33cca5f552
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
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
"""
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Audio noise reduction using DeepFilterNet.
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Falls back to a basic FFmpeg noise filter if DeepFilterNet is not installed.
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"""
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import logging
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import subprocess
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import tempfile
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from pathlib import Path
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logger = logging.getLogger(__name__)
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try:
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from df.enhance import enhance, init_df, load_audio, save_audio
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DEEPFILTER_AVAILABLE = True
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except ImportError:
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DEEPFILTER_AVAILABLE = False
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_df_model = None
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_df_state = None
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def _init_deepfilter():
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global _df_model, _df_state
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if _df_model is None:
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logger.info("Initializing DeepFilterNet model")
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_df_model, _df_state, _ = init_df()
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return _df_model, _df_state
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def clean_audio(
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input_path: str,
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output_path: str = "",
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) -> str:
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"""
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Apply noise reduction to an audio file.
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If DeepFilterNet is available, uses it for high-quality results.
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Otherwise falls back to FFmpeg's anlmdn filter.
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Returns: path to the cleaned audio file.
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"""
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input_path = Path(input_path)
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if not output_path:
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output_path = str(input_path.with_stem(input_path.stem + "_clean"))
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if DEEPFILTER_AVAILABLE:
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return _clean_with_deepfilter(str(input_path), output_path)
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else:
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return _clean_with_ffmpeg(str(input_path), output_path)
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def _clean_with_deepfilter(input_path: str, output_path: str) -> str:
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model, state = _init_deepfilter()
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audio, info = load_audio(input_path, sr=state.sr())
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enhanced = enhance(model, state, audio)
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save_audio(output_path, enhanced, sr=state.sr())
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logger.info(f"DeepFilterNet cleaned audio saved to {output_path}")
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return output_path
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def _clean_with_ffmpeg(input_path: str, output_path: str) -> str:
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"""Fallback: basic noise reduction using FFmpeg's anlmdn filter."""
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cmd = [
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"ffmpeg", "-y",
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"-i", input_path,
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"-af", "anlmdn=s=7:p=0.002:r=0.002:m=15",
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output_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode != 0:
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raise RuntimeError(f"FFmpeg audio cleaning failed: {result.stderr[-300:]}")
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logger.info(f"FFmpeg cleaned audio saved to {output_path}")
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return output_path
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def is_deepfilter_available() -> bool:
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return DEEPFILTER_AVAILABLE
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