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

backend/services/audio_cleaner.py

@@ -0,0 +1,79 @@
+"""
+Audio noise reduction using DeepFilterNet.
+Falls back to a basic FFmpeg noise filter if DeepFilterNet is not installed.
+"""
+
+import logging
+import subprocess
+import tempfile
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+try:
+    from df.enhance import enhance, init_df, load_audio, save_audio
+    DEEPFILTER_AVAILABLE = True
+except ImportError:
+    DEEPFILTER_AVAILABLE = False
+
+
+_df_model = None
+_df_state = None
+
+
+def _init_deepfilter():
+    global _df_model, _df_state
+    if _df_model is None:
+        logger.info("Initializing DeepFilterNet model")
+        _df_model, _df_state, _ = init_df()
+    return _df_model, _df_state
+
+
+def clean_audio(
+    input_path: str,
+    output_path: str = "",
+) -> str:
+    """
+    Apply noise reduction to an audio file.
+
+    If DeepFilterNet is available, uses it for high-quality results.
+    Otherwise falls back to FFmpeg's anlmdn filter.
+
+    Returns: path to the cleaned audio file.
+    """
+    input_path = Path(input_path)
+    if not output_path:
+        output_path = str(input_path.with_stem(input_path.stem + "_clean"))
+
+    if DEEPFILTER_AVAILABLE:
+        return _clean_with_deepfilter(str(input_path), output_path)
+    else:
+        return _clean_with_ffmpeg(str(input_path), output_path)
+
+
+def _clean_with_deepfilter(input_path: str, output_path: str) -> str:
+    model, state = _init_deepfilter()
+    audio, info = load_audio(input_path, sr=state.sr())
+    enhanced = enhance(model, state, audio)
+    save_audio(output_path, enhanced, sr=state.sr())
+    logger.info(f"DeepFilterNet cleaned audio saved to {output_path}")
+    return output_path
+
+
+def _clean_with_ffmpeg(input_path: str, output_path: str) -> str:
+    """Fallback: basic noise reduction using FFmpeg's anlmdn filter."""
+    cmd = [
+        "ffmpeg", "-y",
+        "-i", input_path,
+        "-af", "anlmdn=s=7:p=0.002:r=0.002:m=15",
+        output_path,
+    ]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"FFmpeg audio cleaning failed: {result.stderr[-300:]}")
+    logger.info(f"FFmpeg cleaned audio saved to {output_path}")
+    return output_path
+
+
+def is_deepfilter_available() -> bool:
+    return DEEPFILTER_AVAILABLE