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/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 +73,18 @@ 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)