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.
With the advancement of these TTS models, they are increasingly being integrated into mobile applications, such as car navigation systems and conver- sational bots, among others
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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.