Initial CutScript release - Open-source AI-powered text-based video editor
CutScript is a local-first, Descript-like video editor where you edit video by editing text. Delete a word from the transcript and it's cut from the video. Features: - Word-level transcription with WhisperX - Text-based video editing with undo/redo - AI filler word removal (Ollama/OpenAI/Claude) - AI clip creation for shorts - Waveform timeline with virtualized transcript - FFmpeg stream-copy (fast) and re-encode (4K) export - Caption burn-in and sidecar SRT generation - Studio Sound audio enhancement (DeepFilterNet) - Keyboard shortcuts (J/K/L, Space, Delete, Ctrl+Z/S/E) - Encrypted API key storage - Project save/load (.aive files) Architecture: - Electron + React + Tailwind (frontend) - FastAPI + Python (backend) - WhisperX for transcription - FFmpeg for video processing - Multi-provider AI support Performance optimizations: - RAF-throttled time updates - Zustand selectors for granular subscriptions - Dual-canvas waveform rendering - Virtualized transcript with react-virtuoso Built on top of DataAnts-AI/VideoTranscriber, completely rewritten as a desktop application. License: MIT
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backend/utils/cache.py
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205
backend/utils/cache.py
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"""
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Caching utilities for the OBS Recording Transcriber.
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Provides functions to cache and retrieve transcription and summarization results.
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"""
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import json
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import hashlib
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import os
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from pathlib import Path
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import logging
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import time
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Default cache directory
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CACHE_DIR = Path.home() / ".obs_transcriber_cache"
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def get_file_hash(file_path):
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"""
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Generate a hash for a file based on its content and modification time.
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Args:
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file_path (Path): Path to the file
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Returns:
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str: Hash string representing the file
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"""
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file_path = Path(file_path)
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if not file_path.exists():
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return None
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# Get file stats
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stats = file_path.stat()
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file_size = stats.st_size
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mod_time = stats.st_mtime
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# Create a hash based on path, size and modification time
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# This is faster than hashing the entire file content
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hash_input = f"{file_path.absolute()}|{file_size}|{mod_time}"
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return hashlib.md5(hash_input.encode()).hexdigest()
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def get_cache_path(file_path, model=None, operation=None):
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"""
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Get the cache file path for a given input file and operation.
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Args:
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file_path (Path): Path to the original file
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model (str, optional): Model used for processing
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operation (str, optional): Operation type (e.g., 'transcribe', 'summarize')
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Returns:
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Path: Path to the cache file
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"""
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file_path = Path(file_path)
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file_hash = get_file_hash(file_path)
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if not file_hash:
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return None
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# Create cache directory if it doesn't exist
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cache_dir = CACHE_DIR
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cache_dir.mkdir(parents=True, exist_ok=True)
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# Create a cache filename based on the hash and optional parameters
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cache_name = file_hash
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if model:
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cache_name += f"_{model}"
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if operation:
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cache_name += f"_{operation}"
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return cache_dir / f"{cache_name}.json"
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def save_to_cache(file_path, data, model=None, operation=None):
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"""
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Save data to cache.
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Args:
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file_path (Path): Path to the original file
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data (dict): Data to cache
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model (str, optional): Model used for processing
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operation (str, optional): Operation type
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Returns:
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bool: True if successful, False otherwise
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"""
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cache_path = get_cache_path(file_path, model, operation)
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if not cache_path:
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return False
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try:
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# Add metadata to the cached data
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cache_data = {
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"original_file": str(Path(file_path).absolute()),
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"timestamp": time.time(),
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"model": model,
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"operation": operation,
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"data": data
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}
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with open(cache_path, 'w', encoding='utf-8') as f:
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json.dump(cache_data, f, ensure_ascii=False, indent=2)
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logger.info(f"Cached data saved to {cache_path}")
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return True
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except Exception as e:
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logger.error(f"Error saving cache: {e}")
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return False
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def load_from_cache(file_path, model=None, operation=None, max_age=None):
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"""
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Load data from cache if available and not expired.
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Args:
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file_path (Path): Path to the original file
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model (str, optional): Model used for processing
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operation (str, optional): Operation type
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max_age (float, optional): Maximum age of cache in seconds
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Returns:
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dict or None: Cached data or None if not available
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"""
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cache_path = get_cache_path(file_path, model, operation)
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if not cache_path or not cache_path.exists():
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return None
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try:
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with open(cache_path, 'r', encoding='utf-8') as f:
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cache_data = json.load(f)
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# Check if cache is expired
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if max_age is not None:
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cache_time = cache_data.get("timestamp", 0)
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if time.time() - cache_time > max_age:
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logger.info(f"Cache expired for {file_path}")
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return None
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logger.info(f"Loaded data from cache: {cache_path}")
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return cache_data.get("data")
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except Exception as e:
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logger.error(f"Error loading cache: {e}")
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return None
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def clear_cache(max_age=None):
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"""
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Clear all cache files or only expired ones.
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Args:
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max_age (float, optional): Maximum age of cache in seconds
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Returns:
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int: Number of files deleted
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"""
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if not CACHE_DIR.exists():
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return 0
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count = 0
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for cache_file in CACHE_DIR.glob("*.json"):
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try:
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if max_age is not None:
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# Check if file is expired
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with open(cache_file, 'r', encoding='utf-8') as f:
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cache_data = json.load(f)
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cache_time = cache_data.get("timestamp", 0)
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if time.time() - cache_time <= max_age:
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continue # Skip non-expired files
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# Delete the file
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os.remove(cache_file)
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count += 1
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except Exception as e:
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logger.error(f"Error deleting cache file {cache_file}: {e}")
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logger.info(f"Cleared {count} cache files")
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return count
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def get_cache_size():
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"""
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Get the total size of the cache directory.
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Returns:
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tuple: (size_bytes, file_count)
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"""
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if not CACHE_DIR.exists():
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return 0, 0
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total_size = 0
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file_count = 0
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for cache_file in CACHE_DIR.glob("*.json"):
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try:
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total_size += cache_file.stat().st_size
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file_count += 1
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except Exception:
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pass
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return total_size, file_count
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