Single-Stream Text-to-Speech Synthesis Using LLMs and Decoupled Speech Tokens

I just read the Spark-TTS paper, and it introduces a really clever approach to text-to-speech: a single-stream architecture with decoupled speech tokens that represents both content and acoustic features in a unified sequence.

The key technical highlights: * Uses “DCC” (Duration/Content/Condition) token format in a single stream instead of separate dual-streams * Achieves comparable quality to state-of-the-art models with just 1B parameters (vs competitors’ 7B) * 1.8x faster inference speed than previous approaches * Effectively handles both seen and unseen speaker adaptation * Maintains high speech quality while dramatically reducing computational costs

The researchers conducted extensive evaluations showing that their model outperforms existing approaches like VALL-E in speaker similarity and computational efficiency while maintaining audio quality. They used vector quantization techniques for the speech tokenizer and a two-stage training approach (tokenizer training followed by TTS model training).

I think this work represents an important efficiency breakthrough in TTS. Instead of simply scaling up model size, they’ve found a more elegant architectural solution that could make high-quality speech synthesis practical on more modest hardware. The single-stream approach with decoupled tokens seems like it could become a new standard architecture for efficient TTS systems.

What’s particularly impressive is that they’ve managed to reduce computational requirements without sacrificing quality. This suggests that we can build more accessible speech technologies without waiting for ever-larger models or more powerful hardware.

TLDR: Spark-TTS introduces a single-stream architecture with decoupled speech tokens that achieves state-of-the-art TTS quality with fewer parameters and faster inference than previous models.

Full summary is here. Paper here.