | null>(null);
+  const [detailOpen, setDetailOpen] = useState(false);
 
   const { data: modelStatus, isLoading } = useQuery({
     queryKey: ['modelStatus'],
-    queryFn: () => apiClient.getModelStatus(),
-    refetchInterval: 5000, // Refresh every 5 seconds
+    queryFn: async () => {
+      const result = await apiClient.getModelStatus();
+      return result;
+    },
+    refetchInterval: 5000,
   });
 
-  // Use progress toast hook for the downloading model
+  const { data: activeTasks } = useQuery({
+    queryKey: ['activeTasks'],
+    queryFn: () => apiClient.getActiveTasks(),
+    refetchInterval: (query) => {
+      const data = query.state.data;
+      const hasActive = data?.downloads.some((d) => d.status === 'downloading');
+      return hasActive ? 1000 : 5000;
+    },
+  });
+
+  // HuggingFace model card query - only fetches when modal is open and model has a repo ID
+  const { data: hfModelInfo, isLoading: hfLoading } = useQuery({
+    queryKey: ['hfModelInfo', selectedModel?.hf_repo_id],
+    queryFn: () => fetchHuggingFaceModelInfo(selectedModel!.hf_repo_id!),
+    enabled: detailOpen && !!selectedModel?.hf_repo_id,
+    staleTime: 1000 * 60 * 30, // Cache for 30 minutes
+    retry: 1,
+  });
+
+  // Build a map of errored downloads for quick lookup, excluding dismissed ones
+  const erroredDownloads = new Map<string, ActiveDownloadTask>();
+  if (activeTasks?.downloads) {
+    for (const dl of activeTasks.downloads) {
+      if (dl.status === 'error' && !dismissedErrors.has(dl.model_name)) {
+        const localErr = localErrors.get(dl.model_name);
+        erroredDownloads.set(dl.model_name, localErr ? { ...dl, error: localErr } : dl);
+      }
+    }
+  }
+  for (const [modelName, error] of localErrors) {
+    if (!erroredDownloads.has(modelName) && !dismissedErrors.has(modelName)) {
+      erroredDownloads.set(modelName, {
+        model_name: modelName,
+        status: 'error',
+        started_at: new Date().toISOString(),
+        error,
+      });
+    }
+  }
+
+  const errorCount = erroredDownloads.size;
+
+  // Build progress map from active tasks for inline display
+  const downloadProgressMap = useMemo(() => {
+    const map = new Map<string, ActiveDownloadTask>();
+    if (activeTasks?.downloads) {
+      for (const dl of activeTasks.downloads) {
+        if (dl.status === 'downloading') {
+          map.set(dl.model_name, dl);
+        }
+      }
+    }
+    return map;
+  }, [activeTasks]);
+
+  const handleDownloadComplete = useCallback(() => {
+    setDownloadingModel(null);
+    setDownloadingDisplayName(null);
+    queryClient.invalidateQueries({ queryKey: ['modelStatus'] });
+    queryClient.invalidateQueries({ queryKey: ['activeTasks'] });
+  }, [queryClient]);
+
+  const handleDownloadError = useCallback(
+    (error: string) => {
+      if (downloadingModel) {
+        setLocalErrors((prev) => new Map(prev).set(downloadingModel, error));
+        setConsoleOpen(true);
+      }
+      setDownloadingModel(null);
+      setDownloadingDisplayName(null);
+      queryClient.invalidateQueries({ queryKey: ['activeTasks'] });
+    },
+    [queryClient, downloadingModel],
+  );
+
   useModelDownloadToast({
     modelName: downloadingModel || '',
     displayName: downloadingDisplayName || '',
     enabled: !!downloadingModel && !!downloadingDisplayName,
+    onComplete: handleDownloadComplete,
+    onError: handleDownloadError,
   });
 
   const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
@@ -45,42 +193,103 @@ export function ModelManagement() {
     sizeMb?: number;
   } | null>(null);
 
-  const downloadMutation = useMutation({
-    mutationFn: (modelName: string) => {
+  const handleDownload = async (modelName: string) => {
+    setDismissedErrors((prev) => {
+      const next = new Set(prev);
+      next.delete(modelName);
+      return next;
+    });
+
+    const model = modelStatus?.models.find((m) => m.model_name === modelName);
+    const displayName = model?.display_name || modelName;
+
+    try {
+      await apiClient.triggerModelDownload(modelName);
+
       setDownloadingModel(modelName);
-      // Find display name from model status
-      const model = modelStatus?.models.find((m) => m.model_name === modelName);
-      setDownloadingDisplayName(model?.display_name || modelName);
-      return apiClient.triggerModelDownload(modelName);
-    },
-    onSuccess: () => {
-      // Download completed - clear state and refetch status
-      setDownloadingModel(null);
-      setDownloadingDisplayName(null);
+      setDownloadingDisplayName(displayName);
+
       queryClient.invalidateQueries({ queryKey: ['modelStatus'] });
-    },
-    onError: (error: Error) => {
+      queryClient.invalidateQueries({ queryKey: ['activeTasks'] });
+    } catch (error) {
       setDownloadingModel(null);
       setDownloadingDisplayName(null);
       toast({
         title: 'Download failed',
-        description: error.message,
+        description: error instanceof Error ? error.message : 'Unknown error',
         variant: 'destructive',
       });
+    }
+  };
+
+  const cancelMutation = useMutation({
+    mutationFn: (modelName: string) => apiClient.cancelDownload(modelName),
+    onSuccess: async () => {
+      await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
+      await queryClient.invalidateQueries({ queryKey: ['activeTasks'], refetchType: 'all' });
+    },
+  });
+
+  const handleCancel = (modelName: string) => {
+    const prevDismissed = dismissedErrors;
+    const prevLocalErrors = localErrors;
+    const prevDownloadingModel = downloadingModel;
+    const prevDownloadingDisplayName = downloadingDisplayName;
+
+    setDismissedErrors((prev) => new Set(prev).add(modelName));
+    setLocalErrors((prev) => {
+      const next = new Map(prev);
+      next.delete(modelName);
+      return next;
+    });
+    if (downloadingModel === modelName) {
+      setDownloadingModel(null);
+      setDownloadingDisplayName(null);
+    }
+
+    cancelMutation.mutate(modelName, {
+      onError: () => {
+        setDismissedErrors(prevDismissed);
+        setLocalErrors(prevLocalErrors);
+        setDownloadingModel(prevDownloadingModel);
+        setDownloadingDisplayName(prevDownloadingDisplayName);
+        toast({
+          title: 'Cancel failed',
+          description: 'Could not cancel the download task.',
+          variant: 'destructive',
+        });
+      },
+    });
+  };
+
+  const clearAllMutation = useMutation({
+    mutationFn: () => apiClient.clearAllTasks(),
+    onSuccess: async () => {
+      setDismissedErrors(new Set());
+      setLocalErrors(new Map());
+      setDownloadingModel(null);
+      setDownloadingDisplayName(null);
+      await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
+      await queryClient.invalidateQueries({ queryKey: ['activeTasks'], refetchType: 'all' });
     },
   });
 
   const deleteMutation = useMutation({
-    mutationFn: (modelName: string) => apiClient.deleteModel(modelName),
-    onSuccess: () => {
+    mutationFn: async (modelName: string) => {
+      const result = await apiClient.deleteModel(modelName);
+      return result;
+    },
+    onSuccess: async () => {
       toast({
         title: 'Model deleted',
         description: `${modelToDelete?.displayName || 'Model'} has been deleted successfully.`,
       });
       setDeleteDialogOpen(false);
       setModelToDelete(null);
-      // Refetch status to update UI
-      queryClient.invalidateQueries({ queryKey: ['modelStatus'] });
+      setDetailOpen(false);
+      setSelectedModel(null);
+      await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
+      await queryClient.refetchQueries({ queryKey: ['modelStatus'] });
     },
     onError: (error: Error) => {
       toast({
@@ -92,100 +301,438 @@ export function ModelManagement() {
   });
 
   const formatSize = (sizeMb?: number): string => {
-    if (!sizeMb) return 'Unknown';
+    if (!sizeMb) return 'Unknown size';
     if (sizeMb < 1024) return `${sizeMb.toFixed(1)} MB`;
     return `${(sizeMb / 1024).toFixed(2)} GB`;
   };
 
+  const getModelState = (model: ModelStatus) => {
+    const isDownloading =
+      (model.downloading || downloadingModel === model.model_name) &&
+      !erroredDownloads.has(model.model_name) &&
+      !dismissedErrors.has(model.model_name);
+    const hasError = erroredDownloads.has(model.model_name);
+    return { isDownloading, hasError };
+  };
+
+  const openModelDetail = (model: ModelStatus) => {
+    setSelectedModel(model);
+    setDetailOpen(true);
+  };
+
+  const ttsModels = modelStatus?.models.filter((m) => m.model_name.startsWith('qwen-tts')) ?? [];
+  const otherTtsModels =
+    modelStatus?.models.filter(
+      (m) => m.model_name.startsWith('luxtts') || m.model_name.startsWith('chatterbox'),
+    ) ?? [];
+  const whisperModels = modelStatus?.models.filter((m) => m.model_name.startsWith('whisper')) ?? [];
+
+  // Build sections
+  const sections: { label: string; models: ModelStatus[] }[] = [
+    { label: 'Voice Generation', models: ttsModels },
+    ...(otherTtsModels.length > 0 ? [{ label: 'Other Voice Models', models: otherTtsModels }] : []),
+    { label: 'Transcription', models: whisperModels },
+  ];
+
+  // Get detail modal state for selected model
+  const selectedState = selectedModel ? getModelState(selectedModel) : null;
+  const selectedError = selectedModel ? erroredDownloads.get(selectedModel.model_name) : undefined;
+
+  // Keep selectedModel data fresh from query results
+  const freshSelectedModel =
+    selectedModel && modelStatus
+      ? modelStatus.models.find((m) => m.model_name === selectedModel.model_name) || selectedModel
+      : selectedModel;
+
+  // Derive license from HF data
+  const license =
+    hfModelInfo?.cardData?.license ||
+    hfModelInfo?.tags?.find((t) => t.startsWith('license:'))?.replace('license:', '');
+
   return (
-    <Card>
-      <CardHeader>
-        <CardTitle>Model Management</CardTitle>
-        <CardDescription>
+    <div className="flex flex-col h-full">
+      {/* Header */}
+      <div className="shrink-0 pb-4">
+        <h1 className="text-lg font-semibold">Models</h1>
+        <p className="text-sm text-muted-foreground">
           Download and manage AI models for voice generation and transcription
-        </CardDescription>
-      </CardHeader>
-      <CardContent className="space-y-4">
-        {isLoading ? (
-          <div className="flex items-center justify-center py-8">
-            <Loader2 className="h-6 w-6 animate-spin text-muted-foreground" />
-          </div>
-        ) : modelStatus ? (
-          <div className="space-y-4">
-            {/* TTS Models */}
-            <div>
-              <h3 className="text-sm font-semibold mb-3 text-muted-foreground">
-                Voice Generation Models
-              </h3>
-              <div className="space-y-2">
-                {modelStatus.models
-                  .filter((m) => m.model_name.startsWith('qwen-tts'))
-                  .map((model) => (
-                    <ModelItem
+        </p>
+      </div>
+
+      {/* Model list */}
+      {isLoading ? (
+        <div className="flex items-center justify-center py-16">
+          <Loader2 className="h-5 w-5 animate-spin text-muted-foreground" />
+        </div>
+      ) : modelStatus ? (
+        <div className="flex-1 min-h-0 overflow-y-auto space-y-6">
+          {sections.map((section) => (
+            <div key={section.label}>
+              <h2 className="text-xs font-medium text-muted-foreground uppercase tracking-wider mb-1 px-1">
+                {section.label}
+              </h2>
+              <div className="border rounded-lg divide-y overflow-hidden">
+                {section.models.map((model) => {
+                  const { isDownloading, hasError } = getModelState(model);
+                  return (
+                    <button
                       key={model.model_name}
-                      model={model}
-                      onDownload={() => downloadMutation.mutate(model.model_name)}
-                      onDelete={() => {
+                      type="button"
+                      onClick={() => openModelDetail(model)}
+                      className="w-full flex items-center gap-3 px-3 py-2.5 text-left hover:bg-muted/50 transition-colors group"
+                    >
+                      {/* Status indicator */}
+                      <div className="shrink-0">
+                        {hasError ? (
+                          <CircleX className="h-4 w-4 text-destructive" />
+                        ) : isDownloading ? (
+                          <Loader2 className="h-4 w-4 animate-spin text-muted-foreground" />
+                        ) : model.loaded ? (
+                          <CircleCheck className="h-4 w-4 text-accent" />
+                        ) : model.downloaded ? (
+                          <CircleCheck className="h-4 w-4 text-emerald-500" />
+                        ) : (
+                          <Download className="h-4 w-4 text-muted-foreground/50" />
+                        )}
+                      </div>
+
+                      {/* Name + inline progress */}
+                      <div className="flex-1 min-w-0">
+                        <span className="text-sm font-medium">{model.display_name}</span>
+                        {isDownloading &&
+                          (() => {
+                            const dl = downloadProgressMap.get(model.model_name);
+                            const pct = dl?.progress ?? 0;
+                            const hasProgress = dl && dl.total && dl.total > 0;
+                            return (
+                              <div className="mt-1 space-y-0.5">
+                                <Progress value={hasProgress ? pct : undefined} className="h-1" />
+                                <div className="text-[10px] text-muted-foreground truncate">
+                                  {hasProgress
+                                    ? `${formatBytes(dl.current ?? 0)} / ${formatBytes(dl.total!)} (${pct.toFixed(0)}%)`
+                                    : dl?.filename || 'Connecting...'}
+                                </div>
+                              </div>
+                            );
+                          })()}
+                      </div>
+
+                      {/* Right side info */}
+                      <div className="shrink-0 flex items-center gap-2">
+                        {hasError && (
+                          <Badge variant="destructive" className="text-[10px] h-5">
+                            Error
+                          </Badge>
+                        )}
+                        {model.loaded && (
+                          <Badge className="text-[10px] h-5 bg-accent/15 text-accent border-accent/30 hover:bg-accent/15">
+                            Loaded
+                          </Badge>
+                        )}
+                        {model.downloaded && !isDownloading && !hasError && (
+                          <span className="text-xs text-muted-foreground">
+                            {formatSize(model.size_mb)}
+                          </span>
+                        )}
+                        {!model.downloaded && !isDownloading && !hasError && (
+                          <span className="text-xs text-muted-foreground/60">Not downloaded</span>
+                        )}
+                        <ChevronRight className="h-4 w-4 text-muted-foreground/40 group-hover:text-muted-foreground transition-colors" />
+                      </div>
+                    </button>
+                  );
+                })}
+              </div>
+            </div>
+          ))}
+
+          {/* Error console */}
+          {errorCount > 0 && (
+            <div className="border rounded-lg overflow-hidden">
+              <div className="flex items-center justify-between px-3 py-1.5 bg-muted/50 text-xs font-medium text-muted-foreground">
+                <button
+                  type="button"
+                  onClick={() => setConsoleOpen((v) => !v)}
+                  className="flex items-center gap-2 hover:text-foreground transition-colors"
+                >
+                  {consoleOpen ? (
+                    <ChevronUp className="h-3.5 w-3.5" />
+                  ) : (
+                    <ChevronDown className="h-3.5 w-3.5" />
+                  )}
+                  <span>Problems</span>
+                  <Badge variant="destructive" className="text-[10px] h-4 px-1.5 rounded-full">
+                    {errorCount}
+                  </Badge>
+                </button>
+                <Button
+                  size="sm"
+                  variant="ghost"
+                  className="h-6 px-2 text-xs text-muted-foreground hover:text-foreground"
+                  onClick={() => clearAllMutation.mutate()}
+                  disabled={clearAllMutation.isPending}
+                >
+                  <RotateCcw className="h-3 w-3 mr-1" />
+                  Clear All
+                </Button>
+              </div>
+              {consoleOpen && (
+                <div className="bg-[#1e1e1e] text-[#d4d4d4] p-3 max-h-48 overflow-auto font-mono text-xs leading-relaxed">
+                  {Array.from(erroredDownloads.entries()).map(([modelName, dl]) => (
+                    <div key={modelName} className="mb-2 last:mb-0">
+                      <span className="text-[#f44747]">[error]</span>{' '}
+                      <span className="text-[#569cd6]">{modelName}</span>
+                      {dl.error ? (
+                        <>
+                          {': '}
+                          <span className="text-[#ce9178] whitespace-pre-wrap break-all">
+                            {dl.error}
+                          </span>
+                        </>
+                      ) : (
+                        <>
+                          {': '}
+                          <span className="text-[#808080]">
+                            No error details available. Try downloading again.
+                          </span>
+                        </>
+                      )}
+                      <div className="text-[#6a9955] mt-0.5">
+                        started at {new Date(dl.started_at).toLocaleString()}
+                      </div>
+                    </div>
+                  ))}
+                </div>
+              )}
+            </div>
+          )}
+        </div>
+      ) : null}
+
+      {/* Model Detail Modal */}
+      <Dialog open={detailOpen} onOpenChange={setDetailOpen}>
+        <DialogContent className="sm:max-w-md">
+          {freshSelectedModel && (
+            <>
+              <DialogHeader>
+                <DialogTitle>{freshSelectedModel.display_name}</DialogTitle>
+                <DialogDescription className="flex items-center gap-1.5">
+                  {freshSelectedModel.hf_repo_id ? (
+                    <a
+                      href={`https://huggingface.co/${freshSelectedModel.hf_repo_id}`}
+                      target="_blank"
+                      rel="noopener noreferrer"
+                      className="inline-flex items-center gap-1 hover:underline"
+                    >
+                      {freshSelectedModel.hf_repo_id}
+                      <ExternalLink className="h-3 w-3" />
+                    </a>
+                  ) : (
+                    freshSelectedModel.model_name
+                  )}
+                </DialogDescription>
+              </DialogHeader>
+
+              <div className="space-y-4 pt-2">
+                {/* Status badges */}
+                <div className="flex items-center gap-2 flex-wrap">
+                  {freshSelectedModel.loaded && (
+                    <Badge className="text-xs bg-accent/15 text-accent border-accent/30 hover:bg-accent/15">
+                      <CircleCheck className="h-3 w-3 mr-1" />
+                      Loaded
+                    </Badge>
+                  )}
+                  {freshSelectedModel.downloaded && !freshSelectedModel.loaded && (
+                    <Badge variant="secondary" className="text-xs">
+                      <CircleCheck className="h-3 w-3 mr-1" />
+                      Downloaded
+                    </Badge>
+                  )}
+                  {selectedState?.hasError && (
+                    <Badge variant="destructive" className="text-xs">
+                      <CircleX className="h-3 w-3 mr-1" />
+                      Error
+                    </Badge>
+                  )}
+                  {!freshSelectedModel.downloaded &&
+                    !selectedState?.isDownloading &&
+                    !selectedState?.hasError && (
+                      <Badge variant="outline" className="text-xs text-muted-foreground">
+                        Not downloaded
+                      </Badge>
+                    )}
+                </div>
+
+                {/* HuggingFace model card info */}
+                {hfLoading && freshSelectedModel.hf_repo_id && (
+                  <div className="flex items-center gap-2 text-xs text-muted-foreground py-2">
+                    <Loader2 className="h-3 w-3 animate-spin" />
+                    Loading model info...
+                  </div>
+                )}
+
+                {hfModelInfo && (
+                  <div className="space-y-3">
+                    {/* Stats row */}
+                    <div className="flex items-center gap-4 text-xs text-muted-foreground">
+                      <span className="flex items-center gap-1" title="Downloads">
+                        <Download className="h-3.5 w-3.5" />
+                        {formatDownloads(hfModelInfo.downloads)}
+                      </span>
+                      <span className="flex items-center gap-1" title="Likes">
+                        <Heart className="h-3.5 w-3.5" />
+                        {formatDownloads(hfModelInfo.likes)}
+                      </span>
+                      {license && (
+                        <span className="flex items-center gap-1" title="License">
+                          <Scale className="h-3.5 w-3.5" />
+                          {formatLicense(license)}
+                        </span>
+                      )}
+                    </div>
+
+                    {/* Pipeline tag + author */}
+                    <div className="flex flex-wrap gap-1.5">
+                      {hfModelInfo.pipeline_tag && (
+                        <Badge variant="outline" className="text-[10px]">
+                          {formatPipelineTag(hfModelInfo.pipeline_tag)}
+                        </Badge>
+                      )}
+                      {hfModelInfo.library_name && (
+                        <Badge variant="outline" className="text-[10px]">
+                          {hfModelInfo.library_name}
+                        </Badge>
+                      )}
+                      {hfModelInfo.author && (
+                        <Badge variant="outline" className="text-[10px]">
+                          by {hfModelInfo.author}
+                        </Badge>
+                      )}
+                    </div>
+
+                    {/* Languages */}
+                    {hfModelInfo.cardData?.language && hfModelInfo.cardData.language.length > 0 && (
+                      <div>
+                        <span className="text-xs text-muted-foreground">
+                          {hfModelInfo.cardData.language.length > 10
+                            ? `${hfModelInfo.cardData.language.length} languages supported`
+                            : `Languages: ${hfModelInfo.cardData.language.join(', ')}`}
+                        </span>
+                      </div>
+                    )}
+                  </div>
+                )}
+
+                {/* Disk size */}
+                {freshSelectedModel.downloaded && freshSelectedModel.size_mb && (
+                  <div className="flex items-center gap-2 text-sm text-muted-foreground">
+                    <HardDrive className="h-4 w-4" />
+                    <span>{formatSize(freshSelectedModel.size_mb)} on disk</span>
+                  </div>
+                )}
+
+                {/* Error detail */}
+                {selectedError?.error && (
+                  <div className="rounded-md bg-destructive/10 border border-destructive/20 p-3 text-xs text-destructive">
+                    {selectedError.error}
+                  </div>
+                )}
+
+                {/* Actions */}
+                <div className="flex items-center gap-2 pt-2 border-t">
+                  {selectedState?.hasError ? (
+                    <>
+                      <Button
+                        size="sm"
+                        onClick={() => handleDownload(freshSelectedModel.model_name)}
+                        variant="outline"
+                        className="flex-1"
+                      >
+                        <Download className="h-4 w-4 mr-2" />
+                        Retry Download
+                      </Button>
+                      <Button
+                        size="sm"
+                        onClick={() => handleCancel(freshSelectedModel.model_name)}
+                        variant="ghost"
+                        disabled={
+                          cancelMutation.isPending &&
+                          cancelMutation.variables === freshSelectedModel.model_name
+                        }
+                      >
+                        <X className="h-4 w-4" />
+                      </Button>
+                    </>
+                  ) : selectedState?.isDownloading ? (
+                    <>
+                      <div className="flex-1 space-y-2">
+                        {(() => {
+                          const dl = freshSelectedModel
+                            ? downloadProgressMap.get(freshSelectedModel.model_name)
+                            : undefined;
+                          const pct = dl?.progress ?? 0;
+                          const hasProgress = dl && dl.total && dl.total > 0;
+                          return (
+                            <>
+                              <Progress value={hasProgress ? pct : undefined} className="h-2" />
+                              <div className="text-xs text-muted-foreground">
+                                {hasProgress
+                                  ? `${formatBytes(dl.current ?? 0)} / ${formatBytes(dl.total!)} (${pct.toFixed(1)}%)`
+                                  : dl?.filename || 'Connecting to HuggingFace...'}
+                              </div>
+                            </>
+                          );
+                        })()}
+                      </div>
+                      <Button
+                        size="sm"
+                        onClick={() => handleCancel(freshSelectedModel.model_name)}
+                        variant="ghost"
+                        disabled={
+                          cancelMutation.isPending &&
+                          cancelMutation.variables === freshSelectedModel.model_name
+                        }
+                      >
+                        <X className="h-4 w-4" />
+                      </Button>
+                    </>
+                  ) : freshSelectedModel.downloaded ? (
+                    <Button
+                      size="sm"
+                      onClick={() => {
                         setModelToDelete({
-                          name: model.model_name,
-                          displayName: model.display_name,
-                          sizeMb: model.size_mb,
+                          name: freshSelectedModel.model_name,
+                          displayName: freshSelectedModel.display_name,
+                          sizeMb: freshSelectedModel.size_mb,
                         });
                         setDeleteDialogOpen(true);
                       }}
-                      isDownloading={downloadingModel === model.model_name}
-                      formatSize={formatSize}
-                    />
-                  ))}
+                      variant="outline"
+                      disabled={freshSelectedModel.loaded}
+                      title={
+                        freshSelectedModel.loaded ? 'Unload model before deleting' : 'Delete model'
+                      }
+                      className="flex-1"
+                    >
+                      <Trash2 className="h-4 w-4 mr-2" />
+                      {freshSelectedModel.loaded ? 'Unload to Delete' : 'Delete Model'}
+                    </Button>
+                  ) : (
+                    <Button
+                      size="sm"
+                      onClick={() => handleDownload(freshSelectedModel.model_name)}
+                      className="flex-1"
+                    >
+                      <Download className="h-4 w-4 mr-2" />
+                      Download
+                    </Button>
+                  )}
+                </div>
               </div>
-            </div>
-
-            {/* Whisper Models */}
-            <div>
-              <h3 className="text-sm font-semibold mb-3 text-muted-foreground">
-                Transcription Models
-              </h3>
-              <div className="space-y-2">
-                {modelStatus.models
-                  .filter((m) => m.model_name.startsWith('whisper'))
-                  .map((model) => (
-                    <ModelItem
-                      key={model.model_name}
-                      model={model}
-                      onDownload={() => downloadMutation.mutate(model.model_name)}
-                      onDelete={() => {
-                        setModelToDelete({
-                          name: model.model_name,
-                          displayName: model.display_name,
-                          sizeMb: model.size_mb,
-                        });
-                        setDeleteDialogOpen(true);
-                      }}
-                      isDownloading={downloadingModel === model.model_name}
-                      formatSize={formatSize}
-                    />
-                  ))}
-              </div>
-            </div>
-
-            {/* Progress indicators */}
-            <div className="pt-4 border-t">
-              <h3 className="text-sm font-semibold mb-3 text-muted-foreground">
-                Download Progress
-              </h3>
-              <div className="space-y-2">
-                {modelStatus.models.map((model) => (
-                  <ModelProgress
-                    key={model.model_name}
-                    modelName={model.model_name}
-                    displayName={model.display_name}
-                  />
-                ))}
-              </div>
-            </div>
-          </div>
-        ) : null}
-      </CardContent>
+            </>
+          )}
+        </DialogContent>
+      </Dialog>
 
       {/* Delete Confirmation Dialog */}
       <AlertDialog open={deleteDialogOpen} onOpenChange={setDeleteDialogOpen}>
@@ -226,79 +773,6 @@ export function ModelManagement() {
           </AlertDialogFooter>
         </AlertDialogContent>
       </AlertDialog>
-    </Card>
-  );
-}
-
-interface ModelItemProps {
-  model: {
-    model_name: string;
-    display_name: string;
-    downloaded: boolean;
-    size_mb?: number;
-    loaded: boolean;
-  };
-  onDownload: () => void;
-  onDelete: () => void;
-  isDownloading: boolean;
-  formatSize: (sizeMb?: number) => string;
-}
-
-function ModelItem({ model, onDownload, onDelete, isDownloading, formatSize }: ModelItemProps) {
-  return (
-    <div className="flex items-center justify-between p-3 border rounded-lg">
-      <div className="flex-1">
-        <div className="flex items-center gap-2">
-          <span className="font-medium text-sm">{model.display_name}</span>
-          {model.loaded && (
-            <Badge variant="default" className="text-xs">
-              Loaded
-            </Badge>
-          )}
-          {model.downloaded && !model.loaded && (
-            <Badge variant="secondary" className="text-xs">
-              Downloaded
-            </Badge>
-          )}
-        </div>
-        {model.downloaded && model.size_mb && (
-          <div className="text-xs text-muted-foreground mt-1">
-            Size: {formatSize(model.size_mb)}
-          </div>
-        )}
-      </div>
-      <div className="flex items-center gap-2">
-        {model.downloaded ? (
-          <div className="flex items-center gap-2">
-            <div className="flex items-center gap-1 text-sm text-muted-foreground">
-              <span>Ready</span>
-            </div>
-            <Button
-              size="sm"
-              onClick={onDelete}
-              variant="outline"
-              disabled={model.loaded}
-              title={model.loaded ? 'Unload model before deleting' : 'Delete model'}
-            >
-              <Trash2 className="h-4 w-4" />
-            </Button>
-          </div>
-        ) : (
-          <Button size="sm" onClick={onDownload} disabled={isDownloading} variant="outline">
-            {isDownloading ? (
-              <>
-                <Loader2 className="h-4 w-4 mr-2 animate-spin" />
-                Downloading...
-              </>
-            ) : (
-              <>
-                <Download className="h-4 w-4 mr-2" />
-                Download
-              </>
-            )}
-          </Button>
-        )}
-      </div>
     </div>
   );
 }

app/src/components/ServerSettings/ModelProgress.tsx

@@ -8,15 +8,23 @@ import { useServerStore } from '@/stores/serverStore';
 interface ModelProgressProps {
   modelName: string;
   displayName: string;
+  /** Only connect to SSE when actively downloading - prevents connection exhaustion */
+  isDownloading?: boolean;
 }
 
-export function ModelProgress({ modelName, displayName }: ModelProgressProps) {
+export function ModelProgress({ modelName, displayName, isDownloading = false }: ModelProgressProps) {
   const [progress, setProgress] = useState<ModelProgressType | null>(null);
-  const [isSubscribed, setIsSubscribed] = useState(false);
   const serverUrl = useServerStore((state) => state.serverUrl);
 
   useEffect(() => {
-    if (!serverUrl || isSubscribed) return;
+    // IMPORTANT: Only connect to SSE when this specific model is downloading
+    // Opening SSE connections for all models exhausts HTTP/1.1 connection limits (6 per origin)
+    // which causes other fetches (like the download trigger) to be queued/blocked
+    if (!serverUrl || !isDownloading) {
+      return;
+    }
+
+    console.log(`[ModelProgress] Connecting SSE for ${modelName}`);
 
     // Subscribe to progress updates via Server-Sent Events
     const eventSource = new EventSource(`${serverUrl}/models/progress/${modelName}`);
@@ -28,8 +36,8 @@ export function ModelProgress({ modelName, displayName }: ModelProgressProps) {
 
         // Close connection if complete or error
         if (data.status === 'complete' || data.status === 'error') {
+          console.log(`[ModelProgress] Download ${data.status} for ${modelName}, closing SSE`);
           eventSource.close();
-          setIsSubscribed(false);
         }
       } catch (error) {
         console.error('Error parsing progress event:', error);
@@ -37,18 +45,15 @@ export function ModelProgress({ modelName, displayName }: ModelProgressProps) {
     };
 
     eventSource.onerror = (error) => {
-      console.error('SSE error:', error);
+      console.error(`[ModelProgress] SSE error for ${modelName}:`, error);
       eventSource.close();
-      setIsSubscribed(false);
     };
 
-    setIsSubscribed(true);
-
     return () => {
+      console.log(`[ModelProgress] Cleanup - closing SSE for ${modelName}`);
       eventSource.close();
-      setIsSubscribed(false);
     };
-  }, [serverUrl, modelName, isSubscribed]);
+  }, [serverUrl, modelName, isDownloading]);
 
   // 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 +5,19 @@ 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'));
-  }, []);
+      .catch(() => setCurrentVersion('Unknown'));
+  }, [platform]);
 
   return (
     <Card>

app/src/components/ServerTab/ServerTab.tsx

@@ -1,16 +1,19 @@
 import { ConnectionForm } from '@/components/ServerSettings/ConnectionForm';
+import { GpuAcceleration } from '@/components/ServerSettings/GpuAcceleration';
 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 && <GpuAcceleration />}
+      {platform.metadata.isTauri && <UpdateStatus />}
       <div className="py-8 text-center text-sm text-muted-foreground">
         Created by{' '}
         <a

app/src/components/Sidebar.tsx

@@ -1,5 +1,5 @@
-import { Link, useMatchRoute } from '@tanstack/react-router';
-import { Box, BookOpen, Loader2, Mic, Server, Speaker, Volume2 } from 'lucide-react';
+import { Link, useMatchRoute, useRouterState } from '@tanstack/react-router';
+import { BookOpen, BookText, Box, Loader2, Mic, Server, Speaker, Volume2 } from 'lucide-react';
 import voiceboxLogo from '@/assets/voicebox-logo.png';
 import { cn } from '@/lib/utils/cn';
 import { useGenerationStore } from '@/stores/generationStore';
@@ -12,6 +12,7 @@ interface SidebarProps {
 const tabs = [
   { id: 'main', path: '/', icon: Volume2, label: 'Generate' },
   { id: 'stories', path: '/stories', icon: BookOpen, label: 'Stories' },
+  { id: 'audiobook', path: '/audiobook', icon: BookText, label: 'Audiobook' },
   { id: 'voices', path: '/voices', icon: Mic, label: 'Voices' },
   { id: 'audio', path: '/audio', icon: Speaker, label: 'Audio' },
   { id: 'models', path: '/models', icon: Box, label: 'Models' },
@@ -23,6 +24,9 @@ export function Sidebar({ isMacOS }: SidebarProps) {
   const audioUrl = usePlayerStore((state) => state.audioUrl);
   const isPlayerVisible = !!audioUrl;
   const matchRoute = useMatchRoute();
+  const pathname = useRouterState({
+    select: (state) => state.location.pathname,
+  });
 
   return (
     <div
@@ -41,10 +45,7 @@ export function Sidebar({ isMacOS }: SidebarProps) {
         {tabs.map((tab) => {
           const Icon = tab.icon;
           // For index route, use exact match; for others, use default matching
-          const isActive =
-            tab.path === '/'
-              ? matchRoute({ to: '/', exact: true })
-              : matchRoute({ to: tab.path });
+          const isActive = tab.path === '/' ? pathname === '/' : matchRoute({ to: tab.path });
 
           return (
             <Link

app/src/components/StoriesTab/StoriesTab.tsx

@@ -1,8 +1,11 @@
 import { FloatingGenerateBox } from '@/components/Generation/FloatingGenerateBox';
+import { usePlayerStore } from '@/stores/playerStore';
 import { StoryContent } from './StoryContent';
 import { StoryList } from './StoryList';
 
 export function StoriesTab() {
+  const audioUrl = usePlayerStore((state) => state.audioUrl);
+
   return (
     <div className="flex flex-col h-full min-h-0 overflow-hidden">
       {/* Main content area */}
@@ -18,7 +21,7 @@ export function StoriesTab() {
         </div>
 
         {/* Floating Generate Box - position is managed via storyStore.trackEditorHeight */}
-        <FloatingGenerateBox showVoiceSelector />
+        <FloatingGenerateBox showVoiceSelector isPlayerOpen={!!audioUrl} />
       </div>
     </div>
   );

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">
-          <Mic className="h-5 w-5 text-muted-foreground" />
+        <div className="h-10 w-10 rounded-full bg-muted flex items-center justify-center overflow-hidden">
+          {!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/StoryContent.tsx

@@ -131,13 +131,13 @@ export function StoryContent() {
     }
   }, [isPlaying]);
 
-  const handleRemoveItem = (generationId: string) => {
+  const handleRemoveItem = (itemId: string) => {
     if (!story) return;
 
     removeItem.mutate(
       {
         storyId: story.id,
-        generationId,
+        itemId,
       },
       {
         onError: (error) => {
@@ -360,7 +360,7 @@ export function StoryContent() {
                       item={item}
                       storyId={story.id}
                       index={index}
-                      onRemove={() => handleRemoveItem(item.generation_id)}
+                      onRemove={() => handleRemoveItem(item.id)}
                       currentTimeMs={currentTimeMs}
                       isPlaying={isPlaying && playbackStoryId === story.id}
                     />

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 {
-  useStories,
-  useCreateStory,
-  useUpdateStory,
-  useDeleteStory,
-} from '@/lib/hooks/useStories';
-import { useStoryStore } from '@/stores/storyStore';
+import { useStories, useCreateStory, useUpdateStory, useDeleteStory } from '@/lib/hooks/useStories';
 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>
-              Update the story name and description.
-            </DialogDescription>
+            <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/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 +51,68 @@ 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;
+    if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) 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]">
-              <Button type="button" onClick={onStart} size="lg" className="flex items-center gap-2">
+            <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">
+              {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="flex items-center gap-4">
+            <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">
+              {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 +121,12 @@ export function AudioSampleRecording({
               <Button
                 type="button"
                 onClick={onStop}
-                variant="destructive"
-                className="flex items-center gap-2"
+                className="relative z-10 flex items-center gap-2 bg-accent text-accent-foreground hover:bg-accent/90"
               >
                 <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">
-              <Mic className="h-3.5 w-3.5 text-muted-foreground" />
+            <div className="h-6 w-6 rounded-full bg-muted flex items-center justify-center shrink-0 overflow-hidden">
+              {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 { useEffect, useState } from 'react';
+import { Edit2, Mic, Monitor, Upload, X } from 'lucide-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 { convertToWav, formatAudioDuration, getAudioDuration } from '@/lib/utils/audio';
+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.`,
@@ -363,12 +505,43 @@ export function ProfileForm() {
           language: data.language,
         });
 
+        // Convert non-WAV uploads to WAV so the backend can always use soundfile.
+        // Recorded audio is already WAV (from useAudioRecording's convertToWav call).
+        let fileToUpload: File = sampleFile;
+        if (!sampleFile.type.includes('wav') && !sampleFile.name.toLowerCase().endsWith('.wav')) {
+          try {
+            const wavBlob = await convertToWav(sampleFile);
+            const wavName = sampleFile.name.replace(/\.[^.]+$/, '.wav');
+            fileToUpload = new File([wavBlob], wavName, { type: 'audio/wav' });
+          } catch {
+            // If browser can't decode the format, send the original and let the backend try.
+          }
+        }
+
         try {
           await addSample.mutateAsync({
             profileId: profile.id,
-            file: sampleFile,
+            file: fileToUpload,
             referenceText: referenceText,
           });
+
+          // Handle avatar upload if provided
+          if (data.avatarFile) {
+            try {
+              await uploadAvatar.mutateAsync({
+                profileId: profile.id,
+                file: data.avatarFile,
+              });
+            } catch (avatarError) {
+              toast({
+                title: 'Avatar upload failed',
+                description:
+                  avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
+                variant: 'destructive',
+              });
+            }
+          }
+
           toast({
             title: 'Profile created',
             description: `"${data.name}" has been created with a sample.`,
@@ -383,6 +556,8 @@ export function ProfileForm() {
         }
       }
 
+      // Clear draft and reset form on success
+      setProfileFormDraft(null);
       form.reset();
       setEditingProfileId(null);
       setOpen(false);
@@ -395,12 +570,41 @@ export function ProfileForm() {
     }
   }
 
-  function handleOpenChange(open: boolean) {
-    setOpen(open);
-    if (!open) {
+  async function handleOpenChange(newOpen: boolean) {
+    if (!newOpen && isCreating) {
+      // 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();
-      setSampleMode('upload');
+      // Don't reset form here - let the effect handle it based on draft state
       if (isRecording) {
         cancelRecording();
       }
@@ -413,174 +617,119 @@ export function ProfileForm() {
 
   return (
     <Dialog open={open} onOpenChange={handleOpenChange}>
-      <DialogContent className="max-w-4xl">
-        <DialogHeader>
-          <DialogTitle>{editingProfileId ? 'Edit Voice' : 'Create Voice Profile'}</DialogTitle>
-          <DialogDescription>
-            {editingProfileId
-              ? 'Update your voice profile details and manage samples.'
-              : 'Create a new voice profile with an audio sample to clone the voice.'}
-          </DialogDescription>
-        </DialogHeader>
-
-        <Form {...form}>
-          <form onSubmit={form.handleSubmit(onSubmit)}>
-            <div className="grid gap-6 grid-cols-2">
-              {/* Left column: Profile info */}
-              <div className="space-y-4">
-                <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>
-                  )}
-                />
+      <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">
+        <div className="max-w-5xl max-h-[85vh] mx-auto my-auto w-full flex flex-col">
+          <DialogHeader>
+            <DialogTitle className="text-2xl">
+              {editingProfileId ? 'Edit Voice' : 'Clone voice'}
+            </DialogTitle>
+            <DialogDescription>
+              {editingProfileId
+                ? 'Update your voice profile details and manage samples.'
+                : 'Create a new voice profile with an audio sample to clone the voice.'}
+            </DialogDescription>
+            {isCreating && profileFormDraft && (
+              <div className="flex items-center gap-2 pt-2">
+                <span className="text-xs text-muted-foreground">Draft restored</span>
+                <Button
+                  type="button"
+                  variant="ghost"
+                  size="sm"
+                  className="h-6 px-2 text-xs text-muted-foreground"
+                  onClick={() => {
+                    setProfileFormDraft(null);
+                    form.reset({
+                      name: '',
+                      description: '',
+                      language: 'en',
+                      sampleFile: undefined,
+                      referenceText: '',
+                    });
+                    setSampleMode('record');
+                  }}
+                >
+                  <X className="h-3 w-3 mr-1" />
+                  Discard
+                </Button>
               </div>
+            )}
+          </DialogHeader>
 
-              {/* Right column: Sample management */}
-              <div className="space-y-4 border-l pl-6">
-                {isCreating ? (
-                  <>
-                    <div>
-                      <h3 className="text-sm font-medium mb-2">Add Sample</h3>
-                      <p className="text-sm text-muted-foreground mb-4">
-                        Provide an audio sample to clone the voice. You can add more samples later.
-                      </p>
-                    </div>
-
-                    <Tabs
-                      value={sampleMode}
-                      onValueChange={(v) => {
-                        const newMode = v as 'upload' | 'record' | 'system';
-                        // Cancel any active recordings when switching modes
-                        if (isRecording && newMode !== 'record') {
-                          cancelRecording();
-                        }
-                        if (isSystemRecording && newMode !== 'system') {
-                          cancelSystemRecording();
-                        }
-                        setSampleMode(newMode);
-                      }}
-                    >
-                      <TabsList
-                        className={`grid w-full ${isTauri() && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
+          <Form {...form}>
+            <form onSubmit={form.handleSubmit(onSubmit)} className="flex-1 min-h-0 flex flex-col">
+              <div className="grid gap-6 grid-cols-2 flex-1 overflow-y-auto min-h-0">
+                {/* Left column: Sample management */}
+                <div className="space-y-4 border-r pr-6">
+                  {isCreating ? (
+                    <>
+                      <Tabs
+                        className="pt-4"
+                        value={sampleMode}
+                        onValueChange={(v) => {
+                          const newMode = v as 'upload' | 'record' | 'system';
+                          // Cancel any active recordings when switching modes
+                          if (isRecording && newMode !== 'record') {
+                            cancelRecording();
+                          }
+                          if (isSystemRecording && newMode !== 'system') {
+                            cancelSystemRecording();
+                          }
+                          setSampleMode(newMode);
+                        }}
                       >
-                        <TabsTrigger value="upload" className="flex items-center gap-2">
-                          <Upload className="h-4 w-4 shrink-0" />
-                          Upload
-                        </TabsTrigger>
-                        <TabsTrigger value="record" className="flex items-center gap-2">
-                          <Mic className="h-4 w-4 shrink-0" />
-                          Record
-                        </TabsTrigger>
-                        {isTauri() && isSystemAudioSupported && (
-                          <TabsTrigger value="system" className="flex items-center gap-2">
-                            <Monitor className="h-4 w-4 shrink-0" />
-                            System Audio
+                        <TabsList
+                          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" />
+                            Upload
                           </TabsTrigger>
-                        )}
-                      </TabsList>
-
-                      <TabsContent value="upload" className="space-y-4">
-                        <FormField
-                          control={form.control}
-                          name="sampleFile"
-                          render={({ field: { onChange, name } }) => (
-                            <AudioSampleUpload
-                              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}
-                            />
+                          <TabsTrigger value="record" className="flex items-center gap-2">
+                            <Mic className="h-4 w-4 shrink-0" />
+                            Record
+                          </TabsTrigger>
+                          {platform.metadata.isTauri && isSystemAudioSupported && (
+                            <TabsTrigger value="system" className="flex items-center gap-2">
+                              <Monitor className="h-4 w-4 shrink-0" />
+                              System Audio
+                            </TabsTrigger>
                           )}
-                        />
-                      </TabsContent>
+                        </TabsList>
 
-                      <TabsContent value="record" className="space-y-4">
-                        <FormField
-                          control={form.control}
-                          name="sampleFile"
-                          render={() => (
-                            <AudioSampleRecording
-                              file={selectedFile}
-                              isRecording={isRecording}
-                              duration={duration}
-                              onStart={startRecording}
-                              onStop={stopRecording}
-                              onCancel={handleCancelRecording}
-                              onTranscribe={handleTranscribe}
-                              onPlayPause={handlePlayPause}
-                              isPlaying={isPlaying}
-                              isTranscribing={transcribe.isPending}
-                            />
-                          )}
-                        />
-                      </TabsContent>
+                        <TabsContent value="upload" className="space-y-4">
+                          <FormField
+                            control={form.control}
+                            name="sampleFile"
+                            render={({ field: { onChange, name } }) => (
+                              <AudioSampleUpload
+                                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}
+                              />
+                            )}
+                          />
+                        </TabsContent>
 
-                      {isTauri() && isSystemAudioSupported && (
-                        <TabsContent value="system" className="space-y-4">
+                        <TabsContent value="record" className="space-y-4">
                           <FormField
                             control={form.control}
                             name="sampleFile"
                             render={() => (
-                              <AudioSampleSystem
+                              <AudioSampleRecording
                                 file={selectedFile}
-                                isRecording={isSystemRecording}
-                                duration={systemDuration}
-                                onStart={startSystemRecording}
-                                onStop={stopSystemRecording}
+                                isRecording={isRecording}
+                                duration={duration}
+                                onStart={startRecording}
+                                onStop={stopRecording}
                                 onCancel={handleCancelRecording}
                                 onTranscribe={handleTranscribe}
                                 onPlayPause={handlePlayPause}
@@ -590,55 +739,188 @@ export function ProfileForm() {
                             )}
                           />
                         </TabsContent>
-                      )}
-                    </Tabs>
 
-                    <FormField
-                      control={form.control}
-                      name="referenceText"
-                      render={({ field }) => (
-                        <FormItem>
-                          <FormLabel>Reference Text</FormLabel>
-                          <FormControl>
-                            <Textarea
-                              placeholder="Enter the exact text spoken in the audio..."
-                              className="min-h-[100px]"
-                              {...field}
+                        {platform.metadata.isTauri && isSystemAudioSupported && (
+                          <TabsContent value="system" className="space-y-4">
+                            <FormField
+                              control={form.control}
+                              name="sampleFile"
+                              render={() => (
+                                <AudioSampleSystem
+                                  file={selectedFile}
+                                  isRecording={isSystemRecording}
+                                  duration={systemDuration}
+                                  onStart={startSystemRecording}
+                                  onStop={stopSystemRecording}
+                                  onCancel={handleCancelRecording}
+                                  onTranscribe={handleTranscribe}
+                                  onPlayPause={handlePlayPause}
+                                  isPlaying={isPlaying}
+                                  isTranscribing={transcribe.isPending}
+                                />
+                              )}
                             />
-                          </FormControl>
-                          <FormMessage />
-                        </FormItem>
-                      )}
-                    />
-                  </>
-                ) : (
-                  // Show sample list when editing
-                  editingProfileId && (
-                    <div>
-                      <SampleList profileId={editingProfileId} />
-                    </div>
-                  )
-                )}
-              </div>
-            </div>
+                          </TabsContent>
+                        )}
+                      </Tabs>
 
-            <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>
+                      <FormField
+                        control={form.control}
+                        name="referenceText"
+                        render={({ field }) => (
+                          <FormItem>
+                            <FormLabel>Reference Text</FormLabel>
+                            <FormControl>
+                              <Textarea
+                                placeholder="Enter the exact text spoken in the audio..."
+                                className="min-h-[100px]"
+                                {...field}
+                              />
+                            </FormControl>
+                            <FormMessage />
+                          </FormItem>
+                        )}
+                      />
+                    </>
+                  ) : (
+                    // 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 { useState } from 'react';
+import { Check, Edit, Pause, Play, Plus, Trash2, Volume2, X } from 'lucide-react';
+import { useEffect, useRef, useState } from 'react';
 import { Button } from '@/components/ui/button';
-import { useDeleteSample, useProfileSamples } from '@/lib/hooks/useProfiles';
-import { usePlayerStore } from '@/stores/playerStore';
+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, useUpdateSample } from '@/lib/hooks/useProfiles';
+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 setAudio = usePlayerStore((state) => state.setAudio);
-  const currentAudioId = usePlayerStore((state) => state.audioId);
-  const isPlaying = usePlayerStore((state) => state.isPlaying);
+  const [editingSampleId, setEditingSampleId] = useState<string | null>(null);
+  const [editedText, setEditedText] = useState<string>('');
+  const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
+  const [sampleToDelete, setSampleToDelete] = useState<string | null>(null);
 
-  const handleDelete = (sampleId: string) => {
-    if (confirm('Are you sure you want to delete this sample?')) {
-      deleteSample.mutate(sampleId);
+  const handleDeleteClick = (sampleId: string) => {
+    setSampleToDelete(sampleId);
+    setDeleteDialogOpen(true);
+  };
+
+  const handleDeleteConfirm = () => {
+    if (sampleToDelete) {
+      deleteSample.mutate(sampleToDelete);
+      setDeleteDialogOpen(false);
+      setSampleToDelete(null);
     }
   };
 
-  const handlePlay = (referenceText: string, sampleId: string) => {
-    const audioUrl = apiClient.getSampleUrl(sampleId);
-    setAudio(audioUrl, sampleId, referenceText.substring(0, 50));
+  const handleStartEdit = (sampleId: string, currentText: string) => {
+    setEditingSampleId(sampleId);
+    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="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>
-
+    <div className="space-y-4 pt-4">
       {samples && samples.length === 0 ? (
-        <div className="text-sm text-muted-foreground py-4">
-          No samples yet. Add your first audio sample.
+        <div className="flex flex-col items-center justify-center py-8 text-center border border-dashed rounded-lg">
+          <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) => (
-            <div
-              key={sample.id}
-              className="flex items-center justify-between p-3 border rounded-lg"
-            >
-              <div className="flex-1">
-                <p className="text-sm font-medium">{sample.reference_text}</p>
-                <p className="text-xs text-muted-foreground mt-1">{sample.audio_path}</p>
+          {samples?.map((sample, index) => {
+            const isEditing = editingSampleId === sample.id;
+
+            return (
+              <div
+                key={sample.id}
+                className={cn(
+                  '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/VoicesTab/VoicesTab.tsx

@@ -79,8 +79,8 @@ export function VoicesTab() {
     setDialogOpen(true);
   };
 
-  const handleDelete = (profileId: string) => {
-    if (confirm('Are you sure you want to delete this profile?')) {
+  const handleProfileDelete = async (profileId: string) => {
+    if (await confirm('Are you sure you want to delete this profile?')) {
       deleteProfile.mutate(profileId);
     }
   };
@@ -147,7 +147,7 @@ export function VoicesTab() {
                 channels={channels || []}
                 onChannelChange={(channelIds) => handleChannelChange(profile.id, channelIds)}
                 onEdit={() => handleEdit(profile.id)}
-                onDelete={() => handleDelete(profile.id)}
+                onDelete={() => handleProfileDelete(profile.id)}
               />
             ))}
           </TableBody>

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 +1,47 @@
-import { relaunch } from '@tauri-apps/plugin-process';
-import { check, type Update } from '@tauri-apps/plugin-updater';
-import { useCallback, useEffect, useState } from 'react';
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { usePlatform } from '@/platform/PlatformContext';
+import type { UpdateStatus } from '@/platform/types';
 
-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;
-}
-
-// 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;
-};
+// Re-export UpdateStatus for backwards compatibility
+export type { UpdateStatus };
 
 export function useAutoUpdater(checkOnMount = false) {
-  const [status, setStatus] = useState<UpdateStatus>({
-    checking: false,
-    available: false,
-    downloading: false,
-    installing: false,
-    readyToInstall: false,
-  });
+  const platform = usePlatform();
+  const [status, setStatus] = useState<UpdateStatus>(platform.updater.getStatus());
+  const hasCheckedRef = useRef(false);
 
-  const [update, setUpdate] = useState<Update | null>(null);
+  // Subscribe to updater status changes
+  useEffect(() => {
+    const unsubscribe = platform.updater.subscribe((newStatus) => {
+      setStatus(newStatus);
+    });
+    return unsubscribe;
+    // Empty dependency array - platform is stable from context
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.subscribe]);
 
   const checkForUpdates = useCallback(async () => {
-    if (!isTauri()) {
-      return;
-    }
+    await platform.updater.checkForUpdates();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.checkForUpdates]);
 
-    try {
-      setStatus((prev) => ({ ...prev, checking: true, error: undefined }));
+  const downloadAndInstall = useCallback(async () => {
+    await platform.updater.downloadAndInstall();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.downloadAndInstall]);
 
-      const foundUpdate = await check();
-
-      if (foundUpdate?.available) {
-        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',
-      }));
-    }
-  };
+  const restartAndInstall = useCallback(async () => {
+    await platform.updater.restartAndInstall();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.restartAndInstall]);
 
   useEffect(() => {
-    if (checkOnMount && isTauri()) {
+    if (checkOnMount && platform.metadata.isTauri && !hasCheckedRef.current) {
+      hasCheckedRef.current = true;
       checkForUpdates();
     }
-  }, [checkOnMount, checkForUpdates]);
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.metadata.isTauricheckOnMountcheckForUpdates]);
 
   return {
     status,

app/src/hooks/useAutoUpdater.tsx

@@ -0,0 +1,209 @@
+import { Download, RefreshCw } from 'lucide-react';
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { Progress } from '@/components/ui/progress';
+import { ToastAction } from '@/components/ui/toast';
+import { useToast } from '@/components/ui/use-toast';
+import { usePlatform } from '@/platform/PlatformContext';
+import type { UpdateStatus } from '@/platform/types';
+
+// Re-export UpdateStatus for backwards compatibility
+export type { UpdateStatus };
+
+interface UseAutoUpdaterOptions {
+  checkOnMount?: boolean;
+  showToast?: boolean;
+}
+
+export function useAutoUpdater(options: boolean | UseAutoUpdaterOptions = false) {
+  // Support both old boolean API and new options object
+  const { checkOnMount, showToast } =
+    typeof options === 'boolean'
+      ? { checkOnMount: options, showToast: false }
+      : { checkOnMount: options.checkOnMount ?? false, showToast: options.showToast ?? false };
+
+  const platform = usePlatform();
+  const { toast } = useToast();
+  const [status, setStatus] = useState<UpdateStatus>(platform.updater.getStatus());
+  const hasCheckedRef = useRef(false);
+  const toastIdRef = useRef<string | null>(null);
+  const toastUpdateRef = useRef<
+    | ((props: {
+        title?: React.ReactNode;
+        description?: React.ReactNode;
+        duration?: number;
+        variant?: 'default' | 'destructive';
+        open?: boolean;
+        action?: React.ReactElement<typeof ToastAction>;
+      }) => void)
+    | null
+  >(null);
+
+  // Subscribe to updater status changes
+  useEffect(() => {
+    const unsubscribe = platform.updater.subscribe((newStatus) => {
+      setStatus(newStatus);
+    });
+    return unsubscribe;
+    // Empty dependency array - platform is stable from context
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.subscribe]);
+
+  const checkForUpdates = useCallback(async () => {
+    await platform.updater.checkForUpdates();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.checkForUpdates]);
+
+  const downloadAndInstall = useCallback(async () => {
+    await platform.updater.downloadAndInstall();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.downloadAndInstall]);
+
+  const restartAndInstall = useCallback(async () => {
+    await platform.updater.restartAndInstall();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.updater.restartAndInstall]);
+
+  // Check for updates on mount
+  useEffect(() => {
+    if (checkOnMount && platform.metadata.isTauri && !hasCheckedRef.current) {
+      hasCheckedRef.current = true;
+      checkForUpdates().catch((error) => {
+        console.error('Auto update check failed:', error);
+      });
+    }
+    // Empty dependency array - only run once on mount
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [platform.metadata.isTauricheckOnMountcheckForUpdates]);
+
+  // Show toast when update is available
+  useEffect(() => {
+    if (
+      !showToast ||
+      !status.available ||
+      status.downloading ||
+      status.readyToInstall ||
+      toastIdRef.current
+    ) {
+      return;
+    }
+
+    const handleUpdateNow = async () => {
+      await downloadAndInstall();
+    };
+
+    const toastResult = toast({
+      title: 'Update Available',
+      description: `Version ${status.version} is ready to download.`,
+      duration: Infinity,
+      action: (
+        <ToastAction altText="Update now" onClick={handleUpdateNow}>
+          Update Now
+        </ToastAction>
+      ),
+    });
+
+    toastIdRef.current = toastResult.id;
+    // Type assertion needed because update function has broader type than our ref
+    toastUpdateRef.current = toastResult.update as typeof toastUpdateRef.current;
+  }, [
+    showToast,
+    status.available,
+    status.downloading,
+    status.readyToInstall,
+    status.version,
+    downloadAndInstall,
+    toast,
+  ]);
+
+  // Update toast when downloading
+  useEffect(() => {
+    if (!showToast || !status.downloading || !toastIdRef.current || !toastUpdateRef.current) {
+      return;
+    }
+
+    const progressPercent = status.downloadProgress || 0;
+    const progressText =
+      status.downloadedBytes !== undefined &&
+      status.totalBytes !== undefined &&
+      status.totalBytes > 0
+        ? `${(status.downloadedBytes / 1024 / 1024).toFixed(1)} MB / ${(status.totalBytes / 1024 / 1024).toFixed(1)} MB`
+        : '';
+
+    toastUpdateRef.current({
+      title: (
+        <div className="flex items-center gap-2">
+          <Download className="h-4 w-4 animate-pulse" />
+          <span>Downloading Update</span>
+        </div>
+      ),
+      description: (
+        <div className="space-y-2">
+          <div className="text-sm">Version {status.version}</div>
+          {progressPercent > 0 && (
+            <>
+              <Progress value={progressPercent} className="h-2" />
+              {progressText && <div className="text-xs text-muted-foreground">{progressText}</div>}
+            </>
+          )}
+        </div>
+      ),
+      duration: Infinity,
+    });
+  }, [
+    showToast,
+    status.downloading,
+    status.downloadProgress,
+    status.downloadedBytes,
+    status.totalBytes,
+    status.version,
+  ]);
+
+  // Update toast when ready to install
+  useEffect(() => {
+    if (!showToast || !status.readyToInstall || !toastIdRef.current || !toastUpdateRef.current) {
+      return;
+    }
+
+    const handleRestartNow = async () => {
+      await restartAndInstall();
+    };
+
+    toastUpdateRef.current({
+      title: 'Update Ready',
+      description: `Version ${status.version} has been downloaded and is ready to install.`,
+      duration: Infinity,
+      action: (
+        <ToastAction altText="Restart now" onClick={handleRestartNow}>
+          <RefreshCw className="h-3 w-3 mr-1" />
+          Restart Now
+        </ToastAction>
+      ),
+    });
+  }, [showToast, status.readyToInstall, status.version, restartAndInstall]);
+
+  // Handle errors in toast
+  useEffect(() => {
+    if (!showToast || !status.error || !toastIdRef.current || !toastUpdateRef.current) {
+      return;
+    }
+
+    toastUpdateRef.current({
+      title: 'Update Failed',
+      description: status.error,
+      variant: 'destructive',
+      duration: 5000,
+    });
+
+    setTimeout(() => {
+      toastIdRef.current = null;
+      toastUpdateRef.current = null;
+    }, 5000);
+  }, [showToast, status.error]);
+
+  return {
+    status,
+    checkForUpdates,
+    downloadAndInstall,
+    restartAndInstall,
+  };
+}

app/src/index.css

@@ -125,6 +125,32 @@
     text-orientation: mixed;
     letter-spacing: 0.1em;
   }
+
+  .scrollbar-visible {
+    scrollbar-width: thin;
+    -ms-overflow-style: auto;
+    scrollbar-color: #d8ab4f #2b2b2b;
+  }
+
+  .scrollbar-visible::-webkit-scrollbar {
+    display: block;
+    width: 10px;
+    height: 10px;
+  }
+
+  .scrollbar-visible::-webkit-scrollbar-track {
+    background: #2b2b2b;
+  }
+
+  .scrollbar-visible::-webkit-scrollbar-thumb {
+    background: #d8ab4f;
+    border-radius: 9999px;
+    border: 2px solid #131313;
+  }
+
+  .scrollbar-visible::-webkit-scrollbar-thumb:hover {
+    background: #e2b85e;
+  }
 }
 
 @keyframes fadeInScale {

app/src/lib/api/client.ts

@@ -1,26 +1,30 @@
+import type { LanguageCode } from '@/lib/constants/languages';
 import { useServerStore } from '@/stores/serverStore';
 import type {
-  VoiceProfileCreate,
-  VoiceProfileResponse,
-  ProfileSampleResponse,
+  ActiveTasksResponse,
+  CudaStatus,
   GenerationRequest,
   GenerationResponse,
-  HistoryQuery,
-  HistoryListResponse,
-  HistoryResponse,
-  TranscriptionResponse,
   HealthResponse,
-  ModelStatusListResponse,
+  HistoryListResponse,
+  HistoryQuery,
+  HistoryResponse,
   ModelDownloadRequest,
-  ActiveTasksResponse,
+  ModelStatusListResponse,
+  ProfileSampleResponse,
   StoryCreate,
-  StoryResponse,
   StoryDetailResponse,
+  StoryItemBatchUpdate,
   StoryItemCreate,
   StoryItemDetail,
-  StoryItemBatchUpdate,
-  StoryItemReorder,
   StoryItemMove,
+  StoryItemReorder,
+  StoryItemSplit,
+  StoryItemTrim,
+  StoryResponse,
+  TranscriptionResponse,
+  VoiceProfileCreate,
+  VoiceProfileResponse,
 } from './types';
 
 class ApiClient {
@@ -118,6 +122,16 @@ 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);
@@ -152,6 +166,32 @@ 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', {
@@ -212,7 +252,13 @@ class ApiClient {
     return response.blob();
   }
 
-  async importGeneration(file: File): Promise<{ id: string; profile_id: string; profile_name: string; text: string; message: string }> {
+  async importGeneration(file: File): Promise<{
+    id: string;
+    profile_id: string;
+    profile_name: string;
+    text: string;
+    message: string;
+  }> {
     const url = `${this.getBaseUrl()}/history/import`;
     const formData = new FormData();
     formData.append('file', file);
@@ -242,7 +288,7 @@ 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) {
@@ -271,10 +317,18 @@ class ApiClient {
   }
 
   async triggerModelDownload(modelName: string): Promise<{ message: string }> {
-    return this.request<{ message: string }>('/models/download', {
+    console.log(
+      '[API] triggerModelDownload called for:',
+      modelName,
+      'at',
+      new Date().toISOString(),
+    );
+    const result = await this.request<{ message: string }>('/models/download', {
       method: 'POST',
       body: JSON.stringify({ model_name: modelName } as ModelDownloadRequest),
     });
+    console.log('[API] triggerModelDownload response:', result);
+    return result;
   }
 
   async deleteModel(modelName: string): Promise<{ message: string }> {
@@ -283,11 +337,22 @@ class ApiClient {
     });
   }
 
+  async cancelDownload(modelName: string): Promise<{ message: string }> {
+    return this.request<{ message: string }>('/models/download/cancel', {
+      method: 'POST',
+      body: JSON.stringify({ model_name: modelName } as ModelDownloadRequest),
+    });
+  }
+
   // Task Management
   async getActiveTasks(): Promise<ActiveTasksResponse> {
     return this.request<ActiveTasksResponse>('/tasks/active');
   }
 
+  async clearAllTasks(): Promise<{ message: string }> {
+    return this.request<{ message: string }>('/tasks/clear', { method: 'POST' });
+  }
+
   // Audio Channels
   async listChannels(): Promise<
     Array<{
@@ -301,10 +366,7 @@ class ApiClient {
     return this.request('/channels');
   }
 
-  async createChannel(data: {
-    name: string;
-    device_ids: string[];
-  }): Promise<{
+  async createChannel(data: { name: string; device_ids: string[] }): Promise<{
     id: string;
     name: string;
     is_default: boolean;
@@ -346,10 +408,7 @@ class ApiClient {
     return this.request(`/channels/${channelId}/voices`);
   }
 
-  async setChannelVoices(
-    channelId: string,
-    profileIds: string[],
-  ): Promise<{ message: string }> {
+  async setChannelVoices(channelId: string, profileIds: string[]): Promise<{ message: string }> {
     return this.request(`/channels/${channelId}/voices`, {
       method: 'PUT',
       body: JSON.stringify({ profile_ids: profileIds }),
@@ -360,16 +419,30 @@ class ApiClient {
     return this.request(`/profiles/${profileId}/channels`);
   }
 
-  async setProfileChannels(
-    profileId: string,
-    channelIds: string[],
-  ): Promise<{ message: string }> {
+  async setProfileChannels(profileId: string, channelIds: string[]): Promise<{ message: string }> {
     return this.request(`/profiles/${profileId}/channels`, {
       method: 'PUT',
       body: JSON.stringify({ channel_ids: channelIds }),
     });
   }
 
+  // CUDA Backend Management
+  async getCudaStatus(): Promise<CudaStatus> {
+    return this.request<CudaStatus>('/backend/cuda-status');
+  }
+
+  async downloadCudaBackend(): Promise<{ message: string; progress_key: string }> {
+    return this.request<{ message: string; progress_key: string }>('/backend/download-cuda', {
+      method: 'POST',
+    });
+  }
+
+  async deleteCudaBackend(): Promise<{ message: string }> {
+    return this.request<{ message: string }>('/backend/cuda', {
+      method: 'DELETE',
+    });
+  }
+
   // Stories
   async listStories(): Promise<StoryResponse[]> {
     return this.request<StoryResponse[]>('/stories');
@@ -406,8 +479,8 @@ class ApiClient {
     });
   }
 
-  async removeStoryItem(storyId: string, generationId: string): Promise<void> {
-    await this.request<void>(`/stories/${storyId}/items/${generationId}`, {
+  async removeStoryItem(storyId: string, itemId: string): Promise<void> {
+    await this.request<void>(`/stories/${storyId}/items/${itemId}`, {
       method: 'DELETE',
     });
   }
@@ -426,13 +499,45 @@ class ApiClient {
     });
   }
 
-  async moveStoryItem(storyId: string, generationId: string, data: StoryItemMove): Promise<StoryItemDetail> {
-    return this.request<StoryItemDetail>(`/stories/${storyId}/items/${generationId}/move`, {
+  async moveStoryItem(
+    storyId: string,
+    itemId: string,
+    data: StoryItemMove,
+  ): Promise<StoryItemDetail> {
+    return this.request<StoryItemDetail>(`/stories/${storyId}/items/${itemId}/move`, {
       method: 'PUT',
       body: JSON.stringify(data),
     });
   }
 
+  async trimStoryItem(
+    storyId: string,
+    itemId: string,
+    data: StoryItemTrim,
+  ): Promise<StoryItemDetail> {
+    return this.request<StoryItemDetail>(`/stories/${storyId}/items/${itemId}/trim`, {
+      method: 'PUT',
+      body: JSON.stringify(data),
+    });
+  }
+
+  async splitStoryItem(
+    storyId: string,
+    itemId: string,
+    data: StoryItemSplit,
+  ): Promise<StoryItemDetail[]> {
+    return this.request<StoryItemDetail[]>(`/stories/${storyId}/items/${itemId}/split`, {
+      method: 'POST',
+      body: JSON.stringify(data),
+    });
+  }
+
+  async duplicateStoryItem(storyId: string, itemId: string): Promise<StoryItemDetail> {
+    return this.request<StoryItemDetail>(`/stories/${storyId}/items/${itemId}/duplicate`, {
+      method: 'POST',
+    });
+  }
+
   async exportStoryAudio(storyId: string): Promise<Blob> {
     const url = `${this.getBaseUrl()}/stories/${storyId}/export-audio`;
     const response = await fetch(url);

app/src/lib/api/models/ModelStatus.ts

@@ -9,6 +9,7 @@ export type ModelStatus = {
   model_name: string;
   display_name: string;
   downloaded: boolean;
+  downloading?: boolean;  // True if download is in progress
   size_mb?: number | null;
   loaded?: boolean;
 };

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;
 }
@@ -29,9 +31,11 @@ export interface ProfileSampleResponse {
 export interface GenerationRequest {
   profile_id: string;
   text: string;
-  language: 'en' | 'zh';
+  language: LanguageCode;
   seed?: number;
   model_size?: '1.7B' | '0.6B';
+  engine?: 'qwen' | 'luxtts' | 'chatterbox';
+  instruct?: string;
 }
 
 export interface GenerationResponse {
@@ -62,7 +66,7 @@ export interface HistoryListResponse {
 }
 
 export interface TranscriptionRequest {
-  language?: 'en' | 'zh';
+  language?: LanguageCode;
 }
 
 export interface TranscriptionResponse {
@@ -76,7 +80,29 @@ export interface HealthResponse {
   model_downloaded?: boolean;
   model_size?: string;
   gpu_available: boolean;
+  gpu_type?: string;
   vram_used_mb?: number;
+  backend_type?: string;
+  backend_variant?: string; // "cpu" or "cuda"
+}
+
+export interface CudaDownloadProgress {
+  model_name: string;
+  current: number;
+  total: number;
+  progress: number;
+  filename?: string;
+  status: 'downloading' | 'extracting' | 'complete' | 'error';
+  timestamp: string;
+  error?: string;
+}
+
+export interface CudaStatus {
+  available: boolean; // CUDA binary exists on disk
+  active: boolean; // Currently running the CUDA binary
+  binary_path?: string;
+  downloading: boolean; // Download in progress
+  download_progress?: CudaDownloadProgress;
 }
 
 export interface ModelProgress {
@@ -93,11 +119,29 @@ export interface ModelProgress {
 export interface ModelStatus {
   model_name: string;
   display_name: string;
+  hf_repo_id?: string; // HuggingFace repository ID
   downloaded: boolean;
+  downloading: boolean; // True if download is in progress
   size_mb?: number;
   loaded: boolean;
 }
 
+export interface HuggingFaceModelInfo {
+  id: string;
+  author: string;
+  lastModified: string;
+  pipeline_tag?: string;
+  library_name?: string;
+  downloads: number;
+  likes: number;
+  tags: string[];
+  cardData?: {
+    license?: string;
+    language?: string[];
+    pipeline_tag?: string;
+  };
+}
+
 export interface ModelStatusListResponse {
   models: ModelStatus[];
 }
@@ -110,6 +154,11 @@ export interface ActiveDownloadTask {
   model_name: string;
   status: string;
   started_at: string;
+  error?: string;
+  progress?: number; // 0-100 percentage
+  current?: number; // bytes downloaded
+  total?: number; // total bytes
+  filename?: string; // current file being downloaded
 }
 
 export interface ActiveGenerationTask {
@@ -144,6 +193,8 @@ export interface StoryItemDetail {
   generation_id: string;
   start_time_ms: number;
   track: number;
+  trim_start_ms: number;
+  trim_end_ms: number;
   created_at: string;
   profile_id: string;
   profile_name: string;
@@ -188,3 +239,12 @@ export interface StoryItemMove {
   start_time_ms: number;
   track: number;
 }
+
+export interface StoryItemTrim {
+  trim_start_ms: number;
+  trim_end_ms: number;
+}
+
+export interface StoryItemSplit {
+  split_time_ms: number;
+}

app/src/lib/constants/languages.ts

@@ -1,6 +1,6 @@
 /**
- * Supported languages for Qwen3-TTS
- * Based on: https://github.com/QwenLM/Qwen3-TTS
+ * Supported languages for voice generation.
+ * Most languages use Qwen3-TTS; Hebrew uses Chatterbox TTS.
  */
 
 export const SUPPORTED_LANGUAGES = {
@@ -14,6 +14,7 @@ export const SUPPORTED_LANGUAGES = {
   pt: 'Portuguese',
   es: 'Spanish',
   it: 'Italian',
+  he: 'Hebrew',
 } as const;
 
 export type LanguageCode = keyof typeof SUPPORTED_LANGUAGES;

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);
@@ -19,11 +20,13 @@ export function useAudioRecording({
   const streamRef = useRef<MediaStream | null>(null);
   const timerRef = useRef<number | null>(null);
   const startTimeRef = useRef<number | null>(null);
+  const cancelledRef = useRef<boolean>(false);
 
   const startRecording = useCallback(async () => {
     try {
       setError(null);
       chunksRef.current = [];
+      cancelledRef.current = false;
       setDuration(0);
 
       // Check if getUserMedia is available
@@ -40,15 +43,14 @@ 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);
@@ -87,31 +89,34 @@ export function useAudioRecording({
       };
 
       mediaRecorder.onstop = async () => {
+        // Snapshot the cancellation flag and recorded duration immediately —
+        // cancelRecording() clears chunks and sets cancelledRef synchronously
+        // before this async handler runs, so we must check it first.
+        const wasCancelled = cancelledRef.current;
+        const recordedDuration = startTimeRef.current
+          ? (Date.now() - startTimeRef.current) / 1000
+          : undefined;
+
         const webmBlob = new Blob(chunksRef.current, { type: 'audio/webm' });
 
-        // Convert to WAV format to avoid needing ffmpeg on backend
-        try {
-          const wavBlob = await convertToWav(webmBlob);
-
-          // Pass the actual recorded duration
-          const recordedDuration = startTimeRef.current
-            ? (Date.now() - startTimeRef.current) / 1000
-            : undefined;
-          onRecordingComplete?.(wavBlob, recordedDuration);
-        } catch (err) {
-          console.error('Error converting audio to WAV:', err);
-          // Fallback to original blob if conversion fails
-          const recordedDuration = startTimeRef.current
-            ? (Date.now() - startTimeRef.current) / 1000
-            : undefined;
-          onRecordingComplete?.(webmBlob, recordedDuration);
-        }
-
-        // Stop all tracks
+        // Stop all tracks now that we have the data
         streamRef.current?.getTracks().forEach((track) => {
           track.stop();
         });
         streamRef.current = null;
+
+        // Don't fire completion callback if the recording was cancelled
+        if (wasCancelled) return;
+
+        // Convert to WAV format to avoid needing ffmpeg on backend
+        try {
+          const wavBlob = await convertToWav(webmBlob);
+          onRecordingComplete?.(wavBlob, recordedDuration);
+        } catch (err) {
+          console.error('Error converting audio to WAV:', err);
+          // Fallback to original blob if conversion fails
+          onRecordingComplete?.(webmBlob, recordedDuration);
+        }
       };
 
       mediaRecorder.onerror = (event) => {
@@ -167,9 +172,10 @@ export function useAudioRecording({
 
   const cancelRecording = useCallback(() => {
     if (mediaRecorderRef.current) {
+      cancelledRef.current = true; // Must be set before stop() triggers onstop
+      chunksRef.current = [];
       mediaRecorderRef.current.stop();
       setIsRecording(false);
-      chunksRef.current = [];
       setDuration(0);
     }
 

app/src/lib/hooks/useGenerationForm.ts

@@ -16,6 +16,7 @@ const generationSchema = z.object({
   seed: z.number().int().optional(),
   modelSize: z.enum(['1.7B', '0.6B']).optional(),
   instruct: z.string().max(500).optional(),
+  engine: z.enum(['qwen', 'luxtts', 'chatterbox']).optional(),
 });
 
 export type GenerationFormValues = z.infer<typeof generationSchema>;
@@ -28,7 +29,7 @@ interface UseGenerationFormOptions {
 export function useGenerationForm(options: UseGenerationFormOptions = {}) {
   const { toast } = useToast();
   const generation = useGeneration();
-  const setAudio = usePlayerStore((state) => state.setAudio);
+  const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
   const setIsGenerating = useGenerationStore((state) => state.setIsGenerating);
   const [downloadingModelName, setDownloadingModelName] = useState<string | null>(null);
   const [downloadingDisplayName, setDownloadingDisplayName] = useState<string | null>(null);
@@ -47,6 +48,7 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
       seed: undefined,
       modelSize: '1.7B',
       instruct: '',
+      engine: 'qwen',
       ...options.defaultValues,
     },
   });
@@ -67,8 +69,21 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
     try {
       setIsGenerating(true);
 
-      const modelName = `qwen-tts-${data.modelSize}`;
-      const displayName = data.modelSize === '1.7B' ? 'Qwen TTS 1.7B' : 'Qwen TTS 0.6B';
+      const engine = data.engine || 'qwen';
+      const modelName =
+        engine === 'luxtts'
+          ? 'luxtts'
+          : engine === 'chatterbox'
+            ? 'chatterbox-tts'
+            : `qwen-tts-${data.modelSize}`;
+      const displayName =
+        engine === 'luxtts'
+          ? 'LuxTTS'
+          : engine === 'chatterbox'
+            ? 'Chatterbox TTS'
+            : data.modelSize === '1.7B'
+              ? 'Qwen TTS 1.7B'
+              : 'Qwen TTS 0.6B';
 
       try {
         const modelStatus = await apiClient.getModelStatus();
@@ -82,13 +97,15 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
         console.error('Failed to check model status:', error);
       }
 
+      const isQwen = engine === 'qwen';
       const result = await generation.mutateAsync({
         profile_id: selectedProfileId,
         text: data.text,
         language: data.language,
         seed: data.seed,
-        model_size: data.modelSize,
-        instruct: data.instruct || undefined,
+        model_size: isQwen ? data.modelSize : undefined,
+        engine,
+        instruct: isQwen ? data.instruct || undefined : undefined,
       });
 
       toast({
@@ -97,9 +114,16 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
       });
 
       const audioUrl = apiClient.getAudioUrl(result.id);
-      setAudio(audioUrl, result.id, selectedProfileId, data.text.substring(0, 50));
+      setAudioWithAutoPlay(audioUrl, result.id, selectedProfileId, data.text.substring(0, 50));
 
-      form.reset();
+      form.reset({
+        text: '',
+        language: data.language,
+        seed: undefined,
+        modelSize: data.modelSize,
+        instruct: '',
+        engine: data.engine,
+      });
       options.onSuccess?.(result.id);
     } catch (error) {
       toast({

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()) {
-        // Use Tauri's native save dialog
-        try {
-          const { save } = await import('@tauri-apps/plugin-dialog');
-          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);
-      }
-      
+
+      await platform.filesystem.saveFile(filename, blob, [
+        {
+          name: 'Voicebox Generation',
+          extensions: ['zip'],
+        },
+      ]);
+
       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()) {
-        // Use Tauri's native save dialog
-        try {
-          const { save } = await import('@tauri-apps/plugin-dialog');
-          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);
-      }
-      
+
+      await platform.filesystem.saveFile(filename, blob, [
+        {
+          name: 'Audio File',
+          extensions: ['wav'],
+        },
+      ]);
+
       return blob;
     },
   });

app/src/lib/hooks/useModelDownloadToast.tsx

@@ -1,14 +1,16 @@
-import { useEffect, useRef } from 'react';
-import { useToast } from '@/components/ui/use-toast';
-import { useServerStore } from '@/stores/serverStore';
+import { CheckCircle2, Loader2, XCircle } from 'lucide-react';
+import { useCallback, useEffect, useRef } from 'react';
 import { Progress } from '@/components/ui/progress';
-import { Loader2, CheckCircle2, XCircle } from 'lucide-react';
+import { useToast } from '@/components/ui/use-toast';
 import type { ModelProgress } from '@/lib/api/types';
+import { useServerStore } from '@/stores/serverStore';
 
 interface UseModelDownloadToastOptions {
   modelName: string;
   displayName: string;
   enabled?: boolean;
+  onComplete?: () => void;
+  onError?: (error: string) => void;
 }
 
 /**
@@ -19,47 +21,64 @@ export function useModelDownloadToast({
   modelName,
   displayName,
   enabled = false,
+  onComplete,
+  onError,
 }: UseModelDownloadToastOptions) {
   const { toast } = useToast();
   const serverUrl = useServerStore((state) => state.serverUrl);
   const toastIdRef = useRef<string | null>(null);
-  const toastUpdateRef = useRef<
-    ((props: {
-      title?: React.ReactNode;
-      description?: React.ReactNode;
-      duration?: number;
-      variant?: 'default' | 'destructive';
-      open?: boolean;
-    }) => void) | null
-  >(null);
+  // biome-ignore lint: Using any for toast update ref to handle complex toast types
+  const toastUpdateRef = useRef<any>(null);
   const eventSourceRef = useRef<EventSource | null>(null);
 
-  const formatBytes = (bytes: number): string => {
+  const formatBytes = useCallback((bytes: number): string => {
     if (bytes === 0) return '0 B';
     const k = 1024;
     const sizes = ['B', 'KB', 'MB', 'GB'];
     const i = Math.floor(Math.log(bytes) / Math.log(k));
-    return `${(bytes / Math.pow(k, i)).toFixed(1)} ${sizes[i]}`;
-  };
+    return `${(bytes / k ** i).toFixed(1)} ${sizes[i]}`;
+  }, []);
 
   useEffect(() => {
+    console.log('[useModelDownloadToast] useEffect triggered', {
+      enabled,
+      serverUrl,
+      modelName,
+      displayName,
+    });
+
     if (!enabled || !serverUrl || !modelName) {
+      console.log('[useModelDownloadToast] Not enabled, skipping');
       return;
     }
 
+    console.log('[useModelDownloadToast] Creating toast and EventSource for:', modelName);
+
     // Create initial toast
     const toastResult = toast({
       title: displayName,
-      description: 'Starting download...',
+      description: (
+        <div className="flex items-center gap-2">
+          <Loader2 className="h-4 w-4 animate-spin" />
+          <span>Connecting to download...</span>
+        </div>
+      ),
       duration: Infinity, // Don't auto-dismiss, we'll handle it manually
     });
     toastIdRef.current = toastResult.id;
     toastUpdateRef.current = toastResult.update;
 
     // Subscribe to progress updates via Server-Sent Events
-    const eventSource = new EventSource(`${serverUrl}/models/progress/${modelName}`);
+    const eventSourceUrl = `${serverUrl}/models/progress/${modelName}`;
+    console.log('[useModelDownloadToast] Creating EventSource to:', eventSourceUrl);
+    const eventSource = new EventSource(eventSourceUrl);
+
+    eventSource.onopen = () => {
+      console.log('[useModelDownloadToast] EventSource connection opened for:', modelName);
+    };
 
     eventSource.onmessage = (event) => {
+      console.log('[useModelDownloadToast] Received SSE message:', event.data);
       try {
         const progress = JSON.parse(event.data) as ModelProgress;
 
@@ -82,11 +101,11 @@ export function useModelDownloadToast({
               break;
             case 'error':
               statusIcon = <XCircle className="h-4 w-4 text-destructive" />;
-              statusText = `Error: ${progress.error || 'Unknown error'}`;
+              statusText = 'Download failed. See Problems panel for details.';
               break;
             case 'downloading':
               statusIcon = <Loader2 className="h-4 w-4 animate-spin" />;
-              statusText = progress.filename ? `Downloading ${progress.filename}...` : 'Downloading...';
+              statusText = progress.filename || 'Downloading...';
               break;
             case 'extracting':
               statusIcon = <Loader2 className="h-4 w-4 animate-spin" />;
@@ -112,26 +131,44 @@ export function useModelDownloadToast({
                 )}
               </div>
             ),
-            duration: progress.status === 'complete' ? 5000 : Infinity,
-            variant: progress.status === 'error' ? 'destructive' : 'default',
+            duration: progress.status === 'complete' || progress.status === 'error' ? 5000 : Infinity,
           });
 
           // Close connection and dismiss toast on completion or error
-          if (progress.status === 'complete' || progress.status === 'error') {
+          // Also treat progress >= 100% as complete
+          const isComplete = progress.status === 'complete' || progress.progress >= 100;
+          const isError = progress.status === 'error';
+
+          if (isComplete || isError) {
+            console.log('[useModelDownloadToast] Download finished:', {
+              isComplete,
+              isError,
+              progress: progress.progress,
+            });
             eventSource.close();
             eventSourceRef.current = null;
 
-            // Auto-dismiss on completion after delay
-            if (progress.status === 'complete') {
-              setTimeout(() => {
-                if (toastIdRef.current && toastUpdateRef.current) {
-                  toastUpdateRef.current({
-                    open: false,
-                  });
-                  toastIdRef.current = null;
-                  toastUpdateRef.current = null;
-                }
-              }, 5000);
+            // Update toast to show completion state before callbacks
+            if (isComplete && toastUpdateRef.current) {
+              toastUpdateRef.current({
+                title: (
+                  <div className="flex items-center gap-2">
+                    <CheckCircle2 className="h-4 w-4 text-green-500" />
+                    <span>{displayName}</span>
+                  </div>
+                ),
+                description: 'Download complete',
+                duration: 3000,
+              });
+            }
+
+            // Call callbacks
+            if (isComplete && onComplete) {
+              console.log('[useModelDownloadToast] Download complete, calling onComplete callback');
+              onComplete();
+            } else if (isError && onError) {
+              console.log('[useModelDownloadToast] Download error, calling onError callback');
+              onError(progress.error || 'Unknown error');
             }
           }
         }
@@ -140,8 +177,9 @@ export function useModelDownloadToast({
       }
     };
 
-    eventSource.onerror = () => {
-      console.error('SSE error');
+    eventSource.onerror = (error) => {
+      console.error('[useModelDownloadToast] SSE error for:', modelName, error);
+      console.log('[useModelDownloadToast] EventSource readyState:', eventSource.readyState);
       eventSource.close();
       eventSourceRef.current = null;
 
@@ -162,15 +200,16 @@ export function useModelDownloadToast({
 
     // Cleanup on unmount or when disabled
     return () => {
+      console.log('[useModelDownloadToast] Cleanup - closing EventSource for:', modelName);
       if (eventSourceRef.current) {
         eventSourceRef.current.close();
         eventSourceRef.current = null;
       }
       // Note: We don't dismiss the toast here as it might still be showing completion state
     };
-  }, [enabled, serverUrl, modelName, displayName, toast]);
+  }, [enabled, serverUrl, modelName, displayName, toast, formatBytes, onComplete, onError]);
 
   return {
     isTracking: enabled && 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()) {
-        // Use Tauri's native save dialog
-        try {
-          const { save } = await import('@tauri-apps/plugin-dialog');
-          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);
-      }
-      
+
+      await platform.filesystem.saveFile(filename, blob, [
+        {
+          name: 'Voicebox Profile',
+          extensions: ['zip'],
+        },
+      ]);
+
       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 } from '@/lib/api/types';
-import { isTauri } from '@/lib/tauri';
+import type { StoryCreate, StoryItemCreate, StoryItemBatchUpdate, StoryItemReorder, StoryItemMove, StoryItemTrim, StoryItemSplit } from '@/lib/api/types';
+import { usePlatform } from '@/platform/PlatformContext';
 
 export function useStories() {
   return useQuery({
@@ -70,8 +70,8 @@ export function useRemoveStoryItem() {
   const queryClient = useQueryClient();
 
   return useMutation({
-    mutationFn: ({ storyId, generationId }: { storyId: string; generationId: string }) =>
-      apiClient.removeStoryItem(storyId, generationId),
+    mutationFn: ({ storyId, itemId }: { storyId: string; itemId: string }) =>
+      apiClient.removeStoryItem(storyId, itemId),
     onSuccess: (_, variables) => {
       queryClient.invalidateQueries({ queryKey: ['stories'] });
       queryClient.invalidateQueries({ queryKey: ['stories', variables.storyId] });
@@ -109,8 +109,47 @@ export function useMoveStoryItem() {
   const queryClient = useQueryClient();
 
   return useMutation({
-    mutationFn: ({ storyId, generationId, data }: { storyId: string; generationId: string; data: StoryItemMove }) =>
-      apiClient.moveStoryItem(storyId, generationId, data),
+    mutationFn: ({ storyId, itemId, data }: { storyId: string; itemId: string; data: StoryItemMove }) =>
+      apiClient.moveStoryItem(storyId, itemId, data),
+    onSuccess: (_, variables) => {
+      queryClient.invalidateQueries({ queryKey: ['stories'] });
+      queryClient.invalidateQueries({ queryKey: ['stories', variables.storyId] });
+    },
+  });
+}
+
+export function useTrimStoryItem() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: ({ storyId, itemId, data }: { storyId: string; itemId: string; data: StoryItemTrim }) =>
+      apiClient.trimStoryItem(storyId, itemId, data),
+    onSuccess: (_, variables) => {
+      queryClient.invalidateQueries({ queryKey: ['stories'] });
+      queryClient.invalidateQueries({ queryKey: ['stories', variables.storyId] });
+    },
+  });
+}
+
+export function useSplitStoryItem() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: ({ storyId, itemId, data }: { storyId: string; itemId: string; data: StoryItemSplit }) =>
+      apiClient.splitStoryItem(storyId, itemId, data),
+    onSuccess: (_, variables) => {
+      queryClient.invalidateQueries({ queryKey: ['stories'] });
+      queryClient.invalidateQueries({ queryKey: ['stories', variables.storyId] });
+    },
+  });
+}
+
+export function useDuplicateStoryItem() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: ({ storyId, itemId }: { storyId: string; itemId: string }) =>
+      apiClient.duplicateStoryItem(storyId, itemId),
     onSuccess: (_, variables) => {
       queryClient.invalidateQueries({ queryKey: ['stories'] });
       queryClient.invalidateQueries({ queryKey: ['stories', variables.storyId] });
@@ -119,6 +158,8 @@ export function useMoveStoryItem() {
 }
 
 export function useExportStoryAudio() {
+  const platform = usePlatform();
+
   return useMutation({
     mutationFn: async ({ storyId, storyName }: { storyId: string; storyName: string }) => {
       const blob = await apiClient.exportStoryAudio(storyId);
@@ -127,49 +168,12 @@ export function useExportStoryAudio() {
       const safeName = storyName.substring(0, 50).replace(/[^a-z0-9]/gi, '-').toLowerCase();
       const filename = `${safeName || 'story'}.wav`;
 
-      if (isTauri()) {
-        // Use Tauri's native save dialog
-        try {
-          const { save } = await import('@tauri-apps/plugin-dialog');
-          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);
-      }
+      await platform.filesystem.saveFile(filename, blob, [
+        {
+          name: 'Audio File',
+          extensions: ['wav'],
+        },
+      ]);
 
       return blob;
     },

app/src/lib/hooks/useStoryPlayback.ts

@@ -5,6 +5,7 @@ import { useStoryStore } from '@/stores/storyStore';
 
 interface ActiveSource {
   source: AudioBufferSourceNode;
+  itemId: string;
   generationId: string;
   startTimeMs: number;
   endTimeMs: number;
@@ -26,9 +27,9 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
   const audioContextRef = useRef<AudioContext | null>(null);
   // Master gain for volume control
   const masterGainRef = useRef<GainNode | null>(null);
-  // Preloaded AudioBuffers by generation_id
+  // Preloaded AudioBuffers by generation_id (audio file is shared between split clips)
   const audioBuffersRef = useRef<Map<string, AudioBuffer>>(new Map());
-  // Currently playing AudioBufferSourceNodes by generation_id
+  // Currently playing AudioBufferSourceNodes by item.id (unique per clip)
   const activeSourcesRef = useRef<Map<string, ActiveSource>>(new Map());
   // Animation frame for syncing visual playhead
   const animationFrameRef = useRef<number | null>(null);
@@ -56,16 +57,16 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
     return audioContextRef.current;
   }, []);
 
-  // Stop a source
-  const stopSource = useCallback((generationId: string) => {
-    const activeSource = activeSourcesRef.current.get(generationId);
+  // Stop a source by item id
+  const stopSource = useCallback((itemId: string) => {
+    const activeSource = activeSourcesRef.current.get(itemId);
     if (activeSource) {
       try {
         activeSource.source.stop();
       } catch {
         // Source may have already stopped
       }
-      activeSourcesRef.current.delete(generationId);
+      activeSourcesRef.current.delete(itemId);
     }
   }, []);
 
@@ -123,8 +124,8 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
   useEffect(() => {
     return () => {
       // Stop all sources
-      for (const [generationId] of activeSourcesRef.current) {
-        stopSource(generationId);
+      for (const [itemId] of activeSourcesRef.current) {
+        stopSource(itemId);
       }
       activeSourcesRef.current.clear();
 
@@ -151,7 +152,11 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
     (storyTimeMs: number, itemList: StoryItemDetail[]): StoryItemDetail[] => {
       return itemList.filter((item) => {
         const itemStart = item.start_time_ms;
-        const itemEnd = item.start_time_ms + item.duration * 1000;
+        // Use effective duration (accounting for trims)
+        const trimStartMs = item.trim_start_ms || 0;
+        const trimEndMs = item.trim_end_ms || 0;
+        const effectiveDurationMs = item.duration * 1000 - trimStartMs - trimEndMs;
+        const itemEnd = item.start_time_ms + effectiveDurationMs;
         return storyTimeMs >= itemStart && storyTimeMs < itemEnd;
       });
     },
@@ -185,8 +190,8 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
   // Stop all sources
   const stopAllSources = useCallback(() => {
     console.log('[StoryPlayback] Stopping all sources');
-    for (const [generationId] of activeSourcesRef.current) {
-      stopSource(generationId);
+    for (const [itemId] of activeSourcesRef.current) {
+      stopSource(itemId);
     }
     activeSourcesRef.current.clear();
   }, [stopSource]);
@@ -199,18 +204,18 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
 
       // Find all items that should be playing
       const shouldBePlaying = findActiveItems(storyTimeMs, itemList);
-      const shouldBePlayingIds = new Set(shouldBePlaying.map((item) => item.generation_id));
+      const shouldBePlayingIds = new Set(shouldBePlaying.map((item) => item.id));
 
       // Stop sources that shouldn't be playing anymore
-      for (const [generationId] of activeSourcesRef.current) {
-        if (!shouldBePlayingIds.has(generationId)) {
-          stopSource(generationId);
+      for (const [itemId] of activeSourcesRef.current) {
+        if (!shouldBePlayingIds.has(itemId)) {
+          stopSource(itemId);
         }
       }
 
       // Schedule new sources for items that should be playing
       for (const item of shouldBePlaying) {
-        if (!activeSourcesRef.current.has(item.generation_id)) {
+        if (!activeSourcesRef.current.has(item.id)) {
           const buffer = audioBuffersRef.current.get(item.generation_id);
           if (!buffer) {
             console.warn('[StoryPlayback] Buffer not loaded for:', item.generation_id);
@@ -219,16 +224,24 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
 
           // Calculate when this item should start in AudioContext time
           const itemStartContextTime = storyTimeToContextTime(item.start_time_ms);
-          const itemEndStoryTime = item.start_time_ms + item.duration * 1000;
+          
+          // Calculate effective duration and trim offsets
+          const trimStartSec = (item.trim_start_ms || 0) / 1000;
+          const trimEndSec = (item.trim_end_ms || 0) / 1000;
+          const effectiveDuration = item.duration - trimStartSec - trimEndSec;
+          const itemEndStoryTime = item.start_time_ms + effectiveDuration * 1000;
 
           // Calculate offset into the buffer (if seeking mid-way)
-          const offsetIntoBuffer = Math.max(0, (storyTimeMs - item.start_time_ms) / 1000);
-          const duration = item.duration - offsetIntoBuffer;
+          // Offset is relative to the trimmed start of the clip
+          const offsetIntoEffectiveClip = Math.max(0, (storyTimeMs - item.start_time_ms) / 1000);
+          const offsetIntoBuffer = trimStartSec + offsetIntoEffectiveClip;
+          const duration = effectiveDuration - offsetIntoEffectiveClip;
 
           // If the item should have already started, schedule it to start immediately
           const startAtContextTime = Math.max(currentContextTime, itemStartContextTime);
 
           console.log('[StoryPlayback] Scheduling source:', {
+            itemId: item.id,
             generationId: item.generation_id,
             storyTimeMs,
             itemStart: item.start_time_ms,
@@ -243,20 +256,21 @@ export function useStoryPlayback(items: StoryItemDetail[] | undefined) {
 
           const activeSource: ActiveSource = {
             source,
+            itemId: item.id,
             generationId: item.generation_id,
             startTimeMs: item.start_time_ms,
             endTimeMs: itemEndStoryTime,
           };
 
-          activeSourcesRef.current.set(item.generation_id, activeSource);
+          activeSourcesRef.current.set(item.id, activeSource);
 
           // Schedule playback
           source.start(startAtContextTime, offsetIntoBuffer, duration);
 
           // Clean up when source ends
           source.onended = () => {
-            console.log('[StoryPlayback] Source ended:', item.generation_id);
-            activeSourcesRef.current.delete(item.generation_id);
+            console.log('[StoryPlayback] Source ended:', item.id);
+            activeSourcesRef.current.delete(item.id);
           };
         }
       }

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 { isTauri } from '@/lib/tauri';
+import { usePlatform } from '@/platform/PlatformContext';
 
 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()) {
-      setIsSupported(false);
-      return;
-    }
-
-    invoke<boolean>('is_system_audio_supported')
-      .then((supported) => {
-        setIsSupported(supported);
-      })
-      .catch(() => {
-        setIsSupported(false);
-      });
-  }, []);
+    const supported = platform.audio.isSystemAudioSupported();
+    setIsSupported(supported);
+  }, [platform]);
 
   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', {
-        maxDurationSecs: maxDurationSeconds,
-      });
+      await platform.audio.startSystemAudioCapture(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
-      const base64Data = await invoke<string>('stop_system_audio_capture');
+      // Stop capture and get Blob
+      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/lib/utils/audio.ts

@@ -22,6 +22,11 @@ export function formatAudioDuration(seconds: number): string {
  * If the file has a recordedDuration property (from recording hooks),
  * use that instead of trying to read metadata. This fixes issues on Windows
  * where WebM files from MediaRecorder don't have proper duration metadata.
+ *
+ * For uploaded files we use AudioContext.decodeAudioData which fully decodes
+ * the audio and returns the exact duration. This is more reliable than
+ * HTMLMediaElement.duration which can return incorrect large values for VBR
+ * MP3 files that lack a proper XING/VBRI header.
  */
 export async function getAudioDuration(
   file: File & { recordedDuration?: number },
@@ -30,26 +35,39 @@ export async function getAudioDuration(
     return file.recordedDuration;
   }
 
-  return new Promise((resolve, reject) => {
-    const audio = new Audio();
-    const url = URL.createObjectURL(file);
+  // Use Web Audio API for accurate duration — avoids VBR MP3 metadata issues.
+  try {
+    const audioContext = new AudioContext();
+    try {
+      const arrayBuffer = await file.arrayBuffer();
+      const audioBuffer = await audioContext.decodeAudioData(arrayBuffer);
+      return audioBuffer.duration;
+    } finally {
+      await audioContext.close();
+    }
+  } catch {
+    // Fallback: read duration from the media element (less accurate but works for WAV).
+    return new Promise((resolve, reject) => {
+      const audio = new Audio();
+      const url = URL.createObjectURL(file);
 
-    audio.addEventListener('loadedmetadata', () => {
-      URL.revokeObjectURL(url);
-      if (Number.isFinite(audio.duration) && audio.duration > 0) {
-        resolve(audio.duration);
-      } else {
-        reject(new Error('Audio file has invalid duration metadata'));
-      }
+      audio.addEventListener('loadedmetadata', () => {
+        URL.revokeObjectURL(url);
+        if (Number.isFinite(audio.duration) && audio.duration > 0) {
+          resolve(audio.duration);
+        } else {
+          reject(new Error('Audio file has invalid duration metadata'));
+        }
+      });
+
+      audio.addEventListener('error', () => {
+        URL.revokeObjectURL(url);
+        reject(new Error('Failed to load audio file'));
+      });
+
+      audio.src = url;
     });
-
-    audio.addEventListener('error', () => {
-      URL.revokeObjectURL(url);
-      reject(new Error('Failed to load audio file'));
-    });
-
-    audio.src = url;
-  });
+  }
 }
 
 /**

app/src/lib/utils/textChunking.ts

@@ -0,0 +1,99 @@
+export interface TextChunk {
+  id: string;
+  text: string;
+  charCount: number;
+  wordCount: number;
+}
+
+function normalizeText(text: string): string {
+  return text.replace(/\r\n/g, '\n').replace(/\r/g, '\n').trim();
+}
+
+function splitParagraphIntoSentences(paragraph: string): string[] {
+  const trimmed = paragraph.trim();
+  if (!trimmed) {
+    return [];
+  }
+
+  const matches = trimmed.match(/[^.!?]+[.!?]+(?:["')\]]+)?|[^.!?]+$/g);
+  if (!matches || matches.length === 0) {
+    return [trimmed];
+  }
+
+  return matches.map((sentence) => sentence.trim()).filter(Boolean);
+}
+
+export function chunkText(
+  rawText: string,
+  targetChunkSize: number,
+  maxChunkSize: number,
+): TextChunk[] {
+  const text = normalizeText(rawText);
+  if (!text) {
+    return [];
+  }
+
+  const safeTarget = Math.max(200, Math.min(targetChunkSize, maxChunkSize));
+  const paragraphs = text
+    .split(/\n{2,}/)
+    .map((paragraph) => paragraph.trim())
+    .filter(Boolean);
+
+  const chunks: string[] = [];
+  let current = '';
+
+  const pushCurrent = () => {
+    const normalized = current.trim();
+    if (!normalized) {
+      return;
+    }
+    chunks.push(normalized);
+    current = '';
+  };
+
+  for (const paragraph of paragraphs) {
+    const sentences = splitParagraphIntoSentences(paragraph);
+
+    for (const sentence of sentences) {
+      // Keep sentence integrity. If one sentence exceeds maxChunkSize,
+      // keep it as a single oversized chunk and let UI ask for manual edit.
+      if (sentence.length > maxChunkSize) {
+        pushCurrent();
+        chunks.push(sentence);
+        continue;
+      }
+
+      if (!current) {
+        current = sentence;
+        continue;
+      }
+
+      const candidate = `${current} ${sentence}`;
+      if (candidate.length <= safeTarget) {
+        current = candidate;
+        continue;
+      }
+
+      if (candidate.length <= maxChunkSize && current.length < Math.floor(safeTarget * 0.75)) {
+        current = candidate;
+        continue;
+      }
+
+      pushCurrent();
+      current = sentence;
+    }
+
+    if (current.length >= Math.floor(safeTarget * 0.8)) {
+      pushCurrent();
+    }
+  }
+
+  pushCurrent();
+
+  return chunks.map((chunkTextValue, index) => ({
+    id: `chunk-${index + 1}`,
+    text: chunkTextValue,
+    charCount: chunkTextValue.length,
+    wordCount: chunkTextValue.split(/\s+/).filter(Boolean).length,
+  }));
+}

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 +1,71 @@
+/**
+ * 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>;
+  restartServer(): Promise<string>;
+  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

@@ -1,5 +1,6 @@
 import { createRootRoute, createRoute, createRouter, Outlet } from '@tanstack/react-router';
 import { AppFrame } from '@/components/AppFrame/AppFrame';
+import { AudiobookTab } from '@/components/AudiobookTab/AudiobookTab';
 import { AudioTab } from '@/components/AudioTab/AudioTab';
 import { MainEditor } from '@/components/MainEditor/MainEditor';
 import { ModelsTab } from '@/components/ModelsTab/ModelsTab';
@@ -10,7 +11,9 @@ 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() {
@@ -85,6 +88,13 @@ const storiesRoute = createRoute({
   component: StoriesTab,
 });
 
+// Audiobook route
+const audiobookRoute = createRoute({
+  getParentRoute: () => rootRoute,
+  path: '/audiobook',
+  component: AudiobookTab,
+});
+
 // Voices route
 const voicesRoute = createRoute({
   getParentRoute: () => rootRoute,
@@ -117,6 +127,7 @@ const serverRoute = createRoute({
 const routeTree = rootRoute.addChildren([
   indexRoute,
   storiesRoute,
+  audiobookRoute,
   voicesRoute,
   audioRoute,
   modelsRoute,

app/src/stores/storyStore.ts

@@ -5,6 +5,8 @@ interface StoryPlaybackState {
   // Selection
   selectedStoryId: string | null;
   setSelectedStoryId: (id: string | null) => void;
+  selectedClipId: string | null;
+  setSelectedClipId: (id: string | null) => void;
 
   // Track editor UI state
   trackEditorHeight: number;
@@ -26,6 +28,7 @@ interface StoryPlaybackState {
   stop: () => void;
   seek: (timeMs: number) => void;
   setPlaybackTiming: (contextTime: number, storyTime: number) => void; // Set timing anchors for Web Audio API
+  setActiveStory: (storyId: string, items: StoryItemDetail[], totalDurationMs: number) => void; // Activate story for seeking without playing
 }
 
 const DEFAULT_TRACK_EDITOR_HEIGHT = 250;
@@ -34,6 +37,8 @@ export const useStoryStore = create<StoryPlaybackState>((set, get) => ({
   // Selection
   selectedStoryId: null,
   setSelectedStoryId: (id) => set({ selectedStoryId: id }),
+  selectedClipId: null,
+  setSelectedClipId: (id) => set({ selectedClipId: id }),
 
   // Track editor UI state
   trackEditorHeight: DEFAULT_TRACK_EDITOR_HEIGHT,
@@ -53,14 +58,11 @@ export const useStoryStore = create<StoryPlaybackState>((set, get) => ({
     // Calculate total duration from items
     const maxEndTimeMs = Math.max(
       ...items.map((item) => item.start_time_ms + item.duration * 1000),
-      0
+      0,
     );
 
     // Find the minimum start time (first item)
-    const minStartTimeMs = Math.min(
-      ...items.map((item) => item.start_time_ms),
-      0
-    );
+    const minStartTimeMs = Math.min(...items.map((item) => item.start_time_ms), 0);
 
     // If resuming the same story, keep position; otherwise start at first item
     const currentState = get();
@@ -70,7 +72,11 @@ export const useStoryStore = create<StoryPlaybackState>((set, get) => ({
     console.log('[StoryStore] Play called:', {
       storyId,
       itemCount: items.length,
-      items: items.map(i => ({ id: i.generation_id, start: i.start_time_ms, duration: i.duration })),
+      items: items.map((i) => ({
+        id: i.generation_id,
+        start: i.start_time_ms,
+        duration: i.duration,
+      })),
       maxEndTimeMs,
       minStartTimeMs,
       startTimeMs,
@@ -83,11 +89,14 @@ 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,
     });
   },
 
   pause: () => {
-    set({ 
+    set({
       isPlaying: false,
       // Keep timing anchors so we can resume from same position
     });
@@ -108,7 +117,7 @@ export const useStoryStore = create<StoryPlaybackState>((set, get) => ({
   seek: (timeMs) => {
     const state = get();
     const clampedTime = Math.max(0, Math.min(timeMs, state.totalDurationMs));
-    set({ 
+    set({
       currentTimeMs: clampedTime,
       // Reset timing anchors - will be set by hook when playback resumes
       playbackStartContextTime: null,
@@ -122,4 +131,18 @@ export const useStoryStore = create<StoryPlaybackState>((set, get) => ({
       playbackStartStoryTime: storyTime,
     });
   },
+
+  setActiveStory: (storyId, items, totalDurationMs) => {
+    const currentState = get();
+    // Only update if switching to a different story
+    if (currentState.playbackStoryId !== storyId) {
+      set({
+        playbackStoryId: storyId,
+        playbackItems: items,
+        totalDurationMs,
+        currentTimeMs: 0,
+        isPlaying: false,
+      });
+    }
+  },
 }));

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
-├── transcribe.py       # Whisper ASR
+├── platform_detect.py   # Platform detection for backend selection
+├── 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.
 
@@ -313,18 +334,21 @@ python -m backend.main --host 0.0.0.0 --port 8000
 
 ## Usage Examples
 
+The desktop app, web client, and current development workflow use `http://localhost:17493` by default.
+If you launch the backend manually with a different host or port, substitute that address in the examples below.
+
 ### Creating a Voice Profile
 
 ```bash
 # 1. Create profile
-curl -X POST http://localhost:8000/profiles \
+curl -X POST http://localhost:17493/profiles \
   -H "Content-Type: application/json" \
   -d '{"name": "My Voice", "language": "en"}'
 
 # Response: {"id": "abc-123", ...}
 
 # 2. Add sample
-curl -X POST http://localhost:8000/profiles/abc-123/samples \
+curl -X POST http://localhost:17493/profiles/abc-123/samples \
   -F "[email protected]" \
   -F "reference_text=This is my voice sample"
 ```
@@ -332,7 +356,7 @@ curl -X POST http://localhost:8000/profiles/abc-123/samples \
 ### Generating Speech
 
 ```bash
-curl -X POST http://localhost:8000/generate \
+curl -X POST http://localhost:17493/generate \
   -H "Content-Type: application/json" \
   -d '{
     "profile_id": "abc-123",
@@ -344,13 +368,13 @@ curl -X POST http://localhost:8000/generate \
 # Response: {"id": "gen-456", "audio_path": "/path/to/audio.wav", ...}
 
 # Download audio
-curl http://localhost:8000/audio/gen-456 -o output.wav
+curl http://localhost:17493/audio/gen-456 -o output.wav
 ```
 
 ### Transcribing Audio
 
 ```bash
-curl -X POST http://localhost:8000/transcribe \
+curl -X POST http://localhost:17493/transcribe \
   -F "[email protected]" \
   -F "language=en"
 
@@ -365,12 +389,12 @@ Add multiple samples to a profile for better quality:
 
 ```bash
 # Add first sample
-curl -X POST http://localhost:8000/profiles/abc-123/samples \
+curl -X POST http://localhost:17493/profiles/abc-123/samples \
   -F "[email protected]" \
   -F "reference_text=First sample"
 
 # Add second sample
-curl -X POST http://localhost:8000/profiles/abc-123/samples \
+curl -X POST http://localhost:17493/profiles/abc-123/samples \
   -F "[email protected]" \
   -F "reference_text=Second sample"
 
@@ -391,10 +415,10 @@ Models are lazy-loaded and can be manually unloaded:
 
 ```bash
 # Unload TTS model
-curl -X POST http://localhost:8000/models/unload
+curl -X POST http://localhost:17493/models/unload
 
 # Load specific model size
-curl -X POST "http://localhost:8000/models/load?model_size=0.6B"
+curl -X POST "http://localhost:17493/models/load?model_size=0.6B"
 ```
 
 ## Error Handling

backend/__init__.py

@@ -1 +1,3 @@
 # Backend package
+
+__version__ = "0.1.13"

backend/backends/__init__.py

@@ -0,0 +1,208 @@
+"""
+Backend abstraction layer for TTS and STT.
+
+Provides a unified interface for MLX and PyTorch backends.
+"""
+
+import threading
+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
+_tts_backends: dict[str, TTSBackend] = {}
+_tts_backends_lock = threading.Lock()
+_stt_backend: Optional[STTBackend] = None
+
+# Supported TTS engines
+TTS_ENGINES = {
+    "qwen": "Qwen TTS",
+    "luxtts": "LuxTTS",
+    "chatterbox": "Chatterbox TTS",
+}
+
+
+def get_tts_backend() -> TTSBackend:
+    """
+    Get or create the default (Qwen) TTS backend instance based on platform.
+    
+    Returns:
+        TTS backend instance (MLX or PyTorch)
+    """
+    return get_tts_backend_for_engine("qwen")
+
+
+def get_tts_backend_for_engine(engine: str) -> TTSBackend:
+    """
+    Get or create a TTS backend for the given engine.
+    
+    Args:
+        engine: Engine name ("qwen" or "luxtts")
+    
+    Returns:
+        TTS backend instance
+    """
+    global _tts_backends
+    
+    # Fast path: check without lock
+    if engine in _tts_backends:
+        return _tts_backends[engine]
+    
+    # Slow path: create with lock to avoid duplicate instantiation
+    with _tts_backends_lock:
+        # Double-check after acquiring lock
+        if engine in _tts_backends:
+            return _tts_backends[engine]
+        
+        if engine == "qwen":
+            backend_type = get_backend_type()
+            if backend_type == "mlx":
+                from .mlx_backend import MLXTTSBackend
+                backend = MLXTTSBackend()
+            else:
+                from .pytorch_backend import PyTorchTTSBackend
+                backend = PyTorchTTSBackend()
+        elif engine == "luxtts":
+            from .luxtts_backend import LuxTTSBackend
+            backend = LuxTTSBackend()
+        elif engine == "chatterbox":
+            from .chatterbox_backend import ChatterboxTTSBackend
+            backend = ChatterboxTTSBackend()
+        else:
+            raise ValueError(f"Unknown TTS engine: {engine}. Supported: {list(TTS_ENGINES.keys())}")
+        
+        _tts_backends[engine] = backend
+        return 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, _tts_backends, _stt_backend
+    _tts_backend = None
+    _tts_backends.clear()
+    _stt_backend = None

backend/backends/chatterbox_backend.py

@@ -0,0 +1,326 @@
+"""
+Chatterbox TTS backend implementation.
+
+Wraps ChatterboxMultilingualTTS from chatterbox-tts for zero-shot
+voice cloning. Supports 23 languages including Hebrew. Forces CPU
+on macOS due to known MPS tensor issues.
+"""
+
+import asyncio
+import logging
+import platform
+import threading
+from pathlib import Path
+from typing import ClassVar, List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from ..utils.audio import normalize_audio, load_audio
+from ..utils.progress import get_progress_manager
+from ..utils.tasks import get_task_manager
+
+logger = logging.getLogger(__name__)
+
+CHATTERBOX_HF_REPO = "ResembleAI/chatterbox"
+
+# Files that must be present for the multilingual model
+_MTL_WEIGHT_FILES = [
+    "t3_mtl23ls_v2.safetensors",
+    "s3gen.pt",
+    "ve.pt",
+]
+
+
+class ChatterboxTTSBackend:
+    """Chatterbox Multilingual TTS backend for voice cloning."""
+
+    # Class-level lock for torch.load monkey-patching
+    _load_lock: ClassVar[threading.Lock] = threading.Lock()
+
+    def __init__(self):
+        self.model = None
+        self.model_size = "default"
+        self._device = None
+        self._model_load_lock = asyncio.Lock()
+
+    def _get_device(self) -> str:
+        """Get the best available device. Forces CPU on macOS (MPS issue)."""
+        if platform.system() == "Darwin":
+            return "cpu"
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                return "cuda"
+        except ImportError:
+            pass
+        return "cpu"
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str = "default") -> str:
+        return CHATTERBOX_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        """Check if the Chatterbox multilingual model is cached locally."""
+        try:
+            from huggingface_hub import constants as hf_constants
+
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / (
+                "models--" + CHATTERBOX_HF_REPO.replace("/", "--")
+            )
+
+            if not repo_cache.exists():
+                return False
+
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                return False
+
+            # Check for multilingual weight files
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                for fname in _MTL_WEIGHT_FILES:
+                    if not any(snapshots_dir.rglob(fname)):
+                        return False
+                return True
+
+            return False
+        except Exception as e:
+            logger.warning(f"Error checking Chatterbox cache: {e}")
+            return False
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the Chatterbox multilingual model."""
+        if self.model is not None:
+            return
+        async with self._model_load_lock:
+            if self.model is not None:
+                return
+            await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+
+        progress_manager = get_progress_manager()
+        task_manager = get_task_manager()
+        model_name = "chatterbox-tts"
+
+        is_cached = self._is_model_cached()
+
+        # Set up HF progress tracking (intercepts tqdm for file-level progress)
+        progress_callback = create_hf_progress_callback(model_name, progress_manager)
+        tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+        tracker_context = tracker.patch_download()
+        tracker_context.__enter__()
+
+        if not is_cached:
+            task_manager.start_download(model_name)
+            progress_manager.update_progress(
+                model_name=model_name,
+                current=0,
+                total=0,
+                filename="Connecting to HuggingFace...",
+                status="downloading",
+            )
+
+        try:
+            device = self._get_device()
+            self._device = device
+
+            logger.info(f"Loading Chatterbox Multilingual TTS on {device}...")
+
+            import torch
+            from chatterbox.mtl_tts import ChatterboxMultilingualTTS
+
+            # Monkey-patch torch.load for CPU loading. The model's .pt files
+            # were saved on CUDA; from_pretrained() doesn't pass map_location
+            # so loading on CPU fails without this.
+            try:
+                if device == "cpu":
+                    _orig_torch_load = torch.load
+
+                    def _patched_load(*args, **kwargs):
+                        kwargs.setdefault("map_location", "cpu")
+                        return _orig_torch_load(*args, **kwargs)
+
+                    with ChatterboxTTSBackend._load_lock:
+                        torch.load = _patched_load
+                        try:
+                            self.model = ChatterboxMultilingualTTS.from_pretrained(
+                                device=device,
+                            )
+                        finally:
+                            torch.load = _orig_torch_load
+                else:
+                    self.model = ChatterboxMultilingualTTS.from_pretrained(
+                        device=device,
+                    )
+            finally:
+                tracker_context.__exit__(None, None, None)
+
+            # Fix: transformers >= 4.36 defaults LlamaModel to sdpa attention
+            # which doesn't support output_attentions=True (needed by
+            # Chatterbox's AlignmentStreamAnalyzer). Force eager attention.
+            t3_tfmr = self.model.t3.tfmr
+            if hasattr(t3_tfmr, "config") and hasattr(
+                t3_tfmr.config, "_attn_implementation"
+            ):
+                t3_tfmr.config._attn_implementation = "eager"
+                for layer in getattr(t3_tfmr, "layers", []):
+                    if hasattr(layer, "self_attn"):
+                        layer.self_attn._attn_implementation = "eager"
+
+            if not is_cached:
+                progress_manager.mark_complete(model_name)
+                task_manager.complete_download(model_name)
+
+            logger.info("Chatterbox Multilingual TTS loaded successfully")
+
+        except ImportError as e:
+            logger.error(
+                "chatterbox-tts package not found. "
+                "Install with: pip install chatterbox-tts"
+            )
+            if not is_cached:
+                progress_manager.mark_error(model_name, str(e))
+                task_manager.error_download(model_name, str(e))
+            raise
+        except Exception as e:
+            logger.error(f"Failed to load Chatterbox: {e}")
+            if not is_cached:
+                progress_manager.mark_error(model_name, str(e))
+                task_manager.error_download(model_name, str(e))
+            raise
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            device = self._device
+            del self.model
+            self.model = None
+            self._device = None
+            if device == "cuda":
+                import torch
+
+                torch.cuda.empty_cache()
+            logger.info("Chatterbox 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.
+
+        Chatterbox processes reference audio at generation time, so the
+        prompt just stores the file path. The actual audio is loaded by
+        model.generate() via audio_prompt_path.
+        """
+        voice_prompt = {
+            "ref_audio": str(audio_path),
+            "ref_text": reference_text,
+        }
+        return voice_prompt, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """Combine multiple reference samples."""
+        combined_audio = []
+        for path in audio_paths:
+            audio, _sr = load_audio(path)
+            audio = normalize_audio(audio)
+            combined_audio.append(audio)
+
+        mixed = np.concatenate(combined_audio)
+        mixed = normalize_audio(mixed)
+        combined_text = " ".join(reference_texts)
+        return mixed, combined_text
+
+    # Per-language generation defaults. Lower temp + higher cfg = clearer speech.
+    _LANG_DEFAULTS: ClassVar[dict] = {
+        "he": {
+            "exaggeration": 0.4,
+            "cfg_weight": 0.7,
+            "temperature": 0.65,
+            "repetition_penalty": 2.5,
+        },
+    }
+    _GLOBAL_DEFAULTS: ClassVar[dict] = {
+        "exaggeration": 0.5,
+        "cfg_weight": 0.5,
+        "temperature": 0.8,
+        "repetition_penalty": 2.0,
+    }
+
+    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 using Chatterbox Multilingual TTS.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Dict with ref_audio path
+            language: BCP-47 language code
+            seed: Random seed for reproducibility
+            instruct: Unused (protocol compatibility)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        ref_audio = voice_prompt.get("ref_audio")
+        if ref_audio and not Path(ref_audio).exists():
+            logger.warning(f"Reference audio not found: {ref_audio}")
+            ref_audio = None
+
+        # Merge language-specific defaults with global defaults
+        lang_defaults = self._LANG_DEFAULTS.get(language, self._GLOBAL_DEFAULTS)
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                torch.manual_seed(seed)
+
+            logger.info(f"[Chatterbox] Generating: lang={language}")
+
+            wav = self.model.generate(
+                text,
+                language_id=language,
+                audio_prompt_path=ref_audio,
+                exaggeration=lang_defaults["exaggeration"],
+                cfg_weight=lang_defaults["cfg_weight"],
+                temperature=lang_defaults["temperature"],
+                repetition_penalty=lang_defaults["repetition_penalty"],
+            )
+
+            # Convert tensor -> numpy
+            if isinstance(wav, torch.Tensor):
+                audio = wav.squeeze().cpu().numpy().astype(np.float32)
+            else:
+                audio = np.asarray(wav, dtype=np.float32)
+
+            sample_rate = (
+                getattr(self.model, "sr", None)
+                or getattr(self.model, "sample_rate", 24000)
+            )
+
+            return audio, sample_rate
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/luxtts_backend.py

@@ -0,0 +1,275 @@
+"""
+LuxTTS backend implementation.
+
+Wraps the LuxTTS (ZipVoice) model for zero-shot voice cloning.
+~1GB VRAM, 48kHz output, 150x realtime on CPU.
+"""
+
+import asyncio
+import logging
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from ..utils.audio import normalize_audio, load_audio
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+from ..utils.progress import get_progress_manager
+from ..utils.tasks import get_task_manager
+
+logger = logging.getLogger(__name__)
+
+# HuggingFace repo for model weight detection
+LUXTTS_HF_REPO = "YatharthS/LuxTTS"
+
+
+class LuxTTSBackend:
+    """LuxTTS backend for zero-shot voice cloning."""
+
+    def __init__(self):
+        self.model = None
+        self.model_size = "default"  # LuxTTS has only one model size
+        self._device = None
+
+    def _get_device(self) -> str:
+        """Get the best available device."""
+        import torch
+
+        if torch.cuda.is_available():
+            return "cuda"
+        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            return "mps"
+        return "cpu"
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    @property
+    def device(self) -> str:
+        if self._device is None:
+            self._device = self._get_device()
+        return self._device
+
+    def _get_model_path(self, model_size: str) -> str:
+        return LUXTTS_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        """Check if LuxTTS model weights are cached locally."""
+        try:
+            from huggingface_hub import constants as hf_constants
+
+            repo_cache = (
+                Path(hf_constants.HF_HUB_CACHE)
+                / ("models--" + LUXTTS_HF_REPO.replace("/", "--"))
+            )
+
+            if not repo_cache.exists():
+                return False
+
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                return False
+
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                has_weights = any(snapshots_dir.rglob("*.pt")) or any(
+                    snapshots_dir.rglob("*.safetensors")
+                ) or any(snapshots_dir.rglob("*.onnx")) or any(
+                    snapshots_dir.rglob("*.bin")
+                )
+                return has_weights
+
+            return False
+        except Exception as e:
+            logger.warning(f"Error checking LuxTTS cache: {e}")
+            return False
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the LuxTTS model."""
+        if self.model is not None:
+            return
+
+        await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+
+        progress_manager = get_progress_manager()
+        task_manager = get_task_manager()
+        model_name = "luxtts"
+
+        is_cached = self._is_model_cached()
+
+        # Set up HF progress tracking (intercepts tqdm for file-level progress)
+        progress_callback = create_hf_progress_callback(model_name, progress_manager)
+        tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+        tracker_context = tracker.patch_download()
+        tracker_context.__enter__()
+
+        if not is_cached:
+            task_manager.start_download(model_name)
+            progress_manager.update_progress(
+                model_name=model_name,
+                current=0,
+                total=0,
+                filename="Connecting to HuggingFace...",
+                status="downloading",
+            )
+
+        try:
+            from zipvoice.luxvoice import LuxTTS
+
+            device = self.device
+            logger.info(f"Loading LuxTTS on {device}...")
+
+            # LuxTTS constructor downloads model and loads everything
+            try:
+                if device == "cpu":
+                    import os
+                    threads = os.cpu_count() or 4
+                    self.model = LuxTTS(
+                        model_path=LUXTTS_HF_REPO,
+                        device="cpu",
+                        threads=min(threads, 8),
+                    )
+                else:
+                    self.model = LuxTTS(
+                        model_path=LUXTTS_HF_REPO,
+                        device=device,
+                    )
+            finally:
+                tracker_context.__exit__(None, None, None)
+
+            if not is_cached:
+                progress_manager.mark_complete(model_name)
+                task_manager.complete_download(model_name)
+
+            logger.info("LuxTTS loaded successfully")
+
+        except Exception as e:
+            logger.error(f"Failed to load LuxTTS: {e}")
+            if not is_cached:
+                progress_manager.mark_error(model_name, str(e))
+                task_manager.error_download(model_name, str(e))
+            raise
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+            logger.info("LuxTTS 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.
+
+        LuxTTS uses its own encode_prompt() which runs Whisper ASR internally
+        to transcribe the reference. The reference_text parameter is not used
+        by LuxTTS itself, but we include it in the cache key for consistency.
+        """
+        await self.load_model()
+
+        # Compute cache key once for both lookup and storage
+        cache_key = ("luxtts_" + get_cache_key(audio_path, reference_text)) if use_cache else None
+
+        if cache_key:
+            cached = get_cached_voice_prompt(cache_key)
+            if cached is not None and isinstance(cached, dict):
+                return cached, True
+
+        def _encode_sync():
+            return self.model.encode_prompt(
+                prompt_audio=str(audio_path),
+                duration=5,
+                rms=0.01,
+            )
+
+        encoded = await asyncio.to_thread(_encode_sync)
+
+        if cache_key:
+            cache_voice_prompt(cache_key, encoded)
+
+        return encoded, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """
+        Combine multiple reference samples.
+
+        LuxTTS doesn't have native multi-prompt support, so we concatenate
+        the audio and let encode_prompt handle the combined clip.
+        """
+        combined_audio = []
+        for path in audio_paths:
+            audio, _sr = load_audio(path, sample_rate=24000)
+            audio = normalize_audio(audio)
+            combined_audio.append(audio)
+
+        mixed = np.concatenate(combined_audio)
+        mixed = normalize_audio(mixed)
+        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 LuxTTS.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Encoded prompt dict from encode_prompt()
+            language: Language code (LuxTTS is English-focused)
+            seed: Random seed for reproducibility
+            instruct: Not supported by LuxTTS (ignored)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+
+            wav = self.model.generate_speech(
+                text=text,
+                encode_dict=voice_prompt,
+                num_steps=4,
+                guidance_scale=3.0,
+                t_shift=0.5,
+                speed=1.0,
+                return_smooth=False,  # 48kHz output
+            )
+
+            # LuxTTS returns a tensor (may be on GPU/MPS), move to CPU first
+            audio = wav.detach().cpu().numpy().squeeze()
+            return audio, 48000
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/mlx_backend.py

@@ -0,0 +1,581 @@
+"""
+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
+    
+    def _is_model_cached(self, model_size: str) -> bool:
+        """
+        Check if the model is already cached locally AND fully downloaded.
+        
+        Args:
+            model_size: Model size to check
+            
+        Returns:
+            True if model is fully cached, False if missing or incomplete
+        """
+        try:
+            from huggingface_hub import constants as hf_constants
+            model_path = self._get_model_path(model_size)
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_path.replace("/", "--"))
+            
+            if not repo_cache.exists():
+                return False
+            
+            # Check for .incomplete files - if any exist, download is still in progress
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                print(f"[_is_model_cached] Found .incomplete files for {model_size}, treating as not cached")
+                return False
+            
+            # Check that actual model weight files exist in snapshots
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                has_weights = (
+                    any(snapshots_dir.rglob("*.safetensors")) or
+                    any(snapshots_dir.rglob("*.bin")) or
+                    any(snapshots_dir.rglob("*.npz"))
+                )
+                if not has_weights:
+                    print(f"[_is_model_cached] No model weights found for {model_size}, treating as not cached")
+                    return False
+            
+            return True
+        except Exception as e:
+            print(f"[_is_model_cached] Error checking cache for {model_size}: {e}")
+            return False
+    
+    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:
+            # Get model path BEFORE importing mlx_audio
+            model_path = self._get_model_path(model_size)
+            
+            # Set up progress tracking
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            model_name = f"qwen-tts-{model_size}"
+            
+            # Check if model is already cached
+            is_cached = self._is_model_cached(model_size)
+            
+            # Set up progress callback
+            # If cached: filter out non-download progress
+            # If not cached: report all progress (we're actually downloading)
+            progress_callback = create_hf_progress_callback(model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+            
+            print(f"Loading MLX TTS model {model_size}...")
+            
+            # Only track download progress if model is NOT cached
+            if not is_cached:
+                # Start tracking download task
+                task_manager.start_download(model_name)
+                
+                # Initialize progress state so SSE endpoint has initial data to send
+                # This provides immediate feedback while HuggingFace fetches metadata
+                progress_manager.update_progress(
+                    model_name=model_name,
+                    current=0,
+                    total=0,  # Will be updated once actual total is known
+                    filename="Connecting to HuggingFace...",
+                    status="downloading",
+                )
+            
+            # IMPORTANT: Patch tqdm BEFORE importing mlx_audio
+            # Otherwise mlx_audio caches reference to original tqdm
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+            
+            # Import mlx_audio AFTER patching tqdm
+            from mlx_audio.tts import load
+            
+            # Load MLX model (downloads automatically)
+            try:
+                self.model = load(model_path)
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
+            
+            # Only mark download as complete if we were tracking it
+            if not is_cached:
+                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"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
+
+
+WHISPER_HF_REPOS = {
+    "base": "openai/whisper-base",
+    "small": "openai/whisper-small",
+    "medium": "openai/whisper-medium",
+    "large": "openai/whisper-large-v3",
+}
+
+
+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
+    
+    def _is_model_cached(self, model_size: str) -> bool:
+        """
+        Check if the Whisper model is already cached locally AND fully downloaded.
+        
+        Args:
+            model_size: Model size to check
+            
+        Returns:
+            True if model is fully cached, False if missing or incomplete
+        """
+        try:
+            from huggingface_hub import constants as hf_constants
+            hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + hf_repo.replace("/", "--"))
+            
+            if not repo_cache.exists():
+                return False
+            
+            # Check for .incomplete files - if any exist, download is still in progress
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                print(f"[_is_model_cached] Found .incomplete files for whisper-{model_size}, treating as not cached")
+                return False
+            
+            # Check that actual model weight files exist in snapshots
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                has_weights = (
+                    any(snapshots_dir.rglob("*.safetensors")) or
+                    any(snapshots_dir.rglob("*.bin")) or
+                    any(snapshots_dir.rglob("*.npz"))
+                )
+                if not has_weights:
+                    print(f"[_is_model_cached] No model weights found for whisper-{model_size}, treating as not cached")
+                    return False
+            
+            return True
+        except Exception as e:
+            print(f"[_is_model_cached] Error checking cache for whisper-{model_size}: {e}")
+            return False
+    
+    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:
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            progress_model_name = f"whisper-{model_size}"
+
+            # Check if model is already cached
+            is_cached = self._is_model_cached(model_size)
+
+            # Set up progress callback and tracker
+            # If cached: filter out non-download progress
+            # If not cached: report all progress (we're actually downloading)
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+
+            # Patch tqdm BEFORE importing mlx_audio
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+
+            # Import mlx_audio
+            from mlx_audio.stt import load
+
+            # MLX Whisper uses the standard OpenAI models
+            model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+
+            print(f"Loading MLX Whisper model {model_size}...")
+
+            # Only track download progress if model is NOT cached
+            if not is_cached:
+                # Start tracking download task
+                task_manager.start_download(progress_model_name)
+                
+                # Initialize progress state so SSE endpoint has initial data to send
+                progress_manager.update_progress(
+                    model_name=progress_model_name,
+                    current=0,
+                    total=0,
+                    filename="Connecting to HuggingFace...",
+                    status="downloading",
+                )
+
+            # Load the model (tqdm is patched, but filters out non-download progress)
+            try:
+                self.model = load(model_name)
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
+            
+            # Only mark download as complete if we were tracking it
+            if not is_cached:
+                progress_manager.mark_complete(progress_model_name)
+                task_manager.complete_download(progress_model_name)
+            
+            self.model_size = model_size
+            
+            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 +1,620 @@
+"""
+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"
+        # Intel Arc / Intel Xe GPU via intel-extension-for-pytorch (IPEX)
+        try:
+            import intel_extension_for_pytorch  # noqa: F401
+            if hasattr(torch, 'xpu') and torch.xpu.is_available():
+                return "xpu"
+        except ImportError:
+            pass
+        # Any GPU on Windows via DirectML (torch-directml)
+        try:
+            import torch_directml
+            if torch_directml.device_count() > 0:
+                return torch_directml.device(0)
+        except ImportError:
+            pass
+        # MPS (Apple Silicon) — kept for completeness but MLX backend is preferred
+        if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            return "cpu"  # MPS disabled for stability; MLX backend handles Apple Silicon
+        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]
+    
+    def _is_model_cached(self, model_size: str) -> bool:
+        """
+        Check if the model is already cached locally AND fully downloaded.
+        
+        Args:
+            model_size: Model size to check
+            
+        Returns:
+            True if model is fully cached, False if missing or incomplete
+        """
+        try:
+            from huggingface_hub import constants as hf_constants
+            model_path = self._get_model_path(model_size)
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_path.replace("/", "--"))
+            
+            if not repo_cache.exists():
+                return False
+            
+            # Check for .incomplete files - if any exist, download is still in progress
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                print(f"[_is_model_cached] Found .incomplete files for {model_size}, treating as not cached")
+                return False
+            
+            # Check that actual model weight files exist in snapshots
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                has_weights = (
+                    any(snapshots_dir.rglob("*.safetensors")) or
+                    any(snapshots_dir.rglob("*.bin"))
+                )
+                if not has_weights:
+                    print(f"[_is_model_cached] No model weights found for {model_size}, treating as not cached")
+                    return False
+            
+            return True
+        except Exception as e:
+            print(f"[_is_model_cached] Error checking cache for {model_size}: {e}")
+            return False
+    
+    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:
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            model_name = f"qwen-tts-{model_size}"
+
+            # Check if model is already cached
+            is_cached = self._is_model_cached(model_size)
+
+            # Set up progress callback and tracker
+            # If cached: filter out non-download progress (like "Segment 1/1" during generation)
+            # If not cached: report all progress (we're actually downloading)
+            progress_callback = create_hf_progress_callback(model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+
+            # Patch tqdm BEFORE importing qwen_tts
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+
+            # Import qwen_tts
+            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}...")
+
+            # Only track download progress if model is NOT cached
+            if not is_cached:
+                # Start tracking download task
+                task_manager.start_download(model_name)
+
+                # Initialize progress state so SSE endpoint has initial data to send
+                progress_manager.update_progress(
+                    model_name=model_name,
+                    current=0,
+                    total=0,  # Will be updated once actual total is known
+                    filename="Connecting to HuggingFace...",
+                    status="downloading",
+                )
+
+            # Load the model (tqdm is patched, but filters out non-download progress)
+            try:
+                # Don't pass device_map on CPU: accelerate's meta-tensor mechanism
+                # causes "Cannot copy out of meta tensor" when moving to CPU.
+                # Instead load directly then call .to(device) if needed.
+                if self.device == "cpu":
+                    self.model = Qwen3TTSModel.from_pretrained(
+                        model_path,
+                        torch_dtype=torch.float32,
+                        low_cpu_mem_usage=False,
+                    )
+                else:
+                    self.model = Qwen3TTSModel.from_pretrained(
+                        model_path,
+                        device_map=self.device,
+                        torch_dtype=torch.bfloat16,
+                    )
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
+            
+            # Only mark download as complete if we were tracking it
+            if not is_cached:
+                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
+
+
+WHISPER_HF_REPOS = {
+    "base": "openai/whisper-base",
+    "small": "openai/whisper-small",
+    "medium": "openai/whisper-medium",
+    "large": "openai/whisper-large-v3",
+}
+
+
+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"
+        # Intel Arc / Intel Xe GPU via intel-extension-for-pytorch (IPEX)
+        try:
+            import intel_extension_for_pytorch  # noqa: F401
+            if hasattr(torch, 'xpu') and torch.xpu.is_available():
+                return "xpu"
+        except ImportError:
+            pass
+        # Any GPU on Windows via DirectML (torch-directml)
+        try:
+            import torch_directml
+            if torch_directml.device_count() > 0:
+                return torch_directml.device(0)
+        except ImportError:
+            pass
+        if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            return "cpu"  # MPS disabled for stability
+        return "cpu"
+    
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+    
+    def _is_model_cached(self, model_size: str) -> bool:
+        """
+        Check if the Whisper model is already cached locally AND fully downloaded.
+        
+        Args:
+            model_size: Model size to check
+            
+        Returns:
+            True if model is fully cached, False if missing or incomplete
+        """
+        try:
+            from huggingface_hub import constants as hf_constants
+            hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+            repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + hf_repo.replace("/", "--"))
+
+            if not repo_cache.exists():
+                return False
+
+            # Check for .incomplete files - if any exist, download is still in progress
+            blobs_dir = repo_cache / "blobs"
+            if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+                print(f"[_is_model_cached] Found .incomplete files for whisper-{model_size}, treating as not cached")
+                return False
+
+            # Check that actual model weight files exist in snapshots
+            snapshots_dir = repo_cache / "snapshots"
+            if snapshots_dir.exists():
+                has_weights = (
+                    any(snapshots_dir.rglob("*.safetensors")) or
+                    any(snapshots_dir.rglob("*.bin"))
+                )
+                if not has_weights:
+                    print(f"[_is_model_cached] No model weights found for whisper-{model_size}, treating as not cached")
+                    return False
+
+            return True
+        except Exception as e:
+            print(f"[_is_model_cached] Error checking cache for whisper-{model_size}: {e}")
+            return False
+
+    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:
+            progress_manager = get_progress_manager()
+            task_manager = get_task_manager()
+            progress_model_name = f"whisper-{model_size}"
+
+            # Check if model is already cached
+            is_cached = self._is_model_cached(model_size)
+
+            # Set up progress callback and tracker
+            # If cached: filter out non-download progress
+            # If not cached: report all progress (we're actually downloading)
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
+
+            # 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")
+
+            # Import transformers
+            from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+            model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+            print(f"[DEBUG] Model name: {model_name}")
+
+            print(f"Loading Whisper model {model_size} on {self.device}...")
+
+            # Only track download progress if model is NOT cached
+            if not is_cached:
+                # Start tracking download task
+                task_manager.start_download(progress_model_name)
+
+                # Initialize progress state so SSE endpoint has initial data to send
+                progress_manager.update_progress(
+                    model_name=progress_model_name,
+                    current=0,
+                    total=0,  # Will be updated once actual total is known
+                    filename="Connecting to HuggingFace...",
+                    status="downloading",
+                )
+
+            # Load models (tqdm is patched, but filters out non-download progress)
+            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)
+            
+            # Only mark download as complete if we were tracking it
+            if not is_cached:
+                progress_manager.mark_complete(progress_model_name)
+                task_manager.complete_download(progress_model_name)
+            
+            self.model.to(self.device)
+            self.model_size = model_size
+            
+            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

@@ -1,32 +1,48 @@
 """
 PyInstaller build script for creating standalone Python server binary.
+
+Usage:
+    python build_binary.py           # Build default (CPU) server binary
+    python build_binary.py --cuda    # Build CUDA-enabled server binary
 """
 
 import PyInstaller.__main__
+import argparse
 import os
+import platform
 from pathlib import Path
 
 
-def build_server():
-    """Build Python server as standalone binary."""
+def is_apple_silicon():
+    """Check if running on Apple Silicon."""
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def build_server(cuda=False):
+    """Build Python server as standalone binary.
+
+    Args:
+        cuda: If True, build with CUDA support and name the binary
+              voicebox-server-cuda instead of voicebox-server.
+    """
     backend_dir = Path(__file__).parent
 
-    # Check for local editable qwen_tts install
-    local_qwen_path = Path.home() / 'Projects' / 'voice' / 'Qwen3-TTS'
+    binary_name = 'voicebox-server-cuda' if cuda else 'voicebox-server'
 
     # PyInstaller arguments
     args = [
         'server.py',  # Use server.py as entry point instead of main.py
         '--onefile',
-        '--name', 'voicebox-server',
+        '--name', binary_name,
     ]
 
-    # Add local qwen_tts path if it exists (for editable installs)
-    if local_qwen_path.exists():
-        args.extend(['--paths', str(local_qwen_path)])
-        print(f"Using local qwen_tts source from: {local_qwen_path}")
+    # Add local qwen_tts path if specified (for editable installs)
+    qwen_tts_path = os.getenv('QWEN_TTS_PATH')
+    if qwen_tts_path and Path(qwen_tts_path).exists():
+        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',
@@ -37,11 +53,15 @@ 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',
         '--hidden-import', 'backend.utils.hf_progress',
         '--hidden-import', 'backend.utils.validation',
+        '--hidden-import', 'backend.cuda_download',
         '--hidden-import', 'torch',
         '--hidden-import', 'transformers',
         '--hidden-import', 'fastapi',
@@ -61,6 +81,41 @@ def build_server():
         # Fix for pkg_resources and jaraco namespace packages
         '--hidden-import', 'pkg_resources.extern',
         '--collect-submodules', 'jaraco',
+    ])
+
+    # Add CUDA-specific hidden imports
+    if cuda:
+        print("Building with CUDA support")
+        args.extend([
+            '--hidden-import', 'torch.cuda',
+            '--hidden-import', 'torch.backends.cudnn',
+        ])
+
+    # Add MLX-specific imports if building on Apple Silicon (never for CUDA builds)
+    if is_apple_silicon() and not cuda:
+        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',
+            # Use --collect-all so PyInstaller bundles both data files AND
+            # native shared libraries (.dylib, .metallib) for MLX.
+            # Previously only --collect-data was used, which caused MLX to
+            # raise OSError at runtime inside the bundled binary because
+            # the Metal shader libraries were missing.
+            '--collect-all', 'mlx',
+            '--collect-all', 'mlx_audio',
+        ])
+    elif not cuda:
+        print("Building for non-Apple Silicon platform - PyTorch only")
+
+    args.extend([
         '--noconfirm',
         '--clean',
     ])
@@ -71,8 +126,15 @@ def build_server():
     # Run PyInstaller
     PyInstaller.__main__.run(args)
     
-    print(f"Binary built in {backend_dir / 'dist' / 'voicebox-server'}")
+    print(f"Binary built in {backend_dir / 'dist' / binary_name}")
 
 
 if __name__ == '__main__':
-    build_server()
+    parser = argparse.ArgumentParser(description="Build voicebox-server binary")
+    parser.add_argument(
+        '--cuda',
+        action='store_true',
+        help="Build CUDA-enabled binary (voicebox-server-cuda)",
+    )
+    cli_args = parser.parse_args()
+    build_server(cuda=cli_args.cuda)

backend/config.py

@@ -4,8 +4,17 @@ Configuration module for voicebox backend.
 Handles data directory configuration for production bundling.
 """
 
+import os
 from pathlib import Path
 
+# Allow users to override the HuggingFace model download directory.
+# Set VOICEBOX_MODELS_DIR to an absolute path before starting the server.
+# This sets HF_HUB_CACHE so all huggingface_hub downloads go to that path.
+_custom_models_dir = os.environ.get("VOICEBOX_MODELS_DIR")
+if _custom_models_dir:
+    os.environ["HF_HUB_CACHE"] = _custom_models_dir
+    print(f"[config] Model download path set to: {_custom_models_dir}")
+
 # Default data directory (used in development)
 _data_dir = Path("data")
 

backend/cuda_download.py

@@ -0,0 +1,198 @@
+"""
+CUDA backend binary download, assembly, and verification.
+
+Downloads split parts of the CUDA-enabled voicebox-server binary from
+GitHub Releases, reassembles them, verifies integrity via SHA-256,
+and places the binary in the app's data directory for use on next
+backend restart.
+"""
+
+import hashlib
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Optional
+
+from .config import get_data_dir
+from .utils.progress import get_progress_manager
+from . import __version__
+
+logger = logging.getLogger(__name__)
+
+GITHUB_RELEASES_URL = "https://github.com/jamiepine/voicebox/releases/download"
+
+PROGRESS_KEY = "cuda-backend"
+
+
+def get_backends_dir() -> Path:
+    """Directory where downloaded backend binaries are stored."""
+    d = get_data_dir() / "backends"
+    d.mkdir(parents=True, exist_ok=True)
+    return d
+
+
+def get_cuda_binary_name() -> str:
+    """Platform-specific CUDA binary filename."""
+    if sys.platform == "win32":
+        return "voicebox-server-cuda.exe"
+    return "voicebox-server-cuda"
+
+
+def get_cuda_binary_path() -> Optional[Path]:
+    """Return path to CUDA binary if it exists."""
+    p = get_backends_dir() / get_cuda_binary_name()
+    if p.exists():
+        return p
+    return None
+
+
+def is_cuda_active() -> bool:
+    """Check if the current process is the CUDA binary.
+
+    The CUDA binary sets this env var on startup (see server.py).
+    """
+    return os.environ.get("VOICEBOX_BACKEND_VARIANT") == "cuda"
+
+
+def get_cuda_status() -> dict:
+    """Get current CUDA backend status for the API."""
+    progress_manager = get_progress_manager()
+    cuda_path = get_cuda_binary_path()
+    progress = progress_manager.get_progress(PROGRESS_KEY)
+
+    return {
+        "available": cuda_path is not None,
+        "active": is_cuda_active(),
+        "binary_path": str(cuda_path) if cuda_path else None,
+        "downloading": progress is not None and progress.get("status") == "downloading",
+        "download_progress": progress,
+    }
+
+
+async def download_cuda_binary(version: Optional[str] = None):
+    """Download the CUDA backend binary from GitHub Releases.
+
+    Downloads split parts listed in a manifest file, concatenates them,
+    and verifies the SHA-256 checksum for integrity. Atomic write
+    (temp file -> rename).
+
+    Args:
+        version: Version tag (e.g. "v0.2.0"). Defaults to current app version.
+    """
+    import httpx
+
+    if version is None:
+        version = f"v{__version__}"
+
+    progress = get_progress_manager()
+    binary_name = get_cuda_binary_name()
+    dest_dir = get_backends_dir()
+    final_path = dest_dir / binary_name
+    temp_path = dest_dir / f"{binary_name}.download"
+
+    # Clean up any leftover partial download
+    if temp_path.exists():
+        temp_path.unlink()
+
+    logger.info(f"Starting CUDA backend download for {version}")
+    progress.update_progress(
+        PROGRESS_KEY, current=0, total=0,
+        filename="Fetching manifest...", status="downloading",
+    )
+
+    base_url = f"{GITHUB_RELEASES_URL}/{version}"
+    stem = Path(binary_name).stem  # voicebox-server-cuda
+
+    try:
+        async with httpx.AsyncClient(follow_redirects=True, timeout=30.0) as client:
+            # Fetch the manifest (list of split part filenames)
+            manifest_url = f"{base_url}/{stem}.manifest"
+            manifest_resp = await client.get(manifest_url)
+            manifest_resp.raise_for_status()
+            parts = [p.strip() for p in manifest_resp.text.strip().splitlines() if p.strip()]
+
+            if not parts:
+                raise ValueError("Empty manifest — no split parts found")
+
+            logger.info(f"Found {len(parts)} split parts to download")
+
+            # Fetch expected checksum (optional — for integrity verification)
+            expected_sha = None
+            try:
+                sha_url = f"{base_url}/{stem}.sha256"
+                sha_resp = await client.get(sha_url)
+                if sha_resp.status_code == 200:
+                    # Format: "sha256hex  filename\n"
+                    expected_sha = sha_resp.text.strip().split()[0]
+                    logger.info(f"Expected SHA-256: {expected_sha[:16]}...")
+            except Exception as e:
+                logger.warning(f"Could not fetch checksum file — skipping verification: {e}")
+
+            # Download and concatenate parts
+            total_downloaded = 0
+            with open(temp_path, "wb") as f:
+                for i, part_name in enumerate(parts):
+                    part_url = f"{base_url}/{part_name}"
+                    logger.info(f"Downloading part {i + 1}/{len(parts)}: {part_name}")
+
+                    async with client.stream("GET", part_url) as response:
+                        response.raise_for_status()
+                        async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
+                            f.write(chunk)
+                            total_downloaded += len(chunk)
+                            progress.update_progress(
+                                PROGRESS_KEY, current=total_downloaded, total=0,
+                                filename=f"Part {i + 1}/{len(parts)}",
+                                status="downloading",
+                            )
+
+        # Verify integrity if checksum was available
+        if expected_sha:
+            progress.update_progress(
+                PROGRESS_KEY, current=total_downloaded, total=total_downloaded,
+                filename="Verifying integrity...", status="downloading",
+            )
+            sha256 = hashlib.sha256()
+            with open(temp_path, "rb") as f:
+                while True:
+                    chunk = f.read(1024 * 1024)
+                    if not chunk:
+                        break
+                    sha256.update(chunk)
+
+            actual = sha256.hexdigest()
+            if actual != expected_sha:
+                raise ValueError(
+                    f"Integrity check failed: expected {expected_sha[:16]}..., "
+                    f"got {actual[:16]}..."
+                )
+            logger.info(f"Integrity verified: {actual[:16]}...")
+
+        # Atomic move into place (replace handles existing target on all platforms)
+        temp_path.replace(final_path)
+
+        # Make executable on Unix
+        if sys.platform != "win32":
+            final_path.chmod(0o755)
+
+        logger.info(f"CUDA backend downloaded to {final_path}")
+        progress.mark_complete(PROGRESS_KEY)
+
+    except Exception as e:
+        # Clean up on failure
+        if temp_path.exists():
+            temp_path.unlink()
+        logger.error(f"CUDA backend download failed: {e}")
+        progress.mark_error(PROGRESS_KEY, str(e))
+        raise
+
+
+async def delete_cuda_binary() -> bool:
+    """Delete the downloaded CUDA binary. Returns True if deleted."""
+    path = get_cuda_binary_path()
+    if path and path.exists():
+        path.unlink()
+        logger.info(f"Deleted CUDA binary: {path}")
+        return True
+    return False

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)
 
@@ -71,6 +72,8 @@ class StoryItem(Base):
     generation_id = Column(String, ForeignKey("generations.id"), nullable=False)
     start_time_ms = Column(Integer, nullable=False, default=0)  # Milliseconds from story start
     track = Column(Integer, nullable=False, default=0)  # Track number (0 = main track)
+    trim_start_ms = Column(Integer, nullable=False, default=0)  # Milliseconds trimmed from start
+    trim_end_ms = Column(Integer, nullable=False, default=0)  # Milliseconds trimmed from end
     created_at = Column(DateTime, default=datetime.utcnow)
 
 
@@ -256,6 +259,34 @@ def _run_migrations(engine):
             conn.execute(text("ALTER TABLE story_items ADD COLUMN track INTEGER NOT NULL DEFAULT 0"))
             conn.commit()
             print("Added track column to story_items")
+    
+    # Migration: Add trim columns if they don't exist
+    # Re-check columns after potential track migration
+    columns = {col['name'] for col in inspector.get_columns('story_items')}
+    if 'trim_start_ms' not in columns:
+        print("Migrating story_items: adding trim_start_ms column")
+        with engine.connect() as conn:
+            conn.execute(text("ALTER TABLE story_items ADD COLUMN trim_start_ms INTEGER NOT NULL DEFAULT 0"))
+            conn.commit()
+            print("Added trim_start_ms column to story_items")
+    
+    columns = {col['name'] for col in inspector.get_columns('story_items')}
+    if 'trim_end_ms' not in columns:
+        print("Migrating story_items: adding trim_end_ms column")
+        with engine.connect() as conn:
+            conn.execute(text("ALTER TABLE story_items ADD COLUMN trim_end_ms INTEGER NOT NULL DEFAULT 0"))
+            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():

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/models.py

@@ -11,7 +11,7 @@ class VoiceProfileCreate(BaseModel):
     """Request model for creating a voice profile."""
     name: str = Field(..., min_length=1, max_length=100)
     description: Optional[str] = Field(None, max_length=500)
-    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it)$")
+    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he)$")
 
 
 class VoiceProfileResponse(BaseModel):
@@ -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
@@ -47,10 +53,11 @@ class GenerationRequest(BaseModel):
     """Request model for voice generation."""
     profile_id: str
     text: str = Field(..., min_length=1, max_length=5000)
-    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it)$")
+    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he)$")
     seed: Optional[int] = Field(None, ge=0)
     model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B)$")
     instruct: Optional[str] = Field(None, max_length=500)
+    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox)$")
 
 
 class GenerationResponse(BaseModel):
@@ -118,14 +125,19 @@ 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)
+    backend_variant: Optional[str] = None  # Binary variant (cpu or cuda)
 
 
 class ModelStatus(BaseModel):
     """Response model for model status."""
     model_name: str
     display_name: str
+    hf_repo_id: Optional[str] = None  # HuggingFace repository ID
     downloaded: bool
+    downloading: bool = False  # True if download is in progress
     size_mb: Optional[float] = None
     loaded: bool = False
 
@@ -145,6 +157,11 @@ class ActiveDownloadTask(BaseModel):
     model_name: str
     status: str
     started_at: datetime
+    error: Optional[str] = None
+    progress: Optional[float] = None  # 0-100 percentage
+    current: Optional[int] = None     # bytes downloaded
+    total: Optional[int] = None       # total bytes
+    filename: Optional[str] = None    # current file being downloaded
 
 
 class ActiveGenerationTask(BaseModel):
@@ -221,6 +238,8 @@ class StoryItemDetail(BaseModel):
     generation_id: str
     start_time_ms: int
     track: int = 0
+    trim_start_ms: int = 0
+    trim_end_ms: int = 0
     created_at: datetime
     # Generation details
     profile_id: str
@@ -277,3 +296,14 @@ class StoryItemMove(BaseModel):
     """Request model for moving a story item (position and/or track)."""
     start_time_ms: int = Field(..., ge=0)
     track: int = 0
+
+
+class StoryItemTrim(BaseModel):
+    """Request model for trimming a story item."""
+    trim_start_ms: int = Field(..., ge=0)
+    trim_end_ms: int = Field(..., ge=0)
+
+
+class StoryItemSplit(BaseModel):
+    """Request model for splitting a story item."""
+    split_time_ms: int = Field(..., ge=0)  # Time within the clip to split at (relative to clip start)

backend/platform_detect.py

@@ -0,0 +1,35 @@
+"""
+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 and functional), "pytorch" otherwise
+    """
+    if is_apple_silicon():
+        try:
+            import mlx.core  # noqa: F401 — triggers native lib loading
+            return "mlx"
+        except (ImportError, OSError, RuntimeError):
+            # MLX not installed, or native libraries failed to load inside a
+            # PyInstaller bundle (OSError on missing .dylib / .metallib).
+            # Fall through 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
 
@@ -36,14 +38,22 @@ async def create_profile(
 ) -> VoiceProfileResponse:
     """
     Create a new voice profile.
-    
+
     Args:
         data: Profile creation data
         db: Database session
-        
+
     Returns:
         Created profile
+
+    Raises:
+        ValueError: If a profile with the same name already exists
     """
+    # Check if profile name already exists
+    existing_profile = db.query(DBVoiceProfile).filter_by(name=data.name).first()
+    if existing_profile:
+        raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
+
     # Create profile in database
     db_profile = DBVoiceProfile(
         id=str(uuid.uuid4()),
@@ -53,15 +63,15 @@ async def create_profile(
         created_at=datetime.utcnow(),
         updated_at=datetime.utcnow(),
     )
-    
+
     db.add(db_profile)
     db.commit()
     db.refresh(db_profile)
-    
+
     # Create profile directory
     profile_dir = _get_profiles_dir() / db_profile.id
     profile_dir.mkdir(parents=True, exist_ok=True)
-    
+
     return VoiceProfileResponse.model_validate(db_profile)
 
 
@@ -119,6 +129,10 @@ 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)
 
 
@@ -185,28 +199,37 @@ async def update_profile(
 ) -> Optional[VoiceProfileResponse]:
     """
     Update a voice profile.
-    
+
     Args:
         profile_id: Profile ID
         data: Updated profile data
         db: Database session
-        
+
     Returns:
         Updated profile or None if not found
+
+    Raises:
+        ValueError: If a profile with the same name already exists (different profile)
     """
     profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
     if not profile:
         return None
-    
+
+    # Check if the new name conflicts with another profile
+    if profile.name != data.name:
+        existing_profile = db.query(DBVoiceProfile).filter_by(name=data.name).first()
+        if existing_profile:
+            raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
+
     # Update fields
     profile.name = data.name
     profile.description = data.description
     profile.language = data.language
     profile.updated_at = datetime.utcnow()
-    
+
     db.commit()
     db.refresh(profile)
-    
+
     return VoiceProfileResponse.model_validate(profile)
 
 
@@ -240,6 +263,9 @@ 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 +287,9 @@ 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():
@@ -270,33 +299,75 @@ 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,
     use_cache: bool = True,
+    engine: str = "qwen",
 ) -> 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
-        
+        engine: TTS engine to create prompt for ("qwen" or "luxtts")
+
     Returns:
         Voice prompt dictionary
     """
+    from .backends import get_tts_backend_for_engine
+
     # 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()
-    
+
+    tts_model = get_tts_backend_for_engine(engine)
+
     if len(samples) == 1:
         # Single sample - use directly
         sample = samples[0]
@@ -310,27 +381,127 @@ 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
-        import tempfile
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
-            save_audio(combined_audio, tmp.name, 24000)
-            tmp_path = tmp.name
+        # Store in cache directory
+        cache_dir = _get_cache_dir()
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        combined_path = cache_dir / f"combined_{profile_id}_{combination_hash}.wav"
         
-        try:
-            # Create prompt from combined audio
-            voice_prompt, _ = await tts_model.create_voice_prompt(
-                tmp_path,
-                combined_text,
-                use_cache=use_cache,
-            )
-            return voice_prompt
-        finally:
-            # Clean up temp file
-            Path(tmp_path).unlink(missing_ok=True)
+        # Save combined audio
+        save_audio(combined_audio, str(combined_path), 24000)
+
+        # Create prompt from combined audio
+        voice_prompt, _ = await tts_model.create_voice_prompt(
+            str(combined_path),
+            combined_text,
+            use_cache=use_cache,
+        )
+        return voice_prompt
+
+
+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

@@ -9,15 +9,39 @@ alembic>=1.13.0
 
 # ML models
 torch>=2.1.0
-transformers>=4.36.0
+transformers>=4.36.0,<=4.57.6
 accelerate>=0.26.0
 huggingface_hub>=0.20.0
 qwen-tts>=0.0.5
 
+# LuxTTS (voice cloning engine)
+# piper-phonemize needs custom index (no PyPI wheels)
+--find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
+# linacodec is a git-only dep of Zipvoice (uv-only source, pip can't resolve it)
+linacodec @ git+https://github.com/ysharma3501/LinaCodec.git
+Zipvoice @ git+https://github.com/ysharma3501/LuxTTS.git
+
+# Chatterbox TTS sub-dependencies (chatterbox-tts itself is installed
+# --no-deps in the setup script because it pins numpy<1.26 / torch==2.6
+# which are incompatible with Python 3.12+)
+conformer>=0.3.2
+diffusers>=0.29.0
+omegaconf
+pykakasi
+resemble-perth>=1.0.1
+s3tokenizer
+spacy-pkuseg
+pyloudnorm
+
 # Audio processing
 librosa>=0.10.0
 soundfile>=0.12.0
 numpy>=1.24.0
+numba>=0.60.0,<0.61.0
+
+# HTTP client (for CUDA backend download)
+httpx>=0.27.0
 
 # Utilities
 python-multipart>=0.0.6
+Pillow>=10.0.0

backend/server.py

@@ -64,7 +64,29 @@ if __name__ == "__main__":
             default=None,
             help="Data directory for database, profiles, and generated audio",
         )
+        parser.add_argument(
+            "--version",
+            action="store_true",
+            help="Print version and exit",
+        )
         args = parser.parse_args()
+
+        if args.version:
+            from backend import __version__
+            print(f"voicebox-server {__version__}")
+            sys.exit(0)
+
+        # Detect backend variant from binary name
+        # voicebox-server-cuda → sets VOICEBOX_BACKEND_VARIANT=cuda
+        import os
+        binary_name = os.path.basename(sys.executable).lower()
+        if "cuda" in binary_name:
+            os.environ["VOICEBOX_BACKEND_VARIANT"] = "cuda"
+            logger.info("Backend variant: CUDA")
+        else:
+            os.environ["VOICEBOX_BACKEND_VARIANT"] = "cpu"
+            logger.info("Backend variant: CPU")
+
         logger.info(f"Parsed arguments: host={args.host}, port={args.port}, data_dir={args.data_dir}")
 
         # Set data directory if provided

backend/stories.py

@@ -18,6 +18,8 @@ from .models import (
     StoryItemCreate,
     StoryItemBatchUpdate,
     StoryItemMove,
+    StoryItemTrim,
+    StoryItemSplit,
 )
 from .database import Story as DBStory, StoryItem as DBStoryItem, Generation as DBGeneration, VoiceProfile as DBVoiceProfile
 from .utils.audio import load_audio, save_audio
@@ -129,6 +131,8 @@ async def get_story(
             generation_id=item.generation_id,
             start_time_ms=item.start_time_ms,
             track=item.track,
+            trim_start_ms=getattr(item, 'trim_start_ms', 0),
+            trim_end_ms=getattr(item, 'trim_end_ms', 0),
             created_at=item.created_at,
             profile_id=generation.profile_id,
             profile_name=profile_name,
@@ -252,6 +256,8 @@ async def add_item_to_story(
             generation_id=existing.generation_id,
             start_time_ms=existing.start_time_ms,
             track=existing.track,
+            trim_start_ms=getattr(existing, 'trim_start_ms', 0),
+            trim_end_ms=getattr(existing, 'trim_end_ms', 0),
             created_at=existing.created_at,
             profile_id=generation.profile_id,
             profile_name=profile.name if profile else "Unknown",
@@ -321,6 +327,8 @@ async def add_item_to_story(
         generation_id=item.generation_id,
         start_time_ms=item.start_time_ms,
         track=item.track,
+        trim_start_ms=getattr(item, 'trim_start_ms', 0),
+        trim_end_ms=getattr(item, 'trim_end_ms', 0),
         created_at=item.created_at,
         profile_id=generation.profile_id,
         profile_name=profile.name if profile else "Unknown",
@@ -336,7 +344,7 @@ async def add_item_to_story(
 
 async def move_story_item(
     story_id: str,
-    generation_id: str,
+    item_id: str,
     data: StoryItemMove,
     db: Session,
 ) -> Optional[StoryItemDetail]:
@@ -345,7 +353,7 @@ async def move_story_item(
 
     Args:
         story_id: Story ID
-        generation_id: Generation ID of the item to move
+        item_id: Story item ID
         data: New position and track data
         db: Database session
 
@@ -354,14 +362,14 @@ async def move_story_item(
     """
     # Get the item
     item = db.query(DBStoryItem).filter_by(
+        id=item_id,
         story_id=story_id,
-        generation_id=generation_id
     ).first()
     if not item:
         return None
 
     # Get the generation
-    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
     if not generation:
         return None
 
@@ -386,6 +394,8 @@ async def move_story_item(
         generation_id=item.generation_id,
         start_time_ms=item.start_time_ms,
         track=item.track,
+        trim_start_ms=getattr(item, 'trim_start_ms', 0),
+        trim_end_ms=getattr(item, 'trim_end_ms', 0),
         created_at=item.created_at,
         profile_id=generation.profile_id,
         profile_name=profile.name if profile else "Unknown",
@@ -401,23 +411,23 @@ async def move_story_item(
 
 async def remove_item_from_story(
     story_id: str,
-    generation_id: str,
+    item_id: str,
     db: Session,
 ) -> bool:
     """
-    Remove a generation from a story.
+    Remove a story item from a story.
 
     Args:
         story_id: Story ID
-        generation_id: Generation ID to remove
+        item_id: Story item ID to remove
         db: Database session
 
     Returns:
         True if removed, False if not found
     """
     item = db.query(DBStoryItem).filter_by(
+        id=item_id,
         story_id=story_id,
-        generation_id=generation_id
     ).first()
     if not item:
         return False
@@ -434,6 +444,277 @@ async def remove_item_from_story(
     return True
 
 
+async def trim_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemTrim,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Trim a story item (update trim_start_ms and trim_end_ms).
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID
+        data: Trim data (trim_start_ms, trim_end_ms)
+        db: Database session
+
+    Returns:
+        Updated item detail or None if not found
+    """
+    # Get the item
+    item = db.query(DBStoryItem).filter_by(
+        id=item_id,
+        story_id=story_id,
+    ).first()
+    if not item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
+    if not generation:
+        return None
+
+    # Validate trim values don'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 - would result in zero or negative duration
+
+    # Update trim values
+    item.trim_start_ms = data.trim_start_ms
+    item.trim_end_ms = data.trim_end_ms
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return StoryItemDetail(
+        id=item.id,
+        story_id=item.story_id,
+        generation_id=item.generation_id,
+        start_time_ms=item.start_time_ms,
+        track=item.track,
+        trim_start_ms=item.trim_start_ms,
+        trim_end_ms=item.trim_end_ms,
+        created_at=item.created_at,
+        profile_id=generation.profile_id,
+        profile_name=profile.name if profile else "Unknown",
+        text=generation.text,
+        language=generation.language,
+        audio_path=generation.audio_path,
+        duration=generation.duration,
+        seed=generation.seed,
+        instruct=generation.instruct,
+        generation_created_at=generation.created_at,
+    )
+
+
+async def split_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemSplit,
+    db: Session,
+) -> Optional[List[StoryItemDetail]]:
+    """
+    Split a story item at a given time, creating two clips.
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID to split
+        data: Split data (split_time_ms - time within clip to split at)
+        db: Database session
+
+    Returns:
+        List of two updated item details (original and new) or None if not found/invalid
+    """
+    # Get the item
+    item = db.query(DBStoryItem).filter_by(
+        id=item_id,
+        story_id=story_id,
+    ).first()
+    if not item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
+    if not generation:
+        return None
+
+    # Calculate effective duration and validate split point
+    current_trim_start = getattr(item, 'trim_start_ms', 0)
+    current_trim_end = getattr(item, 'trim_end_ms', 0)
+    original_duration_ms = int(generation.duration * 1000)
+    effective_duration_ms = original_duration_ms - current_trim_start - current_trim_end
+
+    # Validate split_time_ms is within the effective duration
+    if data.split_time_ms <= 0 or data.split_time_ms >= effective_duration_ms:
+        return None  # Invalid split point
+
+    # Calculate the absolute time in the original audio where we're splitting
+    absolute_split_ms = current_trim_start + data.split_time_ms
+
+    # Update original clip: trim from the end
+    item.trim_end_ms = original_duration_ms - absolute_split_ms
+
+    # Create new clip: starts after the split, trimmed from the start
+    new_item = DBStoryItem(
+        id=str(uuid.uuid4()),
+        story_id=story_id,
+        generation_id=item.generation_id,  # Same generation, different trim
+        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,
+        created_at=datetime.utcnow(),
+    )
+
+    db.add(new_item)
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+    db.refresh(new_item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+    profile_name = profile.name if profile else "Unknown"
+
+    # Build response items
+    original_item_detail = StoryItemDetail(
+        id=item.id,
+        story_id=item.story_id,
+        generation_id=item.generation_id,
+        start_time_ms=item.start_time_ms,
+        track=item.track,
+        trim_start_ms=item.trim_start_ms,
+        trim_end_ms=item.trim_end_ms,
+        created_at=item.created_at,
+        profile_id=generation.profile_id,
+        profile_name=profile_name,
+        text=generation.text,
+        language=generation.language,
+        audio_path=generation.audio_path,
+        duration=generation.duration,
+        seed=generation.seed,
+        instruct=generation.instruct,
+        generation_created_at=generation.created_at,
+    )
+
+    new_item_detail = StoryItemDetail(
+        id=new_item.id,
+        story_id=new_item.story_id,
+        generation_id=new_item.generation_id,
+        start_time_ms=new_item.start_time_ms,
+        track=new_item.track,
+        trim_start_ms=new_item.trim_start_ms,
+        trim_end_ms=new_item.trim_end_ms,
+        created_at=new_item.created_at,
+        profile_id=generation.profile_id,
+        profile_name=profile_name,
+        text=generation.text,
+        language=generation.language,
+        audio_path=generation.audio_path,
+        duration=generation.duration,
+        seed=generation.seed,
+        instruct=generation.instruct,
+        generation_created_at=generation.created_at,
+    )
+
+    return [original_item_detail, new_item_detail]
+
+
+async def duplicate_story_item(
+    story_id: str,
+    item_id: str,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Duplicate a story item, creating a copy with all properties.
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID to duplicate
+        db: Database session
+
+    Returns:
+        New item detail or None if not found
+    """
+    # Get the original item
+    original_item = db.query(DBStoryItem).filter_by(
+        id=item_id,
+        story_id=story_id,
+    ).first()
+    if not original_item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=original_item.generation_id).first()
+    if not generation:
+        return None
+
+    # Calculate effective duration
+    current_trim_start = getattr(original_item, 'trim_start_ms', 0)
+    current_trim_end = getattr(original_item, 'trim_end_ms', 0)
+    original_duration_ms = int(generation.duration * 1000)
+    effective_duration_ms = original_duration_ms - current_trim_start - current_trim_end
+
+    # Create duplicate item - place it right after the original
+    new_item = DBStoryItem(
+        id=str(uuid.uuid4()),
+        story_id=story_id,
+        generation_id=original_item.generation_id,  # Same generation as original
+        start_time_ms=original_item.start_time_ms + effective_duration_ms + 200,  # 200ms gap
+        track=original_item.track,
+        trim_start_ms=current_trim_start,
+        trim_end_ms=current_trim_end,
+        created_at=datetime.utcnow(),
+    )
+
+    db.add(new_item)
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(new_item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return StoryItemDetail(
+        id=new_item.id,
+        story_id=new_item.story_id,
+        generation_id=new_item.generation_id,
+        start_time_ms=new_item.start_time_ms,
+        track=new_item.track,
+        trim_start_ms=new_item.trim_start_ms,
+        trim_end_ms=new_item.trim_end_ms,
+        created_at=new_item.created_at,
+        profile_id=generation.profile_id,
+        profile_name=profile.name if profile else "Unknown",
+        text=generation.text,
+        language=generation.language,
+        audio_path=generation.audio_path,
+        duration=generation.duration,
+        seed=generation.seed,
+        instruct=generation.instruct,
+        generation_created_at=generation.created_at,
+    )
+
+
 async def update_story_item_times(
     story_id: str,
     data: StoryItemBatchUpdate,
@@ -538,6 +819,8 @@ async def reorder_story_items(
             generation_id=item.generation_id,
             start_time_ms=item.start_time_ms,
             track=item.track,
+            trim_start_ms=getattr(item, 'trim_start_ms', 0),
+            trim_end_ms=getattr(item, 'trim_end_ms', 0),
             created_at=item.created_at,
             profile_id=generation.profile_id,
             profile_name=profile_name,
@@ -602,14 +885,31 @@ async def export_story_audio(
             audio, sr = load_audio(str(audio_path), sample_rate=sample_rate)
             sample_rate = sr  # Use actual sample rate from first file
             
+            # Get trim values
+            trim_start_ms = getattr(item, 'trim_start_ms', 0)
+            trim_end_ms = getattr(item, 'trim_end_ms', 0)
+            
+            # Calculate effective duration
+            original_duration_ms = int(generation.duration * 1000)
+            effective_duration_ms = original_duration_ms - trim_start_ms - trim_end_ms
+            
+            # Slice audio based on trim values
+            trim_start_sample = int((trim_start_ms / 1000.0) * sample_rate)
+            trim_end_sample = int((trim_end_ms / 1000.0) * sample_rate)
+            
+            # Extract the trimmed portion
+            if trim_end_ms > 0:
+                trimmed_audio = audio[trim_start_sample:-trim_end_sample] if trim_end_sample > 0 else audio[trim_start_sample:]
+            else:
+                trimmed_audio = audio[trim_start_sample:]
+            
             # Store audio with its timecode info
             start_time_ms = item.start_time_ms
-            duration_ms = int(generation.duration * 1000)
             
             audio_data.append({
-                'audio': audio,
+                'audio': trimmed_audio,
                 'start_time_ms': start_time_ms,
-                'duration_ms': duration_ms,
+                'duration_ms': effective_duration_ms,
             })
         except Exception:
             # Skip files that can't be loaded

backend/tests/README.md

@@ -0,0 +1,58 @@
+# Backend Tests
+
+Manual test scripts for debugging and validating backend functionality.
+
+## Test Files
+
+### `test_generation_progress.py`
+Tests TTS generation with SSE progress monitoring to identify UX issues where users see download progress even when the model is already cached.
+
+**Usage:**
+```bash
+cd backend
+python tests/test_generation_progress.py
+```
+
+**Prerequisites:**
+- Server must be running (`python main.py`)
+- At least one voice profile must exist
+
+### `test_real_download.py`
+Tests real model download with SSE progress monitoring.
+
+**Usage:**
+```bash
+cd backend
+# Delete cache first to force fresh download
+rm -rf ~/.cache/huggingface/hub/models--openai--whisper-base
+python tests/test_real_download.py
+```
+
+**Prerequisites:**
+- Server must be running (`python main.py`)
+
+### `test_progress.py`
+Unit tests for ProgressManager and HFProgressTracker functionality.
+
+**Usage:**
+```bash
+cd backend
+python tests/test_progress.py
+```
+
+### `test_check_progress_state.py`
+Debugging script to inspect the internal state of ProgressManager and TaskManager.
+
+**Usage:**
+```bash
+cd backend
+python tests/test_check_progress_state.py
+```
+
+## Notes
+
+These are manual test scripts, not automated unit tests. They're designed for:
+- Debugging progress tracking issues
+- Validating SSE event streams
+- Monitoring real-time download behavior
+- Inspecting internal state during development

backend/tests/__init__.py

@@ -0,0 +1,6 @@
+"""
+Test suite for Voicebox backend.
+
+This directory contains manual test scripts for debugging and validating
+progress tracking, model downloads, and generation functionality.
+"""

backend/tests/test_generation_download.py

@@ -0,0 +1,321 @@
+"""
+Test TTS generation with SSE progress monitoring.
+This test captures the exact SSE events triggered during generation
+to identify UX issues where users see download progress even when
+the model is already cached.
+"""
+
+import asyncio
+import json
+import httpx
+from typing import List, Dict, Optional
+from datetime import datetime
+
+
+async def monitor_sse_stream(model_name: str, timeout: int = 120):
+    """Monitor SSE stream for a model during generation."""
+    events: List[Dict] = []
+    url = f"http://localhost:8000/models/progress/{model_name}"
+
+    print(f"[{_timestamp()}] Connecting to SSE endpoint: {url}")
+
+    try:
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            async with client.stream("GET", url) as response:
+                print(f"[{_timestamp()}] SSE connected, status: {response.status_code}")
+
+                if response.status_code != 200:
+                    print(f"[{_timestamp()}] Error: SSE endpoint returned {response.status_code}")
+                    return events
+
+                async for line in response.aiter_lines():
+                    if not line:
+                        continue
+
+                    timestamp = _timestamp()
+
+                    if line.startswith("data: "):
+                        try:
+                            data = json.loads(line[6:])
+                            print(f"[{timestamp}] → SSE Event: {data['status']:12} {data.get('progress', 0):6.1f}% {data.get('filename', '')}")
+                            events.append({
+                                **data,
+                                "_timestamp": timestamp
+                            })
+
+                            # Stop if complete or error
+                            if data.get("status") in ("complete", "error"):
+                                print(f"[{timestamp}] → Model {data['status']}!")
+                                break
+
+                        except json.JSONDecodeError as e:
+                            print(f"[{timestamp}] Error parsing JSON: {e}")
+                            print(f"  Line was: {line}")
+
+                    elif line.startswith(": heartbeat"):
+                        print(f"[{timestamp}] ♥ heartbeat")
+
+    except asyncio.TimeoutError:
+        print(f"[{_timestamp()}] SSE monitoring timed out")
+    except Exception as e:
+        print(f"[{_timestamp()}] SSE error: {e}")
+
+    return events
+
+
+async def trigger_generation(profile_id: str, text: str, model_size: str = "1.7B"):
+    """Trigger TTS generation via the API."""
+    url = "http://localhost:8000/generate"
+
+    print(f"\n[{_timestamp()}] Triggering generation...")
+    print(f"  Profile: {profile_id}")
+    print(f"  Text: {text[:50]}...")
+    print(f"  Model: {model_size}")
+
+    try:
+        async with httpx.AsyncClient(timeout=120) as client:
+            response = await client.post(url, json={
+                "profile_id": profile_id,
+                "text": text,
+                "language": "en",
+                "model_size": model_size,
+            })
+
+            print(f"[{_timestamp()}] Response: {response.status_code}")
+
+            if response.status_code == 200:
+                result = response.json()
+                print(f"[{_timestamp()}] ✓ Generation successful!")
+                print(f"  Generation ID: {result.get('id')}")
+                print(f"  Duration: {result.get('duration', 0):.2f}s")
+                return True, result
+            elif response.status_code == 202:
+                # Model is being downloaded
+                result = response.json()
+                print(f"[{_timestamp()}] → Model download in progress")
+                print(f"  Detail: {result}")
+                return False, result
+            else:
+                print(f"[{_timestamp()}] ✗ Error: {response.text}")
+                return False, None
+
+    except Exception as e:
+        print(f"[{_timestamp()}] ✗ Exception: {e}")
+        return False, None
+
+
+async def get_first_profile():
+    """Get the first available voice profile."""
+    url = "http://localhost:8000/profiles"
+
+    try:
+        async with httpx.AsyncClient(timeout=10) as client:
+            response = await client.get(url)
+            if response.status_code == 200:
+                profiles = response.json()
+                if profiles:
+                    return profiles[0]["id"]
+    except Exception as e:
+        print(f"Error getting profiles: {e}")
+
+    return None
+
+
+async def check_server():
+    """Check if the server is running."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            response = await client.get("http://localhost:8000/health")
+            return response.status_code == 200
+    except Exception as e:
+        print(f"Server not running: {e}")
+        return False
+
+
+def _timestamp():
+    """Get current timestamp for logging."""
+    return datetime.now().strftime("%H:%M:%S.%f")[:-3]
+
+
+async def test_generation_with_cached_model():
+    """
+    Test Case 1: Generation when model is already cached.
+    
+    This should NOT show any download progress events.
+    If it does, that's the UX bug we're trying to fix.
+    """
+    print("\n" + "=" * 80)
+    print("TEST CASE 1: Generation with Cached Model")
+    print("=" * 80)
+    print("Expected: No download progress events (or minimal/instant completion)")
+    print("Actual UX Issue: Users see 'started' and 'finished' events even for cached models")
+    print("=" * 80)
+
+    model_size = "1.7B"
+    model_name = f"qwen-tts-{model_size}"
+
+    # Get a profile
+    profile_id = await get_first_profile()
+    if not profile_id:
+        print("✗ No voice profiles found. Please create a profile first.")
+        return False
+
+    print(f"\nUsing profile: {profile_id}")
+
+    # Start SSE monitor BEFORE triggering generation
+    monitor_task = asyncio.create_task(monitor_sse_stream(model_name, timeout=30))
+
+    # Wait for SSE to connect
+    await asyncio.sleep(1)
+
+    # Trigger generation
+    test_text = "Hello, this is a test of the voice generation system."
+    success, result = await trigger_generation(profile_id, test_text, model_size)
+
+    if not success and result and result.get("downloading"):
+        print("\n⚠ Model is being downloaded. Waiting for download to complete...")
+        # Wait for SSE monitor to capture download events
+        events = await monitor_task
+        return events
+
+    # Wait a bit more to catch any progress events
+    await asyncio.sleep(3)
+
+    # Cancel SSE monitor
+    monitor_task.cancel()
+    try:
+        events = await monitor_task
+    except asyncio.CancelledError:
+        events = []
+
+    return events
+
+
+async def test_generation_with_fresh_download():
+    """
+    Test Case 2: Generation when model needs to be downloaded.
+    
+    This SHOULD show download progress events.
+    """
+    print("\n" + "=" * 80)
+    print("TEST CASE 2: Generation with Model Download")
+    print("=" * 80)
+    print("Expected: Download progress events from 0% to 100%")
+    print("=" * 80)
+
+    # Use a different model size to force download
+    model_size = "0.6B"  # Smaller model for faster testing
+    model_name = f"qwen-tts-{model_size}"
+
+    # Get a profile
+    profile_id = await get_first_profile()
+    if not profile_id:
+        print("✗ No voice profiles found. Please create a profile first.")
+        return False
+
+    print(f"\nUsing profile: {profile_id}")
+    print("Note: This will download the model if not cached")
+
+    # Start SSE monitor BEFORE triggering generation
+    monitor_task = asyncio.create_task(monitor_sse_stream(model_name, timeout=300))
+
+    # Wait for SSE to connect
+    await asyncio.sleep(1)
+
+    # Trigger generation
+    test_text = "This should trigger a model download if the model is not cached."
+    success, result = await trigger_generation(profile_id, test_text, model_size)
+
+    if not success and result and result.get("downloading"):
+        print("\n→ Model download initiated. Monitoring progress...")
+        # Wait for download to complete
+        events = await monitor_task
+
+        # Try generation again
+        print(f"\n[{_timestamp()}] Retrying generation after download...")
+        await asyncio.sleep(2)
+        success, result = await trigger_generation(profile_id, test_text, model_size)
+
+        if success:
+            print("✓ Generation successful after download")
+
+        return events
+
+    # If model was already cached
+    await asyncio.sleep(3)
+    monitor_task.cancel()
+    try:
+        events = await monitor_task
+    except asyncio.CancelledError:
+        events = []
+
+    return events
+
+
+async def main():
+    print("=" * 80)
+    print("TTS Generation Progress Test")
+    print("=" * 80)
+    print("Purpose: Capture exact SSE events during generation to identify UX issues")
+    print("=" * 80)
+
+    # Check if server is running
+    print(f"\n[{_timestamp()}] Checking if server is running...")
+    if not await check_server():
+        print("✗ Server is not running on http://localhost:8000")
+        print("\nPlease start the server first:")
+        print("  cd backend && python main.py")
+        return False
+
+    print("✓ Server is running")
+
+    # Test Case 1: Cached model
+    print("\n" + "🧪 " * 20)
+    events_cached = await test_generation_with_cached_model()
+
+    # Results for Test Case 1
+    print("\n" + "=" * 80)
+    print("TEST CASE 1 RESULTS: Generation with Cached Model")
+    print("=" * 80)
+
+    if not events_cached:
+        print("✓ GOOD: No SSE progress events received")
+        print("  This is the expected behavior for a cached model.")
+    else:
+        print(f"⚠ ISSUE FOUND: Received {len(events_cached)} SSE events:")
+        print("\nEvent Timeline:")
+        for i, event in enumerate(events_cached, 1):
+            timestamp = event.pop("_timestamp", "??:??:??.???")
+            print(f"  {i}. [{timestamp}] {event}")
+
+        print("\n⚠ This explains the UX issue!")
+        print("  Users see progress events even when the model is already cached,")
+        print("  making them think the model is downloading again.")
+
+    # Test Case 2: Fresh download (optional, commented out by default)
+    # Uncomment if you want to test download progress
+    # print("\n" + "🧪 " * 20)
+    # events_download = await test_generation_with_fresh_download()
+    #
+    # print("\n" + "=" * 80)
+    # print("TEST CASE 2 RESULTS: Generation with Model Download")
+    # print("=" * 80)
+    #
+    # if not events_download:
+    #     print("ℹ Model was already cached, no download occurred")
+    # else:
+    #     print(f"✓ Received {len(events_download)} download progress events")
+    #     print("\nDownload Timeline:")
+    #     for i, event in enumerate(events_download, 1):
+    #         timestamp = event.pop("_timestamp", "??:??:??.???")
+    #         print(f"  {i}. [{timestamp}] {event}")
+
+    print("\n" + "=" * 80)
+    print("Test Complete!")
+    print("=" * 80)
+
+    return True
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

backend/tests/test_profile_duplicate_names.py

@@ -0,0 +1,217 @@
+"""
+Tests for profile duplicate name validation.
+
+This test suite verifies that the application correctly handles
+duplicate profile names and provides user-friendly error messages.
+"""
+
+import pytest
+import tempfile
+import shutil
+from pathlib import Path
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+# Add parent directory to path to import backend modules
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from database import Base, VoiceProfile as DBVoiceProfile
+from models import VoiceProfileCreate
+from profiles import create_profile, update_profile
+
+
+@pytest.fixture
+def test_db():
+    """Create a temporary test database."""
+    # Create temporary directory for test database
+    temp_dir = tempfile.mkdtemp()
+    db_path = Path(temp_dir) / "test.db"
+
+    # Create engine and session
+    engine = create_engine(f"sqlite:///{db_path}")
+    Base.metadata.create_all(bind=engine)
+    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+    db = SessionLocal()
+
+    yield db
+
+    # Cleanup
+    db.close()
+    shutil.rmtree(temp_dir)
+
+
+@pytest.fixture
+def mock_profiles_dir(monkeypatch, tmp_path):
+    """Mock the profiles directory to use a temporary path."""
+    import profiles
+    monkeypatch.setattr(profiles, '_get_profiles_dir', lambda: tmp_path)
+    return tmp_path
+
+
+@pytest.mark.asyncio
+async def test_create_profile_duplicate_name_raises_error(test_db, mock_profiles_dir):
+    """Test that creating a profile with a duplicate name raises a ValueError."""
+    # Create first profile
+    profile_data_1 = VoiceProfileCreate(
+        name="Test Profile",
+        description="First profile",
+        language="en"
+    )
+
+    profile_1 = await create_profile(profile_data_1, test_db)
+    assert profile_1.name == "Test Profile"
+
+    # Try to create second profile with same name
+    profile_data_2 = VoiceProfileCreate(
+        name="Test Profile",
+        description="Second profile",
+        language="en"
+    )
+
+    with pytest.raises(ValueError) as exc_info:
+        await create_profile(profile_data_2, test_db)
+
+    # Verify error message is user-friendly
+    assert "already exists" in str(exc_info.value)
+    assert "Test Profile" in str(exc_info.value)
+    assert "choose a different name" in str(exc_info.value).lower()
+
+
+@pytest.mark.asyncio
+async def test_create_profile_different_names_succeeds(test_db, mock_profiles_dir):
+    """Test that creating profiles with different names succeeds."""
+    # Create first profile
+    profile_data_1 = VoiceProfileCreate(
+        name="Profile One",
+        description="First profile",
+        language="en"
+    )
+
+    profile_1 = await create_profile(profile_data_1, test_db)
+    assert profile_1.name == "Profile One"
+
+    # Create second profile with different name
+    profile_data_2 = VoiceProfileCreate(
+        name="Profile Two",
+        description="Second profile",
+        language="en"
+    )
+
+    profile_2 = await create_profile(profile_data_2, test_db)
+    assert profile_2.name == "Profile Two"
+
+    # Verify both profiles exist
+    assert profile_1.id != profile_2.id
+
+
+@pytest.mark.asyncio
+async def test_update_profile_to_duplicate_name_raises_error(test_db, mock_profiles_dir):
+    """Test that updating a profile to a duplicate name raises a ValueError."""
+    # Create two profiles with different names
+    profile_data_1 = VoiceProfileCreate(
+        name="Profile A",
+        description="First profile",
+        language="en"
+    )
+    profile_1 = await create_profile(profile_data_1, test_db)
+
+    profile_data_2 = VoiceProfileCreate(
+        name="Profile B",
+        description="Second profile",
+        language="en"
+    )
+    profile_2 = await create_profile(profile_data_2, test_db)
+
+    # Try to update profile_2 to use profile_1's name
+    update_data = VoiceProfileCreate(
+        name="Profile A",  # Duplicate name
+        description="Updated description",
+        language="en"
+    )
+
+    with pytest.raises(ValueError) as exc_info:
+        await update_profile(profile_2.id, update_data, test_db)
+
+    # Verify error message is user-friendly
+    assert "already exists" in str(exc_info.value)
+    assert "Profile A" in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_update_profile_keep_same_name_succeeds(test_db, mock_profiles_dir):
+    """Test that updating a profile while keeping the same name succeeds."""
+    # Create profile
+    profile_data = VoiceProfileCreate(
+        name="My Profile",
+        description="Original description",
+        language="en"
+    )
+    profile = await create_profile(profile_data, test_db)
+
+    # Update profile with same name but different description
+    update_data = VoiceProfileCreate(
+        name="My Profile",  # Same name
+        description="Updated description",
+        language="en"
+    )
+
+    updated_profile = await update_profile(profile.id, update_data, test_db)
+
+    # Verify update succeeded
+    assert updated_profile is not None
+    assert updated_profile.id == profile.id
+    assert updated_profile.name == "My Profile"
+    assert updated_profile.description == "Updated description"
+
+
+@pytest.mark.asyncio
+async def test_update_profile_to_new_unique_name_succeeds(test_db, mock_profiles_dir):
+    """Test that updating a profile to a new unique name succeeds."""
+    # Create profile
+    profile_data = VoiceProfileCreate(
+        name="Original Name",
+        description="Profile description",
+        language="en"
+    )
+    profile = await create_profile(profile_data, test_db)
+
+    # Update profile with new unique name
+    update_data = VoiceProfileCreate(
+        name="New Unique Name",
+        description="Updated description",
+        language="en"
+    )
+
+    updated_profile = await update_profile(profile.id, update_data, test_db)
+
+    # Verify update succeeded
+    assert updated_profile is not None
+    assert updated_profile.id == profile.id
+    assert updated_profile.name == "New Unique Name"
+
+
+@pytest.mark.asyncio
+async def test_case_sensitive_names_allowed(test_db, mock_profiles_dir):
+    """Test that profile names are case-sensitive (e.g., 'Test' and 'test' are different)."""
+    # Create profile with lowercase name
+    profile_data_1 = VoiceProfileCreate(
+        name="test profile",
+        description="Lowercase",
+        language="en"
+    )
+    profile_1 = await create_profile(profile_data_1, test_db)
+
+    # Create profile with different case
+    profile_data_2 = VoiceProfileCreate(
+        name="Test Profile",
+        description="Title case",
+        language="en"
+    )
+    profile_2 = await create_profile(profile_data_2, test_db)
+
+    # Both should succeed since SQLite unique constraint is case-sensitive by default
+    assert profile_1.name == "test profile"
+    assert profile_2.name == "Test Profile"
+    assert profile_1.id != profile_2.id

backend/tests/test_progress.py

@@ -0,0 +1,313 @@
+"""
+Test script to debug model download progress tracking.
+"""
+
+import asyncio
+import json
+import time
+from typing import List, Dict
+import logging
+
+# Set up logging to see what's happening
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+from utils.progress import ProgressManager, get_progress_manager
+from utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+
+
+def test_progress_manager_basic():
+    """Test 1: Basic ProgressManager functionality."""
+    print("\n" + "=" * 60)
+    print("Test 1: ProgressManager Basic Operations")
+    print("=" * 60)
+
+    pm = ProgressManager()
+
+    # Test update_progress
+    pm.update_progress(
+        model_name="test-model",
+        current=50,
+        total=100,
+        filename="test.bin",
+        status="downloading"
+    )
+
+    # Test get_progress
+    progress = pm.get_progress("test-model")
+    print(f"✓ Progress stored: {progress}")
+    assert progress is not None
+    assert progress["progress"] == 50.0
+    assert progress["filename"] == "test.bin"
+    assert progress["status"] == "downloading"
+
+    # Test mark_complete
+    pm.mark_complete("test-model")
+    progress = pm.get_progress("test-model")
+    print(f"✓ Marked complete: {progress}")
+    assert progress["status"] == "complete"
+    assert progress["progress"] == 100.0
+
+    print("✓ Test 1 PASSED\n")
+    return True
+
+
+async def test_progress_manager_sse():
+    """Test 2: ProgressManager SSE streaming."""
+    print("\n" + "=" * 60)
+    print("Test 2: ProgressManager SSE Streaming")
+    print("=" * 60)
+
+    pm = ProgressManager()
+    collected_events: List[Dict] = []
+
+    # Simulate SSE client
+    async def sse_client():
+        """Simulates a frontend SSE connection."""
+        print("  SSE client: Subscribing to test-model-sse...")
+        async for event in pm.subscribe("test-model-sse"):
+            # Parse SSE event
+            if event.startswith("data: "):
+                data = json.loads(event[6:])
+                print(f"  SSE client: Received event: {data['status']} - {data.get('progress', 0):.1f}%")
+                collected_events.append(data)
+
+                # Stop when complete
+                if data.get("status") in ("complete", "error"):
+                    break
+            elif event.startswith(": heartbeat"):
+                print("  SSE client: Received heartbeat")
+
+    # Simulate download progress updates (from backend thread)
+    async def simulate_download():
+        """Simulates backend sending progress updates."""
+        print("  Backend: Starting simulated download...")
+        await asyncio.sleep(0.2)  # Let SSE client subscribe first
+
+        # Send progress updates
+        for i in range(0, 101, 20):
+            print(f"  Backend: Updating progress to {i}%")
+            pm.update_progress(
+                model_name="test-model-sse",
+                current=i,
+                total=100,
+                filename=f"file_{i}.bin",
+                status="downloading" if i < 100 else "downloading"
+            )
+            await asyncio.sleep(0.1)
+
+        # Mark complete
+        print("  Backend: Marking download complete")
+        pm.mark_complete("test-model-sse")
+
+    # Run SSE client and download simulation concurrently
+    await asyncio.gather(
+        sse_client(),
+        simulate_download()
+    )
+
+    # Verify we got events
+    print(f"\n  Collected {len(collected_events)} events")
+    assert len(collected_events) > 0, "Should have received at least one event"
+    assert collected_events[-1]["status"] == "complete", "Last event should be 'complete'"
+
+    print("✓ Test 2 PASSED\n")
+    return True
+
+
+def test_hf_progress_tracker():
+    """Test 3: HFProgressTracker tqdm patching."""
+    print("\n" + "=" * 60)
+    print("Test 3: HFProgressTracker tqdm Patching")
+    print("=" * 60)
+
+    captured_progress: List[tuple] = []
+
+    def progress_callback(downloaded: int, total: int, filename: str):
+        """Capture progress updates."""
+        captured_progress.append((downloaded, total, filename))
+        print(f"  Progress callback: {downloaded}/{total} bytes ({filename})")
+
+    tracker = HFProgressTracker(progress_callback)
+
+    # Simulate a download with tqdm
+    with tracker.patch_download():
+        try:
+            from tqdm import tqdm
+
+            # Simulate downloading a file
+            print("  Simulating download with tqdm...")
+            total_size = 1000
+            with tqdm(total=total_size, desc="model.bin", unit="B", unit_scale=True) as pbar:
+                for chunk in range(0, total_size, 100):
+                    pbar.update(100)
+                    time.sleep(0.01)
+
+            print(f"  Captured {len(captured_progress)} progress updates")
+            assert len(captured_progress) > 0, "Should have captured progress updates"
+
+            # Verify progress increases
+            last_downloaded = 0
+            for downloaded, total, filename in captured_progress:
+                assert downloaded >= last_downloaded, "Downloaded bytes should increase"
+                assert total == total_size, "Total should be consistent"
+                last_downloaded = downloaded
+
+            print("✓ Test 3 PASSED\n")
+            return True
+
+        except ImportError:
+            print("✗ tqdm not available, skipping test\n")
+            return None
+
+
+async def test_full_integration():
+    """Test 4: Full integration test."""
+    print("\n" + "=" * 60)
+    print("Test 4: Full Integration (ProgressManager + HFProgressTracker)")
+    print("=" * 60)
+
+    pm = get_progress_manager()
+    collected_events: List[Dict] = []
+
+    # SSE client
+    async def sse_client():
+        print("  SSE client: Subscribing...")
+        async for event in pm.subscribe("integration-test"):
+            if event.startswith("data: "):
+                data = json.loads(event[6:])
+                print(f"  SSE client: {data['status']} - {data.get('progress', 0):.1f}% - {data.get('filename', '')}")
+                collected_events.append(data)
+                if data.get("status") in ("complete", "error"):
+                    break
+
+    # Simulate backend download with HFProgressTracker
+    async def simulate_real_download():
+        await asyncio.sleep(0.2)  # Let SSE subscribe
+
+        print("  Backend: Starting download with HFProgressTracker...")
+
+        # Set up tracking (like the real backend does)
+        progress_callback = create_hf_progress_callback("integration-test", pm)
+        tracker = HFProgressTracker(progress_callback)
+
+        # Initialize progress
+        pm.update_progress(
+            model_name="integration-test",
+            current=0,
+            total=1,
+            filename="",
+            status="downloading"
+        )
+
+        # Simulate download with tqdm patching
+        with tracker.patch_download():
+            try:
+                from tqdm import tqdm
+
+                # Simulate multi-file download (like HuggingFace does)
+                files = [
+                    ("model.safetensors", 5000),
+                    ("config.json", 1000),
+                    ("tokenizer.json", 500),
+                ]
+
+                for filename, size in files:
+                    print(f"  Backend: Downloading {filename}...")
+                    with tqdm(total=size, desc=filename, unit="B") as pbar:
+                        for chunk in range(0, size, 500):
+                            chunk_size = min(500, size - chunk)
+                            pbar.update(chunk_size)
+                            await asyncio.sleep(0.05)
+
+                # Mark complete
+                print("  Backend: Download complete")
+                pm.mark_complete("integration-test")
+
+            except ImportError:
+                print("  ✗ tqdm not available")
+                pm.mark_error("integration-test", "tqdm not available")
+
+    # Run both
+    await asyncio.gather(
+        sse_client(),
+        simulate_real_download()
+    )
+
+    # Verify
+    print(f"\n  Collected {len(collected_events)} events")
+    if len(collected_events) > 0:
+        print(f"  First event: {collected_events[0]}")
+        print(f"  Last event: {collected_events[-1]}")
+        assert collected_events[-1]["status"] == "complete", "Should end with 'complete'"
+        print("✓ Test 4 PASSED\n")
+        return True
+    else:
+        print("✗ Test 4 FAILED - No events received\n")
+        return False
+
+
+async def main():
+    """Run all tests."""
+    print("\n" + "=" * 60)
+    print("Voicebox Progress Tracking Test Suite")
+    print("=" * 60)
+
+    results = []
+
+    # Test 1: Basic operations
+    try:
+        results.append(("Basic Operations", test_progress_manager_basic()))
+    except Exception as e:
+        print(f"✗ Test 1 FAILED: {e}\n")
+        results.append(("Basic Operations", False))
+
+    # Test 2: SSE streaming
+    try:
+        results.append(("SSE Streaming", await test_progress_manager_sse()))
+    except Exception as e:
+        print(f"✗ Test 2 FAILED: {e}\n")
+        results.append(("SSE Streaming", False))
+
+    # Test 3: tqdm patching
+    try:
+        results.append(("tqdm Patching", test_hf_progress_tracker()))
+    except Exception as e:
+        print(f"✗ Test 3 FAILED: {e}\n")
+        results.append(("tqdm Patching", False))
+
+    # Test 4: Full integration
+    try:
+        results.append(("Full Integration", await test_full_integration()))
+    except Exception as e:
+        print(f"✗ Test 4 FAILED: {e}\n")
+        results.append(("Full Integration", False))
+
+    # Summary
+    print("\n" + "=" * 60)
+    print("Test Results Summary")
+    print("=" * 60)
+
+    for name, result in results:
+        status = "✓ PASS" if result else ("⊘ SKIP" if result is None else "✗ FAIL")
+        print(f"  {status:8} {name}")
+
+    passed = sum(1 for _, r in results if r is True)
+    failed = sum(1 for _, r in results if r is False)
+    skipped = sum(1 for _, r in results if r is None)
+
+    print()
+    print(f"  Total: {len(results)} tests")
+    print(f"  Passed: {passed}")
+    print(f"  Failed: {failed}")
+    print(f"  Skipped: {skipped}")
+    print("=" * 60 + "\n")
+
+    return failed == 0
+
+
+if __name__ == "__main__":
+    success = asyncio.run(main())
+    exit(0 if success else 1)

backend/tests/test_qwen_download.py

@@ -0,0 +1,317 @@
+"""
+Test Qwen TTS model download with SSE progress monitoring.
+
+This specifically tests the MLX TTS backend download progress tracking,
+which requires tqdm to be patched BEFORE mlx_audio is imported.
+
+Usage:
+    cd backend && python -m tests.test_qwen_download
+
+Prerequisites:
+    - Server must be running: cd backend && python main.py
+    - Delete model first for fresh download test:
+      curl -X DELETE http://localhost:8000/models/qwen-tts-0.6B
+"""
+
+import asyncio
+import json
+import httpx
+import time
+from typing import List, Dict, Optional
+
+
+async def monitor_sse_stream(model_name: str, timeout: int = 600) -> List[Dict]:
+    """
+    Monitor SSE stream for a model download.
+    
+    Args:
+        model_name: Name of the model to monitor
+        timeout: Maximum time to wait for download (seconds)
+        
+    Returns:
+        List of SSE events received
+    """
+    events: List[Dict] = []
+    url = f"http://localhost:8000/models/progress/{model_name}"
+    last_progress = -1
+    
+    print(f"\n📡 Connecting to SSE endpoint: {url}")
+
+    try:
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            async with client.stream("GET", url) as response:
+                print(f"   SSE connected, status: {response.status_code}")
+
+                if response.status_code != 200:
+                    print(f"   ❌ Error: SSE endpoint returned {response.status_code}")
+                    return events
+
+                async for line in response.aiter_lines():
+                    if not line:
+                        continue
+
+                    if line.startswith("data: "):
+                        try:
+                            data = json.loads(line[6:])
+                            events.append(data)
+                            
+                            # Print progress (only when it changes significantly)
+                            progress = data.get('progress', 0)
+                            status = data.get('status', 'unknown')
+                            filename = data.get('filename', '')
+                            current = data.get('current', 0)
+                            total = data.get('total', 0)
+                            
+                            # Print every 5% change or status change
+                            if abs(progress - last_progress) >= 5 or status in ('complete', 'error'):
+                                current_mb = current / (1024 * 1024)
+                                total_mb = total / (1024 * 1024)
+                                print(f"   📊 {status:12} {progress:6.1f}% ({current_mb:.1f}MB / {total_mb:.1f}MB) {filename[:50]}")
+                                last_progress = progress
+
+                            # Stop if complete or error
+                            if status in ("complete", "error"):
+                                if status == "complete":
+                                    print(f"   ✅ Download complete!")
+                                else:
+                                    print(f"   ❌ Download error: {data.get('error', 'unknown')}")
+                                break
+
+                        except json.JSONDecodeError as e:
+                            print(f"   ⚠️  Error parsing JSON: {e}")
+
+                    elif line.startswith(": heartbeat"):
+                        # Heartbeat every 1 second, don't spam
+                        pass
+
+    except asyncio.CancelledError:
+        print("   ⏹️  SSE monitor cancelled")
+    except Exception as e:
+        print(f"   ❌ SSE error: {e}")
+
+    return events
+
+
+async def trigger_download(model_name: str) -> bool:
+    """Trigger a model download via the API."""
+    url = "http://localhost:8000/models/download"
+
+    print(f"\n🚀 Triggering download for: {model_name}")
+
+    try:
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.post(url, json={"model_name": model_name})
+            result = response.json()
+            print(f"   Response: {response.status_code} - {result}")
+            return response.status_code == 200
+    except Exception as e:
+        print(f"   ❌ Error triggering download: {e}")
+        return False
+
+
+async def delete_model(model_name: str) -> bool:
+    """Delete a model from cache."""
+    url = f"http://localhost:8000/models/{model_name}"
+
+    print(f"\n🗑️  Deleting model: {model_name}")
+
+    try:
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.delete(url)
+            if response.status_code == 200:
+                print(f"   ✅ Model deleted")
+                return True
+            elif response.status_code == 404:
+                print(f"   ℹ️  Model not found (already deleted)")
+                return True
+            else:
+                print(f"   ⚠️  Delete response: {response.status_code} - {response.text}")
+                return False
+    except Exception as e:
+        print(f"   ❌ Error deleting model: {e}")
+        return False
+
+
+async def check_model_status(model_name: str) -> Optional[Dict]:
+    """Check the status of a model."""
+    try:
+        async with httpx.AsyncClient(timeout=10) as client:
+            response = await client.get("http://localhost:8000/models/status")
+            if response.status_code == 200:
+                data = response.json()
+                for model in data.get("models", []):
+                    if model["model_name"] == model_name:
+                        return model
+    except Exception as e:
+        print(f"   ⚠️  Error checking model status: {e}")
+    return None
+
+
+async def check_server() -> bool:
+    """Check if the server is running."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            response = await client.get("http://localhost:8000/health")
+            return response.status_code == 200
+    except Exception:
+        return False
+
+
+async def main():
+    print("=" * 70)
+    print("🧪 Qwen TTS Model Download Progress Test")
+    print("=" * 70)
+    print("\nThis test verifies that MLX TTS download progress tracking works.")
+    print("It specifically tests the tqdm patching for mlx_audio.tts imports.")
+
+    # Check if server is running
+    print("\n📡 Checking if server is running...")
+    if not await check_server():
+        print("   ❌ Server is not running on http://localhost:8000")
+        print("\n   Please start the server first:")
+        print("   cd backend && python main.py")
+        return False
+
+    print("   ✅ Server is running")
+
+    # Test model
+    model_name = "qwen-tts-0.6B"  # Note: 0.6B currently maps to 1.7B on MLX
+    
+    # Check current status
+    print(f"\n📊 Checking status of {model_name}...")
+    status = await check_model_status(model_name)
+    if status:
+        print(f"   Downloaded: {status.get('downloaded', False)}")
+        print(f"   Downloading: {status.get('downloading', False)}")
+        print(f"   Loaded: {status.get('loaded', False)}")
+        if status.get('size_mb'):
+            print(f"   Size: {status['size_mb']:.1f} MB")
+    else:
+        print("   ⚠️  Could not get model status")
+
+    # Ask if user wants to delete first
+    print("\n" + "-" * 70)
+    if status and status.get('downloaded'):
+        print("⚠️  Model is already downloaded. Delete it for a fresh download test?")
+        print("   [y] Yes, delete and download fresh")
+        print("   [n] No, just test SSE connection")
+        print("   [q] Quit")
+        
+        choice = input("\nChoice [y/n/q]: ").strip().lower()
+        
+        if choice == 'q':
+            print("Exiting...")
+            return True
+        
+        if choice == 'y':
+            if not await delete_model(model_name):
+                print("Failed to delete model. Continue anyway? [y/n]")
+                if input().strip().lower() != 'y':
+                    return False
+    else:
+        print("Model not downloaded. Will perform fresh download test.")
+        input("Press Enter to continue...")
+
+    # Run the test
+    print("\n" + "=" * 70)
+    print("🏃 Starting Download Test")
+    print("=" * 70)
+
+    async def run_test():
+        # Start SSE monitor in background FIRST
+        monitor_task = asyncio.create_task(monitor_sse_stream(model_name, timeout=600))
+
+        # Wait for SSE to connect
+        await asyncio.sleep(1)
+
+        # Trigger download
+        success = await trigger_download(model_name)
+
+        if not success:
+            print("   ❌ Failed to trigger download")
+            monitor_task.cancel()
+            try:
+                await monitor_task
+            except asyncio.CancelledError:
+                pass
+            return []
+
+        # Wait for SSE monitor to complete
+        print("\n⏳ Waiting for download to complete (this may take several minutes)...")
+        events = await monitor_task
+
+        return events
+
+    start_time = time.time()
+    events = await run_test()
+    elapsed = time.time() - start_time
+
+    # Results
+    print("\n" + "=" * 70)
+    print("📋 Test Results")
+    print("=" * 70)
+
+    print(f"\n⏱️  Elapsed time: {elapsed:.1f} seconds")
+    print(f"📨 Total SSE events received: {len(events)}")
+
+    if not events:
+        print("\n❌ FAILED - No SSE events received!")
+        print("\nPossible causes:")
+        print("  1. SSE endpoint not working")
+        print("  2. tqdm not patched before mlx_audio import")
+        print("  3. Progress callbacks not firing")
+        print("  4. Model already fully downloaded")
+        print("\nDebug steps:")
+        print("  1. Check server logs for [DEBUG] messages")
+        print("  2. Look for 'tqdm patched' before 'mlx_audio.tts import'")
+        print(f"  3. Delete model: curl -X DELETE http://localhost:8000/models/{model_name}")
+        return False
+
+    # Analyze events
+    first_event = events[0]
+    last_event = events[-1]
+    
+    print(f"\n📊 First event:")
+    print(f"   Status: {first_event.get('status')}")
+    print(f"   Progress: {first_event.get('progress', 0):.1f}%")
+    
+    print(f"\n📊 Last event:")
+    print(f"   Status: {last_event.get('status')}")
+    print(f"   Progress: {last_event.get('progress', 0):.1f}%")
+
+    # Check for expected behaviors
+    has_progress_updates = len(events) > 2
+    has_increasing_progress = False
+    has_complete = any(e.get('status') == 'complete' for e in events)
+    has_100_percent = any(e.get('progress', 0) >= 100 for e in events)
+    
+    # Check if progress increased over time
+    if len(events) >= 2:
+        progress_values = [e.get('progress', 0) for e in events]
+        has_increasing_progress = progress_values[-1] > progress_values[0]
+
+    print("\n📋 Checks:")
+    print(f"   {'✅' if has_progress_updates else '❌'} Multiple progress updates received ({len(events)} events)")
+    print(f"   {'✅' if has_increasing_progress else '❌'} Progress increased over time")
+    print(f"   {'✅' if has_100_percent else '❌'} Reached 100% progress")
+    print(f"   {'✅' if has_complete else '❌'} Received 'complete' status")
+
+    # Overall result
+    success = has_progress_updates and has_complete
+    
+    if success:
+        print("\n" + "=" * 70)
+        print("✅ TEST PASSED - Qwen TTS download progress tracking works!")
+        print("=" * 70)
+    else:
+        print("\n" + "=" * 70)
+        print("❌ TEST FAILED - Progress tracking has issues")
+        print("=" * 70)
+        print("\nCheck the server logs for debug output.")
+
+    return success
+
+
+if __name__ == "__main__":
+    result = asyncio.run(main())
+    exit(0 if result else 1)

backend/tests/test_whisper_download.py

@@ -0,0 +1,178 @@
+"""
+Test real model download with SSE progress monitoring.
+"""
+
+import asyncio
+import json
+import httpx
+import time
+from typing import List, Dict
+
+async def monitor_sse_stream(model_name: str, timeout: int = 300):
+    """Monitor SSE stream for a model download."""
+    events: List[Dict] = []
+    url = f"http://localhost:8000/models/progress/{model_name}"
+
+    print(f"Connecting to SSE endpoint: {url}")
+
+    async with httpx.AsyncClient(timeout=timeout) as client:
+        async with client.stream("GET", url) as response:
+            print(f"SSE connected, status: {response.status_code}")
+
+            if response.status_code != 200:
+                print(f"Error: SSE endpoint returned {response.status_code}")
+                return events
+
+            async for line in response.aiter_lines():
+                if not line:
+                    continue
+
+                print(f"  Raw SSE: {line[:100]}...")  # Print first 100 chars
+
+                if line.startswith("data: "):
+                    try:
+                        data = json.loads(line[6:])
+                        print(f"  → {data['status']:12} {data.get('progress', 0):6.1f}% {data.get('filename', '')}")
+                        events.append(data)
+
+                        # Stop if complete or error
+                        if data.get("status") in ("complete", "error"):
+                            print(f"  Download {data['status']}!")
+                            break
+
+                    except json.JSONDecodeError as e:
+                        print(f"  Error parsing JSON: {e}")
+                        print(f"  Line was: {line}")
+
+                elif line.startswith(": heartbeat"):
+                    print("  ♥ heartbeat")
+
+    return events
+
+
+async def trigger_download(model_name: str):
+    """Trigger a model download via the API."""
+    url = "http://localhost:8000/models/download"
+
+    print(f"\nTriggering download for: {model_name}")
+
+    async with httpx.AsyncClient(timeout=300) as client:
+        response = await client.post(url, json={"model_name": model_name})
+        print(f"Response: {response.status_code} - {response.json()}")
+        return response.status_code == 200
+
+
+async def check_server():
+    """Check if the server is running."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            response = await client.get("http://localhost:8000/health")
+            return response.status_code == 200
+    except Exception as e:
+        print(f"Server not running: {e}")
+        return False
+
+
+async def main():
+    print("=" * 60)
+    print("Real Model Download Progress Test")
+    print("=" * 60)
+
+    # Check if server is running
+    print("\nChecking if server is running...")
+    if not await check_server():
+        print("✗ Server is not running on http://localhost:8000")
+        print("\nPlease start the server first:")
+        print("  cd backend && python main.py")
+        return False
+
+    print("✓ Server is running")
+
+    # Choose a small model for testing
+    model_name = "whisper-base"  # ~150MB, faster to download
+    print(f"\nUsing model: {model_name}")
+
+    # Option to delete model first if it exists
+    print("\nDo you want to delete the model first to force a fresh download? (y/n)")
+    # For automated testing, skip deletion prompt
+    # delete_first = input().strip().lower() == 'y'
+    delete_first = False
+
+    if delete_first:
+        print(f"Deleting {model_name}...")
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.delete(f"http://localhost:8000/models/{model_name}")
+            print(f"Delete response: {response.status_code}")
+
+    print("\n" + "=" * 60)
+    print("Starting Test")
+    print("=" * 60)
+
+    # Start monitoring SSE stream BEFORE triggering download
+    async def run_test():
+        # Start SSE monitor in background
+        monitor_task = asyncio.create_task(monitor_sse_stream(model_name))
+
+        # Wait a bit to ensure SSE is connected
+        await asyncio.sleep(1)
+
+        # Trigger download
+        success = await trigger_download(model_name)
+
+        if not success:
+            print("✗ Failed to trigger download")
+            monitor_task.cancel()
+            return False
+
+        # Wait for SSE monitor to complete
+        events = await monitor_task
+
+        return events
+
+    events = await run_test()
+
+    # Results
+    print("\n" + "=" * 60)
+    print("Test Results")
+    print("=" * 60)
+
+    if not events:
+        print("✗ FAILED - No SSE events received!")
+        print("\nPossible causes:")
+        print("  1. SSE endpoint not working")
+        print("  2. Progress updates not being sent")
+        print("  3. Model already downloaded (no progress to report)")
+        print("\nTry deleting the model first to force a fresh download:")
+        print(f"  curl -X DELETE http://localhost:8000/models/{model_name}")
+        return False
+
+    print(f"✓ Received {len(events)} SSE events")
+    print(f"\nFirst event: {events[0]}")
+    print(f"Last event: {events[-1]}")
+
+    # Check if we got meaningful progress
+    has_progress = any(e.get('progress', 0) > 0 for e in events)
+    has_complete = any(e.get('status') == 'complete' for e in events)
+
+    if has_progress:
+        print("✓ Progress updates received")
+    else:
+        print("✗ No progress updates (might be already downloaded)")
+
+    if has_complete:
+        print("✓ Download completed successfully")
+    else:
+        print("✗ Download did not complete")
+
+    success = has_progress and has_complete
+
+    if success:
+        print("\n✓ TEST PASSED - Progress tracking works!")
+    else:
+        print("\n⊘ TEST INCONCLUSIVE - Try with a fresh download")
+
+    return success
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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
-import asyncio
-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
+from typing import Optional
+from .backends import get_stt_backend, STTBackend
 
 
-class WhisperModel:
-    """Manages Whisper model loading and transcription."""
+def get_whisper_model() -> STTBackend:
+    """
+    Get STT backend instance (MLX or PyTorch based on platform).
     
-    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
-    
-    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
+    Returns:
+        STT backend instance
+    """
+    return get_stt_backend()
 
 
 def unload_whisper_model():
     """Unload Whisper model to free memory."""
-    global _whisper_model
-    if _whisper_model is not None:
-        _whisper_model.unload_model()
+    backend = get_stt_backend()
+    backend.unload_model()

backend/tts.py

@@ -1,372 +1,29 @@
 """
-TTS inference module using Qwen3-TTS.
+TTS inference module - delegates to backend abstraction layer.
 """
 
-from typing import Optional, List, Tuple
-import asyncio
-import torch
+from typing import Optional
 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 .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
+from .backends import get_tts_backend, TTSBackend
 
 
-class TTSModel:
-    """Manages Qwen3-TTS model loading and inference."""
+def get_tts_model() -> TTSBackend:
+    """
+    Get TTS backend instance (MLX or PyTorch based on platform).
     
-    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 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
+    Returns:
+        TTS backend instance
+    """
+    return get_tts_backend()
 
 
 def unload_tts_model():
     """Unload TTS model to free memory."""
-    global _tts_model
-    if _tts_model is not None:
-        _tts_model.unload_model()
+    backend = get_tts_backend()
+    backend.unload_model()
 
 
 def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:

backend/utils/audio.py

@@ -80,6 +80,95 @@ def save_audio(
     sf.write(path, audio, sample_rate)
 
 
+def trim_tts_output(
+    audio: np.ndarray,
+    sample_rate: int = 24000,
+    frame_ms: int = 20,
+    silence_threshold_db: float = -40.0,
+    min_silence_ms: int = 200,
+    max_internal_silence_ms: int = 1000,
+    fade_ms: int = 30,
+) -> np.ndarray:
+    """
+    Trim trailing silence and post-silence hallucination from TTS output.
+
+    Chatterbox sometimes produces ``[speech][silence][hallucinated noise]``.
+    This detects internal silence gaps longer than *max_internal_silence_ms*
+    and cuts the audio at that boundary, then trims trailing silence and
+    applies a short cosine fade-out.
+
+    Args:
+        audio: Input audio array (mono float32)
+        sample_rate: Sample rate in Hz
+        frame_ms: Frame size for RMS energy calculation
+        silence_threshold_db: dB threshold below which a frame is silence
+        min_silence_ms: Minimum trailing silence to keep
+        max_internal_silence_ms: Cut after any silence gap longer than this
+        fade_ms: Cosine fade-out duration in ms
+
+    Returns:
+        Trimmed audio array
+    """
+    frame_len = int(sample_rate * frame_ms / 1000)
+    if frame_len == 0 or len(audio) < frame_len:
+        return audio
+
+    n_frames = len(audio) // frame_len
+    threshold_linear = 10 ** (silence_threshold_db / 20)
+
+    # Compute per-frame RMS
+    rms = np.array(
+        [
+            np.sqrt(np.mean(audio[i * frame_len : (i + 1) * frame_len] ** 2))
+            for i in range(n_frames)
+        ]
+    )
+    is_speech = rms >= threshold_linear
+
+    # Find first speech frame
+    first_speech = 0
+    for i, s in enumerate(is_speech):
+        if s:
+            first_speech = max(0, i - 1)  # keep 1 frame padding
+            break
+
+    # Walk forward from first speech; cut at long internal silence gaps
+    max_silence_frames = int(max_internal_silence_ms / frame_ms)
+    consecutive_silence = 0
+    cut_frame = n_frames
+
+    for i in range(first_speech, n_frames):
+        if is_speech[i]:
+            consecutive_silence = 0
+        else:
+            consecutive_silence += 1
+            if consecutive_silence >= max_silence_frames:
+                cut_frame = i - consecutive_silence + 1
+                break
+
+    # Trim trailing silence from the cut point
+    min_silence_frames = int(min_silence_ms / frame_ms)
+    end_frame = cut_frame
+    while end_frame > first_speech and not is_speech[end_frame - 1]:
+        end_frame -= 1
+    # Keep a short tail
+    end_frame = min(end_frame + min_silence_frames, cut_frame)
+
+    # Convert frames back to samples
+    start_sample = first_speech * frame_len
+    end_sample = min(end_frame * frame_len, len(audio))
+
+    trimmed = audio[start_sample:end_sample].copy()
+
+    # Cosine fade-out
+    fade_samples = int(sample_rate * fade_ms / 1000)
+    if fade_samples > 0 and len(trimmed) > fade_samples:
+        fade = np.cos(np.linspace(0, np.pi / 2, fade_samples)) ** 2
+        trimmed[-fade_samples:] *= fade
+
+    return trimmed
+
+
 def validate_reference_audio(
     audio_path: str,
     min_duration: float = 2.0,

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
-_memory_cache: dict[str, torch.Tensor] = {}
+# In-memory cache - can store dict (voice prompt) or tensor (legacy)
+_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

@@ -11,8 +11,9 @@ import sys
 class HFProgressTracker:
     """Tracks HuggingFace Hub download progress by intercepting tqdm."""
     
-    def __init__(self, progress_callback: Optional[Callable] = None):
+    def __init__(self, progress_callback: Optional[Callable] = None, filter_non_downloads: bool = False):
         self.progress_callback = progress_callback
+        self.filter_non_downloads = filter_non_downloads  # Only filter if True
         self._original_tqdm_class = None
         self._lock = threading.Lock()
         self._total_downloaded = 0
@@ -21,6 +22,7 @@ class HFProgressTracker:
         self._file_downloaded = {}  # Track downloaded bytes per file
         self._current_filename = ""
         self._active_tqdms = {}  # Track active tqdm instances
+        self._hf_tqdm_original_update = None  # For monkey-patching hf's tqdm
     
     def _create_tracked_tqdm_class(self):
         """Create a tqdm subclass that tracks progress."""
@@ -29,7 +31,7 @@ class HFProgressTracker:
         
         class TrackedTqdm(original_tqdm):
             """A tqdm subclass that reports progress to our tracker."""
-            
+
             def __init__(self, *args, **kwargs):
                 # Extract filename from desc before passing to parent
                 desc = kwargs.get("desc", "")
@@ -80,7 +82,7 @@ class HFProgressTracker:
             
             def update(self, n=1):
                 result = super().update(n)
-                
+
                 # Report progress
                 with tracker._lock:
                     if id(self) in tracker._active_tqdms:
@@ -89,6 +91,16 @@ class HFProgressTracker:
                         total = getattr(self, "total", 0)
                         
                         if total and total > 0:
+                            # Always filter out non-byte progress bars (e.g., "Fetching 12 files")
+                            # These cause crazy percentages because they're counting files, not bytes
+                            if self._is_non_byte_progress(filename):
+                                return result
+                            
+                            # When model is cached, also filter out generation-related progress
+                            if tracker.filter_non_downloads:
+                                if not self._is_download_progress(filename):
+                                    return result
+                            
                             # Update per-file tracking
                             tracker._file_sizes[filename] = total
                             tracker._file_downloaded[filename] = current
@@ -97,6 +109,13 @@ class HFProgressTracker:
                             tracker._total_size = sum(tracker._file_sizes.values())
                             tracker._total_downloaded = sum(tracker._file_downloaded.values())
                             
+                            # Only report progress once we have a meaningful total (at least 1MB)
+                            # This avoids the "100% at 0MB" issue when small config
+                            # files are counted before the real model files
+                            MIN_TOTAL_BYTES = 1_000_000  # 1MB
+                            if tracker._total_size < MIN_TOTAL_BYTES:
+                                return result
+                            
                             # Call progress callback
                             if tracker.progress_callback:
                                 tracker.progress_callback(
@@ -107,6 +126,50 @@ class HFProgressTracker:
                 
                 return result
             
+            def _is_non_byte_progress(self, filename: str) -> bool:
+                """Check if this progress bar should be SKIPPED (returns True to skip).
+                
+                We want to track byte-based progress bars. This method identifies
+                progress bars that count files/items instead of bytes, which would
+                cause crazy percentages if mixed with our byte counting.
+                
+                Returns:
+                    True = SKIP this bar (it's not byte-based)
+                    False = TRACK this bar (it counts bytes)
+                """
+                if not filename:
+                    return False
+                
+                filename_lower = filename.lower()
+                
+                # Skip "Fetching X files" - it counts files (total=12), not bytes
+                # Don't skip "Downloading (incomplete total...)" - that IS byte-based
+                skip_patterns = [
+                    'fetching',  # "Fetching 12 files" has total=12 files, not bytes
+                ]
+                return any(pattern in filename_lower for pattern in skip_patterns)
+            
+            def _is_download_progress(self, filename: str) -> bool:
+                """Check if this is a real file download progress bar vs internal processing."""
+                if not filename or filename == "unknown":
+                    return False
+                
+                # Real downloads have file extensions
+                download_extensions = [
+                    '.safetensors', '.bin', '.pt', '.pth',  # Model weights
+                    '.json', '.txt', '.py',  # Config files
+                    '.msgpack', '.h5',  # Other formats
+                ]
+                
+                filename_lower = filename.lower()
+                has_extension = any(filename_lower.endswith(ext) for ext in download_extensions)
+                
+                # Skip generation-related progress indicators
+                skip_patterns = ['segment', 'processing', 'generating', 'loading']
+                has_skip_pattern = any(pattern in filename_lower for pattern in skip_patterns)
+                
+                return has_extension and not has_skip_pattern
+            
             def close(self):
                 with tracker._lock:
                     if id(self) in tracker._active_tqdms:
@@ -120,7 +183,7 @@ class HFProgressTracker:
         """Context manager to patch tqdm for progress tracking."""
         try:
             import tqdm as tqdm_module
-            
+
             # Store original tqdm class
             self._original_tqdm_class = tqdm_module.tqdm
             
@@ -135,10 +198,10 @@ class HFProgressTracker:
             
             # Create our tracked tqdm class
             tracked_tqdm = self._create_tracked_tqdm_class()
-            
+
             # Patch tqdm.tqdm
             tqdm_module.tqdm = tracked_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"):
@@ -146,22 +209,79 @@ class HFProgressTracker:
                 tqdm_module.auto.tqdm = tracked_tqdm
             
             # Patch in sys.modules to catch already-imported references
+            # huggingface_hub uses: from tqdm.auto import tqdm as base_tqdm
+            # So we need to patch both 'tqdm' and 'base_tqdm' attributes
             self._patched_modules = {}
+            tqdm_attr_names = ['tqdm', 'base_tqdm', 'old_tqdm']  # Various names used
+            
+            patched_count = 0
             for module_name in list(sys.modules.keys()):
                 if "huggingface" in module_name or module_name.startswith("tqdm"):
                     try:
                         module = sys.modules[module_name]
-                        if hasattr(module, "tqdm"):
-                            attr = getattr(module, "tqdm")
-                            # Only patch if it's the original tqdm class (not already patched)
-                            if attr is self._original_tqdm_class or (
-                                hasattr(attr, "__name__") and attr.__name__ == "tqdm"
-                            ):
-                                self._patched_modules[module_name] = attr
-                                setattr(module, "tqdm", tracked_tqdm)
+                        for attr_name in tqdm_attr_names:
+                            if hasattr(module, attr_name):
+                                attr = getattr(module, attr_name)
+                                # Only patch if it's a tqdm class (not already patched)
+                                is_tqdm_class = (
+                                    attr is self._original_tqdm_class or 
+                                    (self._original_tqdm_auto and attr is self._original_tqdm_auto) or
+                                    (hasattr(attr, "__name__") and attr.__name__ == "tqdm" and 
+                                     hasattr(attr, "update"))  # tqdm classes have update method
+                                )
+                                if is_tqdm_class:
+                                    key = f"{module_name}.{attr_name}"
+                                    self._patched_modules[key] = (module, attr_name, attr)
+                                    setattr(module, attr_name, tracked_tqdm)
+                                    patched_count += 1
                     except (AttributeError, TypeError):
                         pass
             
+            # ALSO monkey-patch the update method on huggingface_hub's tqdm class
+            # This is needed because the class was already defined at import time
+            self._hf_tqdm_original_update = None
+            try:
+                from huggingface_hub.utils import tqdm as hf_tqdm_module
+                if hasattr(hf_tqdm_module, 'tqdm'):
+                    hf_tqdm_class = hf_tqdm_module.tqdm
+                    self._hf_tqdm_original_update = hf_tqdm_class.update
+                    
+                    # Create a wrapper that calls our tracking
+                    tracker = self  # Reference to HFProgressTracker instance
+                    def patched_update(tqdm_self, n=1):
+                        result = tracker._hf_tqdm_original_update(tqdm_self, n)
+                        
+                        # Track this progress
+                        with tracker._lock:
+                            desc = getattr(tqdm_self, 'desc', '') or ''
+                            current = getattr(tqdm_self, 'n', 0)
+                            total = getattr(tqdm_self, 'total', 0) or 0
+                            
+                            # Skip non-byte progress bars
+                            if 'fetching' in desc.lower():
+                                return result
+                            
+                            # Skip until we have a meaningful total (at least 1MB)
+                            # This avoids the "100% at 0MB" issue when small config
+                            # files are counted before the real model files
+                            MIN_TOTAL_BYTES = 1_000_000  # 1MB
+                            if total >= MIN_TOTAL_BYTES:
+                                tracker._total_downloaded = current
+                                tracker._total_size = total
+                                
+                                if tracker.progress_callback:
+                                    tracker.progress_callback(current, total, desc)
+                        
+                        return result
+                    
+                    hf_tqdm_class.update = patched_update
+                    patched_count += 1
+                    print(f"[HFProgressTracker] Monkey-patched huggingface_hub.utils.tqdm.tqdm.update")
+            except (ImportError, AttributeError) as e:
+                print(f"[HFProgressTracker] Could not monkey-patch hf_tqdm: {e}")
+            
+            print(f"[HFProgressTracker] Patched {patched_count} tqdm references")
+            
             yield
             
         except ImportError:
@@ -178,15 +298,24 @@ class HFProgressTracker:
                         tqdm_module.auto.tqdm = self._original_tqdm_auto
                     
                     # Restore patched modules
-                    for module_name, original in self._patched_modules.items():
+                    for key, (module, attr_name, original) in self._patched_modules.items():
                         try:
-                            module = sys.modules.get(module_name)
                             if module and original:
-                                setattr(module, "tqdm", original)
+                                setattr(module, attr_name, original)
                         except (AttributeError, TypeError):
                             pass
                     self._patched_modules = {}
                     
+                    # Restore hf_tqdm's original update method
+                    if self._hf_tqdm_original_update:
+                        try:
+                            from huggingface_hub.utils import tqdm as hf_tqdm_module
+                            if hasattr(hf_tqdm_module, 'tqdm'):
+                                hf_tqdm_module.tqdm.update = self._hf_tqdm_original_update
+                        except (ImportError, AttributeError):
+                            pass
+                        self._hf_tqdm_original_update = None
+                    
                 except (ImportError, AttributeError):
                     pass
 
@@ -194,13 +323,17 @@ class HFProgressTracker:
 def create_hf_progress_callback(model_name: str, progress_manager):
     """Create a progress callback for HuggingFace downloads."""
     def callback(downloaded: int, total: int, filename: str = ""):
-        """Progress callback."""
-        if total > 0:
-            progress_manager.update_progress(
-                model_name=model_name,
-                current=downloaded,
-                total=total,
-                filename=filename or "",
-                status="downloading",
-            )
+        """Progress callback.
+        
+        Note: We send updates even when total=0 (unknown) to provide feedback
+        during the "incomplete total" phase of huggingface_hub downloads.
+        The frontend handles total=0 gracefully.
+        """
+        progress_manager.update_progress(
+            model_name=model_name,
+            current=downloaded,
+            total=total,
+            filename=filename or "",
+            status="downloading",
+        )
     return callback

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 +6,61 @@ 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).
+    """
+    
+    # Throttle settings to prevent overwhelming SSE clients
+    THROTTLE_INTERVAL_SECONDS = 0.5  # Minimum time between updates
+    THROTTLE_PROGRESS_DELTA = 1.0    # Minimum progress change (%) to force update
     
     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
+        self._last_notify_time: Dict[str, float] = {}  # Last notification time per model
+        self._last_notify_progress: Dict[str, float] = {}  # Last notified progress per model
     
+    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 +71,13 @@ class ProgressManager:
     ):
         """
         Update progress for a model download.
+
+        Thread-safe: can be called from background threads.
         
+        Progress updates are throttled to prevent overwhelming SSE clients.
+        Updates are sent at most every THROTTLE_INTERVAL_SECONDS, or when
+        progress changes by at least THROTTLE_PROGRESS_DELTA percent.
+
         Args:
             model_name: Name of the model (e.g., "qwen-tts-1.7B", "whisper-base")
             current: Current bytes downloaded
@@ -34,9 +85,20 @@ class ProgressManager:
             filename: Current file being downloaded
             status: Status string (downloading, extracting, complete, error)
         """
-        progress_pct = (current / total * 100) if total > 0 else 0
-        
-        self._progress[model_name] = {
+        import logging
+        import time
+        logger = logging.getLogger(__name__)
+
+        # Calculate progress percentage, clamped to 0-100 range
+        # This prevents crazy percentages from edge cases like:
+        # - current > total temporarily during aggregation
+        # - mixing file-count progress with byte-count progress
+        if total > 0:
+            progress_pct = min(100.0, max(0.0, (current / total * 100)))
+        else:
+            progress_pct = 0
+
+        progress_data = {
             "model_name": model_name,
             "current": current,
             "total": total,
@@ -45,26 +107,56 @@ class ProgressManager:
             "status": status,
             "timestamp": datetime.now().isoformat(),
         }
+
+        # Thread-safe update of progress dict (always update internal state)
+        with self._lock:
+            self._progress[model_name] = progress_data
+
+        # Check if we should notify listeners (throttling)
+        current_time = time.time()
+        last_time = self._last_notify_time.get(model_name, 0)
+        last_progress = self._last_notify_progress.get(model_name, -100)
         
-        # Notify all listeners
-        if model_name in self._listeners:
-            for queue in self._listeners[model_name]:
-                try:
-                    queue.put_nowait(self._progress[model_name].copy())
-                except asyncio.QueueFull:
-                    pass
+        time_delta = current_time - last_time
+        progress_delta = abs(progress_pct - last_progress)
+        
+        # Always notify for complete/error status, or if throttle conditions are met
+        should_notify = (
+            status in ("complete", "error") or
+            time_delta >= self.THROTTLE_INTERVAL_SECONDS or
+            progress_delta >= self.THROTTLE_PROGRESS_DELTA
+        )
+        
+        if not should_notify:
+            return  # Skip this update (throttled)
+        
+        # Update throttle tracking
+        self._last_notify_time[model_name] = current_time
+        self._last_notify_progress[model_name] = progress_pct
+
+        # Notify all listeners (thread-safe)
+        listener_count = len(self._listeners.get(model_name, []))
+
+        if listener_count > 0:
+            logger.debug(f"Notifying {listener_count} listeners for {model_name}: {progress_pct:.1f}% ({filename})")
+            self._notify_listeners_threadsafe(model_name, progress_data)
+        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."""
-        return self._progress.get(model_name)
+        """Get current progress for a model. Thread-safe."""
+        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():
-            status = progress.get("status", "")
-            if status in ("downloading", "extracting"):
-                active.append(progress.copy())
+        with self._lock:
+            for model_name, progress in self._progress.items():
+                status = progress.get("status", "")
+                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 +190,57 @@ 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
-            if model_name in self._progress:
-                yield f"data: {json.dumps(self._progress[model_name])}\n\n"
+            # Send initial progress if available and still in progress (thread-safe read)
+            with self._lock:
+                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 +252,53 @@ 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."""
-        if model_name in self._progress:
-            self._progress[model_name]["status"] = "complete"
-            self._progress[model_name]["progress"] = 100.0
-            # Notify listeners
-            if model_name in self._listeners:
-                for queue in self._listeners[model_name]:
-                    try:
-                        queue.put_nowait(self._progress[model_name].copy())
-                    except asyncio.QueueFull:
-                        pass
+        """Mark a model download as complete. Thread-safe."""
+        import logging
+        logger = logging.getLogger(__name__)
+
+        with self._lock:
+            if model_name in self._progress:
+                self._progress[model_name]["status"] = "complete"
+                self._progress[model_name]["progress"] = 100.0
+                progress_data = self._progress[model_name].copy()
+            else:
+                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."""
-        if model_name in self._progress:
-            self._progress[model_name]["status"] = "error"
-            self._progress[model_name]["error"] = error
-            # Notify listeners
-            if model_name in self._listeners:
-                for queue in self._listeners[model_name]:
-                    try:
-                        queue.put_nowait(self._progress[model_name].copy())
-                    except asyncio.QueueFull:
-                        pass
+        """Mark a model download as failed. Thread-safe."""
+        import logging
+        logger = logging.getLogger(__name__)
+
+        with self._lock:
+            if model_name in self._progress:
+                self._progress[model_name]["status"] = "error"
+                self._progress[model_name]["error"] = error
+                progress_data = self._progress[model_name].copy()
+            else:
+                # 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/utils/tasks.py

@@ -72,6 +72,15 @@ class TaskManager:
         """Get all active generations."""
         return list(self._active_generations.values())
     
+    def cancel_download(self, model_name: str) -> bool:
+        """Cancel/dismiss a download task (removes it from active list)."""
+        return self._active_downloads.pop(model_name, None) is not None
+
+    def clear_all(self) -> None:
+        """Clear all download and generation tasks."""
+        self._active_downloads.clear()
+        self._active_generations.clear()
+
     def is_download_active(self, model_name: str) -> bool:
         """Check if a download is active."""
         return model_name in self._active_downloads

backend/voicebox-server.spec

@@ -4,17 +4,26 @@ 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')
+# Use collect_all (not collect_data_files) so native .dylib and .metallib
+# files are bundled as binaries, not data. Without this, MLX raises OSError
+# when loading Metal shaders inside the PyInstaller bundle.
+from PyInstaller.utils.hooks import collect_all as _collect_all
+_mlx_datas, _mlx_bins, _mlx_hidden = _collect_all('mlx')
+_mlxa_datas, _mlxa_bins, _mlxa_hidden = _collect_all('mlx_audio')
+datas += _mlx_datas + _mlxa_datas
 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'],
-    binaries=[],
+    pathex=[],
+    binaries=_mlx_bins + _mlxa_bins,
     datas=datas,
     hiddenimports=hiddenimports,
     hookspath=[],

bun.lock

@@ -13,7 +13,7 @@
     },
     "app": {
       "name": "@voicebox/app",
-      "version": "0.1.5",
+      "version": "0.1.12",
       "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": "0.1.12",
       "dependencies": {
         "@radix-ui/react-separator": "^1.1.8",
         "@radix-ui/react-slot": "^1.2.4",
@@ -92,10 +93,14 @@
     },
     "tauri": {
       "name": "@voicebox/tauri",
-      "version": "0.1.5",
+      "version": "0.1.12",
       "dependencies": {
         "@tauri-apps/api": "^2.0.0",
+        "@tauri-apps/plugin-dialog": "^2.0.0",
+        "@tauri-apps/plugin-fs": "^2.0.0",
+        "@tauri-apps/plugin-process": "^2.0.0",
         "@tauri-apps/plugin-shell": "^2.0.0",
+        "@tauri-apps/plugin-updater": "^2.0.0",
       },
       "devDependencies": {
         "@tailwindcss/vite": "^4.1.18",
@@ -111,7 +116,7 @@
     },
     "web": {
       "name": "@voicebox/web",
-      "version": "0.1.5",
+      "version": "0.1.12",
       "dependencies": {
         "@tanstack/react-query": "^5.0.0",
         "react": "^18.3.0",
@@ -120,6 +125,7 @@
         "zustand": "^4.5.0",
       },
       "devDependencies": {
+        "@tailwindcss/vite": "^4.0.0",
         "@types/react": "^18.3.0",
         "@types/react-dom": "^18.3.0",
         "@typescript-eslint/eslint-plugin": "^7.0.0",
@@ -970,6 +976,8 @@
 
     "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 +1012,8 @@
 
     "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=="],