Gig Splitter

Two hours in, a folder of reels out.

Run as python split_gig.py ~/Movies/Gigs/<gig>/IMG_xxxx.MOV. Every step skips work already on disk, so a rerun picks up where it stopped.

1. Find the songs by how loud they are

   ffmpeg pulls the audio out and librosa measures its loudness, smoothed over 15 seconds so a quiet verse doesn't split a song in half. Anything above a threshold for more than 90 seconds is a song. Anything below is me talking.

   

2. Cut each song out without re-encoding

   ffmpeg stream-copies each band to its own file, so the cut is fast and nothing loses quality.

3. Name the song from garbled lyrics

   Whisper transcribes a minute of each song, and Claude names it from the transcript, knowing the words will be wrong but the phrases close. If the minute turns out to be banter, it tries later in the song.

   Predator Ridge, reels folderreal output, one bug included

   song_02_Van_Morrison_-_Bright_Side_of_the_Road_reel.mp4
   song_03_The_Beatles_-_All_You_Need_Is_Love_reel.mp4
   song_04_Creedence_Clearwater_Revival_-_Have_You_Ever_Seen_the_Rain_reel.mp4
   song_07_Sting_-_Englishman_in_New_York_reel.mp4
   song_09_Van_Morrison_-_Brown_Eyed_Girl_reel.mp4
   song_10_Bruce_Springsteen_-_Dancing_in_the_Dark_reel.mp4
   song_11_Hall_Oates_-_Maneater_reel.mp4
   song_13_Me_by_Ben_E_King_-_Stand_reel.mp4
   song_21_ACDC_-_Highway_to_Hell_reel.mp4
   song_26_ABBA_-_Dancing_Queen_reel.mp4

   Ten of the 23 reels from 8 May 2026. Song 13 is "Stand By Me", and it's explained under "Still gets wrong".

4. Find the chorus

   A second Whisper pass gets word timings for 90 seconds of the song. The reel starts two seconds before the phrase that repeats most, which is almost always the chorus, the bit people know. No repeat, and it starts a quarter of the way in.

   split_gig.pyfind_chorus()

   def find_chorus(transcript: str) -> str:
       words = transcript.lower().split()
       best_phrase, best_score = "", 0
       for n in range(8, 3, -1):
           seen = {}
           for i in range(len(words) - n):
               phrase = " ".join(words[i:i+n])
               seen[phrase] = seen.get(phrase, 0) + 1
           for phrase, count in seen.items():
               score = count * len(phrase)
               if count > 1 and score > best_score:
                   best_phrase, best_score = phrase, score
       return best_phrase

5. Cut the reel

   30 seconds at 1080 by 1920, karaoke captions burned in from a subtitle file, and the captions saved as editable JSON next to it so a wrong word can be fixed and re-burned without rerunning anything.

   

Built with Claude, and runs on it too.

• Built in Claude Code

  All 17 commits carry Claude as co-author, from the first one on 9 May, the night it was built. It came before I packaged the method as my spec-driven development skill, so there's no specs folder behind this one.

• A model that listens

  Whisper transcribes a minute of each song to identify it, and a 90-second window with word timings to find the chorus and time the captions.

• A model that names the song

  Claude Sonnet gets the garbled lyrics, is told the words will be wrong but the phrases close, and answers in one fixed format. That format is also where the "Stand By Me" bug comes from.

• Plain code where plain code is enough

  Finding the songs is loudness and a threshold. Finding the chorus is counting repeated phrases. No model in either, so they're free, fast and predictable.

• What stayed mine

  Playing the gig, picking which reels are worth posting, and the captions, which I do by hand in InShot.

What fought back.

• Shazam can't recognise a live cover

  The first version used it, and it failed on every song, because it matches studio recordings and a live acoustic cover isn't one. It was gone the same night. Reading the lyrics and letting Claude name the song is the decision the whole tool rests on.

• I had the loudness backwards

  My first guess was that the crowd chatting between songs would be louder than me. It was the other way round, so songs are the loud parts.

• A truncated cache hid half the gig

  A cut-short audio cache meant only 15 of 28 songs were found on the first real run. It now checks the cached audio's length against the video before trusting it.

• Knowing which job to hand back

  Since late May I run it with captions off and caption in InShot. The script finds, cuts and names the songs. The words on screen I do myself.

Six gigs so far.

Views from Instagram's own count on the post, synced through Buffer. Runs from the output folders and the project history. The August runs took about 75 minutes for 26 reels.

• Python
• librosa
• ffmpeg
• Whisper
• Claude Sonnet
• Gradio
• Docker