docs: add lineage README mapping v1→v6 evolution

Overview table, engine evolution notes, and per-version caveats.

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

README.md

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+# sing_detect
+
+Consolidated history of my singing/music-segment detection tools, gathered from
+across several projects and ranked by **technical & functional evolution** (not
+by file timestamp). The goal of every version is the same:
+
+> Take a long live-stream recording, find the segments where someone is singing,
+> and export markers (CSV / EDL / JSON) for fast editing in DaVinci Resolve.
+
+Each version lives in its own folder and was committed as a separate step so the
+git history reads as the evolution story.
+
+| Version | Folder | Engine | Interface | Leap |
+|---------|--------|--------|-----------|------|
+| v1 | `v1_svd_prototype/` | HuggingFace `matthijs/svd` (torch.jit) | tkinter file picker | First prototype — single file, proves the idea |
+| v2 | `v2_transformers_gui/` | `transformers` pipeline + pyannote | CustomTkinter GUI | Overlapping-chunk inference, CSV timecode export |
+| v3 | `v3_inaseg_cli/` | inaSpeechSegmenter | CLI (argparse) | Robust pipeline: gap-merge, min-duration, EDL+CSV |
+| v4 | `v4_inaseg_batch/` | inaSpeechSegmenter | CLI batch | Process whole folders, `--recursive`, `--auto-cut` |
+| v5 | `v5_stream_tools_app/` | inaSpeechSegmenter | CustomTkinter multi-tab app | Refactored into modular `core/` engine + UI tab, JSON + ffmpeg-script export |
+| v6 | `v6_stream_studio_tauri/` | inaSpeechSegmenter (Python sidecar) | Tauri (Rust + TS) | Cross-platform rewrite; Python reduced to a thin ML sidecar emitting line-JSON |
+
+## Engine evolution
+`svd model` → `transformers pipeline` → **`inaSpeechSegmenter`** (settled from v3 on,
+which classifies singing voice as "music").
+
+## Notes per version
+- **v4** includes a copy of `detect_singing.py` because `batch_detect.py` imports it at runtime.
+- **v5** keeps the original package layout (`core/`, `ui/`, `utils/`) so the relative imports resolve; it was extracted from the larger `stream_tools` app.
+- **v6** is the detection slice only (`sidecar_detect.py`, `detect.rs`, `detect.ts`), extracted from the `stream-studio` Tauri app — `detect.ts` still references the app's `../dom`, `../ui`, `../api` modules.
+
+## Excluded
+`song_detection_cn.py` (an ACRCloud-style fingerprint *song-identification* API client)
+was **not** included — it identifies/names a track rather than detecting singing segments.

v1_svd_prototype/sing_detect.py

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+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")

v2_transformers_gui/sing_detect.py

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+import customtkinter as ctk
+import tkinter as tk
+from tkinter import filedialog, messagebox
+import os
+import csv
+from datetime import timedelta
+
+import math
+import librosa
+import soundfile as sf
+
+from transformers import pipeline
+from pyannote.core import Segment
+
+# ------------------------
+# Time formatting function
+# ------------------------
+def seconds_to_timecode(total_seconds):
+    """
+    Convert float seconds -> "HH:MM:SS.mmm" for DaVinci Resolve CSV markers.
+    """
+    td = timedelta(seconds=total_seconds)
+    hours, remainder = divmod(td.seconds, 3600)
+    minutes, secs = divmod(remainder, 60)
+    ms = int(td.microseconds / 1000)
+    return f"{hours:02d}:{minutes:02d}:{secs:02d}.{ms:03d}"
+
+def chunk_audio(audio_path, chunk_length_s=10.0, stride_s=5.0, sr=16000):
+    """
+    Loads the entire audio with librosa, then yields overlapping chunks
+    (chunk_data, chunk_start, chunk_end, sr).
+
+    - chunk_length_s = length (seconds) of each chunk
+    - stride_s       = how much we "step back" within each chunk for overlap
+                       e.g. chunk_length=10, stride=5 => chunk #0 covers
+                       0-10s, chunk #1 covers 5-15s, chunk #2 covers 10-20s, etc.
+    - sr            = sampling rate to load the audio
+    """
+    audio, sr = librosa.load(audio_path, sr=sr)
+    total_len_s = len(audio) / sr
+
+    # Step in seconds, between the start points of consecutive chunks
+    # e.g. for chunk=10, stride=5 => step=10 - 5=5
+    # means next chunk starts 5s after the previous chunk start
+    step = chunk_length_s - stride_s
+
+    # Edge case: if stride_s >= chunk_length_s, you won't have overlap
+    if step <= 0:
+        step = chunk_length_s  # no overlap
+
+    # Figure out how many chunks needed so we don’t exceed total length
+    # (a bit of math to ensure we handle trailing audio < chunk_length_s)
+    num_chunks = math.ceil((total_len_s - chunk_length_s) / step) + 1
+    if num_chunks < 1:
+        num_chunks = 1
+
+    for i in range(num_chunks):
+        chunk_start = i * step
+        chunk_end   = chunk_start + chunk_length_s
+
+        # If the chunk goes beyond total_len_s, clamp it
+        if chunk_end > total_len_s:
+            chunk_end = total_len_s
+
+        # Convert times to sample indexes
+        start_sample = int(chunk_start * sr)
+        end_sample   = int(chunk_end * sr)
+
+        # If our chunk_start >= total_len_s, we can stop
+        if chunk_start >= total_len_s:
+            break
+
+        # Slice the waveform
+        chunk_data = audio[start_sample:end_sample]
+
+        yield chunk_data, chunk_start, chunk_end, sr
+
+class MusicMarkerApp(ctk.CTk):
+    def __init__(self):
+        super().__init__()
+
+        self.title("Music Marker Generator")
+        self.geometry("500x350")
+
+        # Variables to hold file paths & threshold
+        self.audio_file_path = tk.StringVar(value="")
+        self.output_csv_path = tk.StringVar(value="")
+        self.merge_threshold_var = tk.DoubleVar(value=2.0)  # default: merge segments within 2 seconds
+        self.pipeline = None  # We'll load it once on demand
+
+        self._create_widgets()
+
+    def _create_widgets(self):
+        # 1) Frame for selecting audio file
+        file_frame = ctk.CTkFrame(self)
+        file_frame.pack(pady=10, padx=10, fill="x")
+
+        file_label = ctk.CTkLabel(file_frame, text="Audio File:")
+        file_label.pack(side="left", padx=5)
+
+        file_entry = ctk.CTkEntry(file_frame, textvariable=self.audio_file_path, width=250)
+        file_entry.pack(side="left", padx=5)
+
+        file_button = ctk.CTkButton(file_frame, text="Browse", command=self._browse_audio_file)
+        file_button.pack(side="left", padx=5)
+
+        # 2) Frame for output CSV
+        output_frame = ctk.CTkFrame(self)
+        output_frame.pack(pady=10, padx=10, fill="x")
+
+        output_label = ctk.CTkLabel(output_frame, text="Output CSV:")
+        output_label.pack(side="left", padx=5)
+
+        output_entry = ctk.CTkEntry(output_frame, textvariable=self.output_csv_path, width=250)
+        output_entry.pack(side="left", padx=5)
+
+        output_button = ctk.CTkButton(output_frame, text="Browse", command=self._browse_output_csv)
+        output_button.pack(side="left", padx=5)
+
+        # 3) Merge Threshold
+        threshold_frame = ctk.CTkFrame(self)
+        threshold_frame.pack(pady=10, padx=10, fill="x")
+
+        threshold_label = ctk.CTkLabel(threshold_frame, text="Merge Gap (sec):")
+        threshold_label.pack(side="left", padx=5)
+
+        threshold_entry = ctk.CTkEntry(threshold_frame, textvariable=self.merge_threshold_var, width=50)
+        threshold_entry.pack(side="left", padx=5)
+
+        # 4) Run button
+        run_button = ctk.CTkButton(self, text="Run Music Detection", command=self._run_detection)
+        run_button.pack(pady=10)
+
+        # 5) Status label
+        self.status_label = ctk.CTkLabel(self, text="", wraplength=400, justify="left")
+        self.status_label.pack(pady=5)
+
+    def _browse_audio_file(self):
+        file_path = filedialog.askopenfilename(
+            title="Select Audio File",
+            filetypes=[("Audio Files", "*.wav *.mp3 *.flac *.m4a *.aac *.ogg *.wma *.aif *.aiff")]
+        )
+        if file_path:
+            self.audio_file_path.set(file_path)
+
+    def _browse_output_csv(self):
+        file_path = filedialog.asksaveasfilename(
+            title="Select Output CSV",
+            defaultextension=".csv",
+            filetypes=[("CSV Files", "*.csv")]
+        )
+        if file_path:
+            self.output_csv_path.set(file_path)
+
+    def _run_detection(self):
+        audio_path = self.audio_file_path.get().strip()
+        output_csv = self.output_csv_path.get().strip()
+        merge_threshold = self.merge_threshold_var.get()
+
+        if not audio_path or not os.path.isfile(audio_path):
+            messagebox.showerror("Error", "Please select a valid audio file.")
+            return
+        if not output_csv:
+            messagebox.showerror("Error", "Please specify an output CSV file.")
+            return
+
+        self._set_status("Loading model, please wait...")
+
+        # Load pipeline if not loaded yet
+        if self.pipeline is None:
+            try:
+                # Standard audio-classification pipeline
+                self.pipeline = pipeline(
+                    "audio-classification",
+                    model="MarekCech/GenreVim-Music-Detection-DistilHuBERT"
+                )
+            except Exception as e:
+                messagebox.showerror("Model Error", f"Could not load the model:\n{e}")
+                return
+
+        self._set_status("Chunking audio and running music detection...")
+
+        # We'll collect "music" segments from each chunk
+        music_segments = []
+
+        # Manually chunk the audio & classify each chunk
+        try:
+            for chunk_data, chunk_start, chunk_end, sr in chunk_audio(
+                audio_path, 
+                chunk_length_s=10.0, 
+                stride_s=5.0, 
+                sr=16000
+            ):
+                # The pipeline expects a waveform plus sampling_rate, not a path
+                # So pass the chunk_data + sr
+                results = self.pipeline(chunk_data, sampling_rate=sr)
+                if not results:
+                    continue
+
+                # best guess from the pipeline for this chunk
+                best_guess = max(results, key=lambda x: x["score"])
+                if best_guess["label"].lower() == "music" and best_guess["score"] > 0.5:
+                    music_segments.append(Segment(chunk_start, chunk_end))
+
+        except Exception as e:
+            messagebox.showerror("Detection Error", f"Error running music detection:\n{e}")
+            return
+
+        # Sort segments by start time
+        music_segments.sort(key=lambda seg: seg.start)
+
+        # Merge close segments based on threshold
+        merged_segments = []
+        if not music_segments:
+            self._set_status("No music segments found. No CSV generated.")
+            return
+        else:
+            current_start = music_segments[0].start
+            current_end   = music_segments[0].end
+
+            for seg in music_segments[1:]:
+                # If the new segment starts within X seconds of the old segment, merge them
+                if seg.start <= current_end + merge_threshold:
+                    current_end = max(current_end, seg.end)
+                else:
+                    merged_segments.append(Segment(current_start, current_end))
+                    current_start = seg.start
+                    current_end   = seg.end
+
+            # finalize last
+            merged_segments.append(Segment(current_start, current_end))
+
+        # Write CSV for DaVinci Resolve
+        try:
+            with open(output_csv, "w", newline="", encoding="utf-8") as csvfile:
+                writer = csv.writer(csvfile)
+                # DaVinci Resolve CSV marker columns
+                writer.writerow(["Name", "Start", "End", "Color", "Marker Type"])
+
+                for i, seg in enumerate(merged_segments, start=1):
+                    start_tc = seconds_to_timecode(seg.start)
+                    end_tc   = seconds_to_timecode(seg.end)
+                    name = f"Music Segment #{i}"
+                    color = "Green"
+                    marker_type = "Comment"
+                    writer.writerow([name, start_tc, end_tc, color, marker_type])
+
+            self._set_status(
+                f"Done! Found {len(merged_segments)} music segments.\n"
+                f"CSV saved to: {output_csv}"
+            )
+        except Exception as e:
+            messagebox.showerror("File Error", f"Could not write CSV:\n{e}")
+
+    def _set_status(self, msg):
+        self.status_label.configure(text=msg)
+        self.update_idletasks()
+
+if __name__ == "__main__":
+    app = MusicMarkerApp()
+    app.mainloop()

v3_inaseg_cli/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`) |

v3_inaseg_cli/setup_windows.bat

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+@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

v4_inaseg_batch/detect_singing.py

@@ -0,0 +1,598 @@
+#!/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()

v5_stream_tools_app/core/exporters.py

@@ -0,0 +1,188 @@
+"""
+Export singing segments to various formats:
+  - EDL (DaVinci Resolve timeline import)
+  - CSV (human-readable)
+  - DaVinci Resolve Markers CSV
+  - JSON (programmatic)
+  - ffmpeg concat script (auto-cut)
+"""
+
+import csv
+import json
+import os
+import sys
+from pathlib import Path
+
+from utils.formats import format_timecode, seconds_to_smpte
+
+
+def export_edl(segments: list, output_path: str, fps: float = 29.97,
+               title: str = "Singing Segments", source_filename: str = None):
+    """Export EDL file for DaVinci Resolve."""
+    if source_filename:
+        clip_name = source_filename
+        reel_name_safe = Path(source_filename).stem[:32].replace(" ", "_")
+    else:
+        clip_name = None
+        reel_name_safe = "001"
+
+    rec_offset = 3600.0
+
+    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_smpte(seg["start"], fps)
+            src_out = seconds_to_smpte(seg["end"], fps)
+            rec_in = seconds_to_smpte(current_rec_pos, fps)
+            rec_out = seconds_to_smpte(current_rec_pos + seg["duration"], fps)
+
+            f.write(f"{edit_num}  {reel_name_safe}  V     C        {src_in} {src_out} {rec_in} {rec_out}\n")
+
+            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"]
+
+
+def export_csv(segments: list, output_path: str):
+    """Export singing segments as CSV."""
+    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"]),
+            ])
+
+
+def export_markers_csv(segments: list, output_path: str, fps: float = 29.97):
+    """Export DaVinci Resolve Markers CSV."""
+    with open(output_path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.writer(f)
+        writer.writerow(["#", "Color", "Name", "Start TC", "End TC", "Duration TC", "Notes"])
+        for seg in segments:
+            writer.writerow([
+                seg["index"],
+                "Blue",
+                f"Song {seg['index']}",
+                seconds_to_smpte(seg["start"], fps),
+                seconds_to_smpte(seg["end"], fps),
+                seconds_to_smpte(seg["duration"], fps),
+                f"Duration: {seg['duration']:.0f}s",
+            ])
+
+
+def export_json(segments: list, output_path: str):
+    """Export as JSON."""
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(segments, f, indent=2, ensure_ascii=False)
+
+
+def export_ffmpeg_script(segments: list, video_path: str, output_dir: str):
+    """
+    Export a script that uses ffmpeg to cut and concatenate all
+    singing segments into a single video file.
+    """
+    video_name = Path(video_path).stem
+    ext = Path(video_path).suffix
+    out_video = str(Path(output_dir) / 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_dir) / f"{video_name}_autocut{script_ext}")
+    concat_list_path = str(Path(output_dir) / f"{video_name}_concat_list.txt")
+
+    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("")
+
+    segment_files = []
+    for seg in segments:
+        seg_file = f"_seg_{seg['index']:03d}{ext}"
+        seg_path = str(Path(output_dir) / seg_file)
+        segment_files.append(seg_file)
+
+        cmd = (f'ffmpeg -y -ss {seg["start"]:.2f} -i "{video_path}" '
+               f'-t {seg["duration"]:.2f} -c copy "{seg_path}"')
+        lines.append(f"echo Extracting Song {seg['index']}...")
+        lines.append(cmd)
+        lines.append("")
+
+    lines.append("echo Creating concat list...")
+    if is_windows:
+        lines.append("(")
+        for sf in segment_files:
+            seg_path = str(Path(output_dir) / 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_dir) / sf)
+            op = ">" if i == 0 else ">>"
+            lines.append(f"echo \"file '{seg_path}'\" {op} \"{concat_list_path}\"")
+
+    lines.append("")
+    lines.append("echo Concatenating all segments...")
+    lines.append(f'ffmpeg -y -f concat -safe 0 -i "{concat_list_path}" -c copy "{out_video}"')
+    lines.append("")
+    lines.append("echo Cleaning up temp files...")
+
+    for sf in segment_files:
+        seg_path = str(Path(output_dir) / 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)
+
+    return script_path
+
+
+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),
+            ])

v5_stream_tools_app/core/fftools.py

@@ -0,0 +1,99 @@
+"""
+ffmpeg / ffprobe discovery and probing helpers.
+Shared by both concat and singing detection features.
+"""
+
+import json
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+# ── Suppress console windows on Windows ──────────────────────────────────────
+_NO_WINDOW: dict = {}
+if sys.platform == "win32":
+    _NO_WINDOW["creationflags"] = subprocess.CREATE_NO_WINDOW
+
+
+def app_dir() -> str:
+    if getattr(sys, "frozen", False):
+        return os.path.dirname(sys.executable)
+    return os.path.dirname(os.path.abspath(__file__))
+
+
+def find_tool(name: str) -> str | None:
+    """Locate ffmpeg / ffprobe next to the exe / script, then on PATH."""
+    base = app_dir()
+    for sub in ["_internal", "..", ""]:
+        for variant in [name + ".exe", name]:
+            p = os.path.join(base, sub, variant)
+            if os.path.exists(p):
+                return os.path.abspath(p)
+    return shutil.which(name + ".exe") or shutil.which(name)
+
+
+def probe_duration(path: str, ffprobe_bin: str | None = None) -> float:
+    """Get video/audio duration in seconds via ffprobe."""
+    ffprobe = ffprobe_bin or find_tool("ffprobe")
+    if not ffprobe:
+        return 0.0
+    try:
+        r = subprocess.run(
+            [ffprobe, "-v", "error",
+             "-show_entries", "format=duration",
+             "-of", "json", path],
+            stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+            text=True, timeout=20,
+            **_NO_WINDOW,
+        )
+        if r.returncode != 0:
+            return 0.0
+        data = json.loads(r.stdout or "{}")
+        return max(0.0, float(data.get("format", {}).get("duration", 0) or 0))
+    except Exception:
+        return 0.0
+
+
+def extract_audio(video_path: str, output_wav: str,
+                  ffmpeg_bin: str | None = None,
+                  progress_cb=None) -> str:
+    """
+    Extract audio from video file as 16kHz mono WAV.
+    Required format for inaSpeechSegmenter.
+    
+    progress_cb: optional callable(message: str) for status updates.
+    """
+    ffmpeg = ffmpeg_bin or find_tool("ffmpeg")
+    if not ffmpeg:
+        raise FileNotFoundError("ffmpeg not found. Install ffmpeg and add to PATH.")
+
+    if progress_cb:
+        progress_cb(f"Extracting audio from: {Path(video_path).name}")
+
+    cmd = [
+        ffmpeg,
+        "-i", video_path,
+        "-vn",
+        "-acodec", "pcm_s16le",
+        "-ar", "16000",
+        "-ac", "1",
+        "-y",
+        output_wav,
+    ]
+
+    result = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+        timeout=1800,
+        **_NO_WINDOW,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"ffmpeg failed:\n{result.stderr[-500:]}")
+
+    if progress_cb:
+        size_mb = os.path.getsize(output_wav) / (1024 * 1024)
+        progress_cb(f"Audio extracted: {size_mb:.1f} MB")
+
+    return output_wav

v5_stream_tools_app/core/segmenter.py

@@ -0,0 +1,118 @@
+"""
+Singing segment detection via inaSpeechSegmenter.
+
+Pipeline:
+  1. Run inaSpeechSegmenter to classify speech/music/noise
+  2. Filter for 'music' segments (singing voice is classified as music)
+  3. Merge nearby segments, apply padding, filter by minimum duration
+"""
+
+import time
+from utils.formats import format_timecode
+
+
+def check_segmenter_available() -> tuple[bool, str]:
+    """Check if inaSpeechSegmenter is installed."""
+    try:
+        from inaSpeechSegmenter import Segmenter  # noqa: F401
+        return True, ""
+    except ImportError:
+        return False, (
+            "inaSpeechSegmenter is not installed.\n\n"
+            "Install it with:\n"
+            "  pip install inaSpeechSegmenter\n\n"
+            "You also need TensorFlow:\n"
+            "  pip install tensorflow[and-cuda]   (GPU)\n"
+            "  pip install tensorflow              (CPU)"
+        )
+
+
+def run_segmentation(wav_path: str, progress_cb=None) -> list:
+    """
+    Run inaSpeechSegmenter on audio file.
+    Returns list of (label, start_sec, end_sec) tuples.
+    Labels: 'music', 'speech', 'noise', 'noEnergy'
+    """
+    from inaSpeechSegmenter import Segmenter
+
+    if progress_cb:
+        progress_cb("Loading segmentation model…")
+
+    start_time = time.time()
+    seg = Segmenter(vad_engine='smn', detect_gender=False)
+
+    if progress_cb:
+        progress_cb("Running audio segmentation (this may take a while)…")
+
+    segments = seg(wav_path)
+    elapsed = time.time() - start_time
+
+    if progress_cb:
+        progress_cb(f"Segmentation complete in {elapsed:.1f}s — {len(segments)} raw segments")
+
+    return 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 and merge nearby ones.
+    
+    Returns list of dicts:
+        [{"index", "start", "end", "duration", "original_start", "original_end"}, ...]
+    """
+    music_segs = [(start, end) for label, start, end in segments if label == "music"]
+
+    if not music_segs:
+        return []
+
+    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:
+            current_end = max(current_end, end)
+        else:
+            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
+
+    return results
+
+
+def get_segment_stats(segments: list) -> dict:
+    """Get summary statistics from raw segmentation output."""
+    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)
+    return {
+        "counts": type_counts,
+        "durations": type_durations,
+    }

v5_stream_tools_app/ui/tab_detect.py

@@ -0,0 +1,505 @@
+"""
+Tab 2: Song Detection
+Detect singing segments in a video and export EDL/CSV/JSON for DaVinci Resolve.
+"""
+
+import os
+import threading
+import tkinter as tk
+from pathlib import Path
+from tkinter import filedialog, messagebox
+
+import customtkinter as ctk
+
+from core.fftools import find_tool, extract_audio
+from core.segmenter import check_segmenter_available, run_segmentation, merge_singing_segments, get_segment_stats
+from core.exporters import export_edl, export_csv, export_markers_csv, export_json, export_ffmpeg_script
+from utils.formats import format_timecode, fmt_dur, fmt_elapsed
+from ui.theme import BTN_NEUTRAL, ACCENT_PURPLE, ACCENT_GREEN
+
+
+class DetectTab:
+    """Song detection tab."""
+
+    VIDEO_EXTS = {".mp4", ".mov", ".mkv", ".avi", ".flv",
+                  ".ts", ".wmv", ".m4v", ".webm", ".mts", ".m2ts"}
+
+    def __init__(self, parent_frame, app):
+        self.frame = parent_frame
+        self.app = app
+        self.ffmpeg = app.ffmpeg
+
+        # State
+        self.input_path_var = tk.StringVar(value="")
+        self.output_dir_var = tk.StringVar(value="")
+        self.gap_var = tk.DoubleVar(value=10.0)
+        self.min_dur_var = tk.DoubleVar(value=20.0)
+        self.padding_var = tk.DoubleVar(value=2.0)
+        self.fps_var = tk.DoubleVar(value=29.97)
+        self.autocut_var = tk.BooleanVar(value=False)
+
+        self._running = False
+        self._stop_req = False
+
+        # Results
+        self._singing_segments = []
+        self._raw_segments = []
+
+        self._build_ui()
+        self._check_deps()
+
+    # ══════════════════════════════════════════════════════════════════════════
+    #  UI
+    # ══════════════════════════════════════════════════════════════════════════
+
+    def _build_ui(self):
+        scroll = ctk.CTkScrollableFrame(self.frame, corner_radius=12)
+        scroll.pack(fill="both", expand=True, padx=4, pady=4)
+
+        self._build_sec_input(scroll)
+        self._build_sec_params(scroll)
+        self._build_sec_run(scroll)
+        self._build_sec_results(scroll)
+
+    def _section(self, parent, title):
+        f = ctk.CTkFrame(parent, corner_radius=12)
+        hdr = ctk.CTkFrame(f, fg_color="transparent")
+        hdr.pack(fill="x", padx=14, pady=(10, 0))
+        ctk.CTkLabel(hdr, text=title,
+                     font=ctk.CTkFont(size=13, weight="bold")).pack(side="left")
+        ctk.CTkFrame(f, height=1, fg_color=("gray78", "gray32")).pack(
+            fill="x", padx=14, pady=(6, 0))
+        return f
+
+    # ── Input ─────────────────────────────────────────────────────────────────
+
+    def _build_sec_input(self, parent):
+        sec = self._section(parent, "1 · Input Video")
+        sec.pack(fill="x", pady=(0, 12))
+
+        body = ctk.CTkFrame(sec, fg_color="transparent")
+        body.pack(fill="x", padx=14, pady=(10, 4))
+
+        r1 = ctk.CTkFrame(body, fg_color="transparent")
+        r1.pack(fill="x", pady=(0, 4))
+        ctk.CTkLabel(r1, text="Video file:", width=96, anchor="w").pack(side="left")
+        ctk.CTkEntry(r1, textvariable=self.input_path_var).pack(side="left", fill="x", expand=True)
+        ctk.CTkButton(r1, text="Browse…", width=100, command=self._pick_input,
+                      **BTN_NEUTRAL).pack(side="left", padx=(8, 0))
+
+        # "Use Concat Output" button
+        self.use_concat_btn = ctk.CTkButton(
+            body, text="📎  Use Concat Output", width=200,
+            fg_color=ACCENT_PURPLE["fg"], text_color=ACCENT_PURPLE["text"],
+            hover_color=ACCENT_PURPLE["hover"],
+            command=self._use_concat_output)
+        self.use_concat_btn.pack(anchor="w", pady=(4, 4))
+
+        r2 = ctk.CTkFrame(body, fg_color="transparent")
+        r2.pack(fill="x", pady=(4, 0))
+        ctk.CTkLabel(r2, text="Output dir:", width=96, anchor="w").pack(side="left")
+        ctk.CTkEntry(r2, textvariable=self.output_dir_var).pack(side="left", fill="x", expand=True)
+        ctk.CTkButton(r2, text="Browse…", width=100, command=self._pick_output_dir,
+                      **BTN_NEUTRAL).pack(side="left", padx=(8, 0))
+
+        ctk.CTkLabel(sec, text="Output defaults to same directory as input video.",
+                     font=ctk.CTkFont(size=11), text_color=("gray50", "gray55")
+                     ).pack(anchor="w", padx=14, pady=(4, 10))
+
+    # ── Parameters ────────────────────────────────────────────────────────────
+
+    def _build_sec_params(self, parent):
+        sec = self._section(parent, "2 · Detection Parameters")
+        sec.pack(fill="x", pady=(0, 12))
+
+        body = ctk.CTkFrame(sec, fg_color="transparent")
+        body.pack(fill="x", padx=14, pady=(10, 10))
+
+        params = [
+            ("Merge gap (s):",     self.gap_var,     1, 60,  "Max silence between song parts to merge"),
+            ("Min duration (s):",  self.min_dur_var,  5, 120, "Ignore segments shorter than this"),
+            ("Padding (s):",       self.padding_var,  0, 15,  "Extra seconds before/after each song"),
+            ("FPS:",               self.fps_var,      23, 60, "Video frame rate for timecodes"),
+        ]
+
+        for label_text, var, lo, hi, tooltip in params:
+            row = ctk.CTkFrame(body, fg_color="transparent")
+            row.pack(fill="x", pady=3)
+
+            ctk.CTkLabel(row, text=label_text, width=130, anchor="w",
+                         font=ctk.CTkFont(size=12)).pack(side="left")
+
+            slider = ctk.CTkSlider(row, from_=lo, to=hi, variable=var,
+                                    width=200, number_of_steps=max(1, hi - lo))
+            slider.pack(side="left", padx=(0, 8))
+
+            val_lbl = ctk.CTkLabel(row, text=f"{var.get():.1f}", width=50, anchor="w",
+                                    font=ctk.CTkFont(size=12, weight="bold"))
+            val_lbl.pack(side="left")
+
+            ctk.CTkLabel(row, text=tooltip, font=ctk.CTkFont(size=11),
+                         text_color=("gray50", "gray55")).pack(side="left", padx=(10, 0))
+
+            # Update label on slider move
+            var.trace_add("write", lambda *_, v=var, l=val_lbl: l.configure(text=f"{v.get():.1f}"))
+
+        # Auto-cut checkbox
+        ctk.CTkCheckBox(body, text="Generate auto-cut ffmpeg script (no DaVinci needed)",
+                        variable=self.autocut_var).pack(anchor="w", pady=(8, 0))
+
+    # ── Run ───────────────────────────────────────────────────────────────────
+
+    def _build_sec_run(self, parent):
+        sec = self._section(parent, "3 · Detect")
+        sec.pack(fill="x", pady=(0, 12))
+
+        body = ctk.CTkFrame(sec, fg_color="transparent")
+        body.pack(fill="x", padx=14, pady=(12, 6))
+
+        btn_row = ctk.CTkFrame(body, fg_color="transparent")
+        btn_row.pack(fill="x", pady=(0, 10))
+
+        self.detect_btn = ctk.CTkButton(
+            btn_row, text="🎤  Start Detection", width=230, height=42,
+            font=ctk.CTkFont(size=14, weight="bold"),
+            fg_color=ACCENT_GREEN["fg"], hover_color=ACCENT_GREEN["hover"],
+            command=self.start_detect)
+        self.detect_btn.pack(side="left")
+
+        self.stop_btn = ctk.CTkButton(
+            btn_row, text="■  Stop", width=106, height=42,
+            fg_color=("gray80", "gray25"), text_color=("gray10", "gray90"),
+            hover_color=("#FCA5A5", "#7F1D1D"),
+            command=self._stop, state="disabled")
+        self.stop_btn.pack(side="left", padx=(12, 0))
+
+        # Dependency warning
+        self.dep_lbl = ctk.CTkLabel(body, text="", font=ctk.CTkFont(size=11),
+                                     text_color="#F97316", wraplength=600, anchor="w", justify="left")
+        self.dep_lbl.pack(fill="x", pady=(0, 4))
+
+        # Progress
+        self.progress = ctk.CTkProgressBar(body, height=16, corner_radius=8, mode="indeterminate")
+        self.progress.pack(fill="x", pady=(0, 6))
+        self.progress.stop()
+        self.progress.set(0)
+
+        self.status_lbl = ctk.CTkLabel(body, text="", anchor="w", font=ctk.CTkFont(size=12))
+        self.status_lbl.pack(fill="x")
+
+        ctk.CTkFrame(sec, height=6, fg_color="transparent").pack()
+
+    # ── Results ───────────────────────────────────────────────────────────────
+
+    def _build_sec_results(self, parent):
+        sec = self._section(parent, "4 · Results")
+        sec.pack(fill="x", pady=(0, 14))
+
+        self.results_body = ctk.CTkFrame(sec, fg_color="transparent")
+        self.results_body.pack(fill="x", padx=14, pady=(10, 10))
+
+        self.results_lbl = ctk.CTkLabel(
+            self.results_body, text="No results yet. Run detection first.",
+            font=ctk.CTkFont(size=12), text_color=("gray50", "gray55"))
+        self.results_lbl.pack(anchor="w")
+
+        # Song list (populated after detection)
+        self.song_list_frame = ctk.CTkFrame(self.results_body, fg_color="transparent")
+
+        # Export buttons (hidden until results)
+        self.export_frame = ctk.CTkFrame(sec, fg_color="transparent")
+
+    # ══════════════════════════════════════════════════════════════════════════
+    #  Logic
+    # ══════════════════════════════════════════════════════════════════════════
+
+    def _check_deps(self):
+        available, msg = check_segmenter_available()
+        if not available:
+            self.dep_lbl.configure(text=f"⚠  {msg}")
+            self.detect_btn.configure(state="disabled")
+        else:
+            self.dep_lbl.configure(text="")
+
+        if not self.ffmpeg:
+            self.dep_lbl.configure(text="⚠  ffmpeg not found. Install ffmpeg and add to PATH.")
+            self.detect_btn.configure(state="disabled")
+
+    def _pick_input(self):
+        f = filedialog.askopenfilename(
+            title="Select video file",
+            filetypes=[("Video files", "*.mp4 *.mov *.mkv *.avi *.flv *.ts *.wmv *.m4v *.webm *.mts"),
+                       ("All files", "*.*")])
+        if f:
+            self.input_path_var.set(f)
+            if not self.output_dir_var.get().strip():
+                self.output_dir_var.set(str(Path(f).parent))
+
+    def _pick_output_dir(self):
+        d = filedialog.askdirectory(title="Choose output folder")
+        if d:
+            self.output_dir_var.set(d)
+
+    def _use_concat_output(self):
+        """Fill input from the Concat tab's output path."""
+        if hasattr(self.app, 'concat_tab'):
+            path = self.app.concat_tab.get_last_output_path()
+            if path and Path(path).exists():
+                self.input_path_var.set(path)
+                if not self.output_dir_var.get().strip():
+                    self.output_dir_var.set(str(Path(path).parent))
+                self._set_status(f"Loaded concat output: {Path(path).name}")
+            elif path:
+                self.input_path_var.set(path)
+                if not self.output_dir_var.get().strip():
+                    self.output_dir_var.set(str(Path(path).parent))
+                self._set_status("Concat output path set (file not yet created — run concat first)")
+            else:
+                messagebox.showinfo("No output", "Set an output path in the Concat tab first.")
+
+    def _set_status(self, text, error=False):
+        color = "#DC2626" if error else ("gray10", "gray90")
+        self.status_lbl.configure(text=text, text_color=color)
+
+    def _stop(self):
+        self._stop_req = True
+        self._set_status("Stopping…")
+
+    def start_detect(self):
+        """Start the detection pipeline in a background thread."""
+        input_path = self.input_path_var.get().strip()
+        if not input_path:
+            messagebox.showerror("No input", "Select a video file first.")
+            return
+        if not os.path.isfile(input_path):
+            messagebox.showerror("File not found", f"Cannot find:\n{input_path}")
+            return
+
+        available, msg = check_segmenter_available()
+        if not available:
+            messagebox.showerror("Missing dependency", msg)
+            return
+
+        self._running = True
+        self._stop_req = False
+        self.detect_btn.configure(state="disabled")
+        self.stop_btn.configure(state="normal")
+        self.progress.configure(mode="indeterminate")
+        self.progress.start()
+        self._set_status("Starting detection…")
+
+        threading.Thread(target=self._detect_worker, args=(input_path,), daemon=True).start()
+
+    def _detect_worker(self, input_path):
+        output_dir = self.output_dir_var.get().strip() or str(Path(input_path).parent)
+        os.makedirs(output_dir, exist_ok=True)
+        stem = Path(input_path).stem
+
+        wav_path = os.path.join(output_dir, f"{stem}_audio.wav")
+
+        try:
+            # Step 1: Extract audio
+            def progress_cb(msg):
+                self.app.after(0, lambda: self._set_status(msg))
+
+            extract_audio(input_path, wav_path, self.ffmpeg, progress_cb=progress_cb)
+
+            if self._stop_req:
+                self._cleanup_and_finish(wav_path, "Stopped by user.")
+                return
+
+            # Step 2: Run segmentation
+            self.app.after(0, lambda: self._set_status("Running audio segmentation (this may take a while)…"))
+            raw_segments = run_segmentation(wav_path, progress_cb=progress_cb)
+
+            if self._stop_req:
+                self._cleanup_and_finish(wav_path, "Stopped by user.")
+                return
+
+            self._raw_segments = raw_segments
+
+            # Step 3: Merge
+            self.app.after(0, lambda: self._set_status("Merging singing segments…"))
+            singing = merge_singing_segments(
+                raw_segments,
+                max_gap=self.gap_var.get(),
+                min_duration=self.min_dur_var.get(),
+                padding=self.padding_var.get(),
+            )
+            self._singing_segments = singing
+
+            # Step 4: Export
+            if singing:
+                fps = self.fps_var.get()
+                source_name = Path(input_path).name
+
+                edl_path = os.path.join(output_dir, f"{stem}_singing.edl")
+                csv_path = os.path.join(output_dir, f"{stem}_singing.csv")
+                markers_path = os.path.join(output_dir, f"{stem}_markers.csv")
+                json_path = os.path.join(output_dir, f"{stem}_singing.json")
+
+                export_edl(singing, edl_path, fps=fps,
+                          title=f"{stem} - Singing", source_filename=source_name)
+                export_csv(singing, csv_path)
+                export_markers_csv(singing, markers_path, fps=fps)
+                export_json(singing, json_path)
+
+                if self.autocut_var.get():
+                    export_ffmpeg_script(singing, input_path, output_dir)
+
+                total_singing = sum(s["duration"] for s in singing)
+                result_msg = (f"✓  Found {len(singing)} songs  ·  "
+                              f"Total singing: {format_timecode(total_singing)}  ·  "
+                              f"Exported to: {output_dir}")
+
+                self.app.after(0, lambda: self._show_results(singing, edl_path, output_dir))
+            else:
+                result_msg = "No singing segments detected. Try lowering min duration or increasing gap."
+
+            # Cleanup WAV
+            if os.path.exists(wav_path):
+                os.remove(wav_path)
+
+            self.app.after(0, lambda: self._set_status(result_msg))
+
+        except Exception as e:
+            self.app.after(0, lambda: self._set_status(f"Error: {e}", error=True))
+            if os.path.exists(wav_path):
+                try:
+                    os.remove(wav_path)
+                except Exception:
+                    pass
+
+        self.app.after(0, self._finish_detect)
+
+    def _cleanup_and_finish(self, wav_path, msg):
+        if os.path.exists(wav_path):
+            try:
+                os.remove(wav_path)
+            except Exception:
+                pass
+        self.app.after(0, lambda: self._set_status(msg))
+        self.app.after(0, self._finish_detect)
+
+    def _finish_detect(self):
+        self._running = False
+        self._stop_req = False
+        self.detect_btn.configure(state="normal")
+        self.stop_btn.configure(state="disabled")
+        self.progress.stop()
+        self.progress.configure(mode="determinate")
+        self.progress.set(1.0 if self._singing_segments else 0)
+
+    def _show_results(self, segments, edl_path, output_dir):
+        """Display detection results in the Results section."""
+        # Clear previous
+        for w in self.song_list_frame.winfo_children():
+            w.destroy()
+        self.export_frame.pack_forget()
+
+        self.results_lbl.configure(
+            text=f"Found {len(segments)} singing segments:",
+            text_color=("gray10", "gray90"))
+
+        self.song_list_frame.pack(fill="x", pady=(8, 0))
+
+        # Header
+        hdr = ctk.CTkFrame(self.song_list_frame, fg_color=("gray88", "gray20"), corner_radius=8)
+        hdr.pack(fill="x", pady=(0, 4))
+        for text, w in [("#", 40), ("Start", 90), ("End", 90), ("Duration", 80)]:
+            ctk.CTkLabel(hdr, text=text, width=w, anchor="center",
+                         font=ctk.CTkFont(size=11, weight="bold")).pack(side="left", padx=4, pady=4)
+
+        # Rows
+        for seg in segments:
+            row = ctk.CTkFrame(self.song_list_frame, corner_radius=6, height=30)
+            row.pack(fill="x", pady=2)
+            row.pack_propagate(False)
+
+            vals = [
+                (f"Song {seg['index']}", 40),
+                (format_timecode(seg["start"]), 90),
+                (format_timecode(seg["end"]), 90),
+                (f"{seg['duration']:.0f}s", 80),
+            ]
+            for text, w in vals:
+                ctk.CTkLabel(row, text=text, width=w, anchor="center",
+                             font=ctk.CTkFont(size=11)).pack(side="left", padx=4)
+
+        # Total
+        total = sum(s["duration"] for s in segments)
+        ctk.CTkLabel(self.song_list_frame,
+                     text=f"Total singing time: {format_timecode(total)}",
+                     font=ctk.CTkFont(size=12, weight="bold")).pack(anchor="w", pady=(8, 0))
+
+        # Export info
+        self.export_frame.pack(fill="x", padx=14, pady=(4, 10))
+        for w in self.export_frame.winfo_children():
+            w.destroy()
+
+        ctk.CTkLabel(self.export_frame,
+                     text=f"Files exported to: {output_dir}",
+                     font=ctk.CTkFont(size=11), text_color=("gray45", "gray55"),
+                     wraplength=600, anchor="w", justify="left").pack(anchor="w")
+
+        info_lines = [
+            "• _singing.edl  →  DaVinci Resolve: File → Import → Timeline",
+            "• _markers.csv  →  DaVinci Resolve: Timeline → Import Markers from CSV",
+            "• _singing.csv  →  Human-readable segment list",
+            "• _singing.json →  Programmatic use",
+        ]
+        if self.autocut_var.get():
+            info_lines.append("• _autocut.bat  →  Run to auto-cut singing segments (no DaVinci needed)")
+
+        for line in info_lines:
+            ctk.CTkLabel(self.export_frame, text=line,
+                         font=ctk.CTkFont(size=11),
+                         text_color=("gray50", "gray55"), anchor="w").pack(anchor="w")
+
+    # ── Public API for Quick Flow ─────────────────────────────────────────────
+
+    def run_detect_headless(self, input_path: str, output_dir: str,
+                            gap: float, min_dur: float, padding: float,
+                            fps: float, autocut: bool,
+                            progress_cb=None) -> list:
+        """
+        Run detection synchronously (called from Quick Flow worker thread).
+        Returns list of singing segments.
+        """
+        os.makedirs(output_dir, exist_ok=True)
+        stem = Path(input_path).stem
+        wav_path = os.path.join(output_dir, f"{stem}_audio.wav")
+
+        try:
+            extract_audio(input_path, wav_path, self.ffmpeg, progress_cb=progress_cb)
+
+            raw_segments = run_segmentation(wav_path, progress_cb=progress_cb)
+
+            if progress_cb:
+                progress_cb("Merging singing segments…")
+
+            singing = merge_singing_segments(raw_segments,
+                                             max_gap=gap, min_duration=min_dur, padding=padding)
+
+            if singing:
+                source_name = Path(input_path).name
+                export_edl(singing, os.path.join(output_dir, f"{stem}_singing.edl"),
+                          fps=fps, title=f"{stem} - Singing", source_filename=source_name)
+                export_csv(singing, os.path.join(output_dir, f"{stem}_singing.csv"))
+                export_markers_csv(singing, os.path.join(output_dir, f"{stem}_markers.csv"), fps=fps)
+                export_json(singing, os.path.join(output_dir, f"{stem}_singing.json"))
+                if autocut:
+                    export_ffmpeg_script(singing, input_path, output_dir)
+
+            if os.path.exists(wav_path):
+                os.remove(wav_path)
+
+            return singing
+
+        except Exception:
+            if os.path.exists(wav_path):
+                try:
+                    os.remove(wav_path)
+                except Exception:
+                    pass
+            raise

v5_stream_tools_app/ui/theme.py

@@ -0,0 +1,64 @@
+"""
+Theme, DPI, and shared styling constants.
+"""
+
+import sys
+
+# ── DPI awareness (call before any Tk/CTk window) ────────────────────────────
+def setup_dpi():
+    if sys.platform == "win32":
+        try:
+            import ctypes
+            ctypes.windll.shcore.SetProcessDpiAwareness(2)
+        except Exception:
+            try:
+                import ctypes
+                ctypes.windll.user32.SetProcessDPIAware()
+            except Exception:
+                pass
+
+
+def dpi_scale(widget) -> float:
+    try:
+        return max(1.0, widget.winfo_fpixels("1i") / 96.0)
+    except Exception:
+        return 1.0
+
+
+# ── Color accents ─────────────────────────────────────────────────────────────
+# Each feature area has its own accent color scheme: (light_mode, dark_mode)
+
+ACCENT_BLUE = {
+    "fg":    ("#3B82F6", "#2563EB"),
+    "hover": ("#2563EB", "#1D4ED8"),
+}
+
+ACCENT_PURPLE = {
+    "fg":       ("#EDE9FE", "#3B1F6E"),
+    "text":     ("#5B21B6", "#C4B5FD"),
+    "hover":    ("#DDD6FE", "#4C2889"),
+    "preview":  ("#7C3AED", "#A78BFA"),
+}
+
+ACCENT_GREEN = {
+    "fg":    ("#10B981", "#059669"),
+    "hover": ("#059669", "#047857"),
+}
+
+ACCENT_AMBER = {
+    "fg":    ("#F59E0B", "#D97706"),
+    "hover": ("#D97706", "#B45309"),
+}
+
+# Neutral button style
+BTN_NEUTRAL = {
+    "fg_color":    ("gray80", "gray25"),
+    "text_color":  ("gray10", "gray90"),
+    "hover_color": ("gray70", "gray35"),
+}
+
+BTN_SIDE = {
+    "fg_color":    ("gray82", "gray22"),
+    "text_color":  ("gray10", "gray90"),
+    "hover_color": ("gray72", "gray32"),
+}

v5_stream_tools_app/utils/formats.py

@@ -0,0 +1,42 @@
+"""
+Timecode & duration formatting utilities.
+"""
+
+
+def fmt_dur(seconds: float) -> str:
+    """Format a duration as H:MM:SS or M:SS for display."""
+    if seconds < 0:
+        return "…"
+    if seconds == 0:
+        return "—"
+    s = int(round(seconds))
+    h, rem = divmod(s, 3600)
+    m, sec = divmod(rem, 60)
+    return f"{h}:{m:02d}:{sec:02d}" if h else f"{m}:{sec:02d}"
+
+
+def fmt_elapsed(s: float) -> str:
+    if s < 60:
+        return f"{int(s)}s"
+    m, sec = divmod(int(s), 60)
+    return f"{m}m {sec:02d}s"
+
+
+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_smpte(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}"

v6_stream_studio_tauri/detect.rs

@@ -0,0 +1,222 @@
+//! Singing-segment detection.
+//!
+//! Rust owns the fast/IO parts (audio extraction via ffmpeg, progress events,
+//! and all file exports). The Python sidecar owns *only* the ML step
+//! (inaSpeechSegmenter + TensorFlow), which has no native equivalent.
+//!
+//! Protocol: the sidecar prints one JSON object per line to stdout:
+//!   {"type":"progress","message":"…"}
+//!   {"type":"result","segments":[{index,start,end,duration}, …],
+//!                     "stats":{"music":123.4,"speech":456.7,…}}
+//!   {"type":"error","message":"…"}
+
+use std::io::{BufRead, BufReader};
+use std::path::{Path, PathBuf};
+use std::process::{Command, Stdio};
+
+use serde::{Deserialize, Serialize};
+use serde_json::Value;
+use tauri::{AppHandle, Emitter};
+
+use crate::tools::hide_window;
+use crate::AppState;
+
+#[derive(Clone, Debug, Serialize, Deserialize)]
+pub struct Segment {
+    pub index: u32,
+    pub start: f64,
+    pub end: f64,
+    pub duration: f64,
+}
+
+#[derive(Clone, Serialize)]
+pub struct DetectResult {
+    pub segments: Vec<Segment>,
+    pub stats: Value,
+}
+
+#[derive(Clone, Serialize)]
+struct DetectProgress {
+    message: String,
+}
+
+fn emit(app: &AppHandle, message: impl Into<String>) {
+    let _ = app.emit("detect-progress", DetectProgress { message: message.into() });
+}
+
+/// Locate the sidecar script: bundled resources first, dev tree as fallback.
+fn sidecar_script() -> Option<PathBuf> {
+    if let Ok(p) = std::env::var("STREAM_STUDIO_SIDECAR") {
+        let pb = PathBuf::from(p);
+        if pb.exists() {
+            return Some(pb);
+        }
+    }
+    if let Ok(exe) = std::env::current_exe() {
+        if let Some(base) = exe.parent() {
+            for sub in ["sidecar", "resources/sidecar", "../sidecar"] {
+                let cand = base.join(sub).join("detect.py");
+                if cand.exists() {
+                    return Some(cand);
+                }
+            }
+        }
+    }
+    // Dev fallback: <crate>/../sidecar/detect.py
+    let dev = Path::new(env!("CARGO_MANIFEST_DIR")).join("../sidecar/detect.py");
+    if dev.exists() {
+        return Some(dev);
+    }
+    None
+}
+
+/// Build the python invocation. Prefers the Windows `py -3.10` launcher
+/// (TensorFlow does not yet support 3.12+), then a venv, then plain `python`.
+fn python_command(script: &Path) -> Command {
+    if let Ok(custom) = std::env::var("STREAM_STUDIO_PYTHON") {
+        let mut c = Command::new(custom);
+        c.arg(script);
+        return c;
+    }
+    #[cfg(windows)]
+    {
+        // The py launcher lets us pin 3.10 regardless of the default install.
+        let mut c = Command::new("py");
+        c.args(["-3.10"]).arg(script);
+        return c;
+    }
+    #[allow(unreachable_code)]
+    {
+        let mut c = Command::new("python");
+        c.arg(script);
+        c
+    }
+}
+
+/// Extract 16 kHz mono WAV (the format inaSpeechSegmenter expects).
+fn extract_audio(app: &AppHandle, ffmpeg: &str, video: &str, wav: &Path) -> Result<(), String> {
+    emit(app, "Extracting audio (16 kHz mono)…");
+    let mut cmd = Command::new(ffmpeg);
+    cmd.args([
+        "-y", "-i", video,
+        "-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1",
+        "-hide_banner", "-loglevel", "error",
+        &wav.to_string_lossy(),
+    ]);
+    cmd.stdout(Stdio::null()).stderr(Stdio::piped());
+    hide_window(&mut cmd);
+    let out = cmd.output().map_err(|e| format!("ffmpeg audio extract failed: {e}"))?;
+    if !out.status.success() {
+        return Err(format!("Audio extraction failed:\n{}", String::from_utf8_lossy(&out.stderr)));
+    }
+    Ok(())
+}
+
+pub fn run(
+    app: &AppHandle,
+    state: &AppState,
+    ffmpeg: &str,
+    video: String,
+    gap: f64,
+    min_duration: f64,
+    padding: f64,
+) -> Result<DetectResult, String> {
+    state.cancel.store(false, std::sync::atomic::Ordering::SeqCst);
+
+    let script = sidecar_script().ok_or(
+        "Detection sidecar not found. Expected sidecar/detect.py next to the app.",
+    )?;
+
+    // Temp WAV beside the source video.
+    let wav = Path::new(&video)
+        .with_extension("")
+        .to_string_lossy()
+        .to_string();
+    let wav = PathBuf::from(format!("{wav}.stream_studio.wav"));
+
+    extract_audio(app, ffmpeg, &video, &wav)?;
+
+    let mut cmd = python_command(&script);
+    cmd.args([
+        "--wav", &wav.to_string_lossy(),
+        "--gap", &gap.to_string(),
+        "--min-duration", &min_duration.to_string(),
+        "--padding", &padding.to_string(),
+    ]);
+    cmd.stdout(Stdio::piped()).stderr(Stdio::piped());
+    cmd.env("PYTHONUNBUFFERED", "1").env("PYTHONIOENCODING", "utf-8");
+    hide_window(&mut cmd);
+
+    emit(app, "Loading detection model…");
+    let mut child = cmd.spawn().map_err(|e| {
+        format!("Could not start Python sidecar ({e}). Is Python 3.10 + inaSpeechSegmenter installed?")
+    })?;
+    let stdout = child.stdout.take().ok_or("No sidecar stdout")?;
+    let stderr = child.stderr.take();
+    *state.proc.lock().unwrap() = Some(child);
+
+    let mut result: Option<DetectResult> = None;
+    let mut err_msg: Option<String> = None;
+
+    for line in BufReader::new(stdout).lines() {
+        let line = match line {
+            Ok(l) => l,
+            Err(_) => break,
+        };
+        let line = line.trim();
+        if line.is_empty() {
+            continue;
+        }
+        let parsed: Value = match serde_json::from_str(line) {
+            Ok(v) => v,
+            Err(_) => continue, // ignore stray non-JSON output
+        };
+        match parsed.get("type").and_then(|t| t.as_str()) {
+            Some("progress") => {
+                if let Some(m) = parsed.get("message").and_then(|m| m.as_str()) {
+                    emit(app, m);
+                }
+            }
+            Some("result") => {
+                let segments: Vec<Segment> = serde_json::from_value(
+                    parsed.get("segments").cloned().unwrap_or(Value::Array(vec![])),
+                )
+                .unwrap_or_default();
+                let stats = parsed.get("stats").cloned().unwrap_or(Value::Null);
+                result = Some(DetectResult { segments, stats });
+            }
+            Some("error") => {
+                err_msg = parsed
+                    .get("message")
+                    .and_then(|m| m.as_str())
+                    .map(|s| s.to_string());
+            }
+            _ => {}
+        }
+    }
+
+    let mut child = state.proc.lock().unwrap().take().ok_or("Sidecar vanished")?;
+    let status = child.wait().map_err(|e| format!("sidecar wait failed: {e}"))?;
+    let mut stderr_text = String::new();
+    if let Some(mut se) = stderr {
+        use std::io::Read;
+        let _ = se.read_to_string(&mut stderr_text);
+    }
+
+    let _ = std::fs::remove_file(&wav);
+
+    if state.cancel.load(std::sync::atomic::Ordering::SeqCst) {
+        return Err("__cancelled__".into());
+    }
+    if let Some(m) = err_msg {
+        return Err(m);
+    }
+    if let Some(r) = result {
+        return Ok(r);
+    }
+    if !status.success() {
+        let tail: String = stderr_text.chars().rev().take(800).collect::<String>().chars().rev().collect();
+        return Err(format!("Detection failed.\n{tail}"));
+    }
+    Err("Detection produced no result.".into())
+}

v6_stream_studio_tauri/detect.ts

@@ -0,0 +1,129 @@
+// Detect view — find singing segments in one video, export editor markers.
+
+import { h, clear, basename, fmtDur } from "../dom";
+import { makeProgress, toast, renderSongs } from "../ui";
+import {
+  pickVideo, detectSongs, cancel, exportSegments, onDetectProgress, reveal,
+  type Segment,
+} from "../api";
+
+export interface View { el: HTMLElement; onDrop: (paths: string[]) => void; setVideo: (p: string) => void; }
+
+const FPS = ["29.97", "25", "23.976", "30", "60"];
+
+function numField(label: string, value: number, step = "1"): [HTMLElement, HTMLInputElement] {
+  const input = h("input", { class: "input", type: "number", value: String(value), step, min: "0" }) as HTMLInputElement;
+  return [h("div", { class: "field" }, h("label", {}, label), input), input];
+}
+
+export function buildDetect(): View {
+  let running = false;
+  let segments: Segment[] = [];
+  let videoPath = "";
+
+  const videoInput = h("input", { class: "input grow", placeholder: "Select a video…", readonly: true }) as HTMLInputElement;
+  const [gapF, gapI] = numField("Merge gap (s)", 10);
+  const [minF, minI] = numField("Min duration (s)", 20);
+  const [padF, padI] = numField("Padding (s)", 2);
+  const fpsSel = h("select", { class: "input" }, ...FPS.map((f) => h("option", { value: f }, f))) as HTMLSelectElement;
+
+  const detectBtn = h("button", { class: "btn primary lg", disabled: true }, "🎤  Detect Songs") as HTMLButtonElement;
+  const stopBtn = h("button", { class: "btn danger", disabled: true }, "■ Stop") as HTMLButtonElement;
+  const exportBtn = h("button", { class: "btn", disabled: true }, "⬇ Export EDL / CSV") as HTMLButtonElement;
+  const revealBtn = h("button", { class: "btn ghost", style: "display:none" }, "Show in folder") as HTMLButtonElement;
+  const progress = makeProgress();
+  const statsEl = h("div", { class: "pills" });
+  const songsEl = h("div", { class: "songs" });
+  let lastExportDir = "";
+
+  function setVideo(p: string) {
+    videoPath = p;
+    videoInput.value = basename(p);
+    videoInput.title = p;
+    detectBtn.disabled = running || !p;
+  }
+
+  async function detect() {
+    if (!videoPath) return;
+    running = true;
+    detectBtn.disabled = true;
+    stopBtn.disabled = false;
+    exportBtn.disabled = true;
+    revealBtn.style.display = "none";
+    clear(songsEl); clear(statsEl);
+    progress.reset();
+    progress.indeterminate(true, "Starting detection…");
+
+    const un = await onDetectProgress((msg) => {
+      progress.indeterminate(true, msg);
+      progress.appendLog(msg);
+    });
+
+    try {
+      const res = await detectSongs(videoPath, +gapI.value, +minI.value, +padI.value);
+      segments = res.segments;
+      progress.indeterminate(false);
+      progress.set(1, `Found ${segments.length} singing segment(s).`);
+      renderSongs(songsEl, segments);
+      const totalSing = segments.reduce((a, s) => a + s.duration, 0);
+      statsEl.replaceChildren(
+        h("span", { class: "pill" }, `${segments.length} songs`),
+        h("span", { class: "pill" }, `singing ${fmtDur(totalSing)}`),
+        ...Object.entries(res.stats || {}).map(([k, v]) =>
+          h("span", { class: "pill" }, `${k} ${fmtDur(v as number)}`)),
+      );
+      exportBtn.disabled = segments.length === 0;
+      if (!segments.length) toast("No singing detected — try a larger gap or smaller min duration.", "");
+    } catch (e) {
+      const msg = String(e);
+      progress.indeterminate(false);
+      if (msg.includes("__cancelled__")) progress.set(0, "Stopped by user.");
+      else { progress.set(0, "Detection failed."); toast(msg, "err"); }
+    } finally {
+      un();
+      running = false;
+      stopBtn.disabled = true;
+      detectBtn.disabled = !videoPath;
+    }
+  }
+
+  exportBtn.addEventListener("click", async () => {
+    if (!segments.length) return;
+    try {
+      const res = await exportSegments(segments, videoPath, null, +fpsSel.value);
+      lastExportDir = res.files[0] ?? "";
+      toast(`Exported ${res.files.length} files`, "ok");
+      if (lastExportDir) revealBtn.style.display = "";
+    } catch (e) { toast(String(e), "err"); }
+  });
+  revealBtn.addEventListener("click", () => lastExportDir && reveal(lastExportDir));
+  detectBtn.addEventListener("click", detect);
+  stopBtn.addEventListener("click", () => cancel());
+
+  const el = h("div", { class: "view" },
+    h("div", { class: "card" },
+      h("h2", {}, h("span", { class: "step" }, "1"), "Source Video"),
+      h("div", { class: "row" }, videoInput,
+        h("button", { class: "btn", onclick: async () => { const p = await pickVideo(); if (p) setVideo(p); } }, "Browse…")),
+      h("div", { class: "hint", style: "margin-top:8px" }, "Tip: drop a file here, or use a Concat output."),
+    ),
+    h("div", { class: "card" },
+      h("h2", {}, h("span", { class: "step" }, "2"), "Detection Settings"),
+      h("div", { class: "row wrap" },
+        h("div", { class: "grow", style: "min-width:130px" }, gapF),
+        h("div", { class: "grow", style: "min-width:130px" }, minF),
+        h("div", { class: "grow", style: "min-width:130px" }, padF),
+        h("div", { class: "field", style: "min-width:120px" }, h("label", {}, "FPS (export)"), fpsSel)),
+      h("div", { class: "hint", style: "margin-top:8px" }, "Singing = \"music\". More chatting between songs → raise the gap. Short covers → lower the min duration."),
+    ),
+    h("div", { class: "card" },
+      h("h2", {}, h("span", { class: "step" }, "3"), "Run"),
+      h("div", { class: "row", style: "margin-bottom:14px" }, detectBtn, stopBtn, exportBtn, revealBtn),
+      progress.root,
+      h("div", { style: "margin-top:12px" }, statsEl),
+      h("div", { style: "margin-top:10px" }, songsEl),
+    ),
+  );
+
+  return { el, onDrop: (paths) => { if (paths[0]) setVideo(paths[0]); }, setVideo };
+}

v6_stream_studio_tauri/sidecar_detect.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Stream Studio — singing-detection sidecar.
+
+This is the ONLY Python in the project. It exists solely because the ML model
+(inaSpeechSegmenter, which classifies singing voice as "music") is TensorFlow-
+based and has no native Rust/JS equivalent. Everything else — UI, ffmpeg
+concat, audio extraction, file exports — is handled by the Tauri/Rust host.
+
+Contract (line-delimited JSON on stdout):
+    {"type": "progress", "message": "..."}
+    {"type": "result", "segments": [{"index","start","end","duration"}...],
+                        "stats": {"music": 123.4, "speech": 456.7, ...}}
+    {"type": "error",   "message": "..."}
+
+The host extracts a 16 kHz mono WAV and passes it via --wav, so this script
+never touches ffmpeg.
+
+Run with Python 3.10 (TensorFlow does not support 3.12+):
+    py -3.10 detect.py --wav audio.wav --gap 10 --min-duration 20 --padding 2
+"""
+
+import argparse
+import json
+import sys
+import time
+
+
+def emit(obj: dict) -> None:
+    """Write one JSON record per line and flush immediately."""
+    sys.stdout.write(json.dumps(obj, ensure_ascii=False) + "\n")
+    sys.stdout.flush()
+
+
+def progress(msg: str) -> None:
+    emit({"type": "progress", "message": msg})
+
+
+def merge_singing_segments(segments, max_gap, min_duration, padding):
+    """Filter for 'music' segments and merge nearby ones into songs."""
+    music = sorted((s, e) for label, s, e in segments if label == "music")
+    if not music:
+        return []
+
+    merged = []
+    cur_start, cur_end = music[0]
+    for s, e in music[1:]:
+        if s - cur_end <= max_gap:
+            cur_end = max(cur_end, e)
+        else:
+            merged.append((cur_start, cur_end))
+            cur_start, cur_end = s, e
+    merged.append((cur_start, cur_end))
+
+    results = []
+    idx = 1
+    for s, e in merged:
+        if (e - s) >= min_duration:
+            ps = max(0.0, s - padding)
+            pe = e + padding
+            results.append({
+                "index": idx,
+                "start": round(ps, 3),
+                "end": round(pe, 3),
+                "duration": round(pe - ps, 3),
+            })
+            idx += 1
+    return results
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--wav", required=True)
+    ap.add_argument("--gap", type=float, default=10.0)
+    ap.add_argument("--min-duration", type=float, default=20.0)
+    ap.add_argument("--padding", type=float, default=2.0)
+    args = ap.parse_args()
+
+    try:
+        progress("Loading detection model (first run downloads weights)…")
+        from inaSpeechSegmenter import Segmenter
+    except ImportError:
+        emit({
+            "type": "error",
+            "message": (
+                "inaSpeechSegmenter is not installed for this Python.\n"
+                "Install it with:\n"
+                "  py -3.10 -m pip install inaSpeechSegmenter tensorflow"
+            ),
+        })
+        return 1
+
+    try:
+        t0 = time.time()
+        seg = Segmenter(vad_engine="smn", detect_gender=False)
+        progress("Analysing audio — this can take a while on long streams…")
+        raw = seg(args.wav)
+        progress(f"Segmentation done in {time.time() - t0:.0f}s ({len(raw)} raw segments).")
+
+        stats = {}
+        for label, s, e in raw:
+            stats[label] = round(stats.get(label, 0.0) + (e - s), 1)
+
+        songs = merge_singing_segments(raw, args.gap, args.min_duration, args.padding)
+        progress(f"Found {len(songs)} singing segment(s) after merge + filter.")
+        emit({"type": "result", "segments": songs, "stats": stats})
+        return 0
+    except Exception as exc:  # noqa: BLE001 — surface any model/runtime error to the host
+        emit({"type": "error", "message": f"{type(exc).__name__}: {exc}"})
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())