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

• URL: http://arxiv.org/abs/2506.03515v1
• Date: Wed, 04 Jun 2025 03:02:18 GMT
• Title: BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing
• Authors: Masaya Kawamura, Takuya Hasumi, Yuma Shirahata, Ryuichi Yamamoto,
• Abstract summary: This paper proposes a highly compact, lightweight text-to-speech (TTS) model for on-device applications.<n>We introduce quantization-aware training (QAT), which quantizes model parameters during training to as low as 1.58-bit.<n>We also propose a method named weight indexing, which saves a group of 1.58-bit weights as a single int8 index.
• Score: 8.513851383288067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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