Avenox Roughcut

Rough cut — transcript-driven (silence + flubs)

Two kinds of cut: silence (dead air, mechanical → auto-editor) and flubs/retakes (a restarted sentence, semantic → transcript + agent judgment). The director approves the flub list before anything is cut.

Pipeline

Job dir (LOCAL — never inside a synced/cloud folder): $STUDIO_JOBS//{raw,cut,transcript,frames}

> Set STUDIO_JOBS to wherever you keep heavy media, e.g. > export STUDIO_JOBS=~/video/projects. Keeping media out of a synced folder > matters: cloud sync will thrash on multi-GB intermediates.

1. Transcribe (local mlx-whisper — Apple Silicon)

cd "$STUDIO_JOBS/"
python3 -c "
import os, certifi; os.environ['SSL_CERT_FILE']=certifi.where(); os.environ['REQUESTS_CA_BUNDLE']=certifi.where()
import mlx_whisper, json
r=mlx_whisper.transcribe('', path_or_hf_repo='mlx-community/whisper-large-v3-turbo', language='', word_timestamps=False)
segs=[{'i':i,'start':round(s['start'],2),'end':round(s['end'],2),'text':s['text'].strip()} for i,s in enumerate(r['segments'])]
json.dump({'text':r['text'].strip(),'segments':segs}, open('transcript/raw_timed.json','w'), ensure_ascii=False, indent=1)
"

~48s for 14 min of audio on an M-series Mac. Local is the default — it is faster and cheaper than any API round trip at this length. Note that most LLM-routing proxies have no whisper endpoint; if you must go remote, use a dedicated speech API.

2. Detect flubs (read transcript, propose to the director)

Scan raw_timed.json for:

• repeated sentence-starts (the same opening said twice)
• cut-off restarts (a half sentence, then the full take)
• self-corrections ("we need X" → "instead of X, …")
• hanging filler words right before a gap

Present as a table (mm:ss + text). The director approves before cutting. This step stays human-gated — an agent cutting semantic content unreviewed will eventually remove a real point.

3. Cut — flubs (ffmpeg) THEN silence (auto-editor)

Order matters: flub timecodes are in RAW coordinates, so cut flubs first; silence removal shifts the timeline underneath them.

Flubs via ffmpeg select (frame-precise; build KEEP as the complement of the cut ranges):

KEEP="between(t,4.96,46.16)+between(t,48.98,69.42)+...+between(t,LAST,99999)"
ffmpeg -y -i "" \
  -vf "select='$KEEP',setpts=N/FRAME_RATE/TB" \
  -af "aselect='$KEEP',asetpts=N/SR/TB" \
  -c:v h264_videotoolbox -b:v 18M -c:a aac -b:a 256k cut/flubcut.mp4

Silence via auto-editor:

export SSL_CERT_FILE="$(python3 -c 'import certifi;print(certifi.where())')"; export REQUESTS_CA_BUNDLE="$SSL_CERT_FILE"
python3 -m auto_editor cut/flubcut.mp4 --edit "audio:threshold=8%" --margin 0.5s \
  -c:v h264_videotoolbox -b:v 16M --no-open -o cut/draft_v1.mp4

Gotchas (learned the hard way — do not rediscover)

• auto-editor needs the certifi SSL fix or its binary download fails with

CERTIFICATE_VERIFY_FAILED. Always export SSL_CERT_FILE first.

• Do NOT use auto-editor --cut-out for flubs. In v29 a multi-range

--cut-out a,b c,d … leaks the last range as a positional input file ("Could not open input file"). Use the ffmpeg select filter for content cuts — it is also frame-precise, where auto-editor's cuts are coarser.

• Pause length is --margin, not --threshold. 0.15s ≈ very tight

(~0.3s pauses); 0.5s ≈ ~1s max pauses, which reads as flowy rather than clipped. Tune margin for rhythm, leave threshold alone.

• threshold=8% is calibrated to one specific voice/mic. Re-calibrate for

your own setup: too low clips soft word-endings, too high leaves dead air.

• The auto-editor binary is a WyattBlue release, auto-downloaded by the pip

wrapper into its own cache.

Output & next

• Render the draft with hardware encoding (h264_videotoolbox) for fast

review; produce the master later via mltgen/.mlt, or a single-pass keep-list at libx264 CRF 18.

• After cutting, re-transcribe the cut (or remap timecodes) so graphics

land accurately → hand to the avenox-video graphics step.

• Show the director the draft. They are the quality gate.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: avenoxai
• Source: avenoxai/avenoxskills
• License: MIT

Install and usage instructions live in the source repository linked above.