VoiceGrab

Cut a time range out of any video file and export a clean, loudness-normalized MP3 voice sample — ready to use for AI voice training / cloning.

Voice / audio file: [ kage.mp4                    ] [Open…]
Start: 0:10.00   Stop: 0:35.00   [Load preview] [▶ Play range]
Voice processing (choose one):
  (•) Fast noise reduction (CPU) — fan / hum / room tone; no downloads
  ( ) AI voice separation (Demucs) — music & second speakers; GPU if available
  ( ) No processing — just loudness-normalize
  strength: 80%   [x] stationary noise
Output MP3: [ .../kage_voicegrab.mp3            ] [Browse…]
──────────────────────────────────────────────────
[ Cancel ] [ Export MP3 ]

Features

• Works with any container ffmpeg understands: mp4, mkv, mov, webm, avi, flv, m4a…
• Voice processing — pick one mode (toggle, mutually exclusive):
  • Fast noise reduction (CPU, default) — STFT spectral gating via noisereduce. Great for fan, hum, room tone. No downloads, no GPU; strength slider + a "stationary noise" mode for constant fans/hum.
  • AI voice separation (Demucs) — deep-learning 4-stem separation (we keep the vocals stem). Handles background music and a second speaker, which spectral gating cannot. Optional install; uses an NVIDIA GPU (CUDA) or Apple GPU if present, otherwise CPU (slower).
  • No processing — just loudness-normalize.
• Export is 320 kbps MP3, loudness-normalized to −16 LUFS (broadcast reference — good levels for voice-training datasets).
• "▶ Play range" lets you audition the exact segment before saving.
• AI runs report chunk progress in the status bar and can be cancelled between chunks.
• Also a headless CLI (see below) for scripting/batch work.

Requirements

• Python 3.10+
• ffmpeg on your PATH (bundled automatically in installer builds)
• Optional, only for AI voice separation: pip install -r requirements-ai.txt (pulls torch + demucs, ~1.5–2.5 GB with CUDA wheels)

Run from source

cd voicegrab2
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/python voicegrab.py

AI voice separation (optional — GPU recommended)

.venv/bin/pip install -r requirements-ai.txt

• Pick AI voice separation (Demucs) in the GUI. The mode is disabled (with an explanation) until torch + demucs are installed — the core app never requires them.
• First use downloads the htdemucs_ft model (~90 MB) to the app data dir (<appdata>/VoiceGrab/models/) and caches it — later runs are instant. GPU (NVIDIA CUDA / Apple) is used automatically when present, otherwise CPU.
• Performance expectations (5 s → 60 s clip):
  • GPU (≥ 6 GB VRAM): ~1–3 s → ~15–40 s
  • CPU (8 cores): ~10–25 s → a few minutes
• Cancel stops an AI run at the next chunk boundary. Fast DSP runs finish in seconds and are not cancellable (by design).
• Quality note: aggressive separation can thin sibilance/breath — for voice training samples the fast DSP mode is usually enough; reach for AI mode when there's music or a competing speaker.
• Licensing: Demucs code is MIT and the htdemucs_ft weights are MIT (see THIRD_PARTY.md).

CLI mode

voicegrab.py --cli input.mp4 10 35 output.mp3              # default: fast DSP
voicegrab.py --cli input.mp4 10 35 output.mp3 --mode dsp   # fast noise reduction
voicegrab.py --cli input.mp4 10 35 output.mp3 --mode ai    # Demucs (requirements-ai.txt)
voicegrab.py --cli input.mp4 10 35 output.mp3 --mode none  # just loudness-normalize

Tests

.venv/bin/python tests/test_separators.py   # or: .venv/bin/pytest tests/

Covers: DSP shape/dtype/NaN invariants, AI graceful degradation without torch, device selection, AI unit separation (44.1 kHz output), and the full CLI pipeline in all three modes (valid 320 kbps MP3, duration 5.0 s ± 0.15 s).

Windows installer (single .exe + installer)

1. pip install pyinstaller

2. python voicegrab.py.spec is not needed — run:

   pyinstaller --onefile --windowed --name VoiceGrab ^
       --add-binary "C:\ffmpeg\bin\ffmpeg.exe;." ^
       --add-binary "C:\ffmpeg\bin\ffprobe.exe;." ^
       --icon assets/icon.ico voicegrab.py
   

3. Build the installer with Inno Setup:

   "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" installer\VoiceGrab.iss
   

   (Copy dist\VoiceGrab.exe to installer\ first — the script assumes dist\VoiceGrab.exe relative to the project root.)

   To ship the AI mode inside the frozen exe (bigger, ~2 GB), install requirements-ai.txt into the venv first and add --collect-all demucs --collect-all torch to the PyInstaller command.

Linux

• AppImage: installer/build_linux.sh (needs PyInstaller + linuxdeploy or appimagetool + your ffmpeg in PATH).
• Debian/Ubuntu: just run the PyInstaller binary, or install ffmpeg via apt and run from source. A .desktop entry template is in installer/.
• Flatpak is also a fine route if you want it in your store.
• AI mode: WITH_AI=1 ./installer/build_linux.sh bundles demucs/torch into the binary (bigger build).

Notes on voice-isolation quality

• Fast mode is classic DSP (spectral gating / STFT noise estimation) — great for fan, hum, light room tone; it can't remove music or a second speaker.
• AI mode (Demucs htdemucs_ft) is a source separator — the right tool for speech-over-music or competing voices. It's a product feature behind a radio button, never a hard dependency: the core app stays small and starts in < 2 s without torch/demucs installed.
• The AI path replaces the DSP path — one or the other, never stacked (double-processing adds artifacts).