Quantization-aware training with ternary weights plus base-3 weight indexing reduces a JETS/HiFi-GAN TTS model from 25.66 MB to 4.39 MB while keeping naturalness MOS around 3.1 to 3.3.
Experimental conditions We conducted experiments to evaluate the effectiveness of quantization in TTS and the proposed methods
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.AS 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing
Quantization-aware training with ternary weights plus base-3 weight indexing reduces a JETS/HiFi-GAN TTS model from 25.66 MB to 4.39 MB while keeping naturalness MOS around 3.1 to 3.3.