v4: batch processing layer over the CLI engine
Wraps detect_singing to process whole folders of recordings at once: --recursive, --pattern, --output-dir, --auto-cut. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
183
v4_inaseg_batch/batch_detect.py
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183
v4_inaseg_batch/batch_detect.py
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#!/usr/bin/env python3
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"""
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Batch Singing Detector
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=======================
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Process multiple stream recordings at once.
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Point it at a folder of recordings and it will process each one.
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Usage:
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python batch_detect.py "D:\\streams"
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python batch_detect.py "D:\\streams" --output-dir "D:\\singing_edits" --auto-cut
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python batch_detect.py "D:\\streams" --pattern "*.mp4" --recursive
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"""
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import argparse
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import os
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import sys
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import time
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import glob
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from pathlib import Path
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def find_videos(input_dir: str, pattern: str = "*", recursive: bool = False,
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extensions: tuple = (".mp4", ".mkv", ".flv", ".ts", ".avi", ".mov", ".webm")) -> list:
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"""Find all video files in directory."""
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videos = []
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if recursive:
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for ext in extensions:
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videos.extend(glob.glob(os.path.join(input_dir, "**", f"*{ext}"), recursive=True))
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else:
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for ext in extensions:
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videos.extend(glob.glob(os.path.join(input_dir, f"*{ext}")))
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# Filter by pattern if specified
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if pattern != "*":
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videos = [v for v in videos if Path(v).match(pattern)]
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# Sort by modification time (newest first)
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videos.sort(key=lambda x: os.path.getmtime(x), reverse=True)
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return videos
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def main():
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parser = argparse.ArgumentParser(
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description="Batch process multiple stream recordings for singing detection.",
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)
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parser.add_argument("input_dir", help="Directory containing stream recordings")
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parser.add_argument("--output-dir", "-o", default=None,
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help="Output directory (default: same as input)")
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parser.add_argument("--pattern", default="*",
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help="Filename pattern to match (default: all video files)")
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parser.add_argument("--recursive", "-r", action="store_true",
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help="Search subdirectories recursively")
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parser.add_argument("--gap", "-g", type=float, default=10.0,
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help="Max gap to merge singing segments (default: 10s)")
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parser.add_argument("--min-duration", "-m", type=float, default=20.0,
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help="Minimum segment duration (default: 20s)")
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parser.add_argument("--padding", "-p", type=float, default=2.0,
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help="Padding before/after segments (default: 2s)")
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parser.add_argument("--fps", type=float, default=29.97,
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help="Frame rate (default: 29.97)")
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parser.add_argument("--auto-cut", action="store_true",
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help="Generate auto-cut ffmpeg scripts")
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parser.add_argument("--skip-existing", action="store_true",
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help="Skip files that already have output .edl files")
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parser.add_argument("--limit", type=int, default=0,
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help="Process only N files (0 = all)")
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args = parser.parse_args()
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if not os.path.isdir(args.input_dir):
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print(f"[ERROR] Directory not found: {args.input_dir}")
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sys.exit(1)
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# Find videos
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videos = find_videos(args.input_dir, args.pattern, args.recursive)
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if not videos:
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print(f"[INFO] No video files found in: {args.input_dir}")
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sys.exit(0)
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# Filter already-processed files
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if args.skip_existing:
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output_dir = args.output_dir or args.input_dir
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unprocessed = []
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for v in videos:
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stem = Path(v).stem
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edl = os.path.join(output_dir, f"{stem}_singing.edl")
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if not os.path.exists(edl):
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unprocessed.append(v)
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else:
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print(f"[SKIP] Already processed: {Path(v).name}")
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videos = unprocessed
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# Apply limit
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if args.limit > 0:
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videos = videos[:args.limit]
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print(f"\n{'='*60}")
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print(f" Batch Singing Detector")
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print(f"{'='*60}")
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print(f" Input: {args.input_dir}")
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print(f" Videos: {len(videos)} to process")
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print(f"{'='*60}\n")
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# Process each video
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# Import the main detect_singing module
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from detect_singing import (
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extract_audio, run_segmentation, merge_singing_segments,
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export_edl, export_csv, export_davinci_markers_csv,
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export_json, export_ffmpeg_concat, format_timecode,
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)
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results = []
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for i, video_path in enumerate(videos, 1):
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video_name = Path(video_path).name
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print(f"\n{'─'*60}")
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print(f" [{i}/{len(videos)}] Processing: {video_name}")
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print(f"{'─'*60}")
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try:
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output_dir = args.output_dir or os.path.dirname(video_path)
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os.makedirs(output_dir, exist_ok=True)
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stem = Path(video_path).stem
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wav_path = os.path.join(output_dir, f"{stem}_audio.wav")
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# Step 1-3
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extract_audio(video_path, wav_path)
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raw_segments = run_segmentation(wav_path)
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singing = merge_singing_segments(
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raw_segments,
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max_gap=args.gap,
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min_duration=args.min_duration,
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padding=args.padding,
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)
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# Step 4: Export
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if singing:
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edl = os.path.join(output_dir, f"{stem}_singing.edl")
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csv_f = os.path.join(output_dir, f"{stem}_singing.csv")
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markers = os.path.join(output_dir, f"{stem}_markers.csv")
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json_f = os.path.join(output_dir, f"{stem}_singing.json")
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export_edl(singing, edl, fps=args.fps,
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source_filename=Path(video_path).name)
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export_csv(singing, csv_f)
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export_davinci_markers_csv(singing, markers, fps=args.fps)
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export_json(singing, json_f)
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if args.auto_cut:
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export_ffmpeg_concat(singing, video_path, json_f)
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total_dur = sum(s["duration"] for s in singing)
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results.append((video_name, len(singing), total_dur, "OK"))
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else:
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results.append((video_name, 0, 0, "No songs found"))
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# Cleanup WAV
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if os.path.exists(wav_path):
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os.remove(wav_path)
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except Exception as e:
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print(f" [ERROR] {e}")
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results.append((video_name, 0, 0, f"Error: {e}"))
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# Summary
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print(f"\n\n{'='*60}")
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print(f" BATCH COMPLETE")
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print(f"{'='*60}")
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print(f" {'Video':<40s} {'Songs':>5s} {'Singing Time':>12s} Status")
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print(f" {'─'*40} {'─'*5} {'─'*12} {'─'*15}")
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for name, count, dur, status in results:
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name_short = name[:38] + ".." if len(name) > 40 else name
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print(f" {name_short:<40s} {count:>5d} {format_timecode(dur):>12s} {status}")
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print(f"{'='*60}")
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if __name__ == "__main__":
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main()
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598
v4_inaseg_batch/detect_singing.py
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598
v4_inaseg_batch/detect_singing.py
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#!/usr/bin/env python3
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"""
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Singing Segment Detector for Live Stream Recordings
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=====================================================
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Detects singing segments in long stream recordings and exports
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DaVinci Resolve-compatible EDL markers for fast video editing.
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Pipeline:
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1. Extract audio from video (ffmpeg → 16kHz mono WAV)
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2. Run inaSpeechSegmenter to classify speech/music/noise
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3. Merge nearby "music" segments (singing) with configurable gap
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4. Export to EDL (Edit Decision List) for DaVinci Resolve import
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5. Optionally export CSV for review
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Usage:
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python detect_singing.py "path/to/stream_recording.mp4"
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python detect_singing.py "path/to/stream_recording.mp4" --gap 15 --min-duration 30
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python detect_singing.py "path/to/stream_recording.mp4" --output-dir "D:/singing_edits"
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"""
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import argparse
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import csv
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import json
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import os
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import subprocess
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import sys
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import tempfile
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import time
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from pathlib import Path
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# ──────────────────────────────────────────────
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# Step 1: Extract audio from video using ffmpeg
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# ──────────────────────────────────────────────
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def extract_audio(video_path: str, output_wav: str, ffmpeg_bin: str = "ffmpeg") -> str:
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"""Extract audio from video file as 16kHz mono WAV (required by inaSpeechSegmenter)."""
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print(f"\n[Step 1/4] Extracting audio from: {video_path}")
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print(f" Output WAV: {output_wav}")
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cmd = [
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ffmpeg_bin,
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"-i", video_path,
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"-vn", # no video
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"-acodec", "pcm_s16le", # 16-bit PCM
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"-ar", "16000", # 16kHz sample rate
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"-ac", "1", # mono
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"-y", # overwrite
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output_wav,
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]
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try:
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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timeout=1800, # 30 min timeout for very long files
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)
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if result.returncode != 0:
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print(f" [ERROR] ffmpeg failed:\n{result.stderr[-500:]}")
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sys.exit(1)
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except FileNotFoundError:
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print(f" [ERROR] ffmpeg not found at '{ffmpeg_bin}'.")
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print(f" Make sure ffmpeg is installed and in your PATH.")
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sys.exit(1)
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size_mb = os.path.getsize(output_wav) / (1024 * 1024)
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print(f" Audio extracted: {size_mb:.1f} MB")
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return output_wav
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# ──────────────────────────────────────────────
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# Step 2: Run inaSpeechSegmenter
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# ──────────────────────────────────────────────
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def run_segmentation(wav_path: str) -> list:
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"""
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Run inaSpeechSegmenter on audio file.
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Returns list of (label, start_sec, end_sec) tuples.
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Labels: 'music', 'speech', 'male', 'female', 'noise', 'noEnergy'
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NOTE: inaSpeechSegmenter classifies singing voice as 'music'.
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"""
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print(f"\n[Step 2/4] Running audio segmentation (this may take a while)...")
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try:
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from inaSpeechSegmenter import Segmenter
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except ImportError:
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print(" [ERROR] inaSpeechSegmenter is not installed.")
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print(" Install it with: pip install inaSpeechSegmenter")
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sys.exit(1)
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start_time = time.time()
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# vad_engine='smn' → speech/music/noise detection
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# detect_gender=False → faster, we don't need gender info
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seg = Segmenter(vad_engine='smn', detect_gender=False)
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segments = seg(wav_path)
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elapsed = time.time() - start_time
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print(f" Segmentation complete in {elapsed:.1f}s")
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print(f" Total segments found: {len(segments)}")
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# Count segment types
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type_counts = {}
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type_durations = {}
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for label, start, end in segments:
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type_counts[label] = type_counts.get(label, 0) + 1
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type_durations[label] = type_durations.get(label, 0) + (end - start)
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for label in sorted(type_counts.keys()):
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count = type_counts[label]
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dur = type_durations[label]
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print(f" {label:>10s}: {count:4d} segments, {format_timecode(dur)} total")
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return segments
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# ──────────────────────────────────────────────
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# Step 3: Filter & merge singing (music) segments
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# ──────────────────────────────────────────────
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def merge_singing_segments(
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segments: list,
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max_gap: float = 10.0,
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min_duration: float = 20.0,
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padding: float = 2.0,
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) -> list:
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"""
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Filter for 'music' segments (which includes singing) and merge
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nearby segments that are likely the same song.
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Args:
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segments: Raw segmentation output [(label, start, end), ...]
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max_gap: Max gap (seconds) between music segments to merge them.
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Stream singers often have brief pauses, audience interaction
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between verses, etc. Default 10s works well.
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min_duration: Minimum duration (seconds) for a merged segment to be kept.
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Filters out short music stings, sound effects, etc.
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A typical song is 2-5 minutes, so 20s is a safe minimum.
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padding: Seconds to add before/after each segment for clean cuts.
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Returns:
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List of dicts: [{"start": float, "end": float, "duration": float, "index": int}, ...]
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"""
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print(f"\n[Step 3/4] Merging singing segments (gap={max_gap}s, min={min_duration}s, pad={padding}s)")
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# Extract only music segments
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music_segs = [(start, end) for label, start, end in segments if label == "music"]
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if not music_segs:
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print(" No music segments found!")
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return []
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# Sort by start time (should already be sorted, but just in case)
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music_segs.sort(key=lambda x: x[0])
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# Merge segments with gaps smaller than max_gap
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merged = []
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current_start, current_end = music_segs[0]
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for start, end in music_segs[1:]:
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if start - current_end <= max_gap:
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# Extend current segment
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current_end = max(current_end, end)
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else:
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# Save current segment and start new one
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merged.append((current_start, current_end))
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current_start, current_end = start, end
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merged.append((current_start, current_end))
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# Apply padding and minimum duration filter
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results = []
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idx = 1
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for start, end in merged:
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duration = end - start
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if duration >= min_duration:
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padded_start = max(0, start - padding)
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padded_end = end + padding
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results.append({
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"index": idx,
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"start": padded_start,
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"end": padded_end,
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"duration": padded_end - padded_start,
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"original_start": start,
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"original_end": end,
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})
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idx += 1
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print(f" Found {len(results)} singing segments after merge+filter:")
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for seg in results:
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print(f" Song {seg['index']:2d}: "
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f"{format_timecode(seg['start'])} → {format_timecode(seg['end'])} "
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f"({seg['duration']:.0f}s)")
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return results
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# ──────────────────────────────────────────────
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# Step 4: Export formats
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# ──────────────────────────────────────────────
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def export_edl(segments: list, output_path: str, fps: float = 29.97,
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title: str = "Singing Segments", source_filename: str = None):
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"""
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Export an EDL (Edit Decision List) file for DaVinci Resolve.
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DaVinci Resolve import: File → Import → Timeline → select the .edl file.
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The EDL creates cut points at each singing segment's in/out points.
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Args:
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source_filename: Original video filename (e.g. "stream_2026-04-01.mp4").
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Used as reel name + FROM CLIP NAME so DaVinci Resolve
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matches the EDL to the correct clip in the Media Pool.
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Without this, Resolve picks a random clip when multiple
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videos are loaded in the same project.
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"""
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print(f"\n[Step 4/4] Exporting EDL: {output_path}")
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# DaVinci Resolve uses both the reel name column AND the "* FROM CLIP NAME:"
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# comment to identify which media clip an EDL edit refers to.
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#
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# Reel name: traditionally 8 chars max, but Resolve accepts longer names.
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# We use the filename without extension, truncated to a safe length.
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# FROM CLIP NAME: Resolve's preferred matching method. Must be the exact
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# filename (with extension) as it appears in the Media Pool.
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if source_filename:
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clip_name = source_filename # full filename with extension
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reel_name = Path(source_filename).stem[:32] # stem, truncated for safety
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# Replace spaces with underscores in reel name (some EDL parsers choke on spaces)
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reel_name_safe = reel_name.replace(" ", "_")
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else:
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clip_name = None
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reel_name_safe = "001"
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# Calculate record timecodes: sequential placement on the output timeline
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# Each segment is placed one after another, starting at 01:00:00:00
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rec_offset = 3600.0 # start at 01:00:00:00
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(f"TITLE: {title}\n")
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f.write("FCM: NON-DROP FRAME\n\n")
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current_rec_pos = rec_offset
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for seg in segments:
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edit_num = f"{seg['index']:03d}"
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src_in = seconds_to_timecode(seg["start"], fps)
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src_out = seconds_to_timecode(seg["end"], fps)
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rec_in = seconds_to_timecode(current_rec_pos, fps)
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rec_out = seconds_to_timecode(current_rec_pos + seg["duration"], fps)
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# EDL format: edit# reel_name track_type transition src_in src_out rec_in rec_out
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f.write(f"{edit_num} {reel_name_safe} V C {src_in} {src_out} {rec_in} {rec_out}\n")
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||||
|
||||
# "* FROM CLIP NAME:" is the key line DaVinci uses for media matching.
|
||||
# It must exactly match the clip name shown in the Media Pool.
|
||||
if clip_name:
|
||||
f.write(f"* FROM CLIP NAME: {clip_name}\n")
|
||||
|
||||
f.write(f"* COMMENT: Song {seg['index']} - Duration {seg['duration']:.0f}s\n\n")
|
||||
|
||||
current_rec_pos += seg["duration"]
|
||||
|
||||
print(f" EDL file written with {len(segments)} edits")
|
||||
if clip_name:
|
||||
print(f" Source clip name: {clip_name}")
|
||||
|
||||
|
||||
def export_csv(segments: list, output_path: str):
|
||||
"""Export singing segments as CSV for review or further processing."""
|
||||
with open(output_path, "w", newline="", encoding="utf-8") as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(["index", "start_sec", "end_sec", "duration_sec",
|
||||
"start_timecode", "end_timecode"])
|
||||
for seg in segments:
|
||||
writer.writerow([
|
||||
seg["index"],
|
||||
f"{seg['start']:.2f}",
|
||||
f"{seg['end']:.2f}",
|
||||
f"{seg['duration']:.1f}",
|
||||
format_timecode(seg["start"]),
|
||||
format_timecode(seg["end"]),
|
||||
])
|
||||
print(f" CSV file written: {output_path}")
|
||||
|
||||
|
||||
def export_davinci_markers_csv(segments: list, output_path: str, fps: float = 29.97):
|
||||
"""
|
||||
Export a CSV file that can be used with DaVinci Resolve's
|
||||
'Import Markers from CSV' function (Resolve 18+).
|
||||
|
||||
Format: #, Color, Name, Start TC, End TC, Duration TC, Notes
|
||||
"""
|
||||
with open(output_path, "w", newline="", encoding="utf-8") as f:
|
||||
writer = csv.writer(f)
|
||||
# DaVinci Resolve Marker CSV header
|
||||
writer.writerow(["#", "Color", "Name", "Start TC", "End TC", "Duration TC", "Notes"])
|
||||
for seg in segments:
|
||||
start_tc = seconds_to_timecode(seg["start"], fps)
|
||||
end_tc = seconds_to_timecode(seg["end"], fps)
|
||||
dur_tc = seconds_to_timecode(seg["duration"], fps)
|
||||
writer.writerow([
|
||||
seg["index"],
|
||||
"Blue",
|
||||
f"Song {seg['index']}",
|
||||
start_tc,
|
||||
end_tc,
|
||||
dur_tc,
|
||||
f"Duration: {seg['duration']:.0f}s",
|
||||
])
|
||||
print(f" DaVinci markers CSV written: {output_path}")
|
||||
|
||||
|
||||
def export_json(segments: list, output_path: str):
|
||||
"""Export as JSON for programmatic use."""
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
json.dump(segments, f, indent=2, ensure_ascii=False)
|
||||
print(f" JSON file written: {output_path}")
|
||||
|
||||
|
||||
def export_ffmpeg_concat(segments: list, video_path: str, output_path: str):
|
||||
"""
|
||||
Export a .bat/.sh script that uses ffmpeg to directly cut and
|
||||
concatenate all singing segments into a single video file.
|
||||
This is the fully automated option — no DaVinci needed.
|
||||
"""
|
||||
video_name = Path(video_path).stem
|
||||
ext = Path(video_path).suffix
|
||||
out_video = str(Path(output_path).parent / f"{video_name}_singing_only{ext}")
|
||||
|
||||
is_windows = os.name == "nt" or sys.platform == "win32"
|
||||
script_ext = ".bat" if is_windows else ".sh"
|
||||
script_path = str(Path(output_path).with_suffix(script_ext))
|
||||
|
||||
lines = []
|
||||
if is_windows:
|
||||
lines.append("@echo off")
|
||||
lines.append("REM Auto-generated ffmpeg script to extract singing segments")
|
||||
lines.append(f'REM Source: {video_path}')
|
||||
lines.append("")
|
||||
else:
|
||||
lines.append("#!/bin/bash")
|
||||
lines.append("# Auto-generated ffmpeg script to extract singing segments")
|
||||
lines.append(f'# Source: {video_path}')
|
||||
lines.append("")
|
||||
|
||||
# Create a concat file list
|
||||
concat_list_path = str(Path(output_path).parent / f"{video_name}_concat_list.txt")
|
||||
|
||||
# Generate individual segment extraction commands
|
||||
segment_files = []
|
||||
for seg in segments:
|
||||
seg_file = f"_seg_{seg['index']:03d}{ext}"
|
||||
seg_path = str(Path(output_path).parent / seg_file)
|
||||
segment_files.append(seg_file)
|
||||
|
||||
start = seg["start"]
|
||||
duration = seg["duration"]
|
||||
|
||||
cmd = (f'ffmpeg -y -ss {start:.2f} -i "{video_path}" '
|
||||
f'-t {duration:.2f} -c copy "{seg_path}"')
|
||||
lines.append(f"echo Extracting Song {seg['index']}...")
|
||||
lines.append(cmd)
|
||||
lines.append("")
|
||||
|
||||
# Generate concat list file
|
||||
lines.append(f"echo Creating concat list...")
|
||||
if is_windows:
|
||||
lines.append(f'(')
|
||||
for sf in segment_files:
|
||||
seg_path = str(Path(output_path).parent / sf)
|
||||
lines.append(f' echo file \'{seg_path}\'')
|
||||
lines.append(f') > "{concat_list_path}"')
|
||||
else:
|
||||
for i, sf in enumerate(segment_files):
|
||||
seg_path = str(Path(output_path).parent / sf)
|
||||
op = ">" if i == 0 else ">>"
|
||||
lines.append(f"echo \"file '{seg_path}'\" {op} \"{concat_list_path}\"")
|
||||
|
||||
lines.append("")
|
||||
lines.append(f"echo Concatenating all segments...")
|
||||
lines.append(f'ffmpeg -y -f concat -safe 0 -i "{concat_list_path}" -c copy "{out_video}"')
|
||||
lines.append("")
|
||||
|
||||
# Cleanup temp segment files
|
||||
lines.append("echo Cleaning up temp files...")
|
||||
for sf in segment_files:
|
||||
seg_path = str(Path(output_path).parent / sf)
|
||||
if is_windows:
|
||||
lines.append(f'del "{seg_path}"')
|
||||
else:
|
||||
lines.append(f'rm -f "{seg_path}"')
|
||||
if is_windows:
|
||||
lines.append(f'del "{concat_list_path}"')
|
||||
else:
|
||||
lines.append(f'rm -f "{concat_list_path}"')
|
||||
|
||||
lines.append("")
|
||||
lines.append(f'echo Done! Output: {out_video}')
|
||||
if is_windows:
|
||||
lines.append("pause")
|
||||
|
||||
with open(script_path, "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(lines))
|
||||
|
||||
if not is_windows:
|
||||
os.chmod(script_path, 0o755)
|
||||
|
||||
print(f" FFmpeg concat script written: {script_path}")
|
||||
print(f" Run it to auto-generate: {out_video}")
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# Export raw segmentation for debugging
|
||||
# ──────────────────────────────────────────────
|
||||
def export_raw_segments(segments: list, output_path: str):
|
||||
"""Export ALL raw segments (speech/music/noise) as CSV for debugging."""
|
||||
with open(output_path, "w", newline="", encoding="utf-8") as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(["label", "start_sec", "end_sec", "duration_sec",
|
||||
"start_timecode", "end_timecode"])
|
||||
for label, start, end in segments:
|
||||
writer.writerow([
|
||||
label,
|
||||
f"{start:.2f}",
|
||||
f"{end:.2f}",
|
||||
f"{end - start:.1f}",
|
||||
format_timecode(start),
|
||||
format_timecode(end),
|
||||
])
|
||||
print(f" Raw segments CSV written: {output_path}")
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# Utility functions
|
||||
# ──────────────────────────────────────────────
|
||||
def format_timecode(seconds: float) -> str:
|
||||
"""Format seconds as HH:MM:SS."""
|
||||
h = int(seconds // 3600)
|
||||
m = int((seconds % 3600) // 60)
|
||||
s = int(seconds % 60)
|
||||
return f"{h:02d}:{m:02d}:{s:02d}"
|
||||
|
||||
|
||||
def seconds_to_timecode(seconds: float, fps: float = 29.97) -> str:
|
||||
"""Convert seconds to SMPTE timecode HH:MM:SS:FF."""
|
||||
total_frames = int(seconds * fps)
|
||||
ff = total_frames % int(round(fps))
|
||||
total_seconds = total_frames // int(round(fps))
|
||||
ss = total_seconds % 60
|
||||
total_minutes = total_seconds // 60
|
||||
mm = total_minutes % 60
|
||||
hh = total_minutes // 60
|
||||
return f"{hh:02d}:{mm:02d}:{ss:02d}:{ff:02d}"
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# Main
|
||||
# ──────────────────────────────────────────────
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Detect singing segments in stream recordings and export DaVinci Resolve markers.",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
python detect_singing.py "D:\\streams\\2026-04-04.mp4"
|
||||
python detect_singing.py "D:\\streams\\2026-04-04.mp4" --gap 15 --min-duration 30
|
||||
python detect_singing.py "D:\\streams\\2026-04-04.mp4" --output-dir "D:\\singing_edits"
|
||||
python detect_singing.py "D:\\streams\\2026-04-04.mp4" --auto-cut
|
||||
|
||||
Tips:
|
||||
--gap 10 Merge music segments with <=10s gap (default). Increase if the singer
|
||||
often pauses >10s between verses in the same song.
|
||||
--min-duration 20 Ignore segments shorter than 20s (default). Decrease if
|
||||
the singer does very short songs or covers.
|
||||
--padding 2 Add 2s padding before/after each segment (default). Helps catch
|
||||
the very start/end of songs.
|
||||
--auto-cut Generate an ffmpeg script to automatically cut and concat all
|
||||
singing segments into a single video file.
|
||||
--fps 29.97 Frame rate for timecode calculation (default: 29.97 for NTSC).
|
||||
Use 25 for PAL or 23.976 for film.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument("video", help="Path to the stream recording video file")
|
||||
parser.add_argument("--output-dir", "-o", default=None,
|
||||
help="Output directory (default: same directory as input video)")
|
||||
parser.add_argument("--gap", "-g", type=float, default=10.0,
|
||||
help="Max gap (seconds) to merge nearby singing segments (default: 10)")
|
||||
parser.add_argument("--min-duration", "-m", type=float, default=20.0,
|
||||
help="Minimum singing segment duration in seconds (default: 20)")
|
||||
parser.add_argument("--padding", "-p", type=float, default=2.0,
|
||||
help="Padding (seconds) before/after each segment (default: 2)")
|
||||
parser.add_argument("--fps", type=float, default=29.97,
|
||||
help="Video frame rate for timecode export (default: 29.97)")
|
||||
parser.add_argument("--keep-wav", action="store_true",
|
||||
help="Keep the extracted WAV file (default: delete after processing)")
|
||||
parser.add_argument("--auto-cut", action="store_true",
|
||||
help="Generate an ffmpeg script to auto-cut and concat singing segments")
|
||||
parser.add_argument("--raw-segments", action="store_true",
|
||||
help="Also export all raw segments (speech/music/noise) as CSV")
|
||||
parser.add_argument("--ffmpeg", default="ffmpeg",
|
||||
help="Path to ffmpeg binary (default: 'ffmpeg' from PATH)")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate input
|
||||
video_path = os.path.abspath(args.video)
|
||||
if not os.path.isfile(video_path):
|
||||
print(f"[ERROR] Video file not found: {video_path}")
|
||||
sys.exit(1)
|
||||
|
||||
# Set output directory
|
||||
if args.output_dir:
|
||||
output_dir = os.path.abspath(args.output_dir)
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
else:
|
||||
output_dir = os.path.dirname(video_path)
|
||||
|
||||
video_stem = Path(video_path).stem
|
||||
|
||||
print("=" * 60)
|
||||
print(" Singing Segment Detector")
|
||||
print("=" * 60)
|
||||
print(f" Input: {video_path}")
|
||||
print(f" Output dir: {output_dir}")
|
||||
print(f" Settings: gap={args.gap}s, min={args.min_duration}s, pad={args.padding}s")
|
||||
print(f" FPS: {args.fps}")
|
||||
|
||||
# Step 1: Extract audio
|
||||
wav_path = os.path.join(output_dir, f"{video_stem}_audio.wav")
|
||||
extract_audio(video_path, wav_path, ffmpeg_bin=args.ffmpeg)
|
||||
|
||||
# Step 2: Run segmentation
|
||||
raw_segments = run_segmentation(wav_path)
|
||||
|
||||
# Step 3: Merge singing segments
|
||||
singing_segments = merge_singing_segments(
|
||||
raw_segments,
|
||||
max_gap=args.gap,
|
||||
min_duration=args.min_duration,
|
||||
padding=args.padding,
|
||||
)
|
||||
|
||||
if not singing_segments:
|
||||
print("\n[RESULT] No singing segments detected. Try lowering --min-duration or increasing --gap.")
|
||||
# Cleanup WAV
|
||||
if not args.keep_wav and os.path.exists(wav_path):
|
||||
os.remove(wav_path)
|
||||
sys.exit(0)
|
||||
|
||||
# Step 4: Export results
|
||||
edl_path = os.path.join(output_dir, f"{video_stem}_singing.edl")
|
||||
csv_path = os.path.join(output_dir, f"{video_stem}_singing.csv")
|
||||
markers_path = os.path.join(output_dir, f"{video_stem}_markers.csv")
|
||||
json_path = os.path.join(output_dir, f"{video_stem}_singing.json")
|
||||
|
||||
export_edl(singing_segments, edl_path, fps=args.fps,
|
||||
title=f"{video_stem} - Singing",
|
||||
source_filename=Path(video_path).name)
|
||||
export_csv(singing_segments, csv_path)
|
||||
export_davinci_markers_csv(singing_segments, markers_path, fps=args.fps)
|
||||
export_json(singing_segments, json_path)
|
||||
|
||||
if args.auto_cut:
|
||||
export_ffmpeg_concat(singing_segments, video_path, json_path)
|
||||
|
||||
if args.raw_segments:
|
||||
raw_csv_path = os.path.join(output_dir, f"{video_stem}_raw_segments.csv")
|
||||
export_raw_segments(raw_segments, raw_csv_path)
|
||||
|
||||
# Cleanup WAV unless --keep-wav
|
||||
if not args.keep_wav and os.path.exists(wav_path):
|
||||
os.remove(wav_path)
|
||||
print(f"\n Temp WAV file deleted.")
|
||||
|
||||
# Summary
|
||||
total_singing = sum(s["duration"] for s in singing_segments)
|
||||
print("\n" + "=" * 60)
|
||||
print(" DONE!")
|
||||
print("=" * 60)
|
||||
print(f" Songs detected: {len(singing_segments)}")
|
||||
print(f" Total singing time: {format_timecode(total_singing)}")
|
||||
print(f" EDL file: {edl_path}")
|
||||
print(f" DaVinci markers CSV: {markers_path}")
|
||||
print(f" Segments CSV: {csv_path}")
|
||||
print(f" Segments JSON: {json_path}")
|
||||
if args.auto_cut:
|
||||
script_ext = ".bat" if (os.name == "nt" or sys.platform == "win32") else ".sh"
|
||||
print(f" Auto-cut script: {json_path.replace('.json', script_ext)}")
|
||||
print()
|
||||
print(" To import into DaVinci Resolve:")
|
||||
print(" Option A: File → Import → Timeline → select the .edl file")
|
||||
print(" Option B: Timeline menu → Import Markers from CSV → select _markers.csv")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user