简体中文 | English

👉🏻 IndexTTS 👈🏻

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

📣 Updates

• 2026/08/10 🔥 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]"

hf download IndexTeam/IndexTTS-2 --local-dir=checkpoints

Or download via modelscope:

uv tool install "modelscope"

modelscope download --model IndexTeam/IndexTTS-2 --local_dir checkpoints

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

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!

📝 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.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/config.yaml", model_dir="checkpoints", 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/Japan 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.

IndexTTS2.5:

@misc{li2026indextts25technicalreport,
      title={IndexTTS 2.5 Technical Report}, 
      author={Yunpei Li and Xun Zhou and Jinchao Wang and Lu Wang and Yong Wu and Siyi Zhou and Yiquan Zhou and Jingchen Shu},
      year={2026},
      eprint={2601.03888},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2601.03888}, 
}

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}
}