Initial commit

backend/utils/cache.py

@@ -0,0 +1,153 @@
+"""
+Voice prompt caching utilities.
+"""
+
+import hashlib
+import logging
+import torch
+from pathlib import Path
+from typing import Optional, Union, Dict, Any
+
+from .. import config
+
+logger = logging.getLogger(__name__)
+
+
+def _get_cache_dir() -> Path:
+    """Get cache directory from config."""
+    return config.get_cache_dir()
+
+
+# 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:
+    """
+    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[Union[torch.Tensor, Dict[str, Any]]]:
+    """
+    Get cached voice prompt if available.
+
+    Args:
+        cache_key: Cache key
+
+    Returns:
+        Cached voice prompt (dict or tensor) or None
+    """
+    # Check in-memory cache
+    if cache_key in _memory_cache:
+        return _memory_cache[cache_key]
+
+    # Check disk cache
+    cache_file = _get_cache_dir() / f"{cache_key}.prompt"
+    if cache_file.exists():
+        try:
+            prompt = torch.load(cache_file, weights_only=True)
+            _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: Union[torch.Tensor, Dict[str, Any]],
+) -> None:
+    """
+    Cache voice prompt to memory and disk.
+
+    Args:
+        cache_key: Cache key
+        voice_prompt: Voice prompt (dict or tensor)
+    """
+    # Store in memory
+    _memory_cache[cache_key] = voice_prompt
+
+    # 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)
+
+
+def clear_voice_prompt_cache() -> int:
+    """
+    Clear all voice prompt caches (memory and disk).
+
+    Returns:
+        Number of cache files deleted
+    """
+    # Clear memory cache
+    _memory_cache.clear()
+
+    # Clear disk cache
+    cache_dir = _get_cache_dir()
+    deleted_count = 0
+
+    if cache_dir.exists():
+        # Delete prompt cache files
+        for cache_file in cache_dir.glob("*.prompt"):
+            try:
+                cache_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                logger.warning("Failed to delete cache file %s: %s", cache_file, e)
+
+        # Delete combined audio files
+        for audio_file in cache_dir.glob("combined_*.wav"):
+            try:
+                audio_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                logger.warning("Failed to delete combined audio file %s: %s", audio_file, e)
+
+    return deleted_count
+
+
+def clear_profile_cache(profile_id: str) -> int:
+    """
+    Clear cache files for a specific profile.
+
+    Args:
+        profile_id: Profile ID
+
+    Returns:
+        Number of cache files deleted
+    """
+    cache_dir = _get_cache_dir()
+    deleted_count = 0
+
+    if cache_dir.exists():
+        # Delete combined audio files for this profile
+        pattern = f"combined_{profile_id}_*.wav"
+        for audio_file in cache_dir.glob(pattern):
+            try:
+                audio_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                logger.warning("Failed to delete combined audio file %s: %s", audio_file, e)
+
+    return deleted_count