Gepard is an autoregressive text-to-speech model built for real-time conversation. Instead of waiting for a complete sentence, it generates audio piece by piece as text arrives. On the vLLM serving path the first audio chunk lands in roughly 50 ms, fast enough that a dialogue feels live rather than played back.
The interesting part is how compact the recipe is. It is a single language model of about 556M parameters (a 14-layer Qwen3.5 backbone) that learned text and speech together, paired with NVIDIA's NanoCodec for audio. Because one model owns both modalities, the output carries natural rhythm and emphasis instead of the flat, stitched tone of classic TTS pipelines. On the public Seed-TTS-eval it leads the open field on perceived audio quality, with the highest naturalness score and the cleanest results on noise and discontinuity. In exchange for that streaming-first design it gives up some word accuracy and speaker similarity.
Voice cloning is zero-shot: a few seconds of reference audio captures the speaker up front, and cloning adds nothing to the per-word cost. It speaks English (US and UK), Mexican Spanish, Brazilian Portuguese, and Dutch.