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arXiv · 2509.17052

Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing

Wataru Nakata·Yuki Saito·Yota Ueda·Hiroshi Saruwatari
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Abstract

Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, multilingual recordings. We introduce Sidon, a fast, open-source speech restoration model that converts noisy in-the-wild speech into studio-quality speech and scales to dozens of languages. Sidon consists of two models: w2v-BERT 2.0 finetuned feature predictor to cleanse features from noisy speech and vocoder trained to synthesize restored speech from the cleansed features. Sidon achieves restoration performance comparable to Miipher: Google's internal speech restoration model with the aim of dataset cleansing for speech synthesis. Sidon is also computationally efficient, running up to 500 times faster than real time on a single GPU. We further show that training a TTS model using a Sidon-cleansed automatic speech recognition corpus improves the quality of synthetic speech in a zero-shot setting. Code and model are released to facilitate reproducible dataset cleansing for the research community.

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Wataru Nakata, Yuki Saito, Yota Ueda, Hiroshi Saruwatari. 2026-01-26. Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing. https://arxiv.org/abs/2509.17052

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