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

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. Tuned Presets: Built-in profiles for ASR (Whisper), TTS, Speaker ID, and Music Generation. Safe Auto-Repairs: Corrects DC offset, mild clipping, and normalizes loudness (EBU R128). Automated Segregation: 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). _logs/ : Complete audit logs (CSV, JSON, and Markdown summaries). Zero-Config Local Web UI: Ships directly inside the Python package — no Node.js build required. Non-Destructive: Your original files are never modified. Install and launch the local web UI in two commands: bash pip install wavewash wavewash app It opens your browser at http://127.0.0.1:8420. You can also run it directly from the terminal: wavewash run --input ./raw_audio --output ./clean_audio --purpose asr Links PyPI: https://pypi.org/project/wavewash/ GitHub: https://github.com/BoobalanArjunan/wavewash It's currently in early alpha (v0.1.0a1). Feedback, issues, and feature suggestions are very welcome!