Update provider documentation and enhance build configurations

- Clarified the bundling of PyTorch CPU providers for Windows and macOS Intel builds in documentation.
- Improved handling of platform-specific dependencies in the build process, including asyncio support for PyInstaller.
- Updated backend logic to gracefully handle missing dependencies and provide clearer error messages.
- Enhanced progress management to ensure compatibility with PyInstaller's async handling.
- Removed unnecessary exclusions from the build scripts for PyTorch providers to streamline the build process.

CONTRIBUTING.md

@@ -148,9 +148,10 @@ Voicebox uses a modular provider system to support different inference backends.
 **Hybrid Provider:**
 
 - `pytorch-cpu` — Can be bundled OR downloaded depending on platform
-  - **Bundled** with macOS Intel builds (`.dmg` for x64)
-    - Configured in `.github/workflows/release.yml` with `backend: "pytorch"`
-  - **Downloaded** on first use for Windows/Linux builds (~300MB)
+  - **Bundled** with Windows and macOS Intel builds
+    - macOS Intel: `.dmg` for x64 with `backend: "pytorch"`
+    - Windows: `.exe` installer with PyTorch CPU included
+  - **Downloaded** on first use for Linux builds (~300MB)
   - Falls back to bundled version if external binary not found
 
 **External-Only Providers:**

README.md

@@ -205,14 +205,14 @@ Voicebox uses a modular provider system to support different inference backends:
 
 - **`pytorch-cpu`** — Universal CPU provider (bundled or downloaded)
 
-  - Bundled with macOS Intel builds
-  - Downloaded on first use for Windows/Linux (~300MB)
+  - Bundled with Windows and macOS Intel builds
+  - Downloaded on first use for Linux (~300MB)
 
 - **`pytorch-cuda`** — Optional NVIDIA GPU-accelerated provider
   - Windows/Linux only (~2.4GB)
   - 4-5x faster inference on CUDA-capable GPUs
 
-macOS builds work out of the box with bundled providers. Windows and Linux users download a provider on first launch. The app automatically detects your hardware and recommends the best option. All downloadable providers are distributed via Cloudflare R2 for fast, global delivery.
+macOS and Windows builds work out of the box with bundled providers. Linux users download a provider on first launch. The app automatically detects your hardware and recommends the best option. All downloadable providers are distributed via Cloudflare R2 for fast, global delivery.
 
 ---
 

backend/backends/__init__.py

@@ -118,22 +118,37 @@ _stt_backend: Optional[STTBackend] = None
 def get_tts_backend() -> TTSBackend:
     """
     Get or create TTS backend instance based on platform.
-    
+
     Returns:
         TTS backend instance (MLX or PyTorch)
+
+    Raises:
+        ImportError: If required dependencies (mlx or torch) are not available
     """
     global _tts_backend
-    
+
     if _tts_backend is None:
         backend_type = get_backend_type()
-        
+
         if backend_type == "mlx":
-            from .mlx_backend import MLXTTSBackend
-            _tts_backend = MLXTTSBackend()
+            try:
+                from .mlx_backend import MLXTTSBackend
+                _tts_backend = MLXTTSBackend()
+            except ImportError as e:
+                raise ImportError(
+                    f"MLX backend dependencies not available. "
+                    f"Please install mlx and mlx_audio or download a provider. Error: {e}"
+                )
         else:
-            from .pytorch_backend import PyTorchTTSBackend
-            _tts_backend = PyTorchTTSBackend()
-    
+            try:
+                from .pytorch_backend import PyTorchTTSBackend
+                _tts_backend = PyTorchTTSBackend()
+            except ImportError as e:
+                raise ImportError(
+                    f"PyTorch backend dependencies not available. "
+                    f"Please download a TTS provider (pytorch-cpu or pytorch-cuda) from the Downloads page. Error: {e}"
+                )
+
     return _tts_backend
 
 

backend/build_binary.py

@@ -59,9 +59,16 @@ def build_server():
         # Fix for pkg_resources and jaraco namespace packages
         '--hidden-import', 'pkg_resources.extern',
         '--collect-submodules', 'jaraco',
+        # Asyncio and threading support for PyInstaller
+        '--hidden-import', 'asyncio',
+        '--hidden-import', 'asyncio.subprocess',
+        '--hidden-import', 'concurrent.futures',
+        '--hidden-import', 'concurrent.futures.thread',
     ])
 
     # Platform-specific TTS backend handling
+    system = platform.system()
+
     if is_apple_silicon():
         print("Building for Apple Silicon - including MLX dependencies (bundled)")
         args.extend([
@@ -79,13 +86,30 @@ def build_server():
             '--collect-data', 'mlx',
             '--collect-data', 'mlx_audio',
         ])
-    else:
-        print("Building for Windows/Linux - excluding PyTorch/Qwen-TTS (providers downloaded separately)")
-        # Note: PyTorch and Qwen-TTS are NOT included - users will download providers separately
-        # Only include backend abstraction (no actual TTS implementation)
+    elif system == "Windows" or (system == "Darwin" and not is_apple_silicon()):
+        # Windows and Intel macOS: Bundle PyTorch CPU provider
+        print(f"Building for {system} - including PyTorch CPU provider (bundled)")
         args.extend([
             '--hidden-import', 'backend.backends',
-            '--hidden-import', 'backend.backends.pytorch_backend',  # Keep for reference, but won't work without PyTorch
+            '--hidden-import', 'backend.backends.pytorch_backend',
+            '--hidden-import', 'torch',
+            '--hidden-import', 'transformers',
+            '--hidden-import', 'qwen_tts',
+            '--hidden-import', 'qwen_tts.inference',
+            '--hidden-import', 'qwen_tts.inference.qwen3_tts_model',
+            '--hidden-import', 'qwen_tts.inference.qwen3_tts_tokenizer',
+            '--hidden-import', 'qwen_tts.core',
+            '--hidden-import', 'qwen_tts.cli',
+            '--copy-metadata', 'qwen-tts',
+            '--collect-submodules', 'qwen_tts',
+            '--collect-data', 'qwen_tts',
+        ])
+    else:
+        # Linux: No bundled provider - users download providers separately
+        print("Building for Linux - no bundled provider (users download separately)")
+        args.extend([
+            '--hidden-import', 'backend.backends',
+            '--hidden-import', 'backend.backends.pytorch_backend',
         ])
 
     args.extend([

backend/main.py

@@ -14,7 +14,6 @@ from datetime import datetime
 import asyncio
 import uvicorn
 import argparse
-import torch
 import tempfile
 import io
 from pathlib import Path
@@ -23,6 +22,14 @@ import asyncio
 import signal
 import os
 
+# Optional torch import - not available on all platforms (e.g. Windows/Linux without bundled provider)
+try:
+    import torch
+    TORCH_AVAILABLE = True
+except ImportError:
+    torch = None  # type: ignore
+    TORCH_AVAILABLE = False
+
 from . import database, models, profiles, history, tts, transcribe, config, export_import, channels, stories, __version__
 from .database import get_db, Generation as DBGeneration, VoiceProfile as DBVoiceProfile
 from .utils.progress import get_progress_manager
@@ -72,20 +79,32 @@ async def shutdown():
 @app.get("/health", response_model=models.HealthResponse)
 async def health():
     """Health check endpoint."""
-    from huggingface_hub import hf_hub_download, constants as hf_constants
+    from huggingface_hub import constants as hf_constants
     from pathlib import Path
-    import os
 
-    tts_model = await tts.get_tts_model_async()
+    # Try to get TTS model provider, but it may not be available if dependencies aren't installed
+    tts_model = None
+    try:
+        tts_model = await tts.get_tts_model_async()
+    except ImportError as e:
+        # Provider dependencies not available (e.g., PyTorch not bundled on this platform)
+        # This is expected on Windows/Linux builds without a bundled provider
+        print(f"Provider not available: {e}")
+
     backend_type = get_backend_type()
 
     # Check for GPU availability (CUDA or MPS)
-    has_cuda = torch.cuda.is_available()
-    has_mps = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
+    # PyTorch might not be available if no provider is bundled
+    has_cuda = False
+    has_mps = False
+    if TORCH_AVAILABLE and torch is not None:
+        has_cuda = torch.cuda.is_available()
+        has_mps = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
+
     gpu_available = has_cuda or has_mps
 
     gpu_type = None
-    if has_cuda:
+    if has_cuda and torch is not None:
         gpu_type = f"CUDA ({torch.cuda.get_device_name(0)})"
     elif has_mps:
         gpu_type = "MPS (Apple Silicon)"
@@ -93,26 +112,27 @@ async def health():
         gpu_type = "Metal (Apple Silicon via MLX)"
 
     vram_used = None
-    if has_cuda:
+    if has_cuda and torch is not None:
         vram_used = torch.cuda.memory_allocated() / 1024 / 1024  # MB
-    
+
     # Check if model is loaded - use the same logic as model status endpoint
     model_loaded = False
     model_size = None
-    try:
-        # Use the same check as model status endpoint
-        if tts_model.is_loaded():
-            model_loaded = True
-            # Get the actual loaded model size
-            # Check _current_model_size first (more reliable for actually loaded models)
-            model_size = getattr(tts_model, '_current_model_size', None)
-            if not model_size:
-                # Fallback to model_size attribute (which should be set when model loads)
-                model_size = getattr(tts_model, 'model_size', None)
-    except Exception:
-        # If there's an error checking, assume not loaded
-        model_loaded = False
-        model_size = None
+    if tts_model is not None:
+        try:
+            # Use the same check as model status endpoint
+            if tts_model.is_loaded():
+                model_loaded = True
+                # Get the actual loaded model size
+                # Check _current_model_size first (more reliable for actually loaded models)
+                model_size = getattr(tts_model, '_current_model_size', None)
+                if not model_size:
+                    # Fallback to model_size attribute (which should be set when model loads)
+                    model_size = getattr(tts_model, 'model_size', None)
+        except Exception:
+            # If there's an error checking, assume not loaded
+            model_loaded = False
+            model_size = None
     
     # Check if default model is downloaded (cached)
     model_downloaded = None
@@ -1836,11 +1856,12 @@ async def get_active_tasks():
 def _get_gpu_status() -> str:
     """Get GPU availability status."""
     backend_type = get_backend_type()
-    if torch.cuda.is_available():
-        return f"CUDA ({torch.cuda.get_device_name(0)})"
-    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-        return "MPS (Apple Silicon)"
-    elif backend_type == "mlx":
+    if TORCH_AVAILABLE and torch is not None:
+        if torch.cuda.is_available():
+            return f"CUDA ({torch.cuda.get_device_name(0)})"
+        elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            return "MPS (Apple Silicon)"
+    if backend_type == "mlx":
         return "Metal (Apple Silicon via MLX)"
     return "None (CPU only)"
 
@@ -1879,8 +1900,14 @@ async def shutdown_event():
     """Run on application shutdown."""
     print("voicebox API shutting down...")
     # Unload models to free memory
-    tts.unload_tts_model()
-    transcribe.unload_whisper_model()
+    try:
+        tts.unload_tts_model()
+    except Exception as e:
+        print(f"Warning: Failed to unload TTS model: {e}")
+    try:
+        transcribe.unload_whisper_model()
+    except Exception as e:
+        print(f"Warning: Failed to unload Whisper model: {e}")
 
 
 # ============================================

backend/providers/__init__.py

@@ -171,15 +171,12 @@ class ProviderManager:
         machine = platform.machine()
 
         if system == "Darwin" and machine == "arm64":
-            # Apple Silicon gets MLX
+            # Apple Silicon gets MLX bundled
             installed.append("apple-mlx")
-
-        # PyTorch CPU is available on all platforms (check if bundled or downloaded)
-        # For now, assume it's bundled on macOS Intel, Windows, Linux
-        # Downloaded binaries will be detected below
-        if not (system == "Darwin" and machine == "arm64"):
-            # Non-Apple Silicon systems have PyTorch CPU bundled
+        elif system == "Windows" or (system == "Darwin" and machine != "arm64"):
+            # Windows and Intel macOS get PyTorch CPU bundled
             installed.append("pytorch-cpu")
+        # Linux: no bundled provider - users must download
         
         # Check for downloaded providers (Phase 2)
         providers_dir = _get_providers_dir()

backend/utils/progress.py

@@ -49,11 +49,17 @@ class ProgressManager:
                     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
+                    # Use asyncio.run_coroutine_threadsafe for better PyInstaller compatibility
                     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
-                        )
+                        async def put_data_async():
+                            try:
+                                queue.put_nowait(progress_data.copy())
+                            except asyncio.QueueFull:
+                                pass  # Queue full, drop update
+                        try:
+                            asyncio.run_coroutine_threadsafe(put_data_async(), self._main_loop)
+                        except Exception as e:
+                            logger.warning(f"Failed to schedule progress update: {e}")
                     else:
                         logger.debug(f"No main loop available for {model_name}, skipping notification")
             except asyncio.QueueFull:

backend/voicebox-server.spec

@@ -3,7 +3,7 @@ from PyInstaller.utils.hooks import collect_data_files
 from PyInstaller.utils.hooks import collect_submodules
 
 datas = []
-hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.providers', 'backend.providers.base', 'backend.providers.bundled', 'backend.providers.types', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'pkg_resources.extern', 'backend.backends', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
+hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.providers', 'backend.providers.base', 'backend.providers.bundled', 'backend.providers.types', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'pkg_resources.extern', 'asyncio', 'asyncio.subprocess', 'concurrent.futures', 'concurrent.futures.thread', 'backend.backends', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
 datas += collect_data_files('mlx')
 datas += collect_data_files('mlx_audio')
 hiddenimports += collect_submodules('jaraco')

providers/pytorch-cpu/build.py

@@ -55,23 +55,7 @@ def build_provider():
         '--hidden-import', 'numpy',
         '--hidden-import', 'librosa',
     ])
-    
-    # Exclude large unused modules to reduce binary size
-    args.extend([
-        '--exclude-module', 'torch.utils.tensorboard',
-        '--exclude-module', 'tensorboard',
-        '--exclude-module', 'triton',
-        '--exclude-module', 'torch._dynamo',
-        '--exclude-module', 'torch._inductor',
-        '--exclude-module', 'torch.utils.benchmark',
-        '--exclude-module', 'IPython',
-        '--exclude-module', 'matplotlib',
-        '--exclude-module', 'PIL',
-        '--exclude-module', 'cv2',
-        '--exclude-module', 'torchvision',
-        '--exclude-module', 'torchaudio',
-    ])
-    
+
     args.extend([
         '--noconfirm',
         '--clean',

providers/pytorch-cuda/build.py

@@ -57,23 +57,7 @@ def build_provider():
         '--hidden-import', 'numpy',
         '--hidden-import', 'librosa',
     ])
-    
-    # Exclude large unused modules to reduce binary size
-    args.extend([
-        '--exclude-module', 'torch.utils.tensorboard',
-        '--exclude-module', 'tensorboard',
-        '--exclude-module', 'triton',
-        '--exclude-module', 'torch._dynamo',
-        '--exclude-module', 'torch._inductor',
-        '--exclude-module', 'torch.utils.benchmark',
-        '--exclude-module', 'IPython',
-        '--exclude-module', 'matplotlib',
-        '--exclude-module', 'PIL',
-        '--exclude-module', 'cv2',
-        '--exclude-module', 'torchvision',
-        '--exclude-module', 'torchaudio',
-    ])
-    
+
     args.extend([
         '--noconfirm',
         '--clean',