Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918304340246528 |
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| author | Nakata, Wataru Saito, Yuki Ueda, Yota Saruwatari, Hiroshi |
| author_facet | Nakata, Wataru Saito, Yuki Ueda, Yota Saruwatari, Hiroshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing Nakata, Wataru Saito, Yuki Ueda, Yota Saruwatari, Hiroshi Sound Audio and Speech Processing 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. |
| title | Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.17052 |