Initialize voicebox project with backend, frontend, and Tauri setup. Added configuration files, dependencies, and basic structure for components, hooks, and utilities. Included README and setup documentation for guidance.

backend/utils/cache.py

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+"""
+Voice prompt caching utilities.
+"""
+
+import hashlib
+import torch
+from pathlib import Path
+from typing import Optional, Tuple
+import soundfile as sf
+
+
+_cache_dir = Path("data/cache")
+_cache_dir.mkdir(parents=True, exist_ok=True)
+
+# In-memory cache
+_memory_cache: dict[str, torch.Tensor] = {}
+
+
+def get_cache_key(audio_path: str, reference_text: str) -> str:
+    """
+    Generate cache key from audio file and reference text.
+    
+    Args:
+        audio_path: Path to audio file
+        reference_text: Reference text
+        
+    Returns:
+        Cache key (MD5 hash)
+    """
+    # Read audio file
+    with open(audio_path, "rb") as f:
+        audio_bytes = f.read()
+    
+    # Combine audio bytes and text
+    combined = audio_bytes + reference_text.encode("utf-8")
+    
+    # Generate hash
+    return hashlib.md5(combined).hexdigest()
+
+
+def get_cached_voice_prompt(
+    cache_key: str,
+) -> Optional[torch.Tensor]:
+    """
+    Get cached voice prompt if available.
+    
+    Args:
+        cache_key: Cache key
+        
+    Returns:
+        Cached voice prompt tensor or None
+    """
+    # Check in-memory cache
+    if cache_key in _memory_cache:
+        return _memory_cache[cache_key]
+    
+    # Check disk cache
+    cache_file = _cache_dir / f"{cache_key}.prompt"
+    if cache_file.exists():
+        try:
+            prompt = torch.load(cache_file)
+            _memory_cache[cache_key] = prompt
+            return prompt
+        except Exception:
+            # Cache file corrupted, delete it
+            cache_file.unlink()
+    
+    return None
+
+
+def cache_voice_prompt(
+    cache_key: str,
+    voice_prompt: torch.Tensor,
+) -> None:
+    """
+    Cache voice prompt to memory and disk.
+    
+    Args:
+        cache_key: Cache key
+        voice_prompt: Voice prompt tensor
+    """
+    # Store in memory
+    _memory_cache[cache_key] = voice_prompt
+    
+    # Store on disk
+    cache_file = _cache_dir / f"{cache_key}.prompt"
+    torch.save(voice_prompt, cache_file)