Lychee-FD

Native end-to-end full-duplex spoken language model for real-time speech interaction.

Lychee-FD is a native end-to-end full-duplex spoken language model. It addresses modality interference and semantic dilution through hierarchical acoustic-semantic modeling, and supports low-latency online inference, digital avatar demos, and robot interaction demos.

Technical blog 01 / Motivation

What is full-duplex?

Full-duplex speech interaction should be understood at two levels. At the interaction level, it moves speech AI beyond the half-duplex “listen, then speak” paradigm toward listening while speaking in a continuous audio stream. At the modeling level, native full-duplex goes beyond system-level orchestration: listening, understanding, speech generation, and interaction control are internalized within the spoken language model itself.

Half-duplex vs. full-duplex

From turn-taking to simultaneous interaction.

Half-duplex systems listen first and speak later. Full-duplex interaction allows the model to keep listening while generating responses, so interruptions, corrections, and short feedback can be handled in a continuous speech stream.

System-level vs. native full-duplex

From external orchestration to internal modeling.

System-level full-duplex stitches ASR, LLM, TTS, VAD, and schedulers together. Lychee-FD instead internalizes listening, understanding, speech generation, and rhythm control inside one end-to-end spoken language model.

Comparison of half-duplex speech AI, system-level full-duplex, and native end-to-end full-duplex

02 Result

Results

03 Demo gallery

Full-duplex conversation demos

Digital avatar demos

Digital avatar demos

Robot demos

Shennie robot demos