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

说明与用途

WaveWash 自动清理、修复并去重音频文件，生成结构化数据集用于训练语音识别、TTS 和说话人识别模型。

问题

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.

结果

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.

工作流

• 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

证据

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.

来源

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.

来源

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

来源