模型介绍
中文整理🐈 GitHub: https://github.com/hexgrad/kokoro
Kokoro** is an open-weight series of small but powerful TTS models.
This model is the result of a short training run that added 100 Chinese speakers from a professional dataset. The Chinese data was freely and permissively granted to us by LongMaoData, a professional dataset company. Thank you for making this model possible.
Separately, some crowdsourced synthetic English data also entered the training mix:[1]
1 hour of Maple, an American female.
1 hour of Sol, another American female.
And 1 hour of Vale, an older British female.
This model is not a strict upgrade over its predecessor since it drops many voices, but it is released early to gather feedback on new voices and tokenization. Aside from the Chinese dataset and the 3 hours of English, the rest of the data was left behind for this training run. The goal is to push the model series forward and ultimately restore some of the voices that were left behind.
Current guidance from the U.S. Copyright Office indicates that synthetic data generally does not qualify for copyright protection. Since this synthetic data is crowdsourced, the model trainer is not bound by any Terms of Service. This Apache licensed model also aligns with OpenAI's stated mission of broadly distributing the benefits of AI. If you would like to help further that mission, consider contributing permissive audio data to the cause.
[1] LongMaoData had no involvement in the crowdsourced synthetic English data.
[2] The following Chinese text is machine-translated.
Kokoro 是一系列体积虽小但功能强大的 TTS 模型。
该模型是经过短期训练的结果，从专业数据集中添加了100名中文使用者。中文数据由专业数据集公司「龙猫数据」免费且无偿地提供给我们。感谢你们让这个模型成为可能。
另外，一些众包合成英语数据也进入了训练组合：
- 1小时的 Maple，美国女性。
- 1小时的 Sol，另一位美国女性。
- 和1小时的 Vale，一位年长的英国女性。
由于该模型删除了许多声音，因此它并不是对其前身的严格升级，但它提前发布以收集有关新声音和标记化的反馈。除了中文数据集和3小时的英语之外，其余数据都留在本次训练中。目标是推动模型系列的发展，并最终恢复一些被遗留的声音。
美国版权局目前的指导表明，合成数据通常不符合版权保护的资格。由于这些合成数据是众包的，因此模型训练师不受任何服务条款的约束。该 Apache 许可模式也符合 OpenAI 所宣称的广泛传播 AI 优势的使命。如果您愿意帮助进一步完成这一使命，请考虑为此贡献许可的音频数据。
Releases
Usage
Samples ↗️
Model Facts
Acknowledgements
Releases
Model Published Training Data Langs & Voices SHA256
- ---- --------- ------------- -------------- ------
- *v1.1-zh** **2025 Feb 26** **>100 hours** **2 & 103** b1d8410f
v1.0 2025 Jan 27 Few hundred hrs 8 & 54 496dba11
v0.19 2024 Dec 25 =0.8.2 "misaki[zh]>=0.8.2" soundfile
!apt-get -qq -y install espeak-ng > /dev/null 2>&1
from IPython.display import display, Audio
!wget https://huggingface.co/hexgrad/Kokoro-82M-v1.1-zh/resolve/main/samples/make en.py
!python make en.py
display(Audio('HEARME en.wav', rate=24000, autoplay=True))
!wget https://huggingface.co/hexgrad/Kokoro-82M-v1.1-zh/resolve/main/samples/make zh.py
!python make zh.py
display(Audio('HEARME zf 001.wav', rate=24000, autoplay=False))
TODO: Improve usage. Similar to https://hf.co/hexgrad/Kokoro-82M#usage but you should pass repo id='hexgrad/Kokoro-82M-v1.1-zh' when constructing a KModel or KPipeline . See make en.py and make zh.py .
Model Facts
Architecture:**
StyleTTS 2: https://arxiv.org/abs/2306.07691
ISTFTNet: https://arxiv.org/abs/2203.02395
Decoder only: no diffusion, no encoder release
82 million parameters, same as https://hf.co/hexgrad/Kokoro-82M
Architected by:** Li et al @ https://github.com/yl4579/StyleTTS2
Trained by**: @rzvzn on Discord
Languages:** English, Chinese
Model SHA256 Hash:** b1d8410fa44dfb5c15471fd6c4225ea6b4e9ac7fa03c98e8bea47a9928476e2b
Acknowledgements
TODO: Write acknowledgements. Similar to https://hf.co/hexgrad/Kokoro-82M#acknowledgements
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
你将获得什么
完整仓库方式包含该固定版本中的权重、配置、分词器及说明文件；指定版本方式只下载所选文件或整组分片。多模态模型的投影文件、配置等依赖，请按作者说明配齐。
完整仓库可能包含多个权重格式，因此需要的磁盘空间可能大于单一格式的模型大小。下载文件不等于完成模型部署。
使用前须知
运行模型需要的设备、工具与依赖，以原作者说明为准。
是否允许商用、修改和分发，请查看模型许可证。
闲社提供资料与镜像下载方法，不代表已运行模型或完成安全审计。下载的代码应先检查，再执行。
资料或下载有问题？
登录后可提交反馈。