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Auto-align double (or triple) ender podcast recordings. Drop in a master track and individual local tracks — PodSync finds the offset, pads/trims, and outputs aligned files ready for your DAW.

2 starsRust

PodSync – Auto-align double-ender podcast recordings (CLI)

Name: PodSync – Auto-align double-ender podcast recordings (CLI)
Availability: InStock
Author: sorcercode

by sorcercode·Mar 20, 2026·2 points·0 comments

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AI Analysis

●●SolidSolve My ProblemBig Brain

MFCC cross-correlation beats manual alignment for double-ender podcasts.

Strengths

•WebRTC VAD + MFCC fingerprinting handles volume and EQ differences between tracks
•Drift measurement detects clock rate differences over long recordings
•Rust implementation means fast processing for hour-long episodes

Weaknesses

•Audio alignment already solved by PluralEyes, DaVinci Resolve, and Desync
•No GUI option — DAW integration requires manual file import

Post Description

I edit a podcast and every episode starts with 10-15 minutes of manually aligning tracks. Each person records locally, but everyone hits record at a different time. Before editing, you have to line them all up against a master recording by ear.

I've wanted to automate this since 2019 (after first hearing about it in the popular podcast - Accidental Tech Podcast). I figured I'd write it in Kotlin (being my language of choice) first, but JVM audio processing wasn't there (or more fairly I just needed to put in way more work than I realized).

With AI ofc, I took another shot at it recently and finally built it in Rust.

"PodSync" takes a master track and individual participant tracks, finds the time offset for each using VAD (voice activity detection), MFCC fingerprinting, and cross-correlation, then outputs aligned WAV files. Drop them into your DAW at 0:00 and they line up!

There's an accompanying blog post with a visual on the mechanics: https://kau.sh/blog/podsync/

Would love to hear feedback!