v3: inaSpeechSegmenter CLI pipeline
Adopts inaSpeechSegmenter (singing classified as music). Full argparse CLI: gap-merge nearby segments, min-duration filter, EDL + CSV export. Engine that all later versions keep. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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README.md
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README.md
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# Singing Segment Detector
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Automatically detect singing segments in live stream recordings and export timeline markers for DaVinci Resolve.
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## What It Does
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```
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4hr stream recording (.mp4/.mkv/.flv)
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│
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▼ [ffmpeg: extract 16kHz mono audio]
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│
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▼ [inaSpeechSegmenter: classify speech/music/noise]
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│
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▼ [merge nearby music segments, filter short ones]
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│
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├──→ .edl (import as DaVinci Resolve timeline)
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├──→ _markers.csv (import as DaVinci Resolve markers)
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├──→ .csv (human-readable segment list)
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├──→ .json (programmatic use)
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└──→ .bat (optional: auto-cut with ffmpeg, no editing needed)
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```
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**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).
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## Setup (Windows with conda)
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### Prerequisites
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- **conda** (Anaconda or Miniconda)
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- **ffmpeg** in your PATH
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- **NVIDIA GPU** recommended (RTX 2080 works great) but CPU also works
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### Step 1: Create conda environment
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```powershell
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conda create -n singing-detector python=3.11 -y
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conda activate singing-detector
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```
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### Step 2: Install PyTorch with CUDA (for GPU acceleration)
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```powershell
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# For RTX 2080 (CUDA 12.x)
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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```
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### Step 3: Install TensorFlow (required by inaSpeechSegmenter)
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```powershell
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pip install tensorflow[and-cuda]
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```
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Or CPU-only (slower but works):
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```powershell
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pip install tensorflow
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```
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### Step 4: Install inaSpeechSegmenter and dependencies
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```powershell
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pip install inaSpeechSegmenter
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```
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### Step 5: Verify installation
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```powershell
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python -c "from inaSpeechSegmenter import Segmenter; print('OK!')"
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```
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## Usage
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### Single file
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```powershell
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conda activate singing-detector
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# Basic usage
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python detect_singing.py "D:\streams\2026-04-04_stream.mp4"
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# Custom settings
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python detect_singing.py "D:\streams\2026-04-04_stream.mp4" --gap 15 --min-duration 30
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# Output to a specific directory
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python detect_singing.py "D:\streams\2026-04-04_stream.mp4" -o "D:\singing_edits"
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# Also generate an auto-cut ffmpeg script (no DaVinci needed)
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python detect_singing.py "D:\streams\2026-04-04_stream.mp4" --auto-cut
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# For 4K F-log footage at 29.97fps (your XT3 settings)
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python detect_singing.py "D:\streams\2026-04-04_stream.mp4" --fps 29.97
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```
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### Batch processing
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```powershell
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# Process all videos in a folder
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python batch_detect.py "D:\streams"
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# Process new files only (skip already-done ones)
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python batch_detect.py "D:\streams" --skip-existing
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# Process and auto-generate singing-only videos
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python batch_detect.py "D:\streams" --auto-cut -o "D:\singing_edits"
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# Only process .flv files
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python batch_detect.py "D:\streams" --pattern "*.flv"
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```
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## Importing Results into DaVinci Resolve
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### Option A: Import EDL as Timeline (recommended)
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1. Open your project in DaVinci Resolve
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2. Import the original stream video into your Media Pool
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3. Go to **File → Import → Timeline**
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4. Select the `_singing.edl` file
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5. DaVinci will create a new timeline with only the singing segments
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### Option B: Import Markers CSV
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1. Create a timeline from your stream video
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2. Right-click on the timeline → **Timelines → Import Markers from CSV**
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3. Select the `_markers.csv` file
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4. Blue markers will appear at each singing segment start/end
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### Option C: Auto-Cut (no DaVinci needed)
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If you used `--auto-cut`, just run the generated `.bat` script:
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```powershell
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D:\singing_edits\2026-04-04_stream_singing.bat
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```
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This uses ffmpeg to directly cut and concatenate all singing segments into a single `_singing_only.mp4` file. Fastest option, but no manual review.
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## Tuning Parameters
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `--gap` | 10s | Max gap between music segments to merge. Increase if singer pauses >10s between verses. |
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| `--min-duration` | 20s | Minimum segment length. Lower if singer does very short songs. |
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| `--padding` | 2s | Extra seconds before/after each segment. Helps catch song intro/outro. |
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| `--fps` | 29.97 | Frame rate for timecode. Use 25 for PAL. |
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### Recommended settings for typical singing streams
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- **Singer with lots of chatting between songs**: `--gap 10 --min-duration 30`
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- **Singer with minimal breaks**: `--gap 5 --min-duration 20`
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- **Singer who does short covers/snippets**: `--gap 8 --min-duration 15`
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- **Conservative (catch everything)**: `--gap 20 --min-duration 15 --padding 5`
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## Troubleshooting
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### "Singing voice detected as speech"
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This can happen if the singer talks over background music. Try `--gap 15` to merge nearby segments.
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### "Too many false positives (non-singing music detected)"
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If there's background music during chatting, increase `--min-duration 45` to only keep longer segments (full songs).
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### "Segments cut off the beginning/end of songs"
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Increase `--padding 5` to add more buffer.
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### GPU not being used
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Check that TensorFlow detects your GPU:
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```python
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python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
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```
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### Processing is very slow
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- A 4-hour stream typically takes 5-15 minutes on GPU, 30-60 minutes on CPU.
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- Make sure you're using GPU-enabled TensorFlow.
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- Close other GPU-heavy applications during processing.
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## Output Files
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For input `stream_2026-04-04.mp4`, you get:
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| File | Purpose |
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|------|---------|
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| `stream_2026-04-04_singing.edl` | DaVinci Resolve timeline import |
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| `stream_2026-04-04_markers.csv` | DaVinci Resolve marker import |
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| `stream_2026-04-04_singing.csv` | Human-readable segment list |
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| `stream_2026-04-04_singing.json` | Programmatic use |
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| `stream_2026-04-04_singing.bat` | Auto-cut script (with `--auto-cut`) |
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| `stream_2026-04-04_raw_segments.csv` | All segments for debugging (with `--raw-segments`) |
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598
detect_singing.py
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detect_singing.py
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#!/usr/bin/env python3
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"""
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Singing Segment Detector for Live Stream Recordings
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=====================================================
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Detects singing segments in long stream recordings and exports
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DaVinci Resolve-compatible EDL markers for fast video editing.
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Pipeline:
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1. Extract audio from video (ffmpeg → 16kHz mono WAV)
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2. Run inaSpeechSegmenter to classify speech/music/noise
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3. Merge nearby "music" segments (singing) with configurable gap
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4. Export to EDL (Edit Decision List) for DaVinci Resolve import
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5. Optionally export CSV for review
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Usage:
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python detect_singing.py "path/to/stream_recording.mp4"
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python detect_singing.py "path/to/stream_recording.mp4" --gap 15 --min-duration 30
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python detect_singing.py "path/to/stream_recording.mp4" --output-dir "D:/singing_edits"
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"""
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import argparse
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import csv
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import json
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import os
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import subprocess
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import sys
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import tempfile
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import time
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from pathlib import Path
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# ──────────────────────────────────────────────
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# Step 1: Extract audio from video using ffmpeg
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# ──────────────────────────────────────────────
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def extract_audio(video_path: str, output_wav: str, ffmpeg_bin: str = "ffmpeg") -> str:
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"""Extract audio from video file as 16kHz mono WAV (required by inaSpeechSegmenter)."""
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print(f"\n[Step 1/4] Extracting audio from: {video_path}")
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print(f" Output WAV: {output_wav}")
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cmd = [
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ffmpeg_bin,
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"-i", video_path,
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"-vn", # no video
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"-acodec", "pcm_s16le", # 16-bit PCM
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"-ar", "16000", # 16kHz sample rate
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"-ac", "1", # mono
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"-y", # overwrite
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output_wav,
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]
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try:
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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timeout=1800, # 30 min timeout for very long files
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)
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if result.returncode != 0:
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print(f" [ERROR] ffmpeg failed:\n{result.stderr[-500:]}")
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sys.exit(1)
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except FileNotFoundError:
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print(f" [ERROR] ffmpeg not found at '{ffmpeg_bin}'.")
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print(f" Make sure ffmpeg is installed and in your PATH.")
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sys.exit(1)
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size_mb = os.path.getsize(output_wav) / (1024 * 1024)
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print(f" Audio extracted: {size_mb:.1f} MB")
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return output_wav
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# ──────────────────────────────────────────────
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# Step 2: Run inaSpeechSegmenter
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# ──────────────────────────────────────────────
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def run_segmentation(wav_path: str) -> list:
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"""
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Run inaSpeechSegmenter on audio file.
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Returns list of (label, start_sec, end_sec) tuples.
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Labels: 'music', 'speech', 'male', 'female', 'noise', 'noEnergy'
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NOTE: inaSpeechSegmenter classifies singing voice as 'music'.
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"""
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print(f"\n[Step 2/4] Running audio segmentation (this may take a while)...")
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try:
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from inaSpeechSegmenter import Segmenter
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except ImportError:
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print(" [ERROR] inaSpeechSegmenter is not installed.")
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print(" Install it with: pip install inaSpeechSegmenter")
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sys.exit(1)
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start_time = time.time()
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# vad_engine='smn' → speech/music/noise detection
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# detect_gender=False → faster, we don't need gender info
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seg = Segmenter(vad_engine='smn', detect_gender=False)
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segments = seg(wav_path)
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elapsed = time.time() - start_time
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print(f" Segmentation complete in {elapsed:.1f}s")
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print(f" Total segments found: {len(segments)}")
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# Count segment types
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type_counts = {}
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type_durations = {}
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for label, start, end in segments:
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type_counts[label] = type_counts.get(label, 0) + 1
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type_durations[label] = type_durations.get(label, 0) + (end - start)
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for label in sorted(type_counts.keys()):
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count = type_counts[label]
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dur = type_durations[label]
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print(f" {label:>10s}: {count:4d} segments, {format_timecode(dur)} total")
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return segments
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# ──────────────────────────────────────────────
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# Step 3: Filter & merge singing (music) segments
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# ──────────────────────────────────────────────
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def merge_singing_segments(
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segments: list,
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max_gap: float = 10.0,
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min_duration: float = 20.0,
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padding: float = 2.0,
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) -> list:
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"""
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Filter for 'music' segments (which includes singing) and merge
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nearby segments that are likely the same song.
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Args:
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segments: Raw segmentation output [(label, start, end), ...]
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max_gap: Max gap (seconds) between music segments to merge them.
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Stream singers often have brief pauses, audience interaction
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between verses, etc. Default 10s works well.
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min_duration: Minimum duration (seconds) for a merged segment to be kept.
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Filters out short music stings, sound effects, etc.
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A typical song is 2-5 minutes, so 20s is a safe minimum.
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padding: Seconds to add before/after each segment for clean cuts.
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Returns:
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List of dicts: [{"start": float, "end": float, "duration": float, "index": int}, ...]
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"""
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print(f"\n[Step 3/4] Merging singing segments (gap={max_gap}s, min={min_duration}s, pad={padding}s)")
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# Extract only music segments
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music_segs = [(start, end) for label, start, end in segments if label == "music"]
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if not music_segs:
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print(" No music segments found!")
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return []
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# Sort by start time (should already be sorted, but just in case)
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music_segs.sort(key=lambda x: x[0])
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# Merge segments with gaps smaller than max_gap
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merged = []
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current_start, current_end = music_segs[0]
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for start, end in music_segs[1:]:
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if start - current_end <= max_gap:
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# Extend current segment
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current_end = max(current_end, end)
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else:
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# Save current segment and start new one
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merged.append((current_start, current_end))
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current_start, current_end = start, end
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merged.append((current_start, current_end))
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# Apply padding and minimum duration filter
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results = []
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idx = 1
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for start, end in merged:
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duration = end - start
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if duration >= min_duration:
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padded_start = max(0, start - padding)
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padded_end = end + padding
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results.append({
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"index": idx,
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"start": padded_start,
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"end": padded_end,
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"duration": padded_end - padded_start,
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"original_start": start,
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"original_end": end,
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})
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idx += 1
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print(f" Found {len(results)} singing segments after merge+filter:")
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for seg in results:
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print(f" Song {seg['index']:2d}: "
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f"{format_timecode(seg['start'])} → {format_timecode(seg['end'])} "
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f"({seg['duration']:.0f}s)")
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return results
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# ──────────────────────────────────────────────
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# Step 4: Export formats
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# ──────────────────────────────────────────────
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def export_edl(segments: list, output_path: str, fps: float = 29.97,
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title: str = "Singing Segments", source_filename: str = None):
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"""
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Export an EDL (Edit Decision List) file for DaVinci Resolve.
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DaVinci Resolve import: File → Import → Timeline → select the .edl file.
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The EDL creates cut points at each singing segment's in/out points.
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Args:
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source_filename: Original video filename (e.g. "stream_2026-04-01.mp4").
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Used as reel name + FROM CLIP NAME so DaVinci Resolve
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matches the EDL to the correct clip in the Media Pool.
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Without this, Resolve picks a random clip when multiple
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videos are loaded in the same project.
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"""
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print(f"\n[Step 4/4] Exporting EDL: {output_path}")
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# DaVinci Resolve uses both the reel name column AND the "* FROM CLIP NAME:"
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# comment to identify which media clip an EDL edit refers to.
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#
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# Reel name: traditionally 8 chars max, but Resolve accepts longer names.
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# We use the filename without extension, truncated to a safe length.
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# FROM CLIP NAME: Resolve's preferred matching method. Must be the exact
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# filename (with extension) as it appears in the Media Pool.
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if source_filename:
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clip_name = source_filename # full filename with extension
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reel_name = Path(source_filename).stem[:32] # stem, truncated for safety
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# Replace spaces with underscores in reel name (some EDL parsers choke on spaces)
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reel_name_safe = reel_name.replace(" ", "_")
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else:
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clip_name = None
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reel_name_safe = "001"
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# Calculate record timecodes: sequential placement on the output timeline
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# Each segment is placed one after another, starting at 01:00:00:00
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rec_offset = 3600.0 # start at 01:00:00:00
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(f"TITLE: {title}\n")
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f.write("FCM: NON-DROP FRAME\n\n")
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current_rec_pos = rec_offset
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for seg in segments:
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edit_num = f"{seg['index']:03d}"
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src_in = seconds_to_timecode(seg["start"], fps)
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src_out = seconds_to_timecode(seg["end"], fps)
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rec_in = seconds_to_timecode(current_rec_pos, fps)
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rec_out = seconds_to_timecode(current_rec_pos + seg["duration"], fps)
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# EDL format: edit# reel_name track_type transition src_in src_out rec_in rec_out
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f.write(f"{edit_num} {reel_name_safe} V C {src_in} {src_out} {rec_in} {rec_out}\n")
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# "* FROM CLIP NAME:" is the key line DaVinci uses for media matching.
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# It must exactly match the clip name shown in the Media Pool.
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if clip_name:
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f.write(f"* FROM CLIP NAME: {clip_name}\n")
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f.write(f"* COMMENT: Song {seg['index']} - Duration {seg['duration']:.0f}s\n\n")
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current_rec_pos += seg["duration"]
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|
||||
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()
|
||||
56
setup_windows.bat
Normal file
56
setup_windows.bat
Normal file
@@ -0,0 +1,56 @@
|
||||
@echo off
|
||||
REM ============================================
|
||||
REM Singing Detector - Quick Setup (Windows)
|
||||
REM ============================================
|
||||
REM Run this in Anaconda Prompt / PowerShell
|
||||
REM Prerequisites: conda, ffmpeg in PATH
|
||||
REM ============================================
|
||||
|
||||
echo.
|
||||
echo ============================================
|
||||
echo Singing Segment Detector - Setup
|
||||
echo ============================================
|
||||
echo.
|
||||
|
||||
REM Step 1: Create conda environment
|
||||
echo [1/4] Creating conda environment (python 3.11)...
|
||||
call conda create -n singing-detector python=3.11 -y
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo ERROR: Failed to create conda environment.
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
REM Activate environment
|
||||
call conda activate singing-detector
|
||||
|
||||
REM Step 2: Install TensorFlow with GPU support
|
||||
echo.
|
||||
echo [2/4] Installing TensorFlow (GPU)...
|
||||
pip install tensorflow[and-cuda]
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo WARNING: GPU TensorFlow install failed, trying CPU version...
|
||||
pip install tensorflow
|
||||
)
|
||||
|
||||
REM Step 3: Install inaSpeechSegmenter
|
||||
echo.
|
||||
echo [3/4] Installing inaSpeechSegmenter...
|
||||
pip install inaSpeechSegmenter
|
||||
|
||||
REM Step 4: Verify
|
||||
echo.
|
||||
echo [4/4] Verifying installation...
|
||||
python -c "from inaSpeechSegmenter import Segmenter; print('inaSpeechSegmenter OK!')"
|
||||
python -c "import tensorflow as tf; gpus = tf.config.list_physical_devices('GPU'); print(f'TensorFlow GPUs: {gpus}' if gpus else 'TensorFlow: CPU only')"
|
||||
|
||||
echo.
|
||||
echo ============================================
|
||||
echo Setup complete!
|
||||
echo ============================================
|
||||
echo.
|
||||
echo Usage:
|
||||
echo conda activate singing-detector
|
||||
echo python detect_singing.py "path\to\stream.mp4"
|
||||
echo.
|
||||
pause
|
||||
261
sing_detect.py
261
sing_detect.py
@@ -1,261 +0,0 @@
|
||||
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()
|
||||
Reference in New Issue
Block a user