WaveWash: Clean, Repair, and Dedupe Audio Datasets Before ML Training

Automated excerpt

Training speech models (Whisper, TTS, voice cloning) often breaks down because of dirty audio data: clipping, DC offset, background noise, low SNR, and duplicate samples quietly inflating validation metrics. I built WaveWash to automate audio quality control and data prep so developers don't have to write one-off scripts or manually listen to thousands of audio files. Safe Auto-Repairs: Corrects DC offset, mild clipping, and normalizes loudness (EBU R128). clean/ : Audio that passed all checks. _error/ : Rejected files categorized by reason (low_snr/, clipping/, etc. ). _duplicates/ : Exact and near-duplicates set aside (nothing is deleted).

Selected automatically from source text; not independently written or fact-checked. Read the original for full context.