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

Developer Boobalan Arjunan released WaveWash, an open-source Python package in early alpha (v0.1.0a1) that cleans, repairs, and dedupes audio datasets before speech-model training for Whisper, TTS, and voice cloning. WaveWash corrects DC offset, mild clipping, and loudness normalization to EBU R128, sorts files into clean/, _error/, _duplicates/, and _logs/ folders without deleting originals, and ships a zero-config local web UI that launches at http://127.0.0.1:8420 via 'pip install wavewash' and 'wavewash app'.

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 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 Key Takeaways - •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 - •This story was reported by Dev.to , covering developments in the dev space. - •AI advancements continue to reshape industries — read the full article on Dev.to for complete coverage. 📖 Continue reading the full article: Read Full Article on Dev.to → https://dev.to/boobalan arjunan/wavewash-clean-repair-and-dedupe-audio-datasets-before-ml-training-4jk4