Auto Clip (V_1 / V_2 / V_3)

A local, offline-friendly toolkit for generating DJ-style album teasers by automatically finding highlights in your tracks and stitching them into a short teaser using Python + FFmpeg.

This repo is organized as:

Auto Clip/
  V_1/
  V_2/
  V_3/

Each version is a step up in “DJ-ness” and automation.

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What it does ✅

Given a folder of audio tracks (WAV/MP3/FLAC/etc.), the scripts can:

• Scan tracks automatically (with a max limit, e.g. 20)
• Select tracks by index (e.g. 1,2,5,7 or 1-4,9) or automatically pick a best-of
• Detect highlight segments (energy + onset/transients)
• Snap cut points to a bar grid (phrase-aligned “DJ” cuts)
• Add optional pre-roll (start 1 bar before the highlight)
• Render clips and merge them with acrossfades
• Export WAV + MP3
• Output a report JSON with timestamps and clip metadata

Note: These scripts are audio analysis + heuristics, not “generative music AI”.
LLMs (Ollama) can help with README/promo/tracklists, but the actual audio cutting is done locally with librosa + FFmpeg.

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Version overview 🧩

V_1 — Minimal MVP

Goal: Quick proof-of-concept teaser builder.

Typical features:

• Highlight detection (energy/onset)
• Simple clip render + acrossfade teaser
• JSON report

Best when: you want a fast starting point.

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V_2 — Selection + Best-of + DJ ordering

Goal: Git-ready CLI and better “DJ flow”.

Adds:

• Folder scan (max 20)
• Track selection by index/range (--select 1-4,7)
• Auto selection (--select auto --auto-n 8)
• Ordering heuristics (tempo clustering + energy ramp)
• WAV + MP3 export
• Report JSON

Best when: you want a practical tool you can keep using.

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V_3 — Harmonic mixing + downbeat-ish snap + 2-pass loudness

Goal: Trance-friendly “DJ teaser” quality.

Adds:

• Key detection (chroma-based) + Camelot mapping (best effort)
• Harmonic ordering (Camelot neighbors) for smoother transitions
• “Downbeat-ish” bar-start snap (beat grid + onset accent heuristic)
• 2-pass loudnorm per clip (more consistent output)

Best when: you want old school trance teasers that feel like mini-mixes.

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Requirements 🛠️

System

• FFmpeg installed and available in PATH
• Python 3.10+ recommended (3.11+ is great)

Python packages

Common:

• numpy
• librosa
• soundfile

Optional:

• requests (only needed if you use the Ollama helper to generate README/promo assets)

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Install (recommended) 🐍

Create a virtual environment:

python -m venv .venv
# Linux/macOS:
source .venv/bin/activate
# Windows PowerShell:
# .\.venv\Scripts\Activate.ps1

pip install -U pip
pip install numpy librosa soundfile

If you plan to use the Ollama helper (v3.1 style extras):

pip install requests

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FFmpeg install hints 🎬

Debian/Ubuntu

sudo apt-get update
sudo apt-get install -y ffmpeg

Windows

Install FFmpeg and add ffmpeg.exe to PATH (so ffmpeg -version works in terminal).

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Usage 🚀

Scripts live under V_1/, V_2/, V_3/ depending on your repo layout. The examples below assume you run from inside a version folder and have a tracks/ folder.

Prepare input

Put audio files in:

tracks/
  01 - Track.wav
  02 - Track.mp3
  ...

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V_2 examples

Use all tracks (max scan still applies)

python dj_teaser_v2.py --tracks-dir ./tracks --select all --mode rollcall --teaser 60 --bars 2 --preroll-bars 1

Select specific tracks by index

python dj_teaser_v2.py --tracks-dir ./tracks --select 1,2,3,7,9 --teaser 60 --bars 2

Select ranges + mix

python dj_teaser_v2.py --tracks-dir ./tracks --select 1-4,7,10-12 --teaser 60 --bars 2

Auto best-of (pick top N tracks)

python dj_teaser_v2.py --tracks-dir ./tracks --select auto --auto-n 8 --mode bestof --teaser 75 --bars 4 --preroll-bars 1

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V_3 examples (recommended for trance)

Rollcall (all tracks, fast DJ flip)

python dj_teaser_v3.py --tracks-dir ./tracks --select all --teaser 60 --bars 2 --preroll-bars 1 --avoid-intro 30 --harmonic

Best-of mini-mix vibe

python dj_teaser_v3.py --tracks-dir ./tracks --select auto --auto-n 8 --teaser 75 --bars 4 --preroll-bars 1 --avoid-intro 30 --harmonic

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Output files 📦

Typical outputs:

• out/album_teaser.wav
• out/album_teaser.mp3
• out/teaser_report.json

The report includes:

• chosen track order
• estimated BPM
• key/camelot (V_3)
• clip start times and durations

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Tuning tips (old school trance) 💡

• Avoid long intros: use --avoid-intro 30 or 45
• DJ phrasing:
  • --bars 2 for rollcall with many tracks (14+)
  • --bars 4 for more “real trance feel”
• Lead-in: --preroll-bars 1 often makes transitions feel natural
• Crossfade:
  • 0.20–0.35 seconds is usually good for teasers
• Harmonic mode (V_3): --harmonic is recommended, but key detection is best-effort

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Limitations ⚠️

• Beat and key detection are heuristics; some tracks will be “weird”, especially with:
  • long breakdowns
  • very pad-heavy sections
  • ambient intros/outros
• “Downbeat” is approximated from beat grid + onset accent (not a trained downbeat model)
• For perfect DJ results, you can always manually tweak:
  • --avoid-intro, --bars, --preroll-bars, --select, and the track order

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Optional: Ollama (text generation) 🤖

If you run Ollama locally (example: http://192.168.2.60:11434) you can use it to generate:

• README snippets
• promo text (TikTok/IG/YouTube)
• tracklists with timestamps (based on teaser_report.json)

Recommended model:

• llama3.1:8b-instruct-q4_0

Ollama is optional and not required for audio processing.

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Repo hygiene 🧼

Suggested .gitignore:

.venv/
__pycache__/
work/
out/
*.wav
*.mp3

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License

Pick whatever fits your repo (MIT is common).
If you haven’t decided yet, add an MIT license later.

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Credits

• FFmpeg for audio processing
• librosa for audio analysis