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

Description and use

WaveWash automatically cleans, repairs, and deduplicates audio files, producing a structured dataset for training speech recognition, TTS, and speaker ID models.

Problem

Training speech models often fails due to dirty audio data such as clipping, DC offset, background noise, low SNR, and duplicate samples that inflate validation metrics.

Outcome

WaveWash automates audio cleaning, repair, and deduplication, producing a structured dataset with clean, error, and duplicate subsets and detailed logs, enabling developers to skip manual listening and scripting.

Workflow

• Install WaveWash via pip
• Launch the local web UI with wavewash app or run from terminal
• Select input directory and output directory
• Choose purpose preset (e.g., asr)
• Run the process
• Review segregated folders and logs

Evidence

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.

Source

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.

Source

Zero-Config Local Web UI: Ships directly inside the Python package — no Node.js build required.

Source