Podcast Edit Skill

Process raw podcast/meeting recordings into polished podcast episodes.

Capabilities

1. Smart trimming, Find where the actual podcast starts/ends by transcribing and detecting intros/outros
2. Filler word removal, Remove verbal tics: 嗯, 呃, 啊, 哦, 对对对, um, uh, etc.
3. Silence trimming, Cut long dead air (>2s) down to natural pauses (~0.6s)
4. Audio enhancement, Noise reduction, EQ, multi-speaker volume balancing, loudness normalization to podcast standard (−16 LUFS)
5. Re-cutting a finished episode, Surgically remove flagged sections from an already-rendered episode without re-running the whole pipeline
6. Highlight clips & reel, Cut shareable soundbites and stitch a ~1-minute reel with music
7. Video cut, Apply the same edit to a Zoom/Riverside video recording (see "Video episodes")

Prerequisites

• ffmpeg and ffprobe installed
• OPENAI_API_KEY in environment (for Whisper API transcription)
• Python 3 with stdlib only (no extra deps for the helper script)
• Optional: resemblyzer (pip install resemblyzer), only for speaker diarization when building highlight reels

Workflow

Step 1: Inspect the audio file

ffprobe -v quiet -print_format json -show_format -show_streams "INPUT_FILE"

Note: duration, sample rate, channels, codec, bitrate.

Step 2: Find podcast start/end (if user says to trim front/back)

Split into 5-minute chunks and transcribe via OpenAI Whisper API with segment-level timestamps:

# Extract chunk
ffmpeg -y -i "INPUT_FILE" -ss OFFSET -t 300 -ar 16000 -ac 1 /tmp/chunk_OFFSET.mp3

# Transcribe
curl -s https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F file="@/tmp/chunk_OFFSET.mp3" \
  -F model="whisper-1" \
  -F response_format="verbose_json" \
  -F language="LANG" \
  -F 'timestamp_granularities[]=segment' > /tmp/transcript_OFFSET.json