IndexTTS 2.5 Local Launcher

这是基于官方 IndexTTS 2.5 制作的 Windows 社区启动器版本，增加了统一的本地语音工作台、运行管理、模型完整性检查、GPU 状态和实时日志。模型推理仍由原始 IndexTTS 代码完成。

Community derivative of IndexTTS 2.5 with a unified Windows launcher, local workbench, runtime status, model checks, GPU information, and live logs.

整合包： 点击跳转下载

本版本增加的内容

• http://127.0.0.1:7859/ 统一入口，可切换“语音工作台”和“运行管理”。
• 独立管理 7860 Gradio 推理服务，页面刷新不会重复加载模型。
• 检查 IndexTTS 2.5 权重目录、Python 环境、GPU 与服务状态。
• 支持启动、停止、重启、实时日志和移动端页面。
• Windows 双击 启动IndexTTS2.5.cmd 即可打开启动器。
• 页面中的项目路径根据仓库位置动态解析，不写死本机盘符。

Windows 快速开始

1. 安装 Python 3.11、uv 和适配 NVIDIA GPU 的 CUDA 环境。
2. 在项目目录执行 uv sync --extra webui --frozen，生成 .venv。
3. 按下方官方文档下载 IndexTTS 2.5 权重，并放入 checkpoints 目录。模型权重不包含在本仓库中。
4. 双击 启动IndexTTS2.5.cmd，或运行：

.\.venv\Scripts\python.exe launcher.py --autostart

默认地址：

• 统一启动器：http://127.0.0.1:7859/
• 内部 Gradio 服务：http://127.0.0.1:7860/

端口、模型相对目录和推理精度可在 launcher_config.json 中调整。

开源范围与许可证

• 本仓库不包含模型权重、虚拟环境、生成音频、参考音频、用户预设或 API 密钥。
• 原始 IndexTTS 代码及本衍生版本继续遵守仓库中的 LICENSE、LICENSE_ZH.txt 和 DISCLAIMER。
• 修改内容和上游归属见 COMMUNITY_NOTICE.md。

Any modifications made to the original model in this Derivative Work are not endorsed, warranted, or guaranteed by the original right-holder of the original model, and the original right-holder disclaims all liability related to this Derivative Work.

作者：Fenglairh · B站主页 · 微信 fenglairh · RunningHub 国内站（邀请码 rh-v1066）

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Upstream IndexTTS README

简体中文 | English

👉🏻 IndexTTS 👈🏻

Model	Demos	Paper	Modelscope	HuggingFace
IndexTTS-2.5	Demos	-	Modelscope	HuggingFace
IndexTTS-2	Demos	Paper	Modelscope	HuggingFace
IndexTTS-1.5	Demos	Paper	Modelscope	HuggingFace
IndexTTS	Demos	Paper	Modelscope	HuggingFace

📣 Updates

• 2026/07/17 🔥 We release IndexTTS-2.5
  • The model now supports Chinese, English, Japanese, Spanish and Arabic, with faster inference speed compared to IndexTTS-2, while maintaining the cross-lingual and timbre-emotion disentanglement capabilities.
  • The model improves the controllability of Chinese Pinyin and English CMU phonemes and Japanese Kana.
• 2025/09/08 🔥 We release IndexTTS-2
  • The first autoregressive TTS model with precise synthesis duration control, supporting both controllable and uncontrollable modes. This functionality is not yet enabled in this release.
  • The model achieves highly expressive emotional speech synthesis, with emotion-controllable capabilities enabled through multiple input modalities.
• 2025/05/14 🔥 We release IndexTTS-1.5, significantly improving the model's stability and its performance in the English language.
• 2025/03/25 🔥 We release IndexTTS-1.0 with model weights and inference code.
• 2025/02/12 🎉 We submitted our paper to arXiv, and released our demos and test sets.

Feel IndexTTS

IndexTTS2.5: The Future of Voice, Now Generating

IndexTTS2: The Future of Voice, Now Generating

Evaluation

Table 1: Zero-shot TTS evaluation results on CV3-Eval test set (Arabic uses an in-house test set). WER (%) ↓ and Speaker Similarity (SS) ↑ are reported. †Results cited from the original paper.

Model	Params	test-zh WER (%) ↓	test-zh SS (%) ↑	test-en WER (%) ↓	test-en SS (%) ↑	test-es WER (%) ↓	test-es SS (%) ↑	test-ja WER (%) ↓	test-ja SS (%) ↑	test-ar WER (%) ↓	test-ar SS (%) ↑	Average WER (%) ↓	Average SS (%) ↑
VoxCPM2	2B	3.88	74.99	5.13	71.57	5.49	74.67	6.69	72.90	14.94	65.99	7.22	72.02
OmniVoice	0.8B	3.41	72.99	3.62	70.13	3.52	74.14	5.38	70.49	17.88	64.22	6.76	70.39
Moss-TTS 1.5	8B	4.02	72.68	4.45	67.46	3.83	71.75	10.97	68.71	23.71	62.21	9.40	68.56
CosyVoice3-0.5B	0.5B	3.84	80.01	4.88	74.16	4.04	78.85	-	76.36	-	-	-	-
CosyVoice3-1.5B	1.5B	3.91†	-	4.99†	-	4.47†	-	7.57†	-	-	-	-	-
FireRedTTS-2	1.5B	8.22	68.10	14.92	56.93	-	-	-	-	-	-	-	-
Fish Audio S2 Pro	4B	3.62	67.79	3.83	61.66	2.93	67.44	5.15	66.15	14.15	59.43	5.94	64.49
Qwen3-TTS	1.7B	3.27	73.02	5.06	67.17	2.87	73.17	5.89	70.18	-	-	-	-
IndexTTS2.5	0.8B	4.36	77.10	5.12	68.06	3.75	76.39	5.66	74.62	14.88	69.74	6.75	73.18
IndexTTS2.5-RL	0.8B	3.93	77.92	3.89	67.79	3.33	76.68	5.30	75.41	13.58	70.36	6.00	73.63

Table 2: Cross-lingual TTS evaluation on CV3-Eval test set (Chinese prompt → target language, Arabic uses an in-house test set). WER (%) ↓ and Speaker Similarity (SS) ↑ are reported.

Model	Params	zh→en WER (%) ↓	zh→en SS (%) ↑	zh→es WER (%) ↓	zh→es SS (%) ↑	zh→ja WER (%) ↓	zh→ja SS (%) ↑	zh→ar WER (%) ↓	zh→ar SS (%) ↑	Average WER (%) ↓	Average SS (%) ↑
VoxCPM2	2B	4.48	64.25	16.38	64.89	11.84	71.54	11.09	67.62	10.95	67.08
OmniVoice	0.8B	3.74	64.91	5.84	62.08	9.09	69.06	19.80	65.27	9.62	65.33
Moss-TTS 1.5	8B	6.13	59.23	4.32	56.63	11.52	65.54	17.03	62.93	9.75	61.08
CosyVoice3-0.5B	0.5B	3.23	62.79	4.58	64.04	-	-	-	-	-	-
CosyVoice3-1.5B	1.5B	4.32	-	-	-	13.70	-	-	-	-	-
FireRedTTS-2	1.5B	9.34	53.19	12.25	58.31	19.05	64.12	-	-	-	-
Fish Audio S2 Pro	4B	4.14	55.89	4.46	55.57	10.48	61.74	14.49	59.80	8.39	58.25
Qwen3-TTS	1.7B	5.74	63.04	5.15	68.02	36.09	65.71	-	-	-	-
IndexTTS2.5	0.8B	3.62	63.83	5.17	65.48	6.57	74.16	9.51	71.02	6.22	68.62
IndexTTS2.5-RL	0.8B	3.55	67.47	4.86	64.47	6.38	75.82	9.89	73.05	6.17	70.20

Contact

QQ Group：663272642(No.4) 1013410623(No.5)
Discord：https://discord.gg/uT32E7KDmy
Email：indexspeech@bilibili.com
You are welcome to join our community! 🌏
欢迎大家来交流讨论！

Caution

Thank you for your support of the bilibili indextts project! Please note that the only official channel maintained by the core team is: https://github.com/index-tts/index-tts. Any other websites or services are not official, and we cannot guarantee their security, accuracy, or timeliness. For the latest updates, please always refer to this official repository.

Tips: Please contact the authors for more detailed information. For commercial usage and cooperation, please contact indexspeech@bilibili.com.

Usage Instructions

⚙️ Environment Setup

1. Ensure that you have both git and git-lfs on your system.

The Git-LFS plugin must also be enabled on your current user account:

git lfs install

2. Download this repository:

git clone https://github.com/index-tts/index-tts.git && cd index-tts
git lfs pull  # download large repository files

3. Install the uv package manager. It is required for a reliable, modern installation environment.

Tip

Quick & Easy Installation Method:

There are many convenient ways to install the uv command on your computer. Please check the link above to see all options. Alternatively, if you want a very quick and easy method, you can install it as follows:

pip install -U uv

Warning

We only support the uv installation method. Other tools, such as conda or pip, don't provide any guarantees that they will install the correct dependency versions. You will almost certainly have random bugs, error messages, missing GPU acceleration, and various other problems if you don't use uv. Please do not report any issues if you use non-standard installations, since almost all such issues are invalid.

Furthermore, uv is up to 115x faster than pip, which is another great reason to embrace the new industry-standard for Python project management.

4. Install required dependencies:

We use uv to manage the project's dependency environment. The following command will automatically create a .venv project-directory and then installs the correct versions of Python and all required dependencies:

uv sync --all-extras

If the download is slow, please try a local mirror, for example any of these local mirrors in China (choose one mirror from the list below):

uv sync --all-extras --default-index "https://mirrors.aliyun.com/pypi/simple"

uv sync --all-extras --default-index "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"

Tip

Available Extra Features:

• --all-extras: Automatically adds every extra feature listed below. You can remove this flag if you want to customize your installation choices.
• --extra webui: Adds WebUI support (recommended).
• --extra deepspeed: Adds DeepSpeed support (may speed up inference on some systems).

Important

Important (Windows): The DeepSpeed library may be difficult to install for some Windows users. You can skip it by removing the --all-extras flag. If you want any of the other extra features above, you can manually add their specific feature flags instead.

Important (Linux/Windows): If you see an error about CUDA during the installation, please ensure that you have installed NVIDIA's CUDA Toolkit version 12.8 (or newer) on your system.

5. Download the required models via uv tool:

Download via huggingface-cli:

uv tool install "huggingface-hub[cli,hf_xet]"

# IndexTTS-2.5
hf download IndexTeam/IndexTTS-2.5 --local-dir=checkpoints

# IndexTTS-2
hf download IndexTeam/IndexTTS-2 --local-dir=checkpoints_2

Or download via modelscope:

uv tool install "modelscope"

# IndexTTS-2.5
modelscope download --model IndexTeam/IndexTTS-2.5 --local_dir checkpoints

# IndexTTS-2
modelscope download --model IndexTeam/IndexTTS-2 --local_dir checkpoints_2

Important

If the commands above aren't available, please carefully read the uv tool output. It will tell you how to add the tools to your system's path.

Note

In addition to the above models, some small models will also be automatically downloaded when the project is run for the first time. If your network environment has slow access to HuggingFace, it is recommended to execute the following command before running the code:

export HF_ENDPOINT="https://hf-mirror.com"

🖥️ Checking PyTorch GPU Acceleration

If you need to diagnose your environment to see which GPUs are detected, you can use our included utility to check your system:

uv run tools/gpu_check.py

🔥 Quickstart

🌐 Web Demo

# IndexTTS2 (default)
uv run webui.py

# IndexTTS2.5
uv run webui.py --version 2.5 --model_dir ./checkpoints_25

Open your browser and visit http://127.0.0.1:7860 to see the demo.

You can also adjust the settings to enable features such as BF16(IndexTTS 2.5)/FP16(IndexTTS 2) inference (lower VRAM usage), DeepSpeed acceleration, compiled CUDA kernels for speed, etc. All available options can be seen via the following command:

uv run webui.py -h

Have fun!

Important

It can be very helpful to use FP16/BF16 (half-precision) inference. It is faster and uses less VRAM, with a very small quality loss.

DeepSpeed may also speed up inference on some systems, but it could also make it slower. The performance impact is highly dependent on your specific hardware, drivers and operating system. Please try with and without it, to discover what works best on your personal system.

Lastly, be aware that all uv commands will automatically activate the correct per-project virtual environments. Do not manually activate any environments before running uv commands, since that could lead to dependency conflicts!

🚀 Serving with vLLM

For production deployment, see the vLLM recipe for IndexTTS.

📝 Using in Python

To run scripts, you must use the uv run <file.py> command to ensure that the code runs inside your current "uv" environment. It may sometimes also be necessary to add the current directory to your PYTHONPATH, to help it find the IndexTTS modules.

Example of running a script via uv:

# IndexTTS2
PYTHONPATH="$PYTHONPATH:." uv run indextts/infer_v2.py

# IndexTTS2.5
PYTHONPATH="$PYTHONPATH:." uv run indextts/infer_v2_5.py \
  --cfg_path checkpoints/config_v2_5.yaml \
  --model_dir checkpoints \
  --text "Hello world" \
  --lang EN

Here are several examples of how to use in your own scripts:

0. Initialize IndexTTS

# IndexTTS2
from indextts.infer_v2 import IndexTTS2
tts = IndexTTS2(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", use_fp16=False, use_cuda_kernel=False, use_deepspeed=False)

# IndexTTS2.5
from indextts.infer_v2_5 import IndexTTS2
tts = IndexTTS2(cfg_path="checkpoints_25/config_v2_5.yaml", model_dir="checkpoints_25", use_bf16=True)

1. Synthesize new speech with a single reference audio file (voice cloning):

text = "Translate for me, what is a surprise!"

# IndexTTS2
tts.infer(spk_audio_prompt='examples/voice_01.wav', text=text, output_path="gen.wav", verbose=True)

# IndexTTS2.5 (multilingual, with language selection)
tts.infer(spk_audio_prompt='examples/voice_01.wav', text=text, lang="EN", output_path="gen.wav", verbose=True)

2. Using a separate, emotional reference audio file to condition the speech synthesis:

text = "酒楼丧尽天良，开始借机竞拍房间，哎，一群蠢货。"

# IndexTTS2
tts.infer(spk_audio_prompt='examples/voice_07.wav', text=text, output_path="gen.wav", emo_audio_prompt="examples/emo_sad.wav", verbose=True)

# IndexTTS2.5
tts.infer(spk_audio_prompt='examples/voice_07.wav', text=text, lang="ZH", output_path="gen.wav", emo_audio_prompt="examples/emo_sad.wav", verbose=True)

3. When an emotional reference audio file is specified, you can optionally set the emo_alpha to adjust how much it affects the output. Valid range is 0.0 - 1.0, and the default value is 1.0 (100%):

text = "酒楼丧尽天良，开始借机竞拍房间，哎，一群蠢货。"

# IndexTTS2
tts.infer(spk_audio_prompt='examples/voice_07.wav', text=text, output_path="gen.wav", emo_audio_prompt="examples/emo_sad.wav", emo_alpha=0.9, verbose=True)

# IndexTTS2.5
tts.infer(spk_audio_prompt='examples/voice_07.wav', text=text, output_path="gen.wav", lang="ZH", emo_audio_prompt="examples/emo_sad.wav", emo_alpha=0.9, verbose=True)

4. It's also possible to omit the emotional reference audio and instead provide an 8-float list specifying the intensity of each emotion, in the following order: [happy, angry, sad, afraid, disgusted, melancholic, surprised, calm]. You can additionally use the use_random parameter to introduce stochasticity during inference; the default is False, and setting it to True enables randomness:

Note

Enabling random sampling will reduce the voice cloning fidelity of the speech synthesis.

text = "对不起嘛！我的记性真的不太好，但是和你在一起的事情，我都会努力记住的~"

# IndexTTS2
tts.infer(spk_audio_prompt='examples/09.wav', text=text, output_path="gen.wav", emo_vector=[0, 0, 0.8, 0, 0, 0, 0, 0], use_random=False, verbose=True)

# IndexTTS2.5
tts.infer(spk_audio_prompt='examples/09.wav', text=text, lang="ZH", output_path="gen.wav", emo_vector=[0, 0, 0.8, 0, 0, 0, 0, 0], use_random=False, verbose=True)

5. Alternatively, you can enable use_emo_text to guide the emotions based on your provided text script. Your text script will then automatically be converted into emotion vectors. It's recommended to use emo_alpha around 0.6 (or lower) when using the text emotion modes, for more natural sounding speech. You can introduce randomness with use_random (default: False; True enables randomness):

text = "快躲起来！是他要来了！他要来抓我们了！"

# IndexTTS2
tts.infer(spk_audio_prompt='examples/voice_12.wav', text=text, output_path="gen.wav", emo_alpha=0.6, use_emo_text=True, use_random=False, verbose=True)

# IndexTTS2.5
tts.infer(spk_audio_prompt='examples/voice_12.wav', text=text, lang="ZH", output_path="gen.wav", emo_alpha=0.6, use_emo_text=True, use_random=False, verbose=True)

6. It's also possible to directly provide a specific text emotion description via the emo_text parameter. Your emotion text will then automatically be converted into emotion vectors. This gives you separate control of the text script and the text emotion description:

text = "快躲起来！是他要来了！他要来抓我们了！"
emo_text = "你吓死我了！你是鬼吗？"

# IndexTTS2
tts.infer(spk_audio_prompt='examples/voice_12.wav', text=text, output_path="gen.wav", emo_alpha=0.6, use_emo_text=True, emo_text=emo_text, use_random=False, verbose=True)

# IndexTTS2.5
tts.infer(spk_audio_prompt='examples/voice_12.wav', text=text, lang="ZH", output_path="gen.wav", emo_alpha=0.6, use_emo_text=True, emo_text=emo_text, use_random=False, verbose=True)

Tip

IndexTTS2.5 Pinyin/English phonemes/Japanese Kana Usage Notes:

IndexTTS2.5 now can support these character replacement, with better instruction-following capability. For the full list of valid entries, please refer to checkpoints/pinyin.vocab for Pinyin, and 'https://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b' for CMU dictionary.

Example:

他在银<行|XING2>里<行|HANG2>走了半天，发现这笔业务办不<行|HANG2>。

He had a <minute|M IH1 . N AH0 T> to examine the <minute|M AY0 . N UW1 T> details of the contract.

彼は料理が<上手|じょうず>だが、囲碁では<上手|うわて>に負けた。

IndexTTS2 Pinyin Usage Notes:

IndexTTS2 still supports mixed modeling of Chinese characters and Pinyin. When you need precise pronunciation control, please provide text with specific Pinyin annotations to activate the Pinyin control feature. Note that Pinyin control does not work for every possible consonant–vowel combination; only valid Chinese Pinyin cases are supported. For the full list of valid entries, please refer to checkpoints/pinyin.vocab.

Example:

之前你做DE5很好，所以这一次也DEI3做DE2很好才XING2，如果这次目标完成得不错的话，我们就直接打DI1去银行取钱。

IndexTTS1 Usage Notes:

You can also use our previous IndexTTS1 model by importing a different module:

from indextts.infer import IndexTTS
tts = IndexTTS(model_dir="checkpoints",cfg_path="checkpoints/config.yaml")
voice = "examples/voice_07.wav"
text = "大家好，我现在正在bilibili 体验 ai 科技，说实话，来之前我绝对想不到！AI技术已经发展到这样匪夷所思的地步了！比如说，现在正在说话的其实是B站为我现场复刻的数字分身，简直就是平行宇宙的另一个我了。如果大家也想体验更多深入的AIGC功能，可以访问 bilibili studio，相信我，你们也会吃惊的。"
tts.infer(voice, text, 'gen.wav')

For more detailed information, see README_INDEXTTS_1_5, or visit the IndexTTS1 repository at index-tts:v1.5.0.

Acknowledgements

1. tortoise-tts
2. XTTSv2
3. BigVGAN
4. wenet
5. icefall
6. maskgct
7. seed-vc

📚 Citation

🌟 If you find our work helpful, please leave us a star and cite our paper.

IndexTTS2:

@article{zhou2025indextts2,
  title={IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech},
  author={Siyi Zhou, Yiquan Zhou, Yi He, Xun Zhou, Jinchao Wang, Wei Deng, Jingchen Shu},
  journal={arXiv preprint arXiv:2506.21619},
  year={2025}
}

IndexTTS:

@article{deng2025indextts,
  title={IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System},
  author={Wei Deng, Siyi Zhou, Jingchen Shu, Jinchao Wang, Lu Wang},
  journal={arXiv preprint arXiv:2502.05512},
  year={2025},
  doi={10.48550/arXiv.2502.05512},
  url={https://arxiv.org/abs/2502.05512}
}