Enhance MLX and PyTorch Backend Integration

- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Implemented platform detection to dynamically select between MLX and PyTorch based on the runtime environment.
- Updated build process to include MLX-specific dependencies and configurations for macOS.
- Refactored backend code to improve model loading and inference logic, accommodating backend-specific requirements.
- Enhanced documentation to clarify backend selection and performance benefits for different platforms.
- Streamlined installation instructions and troubleshooting guidance for MLX-related issues.

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.
 

backend/backends/__init__.py

@@ -8,7 +8,7 @@ from typing import Protocol, Optional, Tuple, List
 from typing_extensions import runtime_checkable
 import numpy as np
 
-from ..platform import get_backend_type
+from ..platform_detect import get_backend_type
 
 
 @runtime_checkable

backend/build_binary.py

@@ -41,7 +41,7 @@ def build_server():
         '--hidden-import', 'backend.history',
         '--hidden-import', 'backend.tts',
         '--hidden-import', 'backend.transcribe',
-        '--hidden-import', 'backend.platform',
+        '--hidden-import', 'backend.platform_detect',
         '--hidden-import', 'backend.backends',
         '--hidden-import', 'backend.backends.pytorch_backend',
         '--hidden-import', 'backend.utils.audio',
@@ -83,6 +83,9 @@ def build_server():
             '--hidden-import', 'mlx_audio.asr',
             '--collect-submodules', 'mlx',
             '--collect-submodules', 'mlx_audio',
+            # Collect MLX data files including Metal shader libraries (.metallib)
+            '--collect-data', 'mlx',
+            '--collect-data', 'mlx_audio',
         ])
     else:
         print("Building for non-Apple Silicon platform - PyTorch only")

backend/main.py

@@ -27,7 +27,7 @@ from . import database, models, profiles, history, tts, transcribe, config, expo
 from .database import get_db, Generation as DBGeneration, VoiceProfile as DBVoiceProfile
 from .utils.progress import get_progress_manager
 from .utils.tasks import get_task_manager
-from .platform import get_backend_type
+from .platform_detect import get_backend_type
 
 app = FastAPI(
     title="voicebox API",

backend/voicebox-server.spec

@@ -0,0 +1,52 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_data_files
+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']
+datas += collect_data_files('qwen_tts')
+datas += collect_data_files('mlx')
+datas += collect_data_files('mlx_audio')
+datas += copy_metadata('qwen-tts')
+hiddenimports += collect_submodules('qwen_tts')
+hiddenimports += collect_submodules('jaraco')
+hiddenimports += collect_submodules('mlx')
+hiddenimports += collect_submodules('mlx_audio')
+
+
+a = Analysis(
+    ['server.py'],
+    pathex=[],
+    binaries=[],
+    datas=datas,
+    hiddenimports=hiddenimports,
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+    optimize=0,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.datas,
+    [],
+    name='voicebox-server',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)