[!IMPORTANT] 2026-08-06 — updated stop_head weights. Every other parameter is unchanged. This fixes premature stopping: multi-sentence inputs are now carried through to the end instead of cutting off at the first sentence boundary, and the effective duration ceiling is lifted. Short-phrase behaviour is unaffected.

GEnerative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue

Gepard is a text-to-speech model built for real-time conversation. It starts speaking the moment text begins arriving, generating audio piece by piece instead of waiting for a full sentence — so it feels like a live voice, not a recording. It's a single language model that learned text and speech together, so the output carries natural rhythm and timing rather than the flat, stitched tone of older pipelines.

The name evokes "Gepard"(/geh-PART/), German for cheetah — a nod to the model's low-latency, high-throughput streaming.

Want to use Gepard in production without hosting it yourself? You can skip the deployment and use our real-time TTS API — a fully managed, Cartesia-compatible service with millisecond time-to-first-chunk, voice cloning, and streaming built in. If you've used Cartesia, you already know how to use it: point the base URL at https://api.nineninesix.ai and the official Cartesia SDKs just work. Roughly 22 hours of audio for $5, and free credits to start (no credit card required). Try the live demo or grab an API key.

Highlights:

• One clean pass per frame — the whole audio frame (32 orthogonal FSQ channels) is sampled in one step, no depth-transformer.
• Extra quality, for free: CFG refinement (normally a two-pass cost) is baked into the weights, with the two-pass mode still available as a quality dial.
• Real-time on vLLM: ~25× real time on a single RTX 5090, with first audio chunk (TTFA) in ~50 ms
• Scales to many callers: One 96GB GPU (RTX Pro 6000 Blackwell) holds up to 256 conversations in parallel.
• Voice cloning from a short clip: A few seconds of reference audio captures the speaker once, up front — cloning adds nothing to the per-word cost.

Languages

• English: US, UK
• Spanish: Mexico
• Portuguese: Brazil
• Dutch: Netherlands

Samples

Try the model live in the Gepard Demo Space.

Model Details

• Developed by: nineninesix.ai
• Model type: Autoregressive (decoder-only) text-to-speech model
• Backbone: Qwen3.5 full-attention transformer (14 layers, hidden 1024, 8 heads; ~500M params)
• Audio codec: NVIDIA NeMo NanoCodec — FSQ, 22.05 kHz, 21.5 frames/s, 1.89 kbps
• Parameters: ≈ 555.7M (backbone + audio interface + voice-cloning compressor)
• Sample rate: 22,050 Hz
• Languages: English, Spanish (es-MX), Portuguese (pt-BR), Dutch — plus five English accents
• License: Apache 2.0 (the codec has NVIDIA Open Model License Agreement)

Benchmarks

Measured on the public Seed-TTS-eval — 1088 paired prompts, identical UUIDs and texts across every model.

Gepard leads on perceived audio quality — highest naturalness (NISQA-MOS) and cleanest on noise, coloration, and discontinuity. In exchange for its streaming-first design and speed, it trades some speaker similarity (SIM) and word accuracy (WER) — a strong fit where a clean, natural real-time voice matters more than exact voice matching.

Links

• Demo Space: huggingface.co/spaces/nineninesix/gepard
• Playground (hosted, no account): nineninesix.ai/playground
• API docs: nineninesix.ai/docs
• Full model guide: gepard-train / docs / MODEL_GUIDE.md
• Technical Report: tech_report
• Inference: github.com/nineninesix-ai/gepard-inference
• Training: github.com/nineninesix-ai/gepard-train
• vLLM serving: github.com/nineninesix-ai/gepard-vllm

What it's good for

Real-time and batch speech synthesis — voice agents, dialogue systems, content voiceover — in the supported languages, with optional zero-shot voice cloning from a short reference clip.

Keep in mind:

• Quality is strongest in English; other languages vary by voice and content.
• Real-time numbers come from the vLLM path (gepard-vllm); the reference PyTorch runner is the source of truth for behavior but isn't tuned for throughput.
• Two-pass CFG dips voice similarity slightly versus single-pass — single-pass is the production default.

Use it responsibly. Don't clone a voice or synthesize speech without the speaker's consent, or create misleading or harmful content. You're responsible for following applicable laws and the licenses below.

Acknowledgments

Built on Qwen3-0.8B-Base as the backbone and NVIDIA NeMo NanoCodec for audio. Training data provided by the LAION team through the Emilia and EmoNet-Voice datasets.

Citation

If you use this work in your research, please cite:

@misc{gepard_2026,
  author        = {Denis Pavlov, Ulanbek Abdurazakov and Nursultan Bakashov},
  title         = {{GEPARD}: Generative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue},
  year          = {2026},
  eprint        = {2609.04222},
  archivePrefix = {arXiv},
  primaryClass  = {eess.AS},
  doi           = {10.48550/arXiv.2609.04222},
  url           = {https://arxiv.org/abs/2609.04222}
}

References

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}


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License

Apache 2.0 — this model and its weights are released under the Apache License 2.0.

This model uses the NVIDIA NeMo NanoCodec. That model is governed by the NVIDIA Open Model License Agreement.