nineninesix/gepard-1.0 · Hugging Face

来源: https://huggingface.co/nineninesix/gepard-1.0 G 生成式，P 韵律感知，A 自回归文本转语音模型，面向R 实时D 对话

Gepard 是一个为实时对话构建的文本转语音模型。它从文本开始输入的那一刻起就开始说话，逐段生成音频，而不是等待完整句子——因此它听起来像现场语音，而非录音。它是一个同时学习文本和语音的单一语言模型，输出具有自然的节奏和时序，而非旧式流水线那种平淡、拼接的感觉。

名称 “Gepard”（发音 /geh-PART/）源自德语中的“猎豹”，寓意该模型的低延迟、高吞吐量流式特性。技术报告：tech_report (https://github.com/nineninesix-ai/gepard-train/blob/main/gepard_techreport.pdf)

https://huggingface.co/nineninesix/gepard-1.0#highlights 亮点:

• 每帧单次干净生成—— 整个音频帧（32个正交FSQ通道）一步采样完成，无需深度Transformer。
• 免费提升质量： CFG精化（通常是两遍开销）被融入权重中，同时保留两遍模式作为质量调节选项。
• 在vLLM上实时运行： 单张RTX 5090上约25倍实时，首个音频块（TTFA）约50毫秒。
• 可扩展到大量通话： 一张96GB GPU（RTX Pro 6000 Blackwell）最多可并行处理256路对话。
• 短片段语音克隆： 几秒的参考音频即可前置捕获一次说话人特征——克隆不会增加每个单词的额外成本。

https://huggingface.co/nineninesix/gepard-1.0#languages 语言

• 英语： 美式、英式
• 西班牙语： 墨西哥
• 葡萄牙语： 巴西
• 荷兰语： 荷兰

https://huggingface.co/nineninesix/gepard-1.0#samples 示例

在 Gepard Demo 空间 (https://huggingface.co/spaces/nineninesix/gepard) 中实时试用模型。

语言 / 口音示例英语 (en-US)英语 (en-GB)西班牙语 (es-MX)葡萄牙语 (pt-BR)荷兰语 (nl)

https://huggingface.co/nineninesix/gepard-1.0#model-details 模型详情

• 开发者： nineninesix.ai (https://www.nineninesix.ai/)
• 模型类型： 自回归（仅解码器）文本转语音模型
• 骨干网络： Qwen3.5 全注意力 Transformer（14层，隐藏层1024，8头；约500M参数）
• 音频编解码器： NVIDIA NeMo NanoCodec — FSQ，22.05 kHz，21.5帧/秒，1.89 kbps
• 参数量： ≈ 555.7M（骨干网络 + 音频接口 + 语音克隆压缩器）
• 采样率： 22,050 Hz
• 语言： 英语、西班牙语 (es-MX)、葡萄牙语 (pt-BR)、荷兰语 — 外加五种英语口音
• 许可协议： Apache 2.0（编解码器遵守 NVIDIA Open Model License Agreement (https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)）

https://huggingface.co/nineninesix/gepard-1.0#benchmarks 基准测试

基于公开的 Seed-TTS-eval 数据集测量——1088对提示词，所有模型使用相同的UUID和文本。

Gepard 在感知音频质量上领先——自然度（NISQA-MOS）最高，且在噪声、染色和不连续性方面最干净。作为牺牲，由于其流式优先的设计和速度，在说话人相似度（SIM）和词准确率（WER）上有所取舍——在需要干净自然的实时语音而非精确语音匹配的场景下非常适用。

https://huggingface.co/nineninesix/gepard-1.0#links 链接

• Demo 空间： huggingface.co/spaces/nineninesix/gepard (https://huggingface.co/spaces/nineninesix/gepard)
• 完整模型指南： gepard-train / docs / MODEL_GUIDE.md (https://github.com/nineninesix-ai/gepard-train/blob/main/docs/MODEL_GUIDE.md)
• 技术报告： tech_report (https://github.com/nineninesix-ai/gepard-train/blob/main/gepard_techreport.pdf)
• 推理： github.com/nineninesix-ai/gepard-inference (https://github.com/nineninesix-ai/gepard-inference)
• 训练： github.com/nineninesix-ai/gepard-train (https://github.com/nineninesix-ai/gepard-train)
• vLLM 服务： github.com/nineninesix-ai/gepard-vllm (https://github.com/nineninesix-ai/gepard-vllm)

https://huggingface.co/nineninesix/gepard-1.0#what-its-good-for 适用场景

实时和批量语音合成——语音助手、对话系统、内容配音——支持上述语言，并可选择从短参考片段进行零样本人声克隆。

请注意：

• 英语质量最佳；其他语言会因人声和内容而有所差异。
• 实时数据来自 vLLM 路径（gepard-vllm (https://github.com/nineninesix-ai/gepard-vllm)）；参考 PyTorch 运行时是行为的标准来源，但未针对吞吐量进行优化。
• 两遍 CFG 的语音相似度略低于单遍——单遍是生产环境的默认选项。

请负责任地使用。 未经说话人同意，不得克隆人声或合成语音，也不得制造误导性或有害内容。用户有责任遵守适用法律和下述许可协议。

https://huggingface.co/nineninesix/gepard-1.0#acknowledgments 致谢

基于 Qwen3-0.8B-Base (https://huggingface.co/Qwen/Qwen3.5-0.8B-Base) 作为骨干网络，音频部分基于 NVIDIA NeMo NanoCodec (https://huggingface.co/nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps)。训练数据由 LAION (https://huggingface.co/laion) 团队通过 Emilia 和 EmoNet-Voice 数据集提供。

https://huggingface.co/nineninesix/gepard-1.0#citation 引用

如果您在研究中使用此工作，请引用：

@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} }

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https://huggingface.co/nineninesix/gepard-1.0#license 许可协议

Apache 2.0 — 本模型及其权重根据 Apache License 2.0 (https://opensource.org/licenses/Apache-2.0) 发布。

本模型使用了 NVIDIA NeMo NanoCodec (https://huggingface.co/nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps)。该模型受 NVIDIA Open Model License Agreement (https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf) 约束。