ADDED MLX FOR SUPER FAST GENERATIONS ON APPLE SILICON

- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Updated release workflow to include MLX-specific dependencies and configurations for macOS platforms.
- Refactored backend code to dynamically select between MLX and PyTorch based on the runtime environment.
- Enhanced model loading and inference logic to accommodate backend-specific requirements, including updated model IDs and hidden imports.
- Improved health check and model status reporting to reflect the active backend type.
- Streamlined caching mechanisms to support both backend types, ensuring compatibility and performance.

backend/backends/__init__.py

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