Enhance README and UI Components for Performance and Features

- Updated README.md to highlight MLX backend performance improvements on Mac with Metal acceleration.
- Refined ProfileCard and ProfileForm components by optimizing imports and improving error handling for avatar uploads.
- Adjusted landing page content to better describe features, including a new multi-voice narrative editor and performance optimizations for different platforms.
- Bumped version to 0.1.11 in Cargo.lock to reflect recent changes.

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.10
+current_version = 0.1.11
 commit = True
 tag = True
 tag_name = v{new_version}

README.md

@@ -68,6 +68,7 @@ Unlike cloud services that lock your voice data behind subscriptions, Voicebox g
 - **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.
 
@@ -97,6 +98,7 @@ 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
 

app/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@voicebox/app",
-  "version": "0.1.10",
+  "version": "0.1.11",
   "private": true,
   "type": "module",
   "scripts": {

app/src/components/VoiceProfiles/ProfileCard.tsx

@@ -1,6 +1,5 @@
 import { Download, Edit, Mic, Trash2 } from 'lucide-react';
 import { useState } from 'react';
-import { useServerStore } from '@/stores/serverStore';
 import { Badge } from '@/components/ui/badge';
 import { Button } from '@/components/ui/button';
 import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
@@ -16,6 +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 {
@@ -35,9 +35,7 @@ export function ProfileCard({ profile }: ProfileCardProps) {
 
   const isSelected = selectedProfileId === profile.id;
 
-  const avatarUrl = profile.avatar_path
-    ? `${serverUrl}/profiles/${profile.id}/avatar`
-    : null;
+  const avatarUrl = profile.avatar_path ? `${serverUrl}/profiles/${profile.id}/avatar` : null;
 
   const handleSelect = () => {
     setSelectedProfileId(isSelected ? null : profile.id);
@@ -81,7 +79,7 @@ export function ProfileCard({ profile }: ProfileCardProps) {
                   alt={`${profile.name} avatar`}
                   className={cn(
                     'h-full w-full object-cover transition-all duration-200',
-                    !isSelected && 'grayscale'
+                    !isSelected && 'grayscale',
                   )}
                   onError={() => setAvatarError(true)}
                 />

app/src/components/VoiceProfiles/ProfileForm.tsx

@@ -45,8 +45,8 @@ 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 { type ProfileFormDraft, useUIStore } from '@/stores/uiStore';
 import { useServerStore } from '@/stores/serverStore';
+import { type ProfileFormDraft, useUIStore } from '@/stores/uiStore';
 import { AudioSampleRecording } from './AudioSampleRecording';
 import { AudioSampleSystem } from './AudioSampleSystem';
 import { AudioSampleUpload } from './AudioSampleUpload';
@@ -427,7 +427,8 @@ export function ProfileForm() {
           } catch (avatarError) {
             toast({
               title: 'Avatar upload failed',
-              description: avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
+              description:
+                avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
               variant: 'destructive',
             });
           }
@@ -520,7 +521,8 @@ export function ProfileForm() {
             } catch (avatarError) {
               toast({
                 title: 'Avatar upload failed',
-                description: avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
+                description:
+                  avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
                 variant: 'destructive',
               });
             }

backend/__init__.py

@@ -1,3 +1,3 @@
 # Backend package
 
-__version__ = "0.1.10"
+__version__ = "0.1.11"

backend/backends/mlx_backend.py

@@ -341,21 +341,34 @@ class MLXSTTBackend:
     def _load_model_sync(self, model_size: str):
         """Synchronous model loading."""
         try:
-            from mlx_audio.asr import load
-            
-            # MLX Whisper model naming
-            model_name = f"mlx-community/whisper-{model_size}"
-            
-            # Set up progress tracking
+            # IMPORTANT: Set up progress tracking BEFORE importing mlx_audio
+            # This ensures tqdm is patched before any HuggingFace Hub imports
             progress_manager = get_progress_manager()
             progress_model_name = f"whisper-{model_size}"
+
+            # Set up progress callback and tracker
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+
+            # Patch tqdm BEFORE importing mlx_audio
+            # This is critical because mlx_audio imports huggingface_hub which imports tqdm
+            print("[DEBUG] Starting tqdm patch BEFORE mlx_audio import")
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+            print("[DEBUG] tqdm patched, now importing mlx_audio")
+
+            # NOW import mlx_audio - it will use our patched tqdm
+            from mlx_audio.stt import load
+
+            # MLX Whisper uses the standard OpenAI models
+            model_name = f"openai/whisper-{model_size}"
             
             # Start tracking download task
             task_manager = get_task_manager()
             task_manager.start_download(progress_model_name)
-            
+
             print(f"Loading MLX Whisper model {model_size}...")
-            
+
             # Initialize progress state
             progress_manager.update_progress(
                 model_name=progress_model_name,
@@ -364,14 +377,13 @@ class MLXSTTBackend:
                 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():
+
+            # Load the model (tqdm is already patched from above)
+            try:
                 self.model = load(model_name)
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
             
             self.model_size = model_size
             
@@ -412,34 +424,35 @@ class MLXSTTBackend:
     ) -> 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)
-            
-            # MLX Whisper transcription
-            # The API may vary - check mlx-audio documentation
-            # For now, assuming similar API to PyTorch Whisper
-            result = self.model.transcribe(audio, language=language)
-            
-            # Extract text from result (format may vary)
+            # 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:
-                # Try to get text attribute
                 return str(result).strip()
-        
+
         # Run blocking transcription in thread pool
         return await asyncio.to_thread(_transcribe_sync)

backend/backends/pytorch_backend.py

@@ -85,21 +85,31 @@ class PyTorchTTSBackend:
     def _load_model_sync(self, model_size: str):
         """Synchronous model loading."""
         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
+            # IMPORTANT: Set up progress tracking BEFORE importing qwen_tts
+            # This ensures tqdm is patched before any HuggingFace Hub imports
             progress_manager = get_progress_manager()
             model_name = f"qwen-tts-{model_size}"
-            
+
+            # Set up progress callback and tracker
+            progress_callback = create_hf_progress_callback(model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+
+            # Patch tqdm BEFORE importing qwen_tts
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+
+            # NOW import qwen_tts - it will use our patched tqdm
+            from qwen_tts import Qwen3TTSModel
+
+            # Get model path (local or HuggingFace Hub ID)
+            model_path = self._get_model_path(model_size)
+
             print(f"Loading TTS model {model_size} on {self.device}...")
-            
+
             # Start tracking download task
             task_manager = get_task_manager()
             task_manager.start_download(model_name)
-            
+
             # Initialize progress state to show download has started
             progress_manager.update_progress(
                 model_name=model_name,
@@ -108,19 +118,17 @@ class PyTorchTTSBackend:
                 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
+
+            # Load the model (tqdm is already patched from above)
+            try:
                 self.model = Qwen3TTSModel.from_pretrained(
                     model_path,
                     device_map=self.device,
                     torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
                 )
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
             
             # Mark as complete
             progress_manager.mark_complete(model_name)
@@ -314,40 +322,61 @@ class PyTorchSTTBackend:
     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:
-            from transformers import WhisperProcessor, WhisperForConditionalGeneration
-            
-            model_name = f"openai/whisper-{model_size}"
-            
-            # Set up progress tracking
+            # IMPORTANT: Set up progress tracking BEFORE importing transformers
+            # This ensures tqdm is patched before any HuggingFace Hub imports
             progress_manager = get_progress_manager()
             progress_model_name = f"whisper-{model_size}"
-            
+
+            # Set up progress callback and tracker
+            progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
+            tracker = HFProgressTracker(progress_callback)
+
+            # Patch tqdm BEFORE importing transformers
+            print("[DEBUG] Starting tqdm patch BEFORE transformers import")
+            tracker_context = tracker.patch_download()
+            tracker_context.__enter__()
+            print("[DEBUG] tqdm patched, now importing transformers")
+
+            # NOW import transformers - it will use our patched tqdm
+            from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+            model_name = f"openai/whisper-{model_size}"
+            print(f"[DEBUG] Model name: {model_name}")
+
             # Start tracking download task
             task_manager = get_task_manager()
             task_manager.start_download(progress_model_name)
-            
+            print(f"[DEBUG] Task manager started download")
+
             print(f"Loading Whisper model {model_size} on {self.device}...")
-            
+
             # Initialize progress state to show download has started
+            print(f"[DEBUG] Calling update_progress...")
             progress_manager.update_progress(
                 model_name=progress_model_name,
                 current=0,
@@ -355,15 +384,15 @@ class PyTorchSTTBackend:
                 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():
+            print(f"[DEBUG] update_progress called, listeners: {len(progress_manager._listeners.get(progress_model_name, []))}")
+
+            # Load models (tqdm is already patched from above)
+            try:
                 self.processor = WhisperProcessor.from_pretrained(model_name)
                 self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
+            finally:
+                # Exit the patch context
+                tracker_context.__exit__(None, None, None)
             
             self.model.to(self.device)
             self.model_size = model_size

backend/build_binary.py

@@ -80,7 +80,7 @@ def build_server():
             '--hidden-import', 'mlx.nn',
             '--hidden-import', 'mlx_audio',
             '--hidden-import', 'mlx_audio.tts',
-            '--hidden-import', 'mlx_audio.asr',
+            '--hidden-import', 'mlx_audio.stt',
             '--collect-submodules', 'mlx',
             '--collect-submodules', 'mlx_audio',
             # Collect MLX data files including Metal shader libraries (.metallib)

backend/main.py

@@ -1393,7 +1393,11 @@ async def trigger_model_download(request: models.ModelDownloadRequest):
     async def download_in_background():
         """Download model in background without blocking the HTTP request."""
         try:
-            await asyncio.to_thread(config["load_func"])
+            # Call the load function (which may be async)
+            result = config["load_func"]()
+            # If it's a coroutine, await it
+            if asyncio.iscoroutine(result):
+                await result
             task_manager.complete_download(request.model_name)
         except Exception as e:
             task_manager.error_download(request.model_name, str(e))

backend/utils/hf_progress.py

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

backend/utils/progress.py

@@ -65,7 +65,7 @@ class ProgressManager:
     ):
         """
         Update progress for a model download.
-        
+
         Thread-safe: can be called from background threads.
 
         Args:
@@ -89,16 +89,26 @@ class ProgressManager:
             "status": status,
             "timestamp": datetime.now().isoformat(),
         }
-        
+
+        print(f"[DEBUG] update_progress called: {model_name}, {progress_pct:.1f}%")
+
         # Thread-safe update of progress dict
         with self._lock:
             self._progress[model_name] = progress_data
 
         # Notify all listeners (thread-safe)
         listener_count = len(self._listeners.get(model_name, []))
+        print(f"[DEBUG] Listener count for {model_name}: {listener_count}")
+        print(f"[DEBUG] All listeners: {list(self._listeners.keys())}")
+        print(f"[DEBUG] Main loop set: {self._main_loop is not None}")
+        if self._main_loop:
+            print(f"[DEBUG] Main loop running: {self._main_loop.is_running()}")
+
         if listener_count > 0:
             logger.debug(f"Notifying {listener_count} listeners for {model_name}: {progress_pct:.1f}% ({filename})")
+            print(f"[DEBUG] About to notify listeners...")
             self._notify_listeners_threadsafe(model_name, progress_data)
+            print(f"[DEBUG] Notified listeners")
         else:
             logger.debug(f"No listeners for {model_name}, progress update stored: {progress_pct:.1f}%")
     

backend/voicebox-server.spec

@@ -4,7 +4,7 @@ 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.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.asr']
+hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
 datas += collect_data_files('qwen_tts')
 datas += collect_data_files('mlx')
 datas += collect_data_files('mlx_audio')

landing/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@voicebox/landing",
-  "version": "0.1.10",
+  "version": "0.1.11",
   "description": "Landing page for voicebox.sh",
   "scripts": {
     "dev": "bun --bun next dev --turbo",

landing/src/app/page.tsx

@@ -5,7 +5,7 @@ import Image from 'next/image';
 import { useEffect, useState } from 'react';
 import { AppleIcon, LinuxIcon, WindowsIcon } from '@/components/PlatformIcons';
 import { Button } from '@/components/ui/button';
-import { Section, SectionTitle } from '@/components/ui/section';
+import { Section } from '@/components/ui/section';
 import { DOWNLOAD_LINKS, GITHUB_REPO } from '@/lib/constants';
 import type { DownloadLinks } from '@/lib/releases';
 import { FeatureCard } from '../components/ui/feature-card';
@@ -39,17 +39,19 @@ export default function Home() {
         "Powered by Alibaba's Qwen3-TTS model for exceptional voice quality and accuracy.",
       icon: <Zap className="h-6 w-6" />,
     },
+    {
+      title: 'Stories Editor',
+      description:
+        'Create multi-voice narratives with a timeline-based editor. Arrange tracks, trim clips, and mix conversations.',
+      icon: <Code className="h-6 w-6" />,
+    },
     {
       title: 'Multi-Sample Support',
       description:
         'Combine multiple voice samples for higher quality and more natural-sounding results.',
       icon: <Code className="h-6 w-6" />,
     },
-    {
-      title: 'Smart Caching',
-      description: 'Instant re-generation with voice prompt caching. No need to reprocess samples.',
-      icon: <Zap className="h-6 w-6" />,
-    },
+
     {
       title: 'Local or Remote',
       description:
@@ -246,6 +248,10 @@ export default function Home() {
               model, clone any voice from a few seconds of audio, and compose multi-voice projects
               with studio-grade editing tools.
             </p>
+            <p>
+              Optimized for performance with <strong>Metal acceleration on Mac</strong> and{' '}
+              <strong>CUDA acceleration on Windows/Linux</strong> for fast, local inference.
+            </p>
             <p className="text-foreground/60">No Python install required.</p>
           </div>
         </div>
@@ -281,10 +287,6 @@ export default function Home() {
 
       {/* Features Section */}
       <Section id="features">
-        <SectionTitle className="mb-4 text-center">Features</SectionTitle>
-        <p className="text-sm text-muted-foreground mb-8 text-center max-w-2xl mx-auto">
-          Everything you need for professional voice cloning in a desktop app.
-        </p>
         <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4 sm:gap-6">
           {features.map((feature) => (
             <FeatureCard

package.json

@@ -1,6 +1,6 @@
 {
   "name": "voicebox",
-  "version": "0.1.10",
+  "version": "0.1.11",
   "private": true,
   "workspaces": [
     "app",

tauri/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@voicebox/tauri",
   "private": true,
-  "version": "0.1.10",
+  "version": "0.1.11",
   "type": "module",
   "scripts": {
     "dev": "vite",

tauri/src-tauri/Cargo.lock

@@ -5041,7 +5041,7 @@ checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
 
 [[package]]
 name = "voicebox"
-version = "0.1.9"
+version = "0.1.11"
 dependencies = [
  "base64 0.22.1",
  "core-foundation-sys",

tauri/src-tauri/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "voicebox"
-version = "0.1.10"
+version = "0.1.11"
 description = "A production-quality desktop app for Qwen3-TTS voice cloning and generation"
 authors = ["you"]
 license = ""

tauri/src-tauri/tauri.conf.json

@@ -1,7 +1,7 @@
 {
   "$schema": "https://schema.tauri.app/config/2",
   "productName": "Voicebox",
-  "version": "0.1.10",
+  "version": "0.1.11",
   "identifier": "sh.voicebox.app",
   "build": {
     "beforeDevCommand": "bun run dev",

web/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@voicebox/web",
   "private": true,
-  "version": "0.1.10",
+  "version": "0.1.11",
   "type": "module",
   "scripts": {
     "dev": "vite",