nineninesix/gepard-1.0

Gepard

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. Technical Report: tech_report

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.

Language / Accent	Sample
English (en-US)
English (en-GB)
Spanish (es-MX)
Portuguese (pt-BR)
Dutch (nl)

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.

Model	WER ↓	SIM ↑	UTMOS ↑	NISQA-MOS ↑	NOI ↑	COL ↑	DIS ↑
VoxCPM2	0.015	0.867	2.42	3.97	3.86	3.96	4.30
Fish-S2	0.016	0.789	2.80	4.18	3.87	4.14	4.44
OmniVoice	0.016	0.848	2.63	4.17	4.14	4.13	4.44
Qwen3-TTS	0.017	0.833	2.87	4.18	3.89	4.14	4.43
Echo-TTS	0.022	0.824	2.60	4.08	3.78	4.07	4.36
Gepard 1.0 (Ours)	0.036	0.585	2.64	4.25	4.16	4.16	4.51
Chatterbox	0.063	0.796	2.70	4.19	4.12	4.12	4.46

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
• 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:

@software{gepard_2026,
  author = {Nineninesix, Inc.},
  title = {Gepard: GEnerative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/nineninesix/gepard-1.0}},
  note = {Open-source, vLLM-native autoregressive TTS}
}

References

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    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.