clean-audio — voice cleanup
Take a locked master cut and remove its background noise, producing a cleaned master whose voice sounds natural and whose visuals are untouched. Runs early (once the cut is locked) so everything downstream — TSX bake, SFX mix, final assemble — sits on the clean voice. Work with the user; the final loudness/limiting is the final-mix step's job, this step is "denoise only, levels preserved."
The engine is tools/clean_voice.py; this skill is the judgment around it: diagnose → pick method
→ A/B → clean → rewire.
The two methods (pick by NOISE TYPE — this is the core decision)
| Method | What it is | Use when | Cost |
|---|---|---|---|
--method eleven | ElevenLabs Voice Isolator (cloud ML voice/noise separation) | Dynamic, broadband noise in the voice band — outdoor running water, wind, traffic, crowd, cafe. Local tools CANNOT remove these. | ELEVENLABS_API_KEY |
--method rnnoise --model sh (or cb) | Local RNNoise via ffmpeg arnndn (models in tools/models/rnnoise/) | Stationary / mild noise (steady hiss, fan, some room tone). Free/offline. Only PARTIALLY removes dynamic noise. | free |
Proven on video-1 (shot outdoors with a stream): afftdn did ~nothing, RNNoise only partially darkened
the water bed, ElevenLabs removed it near-completely (pauses to near-silence, voice + breaths intact).
Rule of thumb: stationary noise → try local first; dynamic broadband (water/wind/traffic) → ElevenLabs.
Inputs (read/measure first, every time)
- The master —
videos/video-N/reference/<cut>.mp4(or the locked cut). Original is NEVER modified; output is a new-clean/-clean-<model>file. - The composited preview (if it exists) —
videos/video-N/output/video-N-preview.mp4, to make a clean in-context preview by swapping audio (its video is identical — no re-bake needed). videos/video-N/work/timeline.json— itsmasterfield; you rewire this to the clean master on approval.
Workflow
- Diagnose the noise BEFORE choosing a method. Measure and look:
- Levels:
ffmpeg -i M -vn -af astats(RMS, peak, noise floor) +ebur128(integrated LUFS, true peak). - Find speech-free gaps (grep
edited-transcript.jsonfor the biggest inter-word gaps) and measure the pure-noise RMS there vs speech RMS → the real SNR. - Spectrogram:
ffmpeg -i M -vn -lavfi showspectrumpic=s=1500x600:legend=1:scale=log out.pngand LOOK at it. Hum = steady horizontal lines (50/60Hz) → notch. Rumble = low band → high-pass. Broadband bed that fills the voice band and fluctuates = dynamic (water/wind) → ElevenLabs. HF hiss = bright top band. - Note if the export is already produced (compressed/normalized/peak-maxed) — it limits what's recoverable.
- Levels:
- Decide the method with the user from the diagnosis (table above). If unsure, A/B both.
- A/B on a short sample FIRST (prove before spending / committing): cut a ~15s pause-rich sample, run each candidate method, level-match them to each other, and compare — by ear (the real test) AND by spectrogram (pauses going dark = noise removed) and residual level. Let the user pick.
- Clean the full master:
python tools/clean_voice.py videos/video-N/reference/<cut>.mp4 --method <chosen> [--model sh]→<cut>-clean.mp4(or-clean-<model>.mp4). Video stream COPIED (fast, non-destructive, keeps 4K60). - Levels are preserved by RMS-match, not LUFS (the tool does this). Never match integrated LUFS — it is gated and inflated by the removed noise, and over-boosts the voice into clipping. The clean file will read a lower integrated LUFS than the noisy original; that is expected (the noise was padding the number), the voice RMS is unchanged. Final loudness to -14 LUFS is the final-mix step's job.
- Give the user an in-context preview (optional but recommended): swap the clean audio onto the
composited preview —
ffmpeg -i preview.mp4 -i <cut>-clean.mp4 -map 0:v -map 1:a -c:v copy -c:a aac -shortest preview-clean.mp4(video identical, no re-bake). For a full A/B, also exportFULL_*.mp3scrub files. - On approval, rewire the pipeline: point
timeline.json"master"at the clean file so every future bake/mix uses the clean voice; re-bake the preview if needed.
Decisions to surface to the user
- Method (from the diagnosis) — and A/B if unsure.
- Dead-silent gaps vs a faint ambience bed. Voice isolation removes ALL background; on an outdoor shot the dead-silent gaps can feel vacuum-sealed. Offer to add back a low-level neutral ambience if wanted.
- Cost for ElevenLabs (~1000 credits/min) — confirm before running on the full master.
Principles (the house style)
- Least processing that works. The goal is to remove distraction, not to make the voice sound processed. Prefer the gentlest method that clears the noise; don't over-strip a clean track.
- Diagnose, then choose. The right tool depends on the noise type — never crank a denoiser blind. Local spectral/RNNoise can't separate dynamic broadband noise; that's ElevenLabs' job.
- A/B before you commit (and before you spend). Prove on a sample; the user's ears decide.
- Preserve levels; loudness is the final-mix step's. RMS-match with a peak ceiling, no compression here.
- Non-destructive. Original master untouched; video stream copied; output is a new file.
Tooling quick reference
- Clean:
python tools/clean_voice.py IN.mp4 [--method eleven|rnnoise] [--model sh|cb] [-o OUT.mp4] [--no-preserve-loudness] [--keep] - Diagnose:
ffmpeg -i M -vn -af astats -f null -·ffmpeg -i M -vn -lavfi showspectrumpic=... out.png(then Read the png). - RNNoise models:
tools/models/rnnoise/<model>.rnnn(sh,cb). - Scratch samples/spectrograms go in the scratchpad, not the project.
Done = the noise is diagnosed, the method is chosen (A/B'd if needed), the full master is cleaned with
levels preserved, the user has approved by ear, and — on approval — timeline.json points at the clean
master. Update memory if a noise-type → method lesson emerges.
