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arXiv · 2506.03515

BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing

Masaya Kawamura·Takuya Hasumi·Yuma Shirahata·Ryuichi Yamamoto
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Abstract

This paper proposes a highly compact, lightweight text-to-speech (TTS) model for on-device applications. To reduce the model size, the proposed model introduces two techniques. First, we introduce quantization-aware training (QAT), which quantizes model parameters during training to as low as 1.58-bit. In this case, most of 32-bit model parameters are quantized to ternary values {-1, 0, 1}. Second, we propose a method named weight indexing. In this method, we save a group of 1.58-bit weights as a single int8 index. This allows for efficient storage of model parameters, even on hardware that treats values in units of 8-bit. Experimental results demonstrate that the proposed method achieved 83 % reduction in model size, while outperforming the baseline of similar model size without quantization in synthesis quality.

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BibTeXRIS

Masaya Kawamura, Takuya Hasumi, Yuma Shirahata, Ryuichi Yamamoto. 2025-06-04. BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing. https://arxiv.org/abs/2506.03515

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