v3: inaSpeechSegmenter CLI pipeline

Adopts inaSpeechSegmenter (singing classified as music). Full argparse CLI: gap-merge nearby segments, min-duration filter, EDL + CSV export. Engine that all later versions keep.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

README.md

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+# Singing Segment Detector
+
+Automatically detect singing segments in live stream recordings and export timeline markers for DaVinci Resolve.
+
+## What It Does
+
+```
+4hr stream recording (.mp4/.mkv/.flv)
+    │
+    ▼  [ffmpeg: extract 16kHz mono audio]
+    │
+    ▼  [inaSpeechSegmenter: classify speech/music/noise]
+    │
+    ▼  [merge nearby music segments, filter short ones]
+    │
+    ├──→ .edl        (import as DaVinci Resolve timeline)
+    ├──→ _markers.csv (import as DaVinci Resolve markers)
+    ├──→ .csv         (human-readable segment list)
+    ├──→ .json        (programmatic use)
+    └──→ .bat         (optional: auto-cut with ffmpeg, no editing needed)
+```
+
+**Key insight**: `inaSpeechSegmenter` classifies **singing voice as "music"**. So in a typical singing stream, talking = "speech", singing = "music". We filter for "music" segments and merge nearby ones (a song might have brief pauses between verses).
+
+## Setup (Windows with conda)
+
+### Prerequisites
+- **conda** (Anaconda or Miniconda)
+- **ffmpeg** in your PATH
+- **NVIDIA GPU** recommended (RTX 2080 works great) but CPU also works
+
+### Step 1: Create conda environment
+
+```powershell
+conda create -n singing-detector python=3.11 -y
+conda activate singing-detector
+```
+
+### Step 2: Install PyTorch with CUDA (for GPU acceleration)
+
+```powershell
+# For RTX 2080 (CUDA 12.x)
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+```
+
+### Step 3: Install TensorFlow (required by inaSpeechSegmenter)
+
+```powershell
+pip install tensorflow[and-cuda]
+```
+
+Or CPU-only (slower but works):
+
+```powershell
+pip install tensorflow
+```
+
+### Step 4: Install inaSpeechSegmenter and dependencies
+
+```powershell
+pip install inaSpeechSegmenter
+```
+
+### Step 5: Verify installation
+
+```powershell
+python -c "from inaSpeechSegmenter import Segmenter; print('OK!')"
+```
+
+## Usage
+
+### Single file
+
+```powershell
+conda activate singing-detector
+
+# Basic usage
+python detect_singing.py "D:\streams\2026-04-04_stream.mp4"
+
+# Custom settings
+python detect_singing.py "D:\streams\2026-04-04_stream.mp4" --gap 15 --min-duration 30
+
+# Output to a specific directory
+python detect_singing.py "D:\streams\2026-04-04_stream.mp4" -o "D:\singing_edits"
+
+# Also generate an auto-cut ffmpeg script (no DaVinci needed)
+python detect_singing.py "D:\streams\2026-04-04_stream.mp4" --auto-cut
+
+# For 4K F-log footage at 29.97fps (your XT3 settings)
+python detect_singing.py "D:\streams\2026-04-04_stream.mp4" --fps 29.97
+```
+
+### Batch processing
+
+```powershell
+# Process all videos in a folder
+python batch_detect.py "D:\streams"
+
+# Process new files only (skip already-done ones)
+python batch_detect.py "D:\streams" --skip-existing
+
+# Process and auto-generate singing-only videos
+python batch_detect.py "D:\streams" --auto-cut -o "D:\singing_edits"
+
+# Only process .flv files
+python batch_detect.py "D:\streams" --pattern "*.flv"
+```
+
+## Importing Results into DaVinci Resolve
+
+### Option A: Import EDL as Timeline (recommended)
+
+1. Open your project in DaVinci Resolve
+2. Import the original stream video into your Media Pool
+3. Go to **File → Import → Timeline**
+4. Select the `_singing.edl` file
+5. DaVinci will create a new timeline with only the singing segments
+
+### Option B: Import Markers CSV
+
+1. Create a timeline from your stream video
+2. Right-click on the timeline → **Timelines → Import Markers from CSV**
+3. Select the `_markers.csv` file
+4. Blue markers will appear at each singing segment start/end
+
+### Option C: Auto-Cut (no DaVinci needed)
+
+If you used `--auto-cut`, just run the generated `.bat` script:
+
+```powershell
+D:\singing_edits\2026-04-04_stream_singing.bat
+```
+
+This uses ffmpeg to directly cut and concatenate all singing segments into a single `_singing_only.mp4` file. Fastest option, but no manual review.
+
+## Tuning Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--gap` | 10s | Max gap between music segments to merge. Increase if singer pauses >10s between verses. |
+| `--min-duration` | 20s | Minimum segment length. Lower if singer does very short songs. |
+| `--padding` | 2s | Extra seconds before/after each segment. Helps catch song intro/outro. |
+| `--fps` | 29.97 | Frame rate for timecode. Use 25 for PAL. |
+
+### Recommended settings for typical singing streams
+
+- **Singer with lots of chatting between songs**: `--gap 10 --min-duration 30`
+- **Singer with minimal breaks**: `--gap 5 --min-duration 20`
+- **Singer who does short covers/snippets**: `--gap 8 --min-duration 15`
+- **Conservative (catch everything)**: `--gap 20 --min-duration 15 --padding 5`
+
+## Troubleshooting
+
+### "Singing voice detected as speech"
+This can happen if the singer talks over background music. Try `--gap 15` to merge nearby segments.
+
+### "Too many false positives (non-singing music detected)"
+If there's background music during chatting, increase `--min-duration 45` to only keep longer segments (full songs).
+
+### "Segments cut off the beginning/end of songs"
+Increase `--padding 5` to add more buffer.
+
+### GPU not being used
+Check that TensorFlow detects your GPU:
+```python
+python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
+```
+
+### Processing is very slow
+- A 4-hour stream typically takes 5-15 minutes on GPU, 30-60 minutes on CPU.
+- Make sure you're using GPU-enabled TensorFlow.
+- Close other GPU-heavy applications during processing.
+
+## Output Files
+
+For input `stream_2026-04-04.mp4`, you get:
+
+| File | Purpose |
+|------|---------|
+| `stream_2026-04-04_singing.edl` | DaVinci Resolve timeline import |
+| `stream_2026-04-04_markers.csv` | DaVinci Resolve marker import |
+| `stream_2026-04-04_singing.csv` | Human-readable segment list |
+| `stream_2026-04-04_singing.json` | Programmatic use |
+| `stream_2026-04-04_singing.bat` | Auto-cut script (with `--auto-cut`) |
+| `stream_2026-04-04_raw_segments.csv` | All segments for debugging (with `--raw-segments`) |

detect_singing.py

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+#!/usr/bin/env python3
+"""
+Singing Segment Detector for Live Stream Recordings
+=====================================================
+Detects singing segments in long stream recordings and exports
+DaVinci Resolve-compatible EDL markers for fast video editing.
+
+Pipeline:
+  1. Extract audio from video (ffmpeg → 16kHz mono WAV)
+  2. Run inaSpeechSegmenter to classify speech/music/noise
+  3. Merge nearby "music" segments (singing) with configurable gap
+  4. Export to EDL (Edit Decision List) for DaVinci Resolve import
+  5. Optionally export CSV for review
+
+Usage:
+  python detect_singing.py "path/to/stream_recording.mp4"
+  python detect_singing.py "path/to/stream_recording.mp4" --gap 15 --min-duration 30
+  python detect_singing.py "path/to/stream_recording.mp4" --output-dir "D:/singing_edits"
+"""
+
+import argparse
+import csv
+import json
+import os
+import subprocess
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+
+# ──────────────────────────────────────────────
+# Step 1: Extract audio from video using ffmpeg
+# ──────────────────────────────────────────────
+def extract_audio(video_path: str, output_wav: str, ffmpeg_bin: str = "ffmpeg") -> str:
+    """Extract audio from video file as 16kHz mono WAV (required by inaSpeechSegmenter)."""
+    print(f"\n[Step 1/4] Extracting audio from: {video_path}")
+    print(f"           Output WAV: {output_wav}")
+
+    cmd = [
+        ffmpeg_bin,
+        "-i", video_path,
+        "-vn",                  # no video
+        "-acodec", "pcm_s16le", # 16-bit PCM
+        "-ar", "16000",         # 16kHz sample rate
+        "-ac", "1",             # mono
+        "-y",                   # overwrite
+        output_wav,
+    ]
+
+    try:
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            timeout=1800,  # 30 min timeout for very long files
+        )
+        if result.returncode != 0:
+            print(f"  [ERROR] ffmpeg failed:\n{result.stderr[-500:]}")
+            sys.exit(1)
+    except FileNotFoundError:
+        print(f"  [ERROR] ffmpeg not found at '{ffmpeg_bin}'.")
+        print(f"          Make sure ffmpeg is installed and in your PATH.")
+        sys.exit(1)
+
+    size_mb = os.path.getsize(output_wav) / (1024 * 1024)
+    print(f"           Audio extracted: {size_mb:.1f} MB")
+    return output_wav
+
+
+# ──────────────────────────────────────────────
+# Step 2: Run inaSpeechSegmenter
+# ──────────────────────────────────────────────
+def run_segmentation(wav_path: str) -> list:
+    """
+    Run inaSpeechSegmenter on audio file.
+    Returns list of (label, start_sec, end_sec) tuples.
+    Labels: 'music', 'speech', 'male', 'female', 'noise', 'noEnergy'
+    
+    NOTE: inaSpeechSegmenter classifies singing voice as 'music'.
+    """
+    print(f"\n[Step 2/4] Running audio segmentation (this may take a while)...")
+
+    try:
+        from inaSpeechSegmenter import Segmenter
+    except ImportError:
+        print("  [ERROR] inaSpeechSegmenter is not installed.")
+        print("          Install it with: pip install inaSpeechSegmenter")
+        sys.exit(1)
+
+    start_time = time.time()
+
+    # vad_engine='smn' → speech/music/noise detection
+    # detect_gender=False → faster, we don't need gender info
+    seg = Segmenter(vad_engine='smn', detect_gender=False)
+    segments = seg(wav_path)
+
+    elapsed = time.time() - start_time
+    print(f"           Segmentation complete in {elapsed:.1f}s")
+    print(f"           Total segments found: {len(segments)}")
+
+    # Count segment types
+    type_counts = {}
+    type_durations = {}
+    for label, start, end in segments:
+        type_counts[label] = type_counts.get(label, 0) + 1
+        type_durations[label] = type_durations.get(label, 0) + (end - start)
+
+    for label in sorted(type_counts.keys()):
+        count = type_counts[label]
+        dur = type_durations[label]
+        print(f"           {label:>10s}: {count:4d} segments, {format_timecode(dur)} total")
+
+    return segments
+
+
+# ──────────────────────────────────────────────
+# Step 3: Filter & merge singing (music) segments
+# ──────────────────────────────────────────────
+def merge_singing_segments(
+    segments: list,
+    max_gap: float = 10.0,
+    min_duration: float = 20.0,
+    padding: float = 2.0,
+) -> list:
+    """
+    Filter for 'music' segments (which includes singing) and merge
+    nearby segments that are likely the same song.
+    
+    Args:
+        segments:     Raw segmentation output [(label, start, end), ...]
+        max_gap:      Max gap (seconds) between music segments to merge them.
+                      Stream singers often have brief pauses, audience interaction
+                      between verses, etc. Default 10s works well.
+        min_duration: Minimum duration (seconds) for a merged segment to be kept.
+                      Filters out short music stings, sound effects, etc.
+                      A typical song is 2-5 minutes, so 20s is a safe minimum.
+        padding:      Seconds to add before/after each segment for clean cuts.
+    
+    Returns:
+        List of dicts: [{"start": float, "end": float, "duration": float, "index": int}, ...]
+    """
+    print(f"\n[Step 3/4] Merging singing segments (gap={max_gap}s, min={min_duration}s, pad={padding}s)")
+
+    # Extract only music segments
+    music_segs = [(start, end) for label, start, end in segments if label == "music"]
+
+    if not music_segs:
+        print("           No music segments found!")
+        return []
+
+    # Sort by start time (should already be sorted, but just in case)
+    music_segs.sort(key=lambda x: x[0])
+
+    # Merge segments with gaps smaller than max_gap
+    merged = []
+    current_start, current_end = music_segs[0]
+
+    for start, end in music_segs[1:]:
+        if start - current_end <= max_gap:
+            # Extend current segment
+            current_end = max(current_end, end)
+        else:
+            # Save current segment and start new one
+            merged.append((current_start, current_end))
+            current_start, current_end = start, end
+
+    merged.append((current_start, current_end))
+
+    # Apply padding and minimum duration filter
+    results = []
+    idx = 1
+    for start, end in merged:
+        duration = end - start
+        if duration >= min_duration:
+            padded_start = max(0, start - padding)
+            padded_end = end + padding
+            results.append({
+                "index": idx,
+                "start": padded_start,
+                "end": padded_end,
+                "duration": padded_end - padded_start,
+                "original_start": start,
+                "original_end": end,
+            })
+            idx += 1
+
+    print(f"           Found {len(results)} singing segments after merge+filter:")
+    for seg in results:
+        print(f"           Song {seg['index']:2d}: "
+              f"{format_timecode(seg['start'])} → {format_timecode(seg['end'])} "
+              f"({seg['duration']:.0f}s)")
+
+    return results
+
+
+# ──────────────────────────────────────────────
+# Step 4: Export formats
+# ──────────────────────────────────────────────
+
+def export_edl(segments: list, output_path: str, fps: float = 29.97,
+               title: str = "Singing Segments", source_filename: str = None):
+    """
+    Export an EDL (Edit Decision List) file for DaVinci Resolve.
+    
+    DaVinci Resolve import: File → Import → Timeline → select the .edl file.
+    The EDL creates cut points at each singing segment's in/out points.
+    
+    Args:
+        source_filename: Original video filename (e.g. "stream_2026-04-01.mp4").
+                         Used as reel name + FROM CLIP NAME so DaVinci Resolve
+                         matches the EDL to the correct clip in the Media Pool.
+                         Without this, Resolve picks a random clip when multiple
+                         videos are loaded in the same project.
+    """
+    print(f"\n[Step 4/4] Exporting EDL: {output_path}")
+
+    # DaVinci Resolve uses both the reel name column AND the "* FROM CLIP NAME:"
+    # comment to identify which media clip an EDL edit refers to.
+    # 
+    # Reel name: traditionally 8 chars max, but Resolve accepts longer names.
+    #   We use the filename without extension, truncated to a safe length.
+    # FROM CLIP NAME: Resolve's preferred matching method. Must be the exact
+    #   filename (with extension) as it appears in the Media Pool.
+    
+    if source_filename:
+        clip_name = source_filename                          # full filename with extension
+        reel_name = Path(source_filename).stem[:32]          # stem, truncated for safety
+        # Replace spaces with underscores in reel name (some EDL parsers choke on spaces)
+        reel_name_safe = reel_name.replace(" ", "_")
+    else:
+        clip_name = None
+        reel_name_safe = "001"
+
+    # Calculate record timecodes: sequential placement on the output timeline
+    # Each segment is placed one after another, starting at 01:00:00:00
+    rec_offset = 3600.0  # start at 01:00:00:00
+
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write(f"TITLE: {title}\n")
+        f.write("FCM: NON-DROP FRAME\n\n")
+
+        current_rec_pos = rec_offset
+        for seg in segments:
+            edit_num = f"{seg['index']:03d}"
+            src_in = seconds_to_timecode(seg["start"], fps)
+            src_out = seconds_to_timecode(seg["end"], fps)
+            rec_in = seconds_to_timecode(current_rec_pos, fps)
+            rec_out = seconds_to_timecode(current_rec_pos + seg["duration"], fps)
+
+            # EDL format: edit# reel_name track_type transition src_in src_out rec_in rec_out
+            f.write(f"{edit_num}  {reel_name_safe}  V     C        {src_in} {src_out} {rec_in} {rec_out}\n")
+
+            # "* 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()

setup_windows.bat

@@ -0,0 +1,56 @@
+@echo off
+REM ============================================
+REM  Singing Detector - Quick Setup (Windows)
+REM ============================================
+REM  Run this in Anaconda Prompt / PowerShell
+REM  Prerequisites: conda, ffmpeg in PATH
+REM ============================================
+
+echo.
+echo ============================================
+echo  Singing Segment Detector - Setup
+echo ============================================
+echo.
+
+REM Step 1: Create conda environment
+echo [1/4] Creating conda environment (python 3.11)...
+call conda create -n singing-detector python=3.11 -y
+if %ERRORLEVEL% neq 0 (
+    echo ERROR: Failed to create conda environment.
+    pause
+    exit /b 1
+)
+
+REM Activate environment
+call conda activate singing-detector
+
+REM Step 2: Install TensorFlow with GPU support
+echo.
+echo [2/4] Installing TensorFlow (GPU)...
+pip install tensorflow[and-cuda]
+if %ERRORLEVEL% neq 0 (
+    echo WARNING: GPU TensorFlow install failed, trying CPU version...
+    pip install tensorflow
+)
+
+REM Step 3: Install inaSpeechSegmenter
+echo.
+echo [3/4] Installing inaSpeechSegmenter...
+pip install inaSpeechSegmenter
+
+REM Step 4: Verify
+echo.
+echo [4/4] Verifying installation...
+python -c "from inaSpeechSegmenter import Segmenter; print('inaSpeechSegmenter OK!')"
+python -c "import tensorflow as tf; gpus = tf.config.list_physical_devices('GPU'); print(f'TensorFlow GPUs: {gpus}' if gpus else 'TensorFlow: CPU only')"
+
+echo.
+echo ============================================
+echo  Setup complete!
+echo ============================================
+echo.
+echo  Usage:
+echo    conda activate singing-detector
+echo    python detect_singing.py "path\to\stream.mp4"
+echo.
+pause

sing_detect.py

@@ -1,108 +0,0 @@
-import subprocess
-import os
-import torch
-import librosa
-import numpy as np
-from huggingface_hub import hf_hub_download
-import tkinter as tk
-from tkinter import filedialog
-
-# ------------------------
-# 1. Extract audio from video
-# ------------------------
-def extract_audio(input_video, output_wav):
-    cmd = [
-        "ffmpeg", "-y",
-        "-i", input_video,
-        "-vn",          # no video
-        "-ac", "1",     # mono
-        "-ar", "16000", # 16kHz
-        output_wav
-    ]
-    subprocess.run(cmd, check=True)
-
-# ------------------------
-# 2. Load pretrained model
-# ------------------------
-def load_model():
-    repo_id = "matthijs/svd"  # HuggingFace singing voice detection model
-    filename = hf_hub_download(repo_id=repo_id, filename="model.pt")
-    model = torch.jit.load(filename)
-    model.eval()
-    return model
-
-# ------------------------
-# 3. Run inference on audio
-# ------------------------
-def detect_singing(audio_path, model, hop_length=512, threshold=0.5, min_duration=10.0):
-    y, sr = librosa.load(audio_path, sr=16000)
-    x = torch.tensor(y).float().unsqueeze(0)
-
-    with torch.no_grad():
-        pred = model(x).squeeze().numpy()
-
-    times = librosa.frames_to_time(np.arange(len(pred)), sr=sr, hop_length=hop_length)
-
-    segments = []
-    in_segment = False
-    seg_start = None
-
-    for t, p in zip(times, pred):
-        if p > threshold and not in_segment:
-            in_segment = True
-            seg_start = t
-        elif p <= threshold and in_segment:
-            in_segment = False
-            seg_end = t
-            if seg_end - seg_start >= min_duration:
-                segments.append((seg_start, seg_end))
-
-    if in_segment:
-        seg_end = times[-1]
-        if seg_end - seg_start >= min_duration:
-            segments.append((seg_start, seg_end))
-
-    return segments
-
-# ------------------------
-# 4. Export CSV
-# ------------------------
-def export_csv(segments, out_file="segments.csv"):
-    with open(out_file, "w", encoding="utf-8") as f:
-        f.write("start_time,end_time\n")
-        for s, e in segments:
-            f.write(f"{s:.2f},{e:.2f}\n")
-    print(f"[+] Saved: {out_file}")
-
-# ------------------------
-# MAIN
-# ------------------------
-if __name__ == "__main__":
-    # Tkinter GUI to choose file
-    root = tk.Tk()
-    root.withdraw()
-    input_video = filedialog.askopenfilename(
-        title="Choose your video file",
-        filetypes=[("Video files", "*.mp4 *.mkv *.flv *.mov *.avi"), ("All files", "*.*")]
-    )
-    if not input_video:
-        print("No file selected. Exiting.")
-        exit()
-
-    audio_file = "audio.wav"
-
-    print(f"[+] Processing: {input_video}")
-
-    # Step 1: extract audio
-    extract_audio(input_video, audio_file)
-
-    # Step 2: load model
-    model = load_model()
-
-    # Step 3: run detection
-    segments = detect_singing(audio_file, model)
-
-    # Step 4: save results
-    export_csv(segments, "segments.csv")
-
-    print("[+] Done! Detected singing segments have been saved to segments.csv")