gepard-1.0

<div align="center">

<h1>Gepard</h1> </div>

[!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}
}


@inproceedings{kwon2023vllm,
  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
  author={Kwon, Woosuk and Li, Zhuohan and Zhuang, Siyuan and Sheng, Ying and Zheng, Lianmin and Yu, Cody Hao and Gonzalez, Joseph E and Zhang, Hao and Stoica, Ion},
  booktitle={Proceedings of the 29th Symposium on Operating Systems Principles (SOSP)},
  pages={611--626},
  year={2023},
  eprint={2309.06180},
  archivePrefix={arXiv}
}

@article{dao2023flashattention2,
  title={FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning},
  author={Dao, Tri},
  journal={arXiv preprint arXiv:2307.08691},
  year={2023}
}

@article{nvidia2025nanocodec,
  title={NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference},
  author={Casanova, Edresson and Neekhara, Paarth and Langman, Ryan and Hussain, Shehzeen and Ghosh, Subhankar and Yang, Xuesong and Juki{\'c}, Ante and Li, Jason and Ginsburg, Boris},
  journal={arXiv preprint arXiv:2508.05835},
  year={2025}
}

@article{mentzer2023fsq,
  title={Finite Scalar Quantization: VQ-VAE Made Simple},
  author={Mentzer, Fabian and Agustsson, Eirikur and Tschannen, Michael and Malireddy, Srikanth and Alshina, Elena},
  journal={arXiv preprint arXiv:2309.15505},
  year={2023}
}

@article{nvidia2024magpie,
  title={Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment},
  author={Neekhara, Paarth and Hussain, Shehzeen and Ghosh, Subhankar and Li, Jason and Valle, Rafael and Badlani, Rohan and Ginsburg, Boris},
  journal={arXiv preprint arXiv:2406.17957},
  year={2024}
}

@article{ho2022cfg,
  title={Classifier-Free Diffusion Guidance},
  author={Ho, Jonathan and Salimans, Tim},
  journal={arXiv preprint arXiv:2207.12598},
  year={2022}
}

@article{rafailov2023dpo,
  title={Direct Preference Optimization: Your Language Model is Secretly a Reward Model},
  author={Rafailov, Rafael and Sharma, Archit and Mitchell, Eric and Ermon, Stefano and Manning, Christopher D and Finn, Chelsea},
  journal={arXiv preprint arXiv:2305.18290},
  year={2023}
}

@article{meng2024simpo,
  title={SimPO: Simple Preference Optimization with a Reference-Free Reward},
  author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
  journal={arXiv preprint arXiv:2405.14734},
  year={2024}
}

@article{li2023blip2,
  title={BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models},
  author={Li, Junnan and Li, Dongxu and Savarese, Silvio and Hoi, Steven},
  journal={arXiv preprint arXiv:2301.12597},
  year={2023}
}

@inproceedings{khosla2020supcon,
  title={Supervised Contrastive Learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  pages={18661--18673},
  year={2020},
  eprint={2004.11362},
  archivePrefix={arXiv}
}

@article{voicestar2025,
  title={VoiceStar: Robust Zero-Shot Autoregressive TTS with Duration Control and Extrapolation},
  author={Peng, Puyuan and Li, Shang-Wen and Mohamed, Abdelrahman and Harwath, David},
  journal={arXiv preprint arXiv:2505.19462},
  year={2025}
}

@article{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  journal={arXiv preprint arXiv:2212.04356},
  year={2022}
}

@inproceedings{emilialarge,
  author={He, Haorui and Shang, Zengqiang and Wang, Chaoren and Li, Xuyuan and Gu, Yicheng and Hua, Hua and Liu, Liwei and Yang, Chen and Li, Jiaqi and Shi, Peiyang and Wang, Yuancheng and Chen, Kai and Zhang, Pengyuan and Wu, Zhizheng},
  title={Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation},
  booktitle={arXiv:2501.15907},
  year={2025}
}

@article{emonet_voice_2025,
  author={Schuhmann, Christoph and Kaczmarczyk, Robert and Rabby, Gollam and Friedrich, Felix and Kraus, Maurice and Nadi, Kourosh and Nguyen, Huu and Kersting, Kristian and Auer, Sören},
  title={EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection},
  journal={arXiv preprint arXiv:2506.09827},
  year={2025}
}

@article{chen2021wavlm,
  title={WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing},
  author={Chen, Sanyuan and Wang, Chengyi and Chen, Zhengyang and Wu, Yu and Liu, Shujie and Chen, Zhuoyuan and Li, Jinyu and others},
  journal={arXiv preprint arXiv:2110.13900},
  year={2021}
}

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.