# index-tts
**Repository Path**: nbedk/index-tts
## Basic Information
- **Project Name**: index-tts
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-19
- **Last Updated**: 2025-09-14
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## 👉🏻 IndexTTS2 👈🏻
IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech
[](assets/IndexTTS2_banner.png)
### Abstract
Existing autoregressive large-scale text-to-speech (TTS) models have advantages in speech naturalness, but their token-by-token generation mechanism makes it difficult to precisely control the duration of synthesized speech. This becomes a significant limitation in applications requiring strict audio-visual synchronization, such as video dubbing.
This paper introduces IndexTTS2, which proposes a novel, general, and autoregressive model-friendly method for speech duration control.
The method supports two generation modes: one explicitly specifies the number of generated tokens to precisely control speech duration; the other freely generates speech in an autoregressive manner without specifying the number of tokens, while faithfully reproducing the prosodic features of the input prompt.
Furthermore, IndexTTS2 achieves disentanglement between emotional expression and speaker identity, enabling independent control over timbre and emotion. In the zero-shot setting, the model can accurately reconstruct the target timbre (from the timbre prompt) while perfectly reproducing the specified emotional tone (from the style prompt).
To enhance speech clarity in highly emotional expressions, we incorporate GPT latent representations and design a novel three-stage training paradigm to improve the stability of the generated speech. Additionally, to lower the barrier for emotional control, we designed a soft instruction mechanism based on text descriptions by fine-tuning Qwen3, effectively guiding the generation of speech with the desired emotional orientation.
Finally, experimental results on multiple datasets show that IndexTTS2 outperforms state-of-the-art zero-shot TTS models in terms of word error rate, speaker similarity, and emotional fidelity. Audio samples are available at: IndexTTS2 demo page.
**Tips:** Please contact the authors for more detailed information. For commercial usage and cooperation, please contact indexspeech@bilibili.com.
### Feel IndexTTS2
**IndexTTS2: The Future of Voice, Now Generating**
[](https://www.bilibili.com/video/BV136a9zqEk5)
*Click the image to watch the IndexTTS2 introduction video.*
### Contact
QQ Group：553460296(No.1) 663272642(No.4) \
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！
## 📣 Updates
- `2025/09/08` 🔥🔥🔥 We release **IndexTTS-2** to the world!
- 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.
## 🖥️ Neural Network Architecture
Architectural overview of IndexTTS2, our state-of-the art speech model:
The key contributions of **IndexTTS2** are summarized as follows:
- We propose a duration adaptation scheme for autoregressive TTS models. IndexTTS2 is the first autoregressive zero-shot TTS model to combine precise duration control with natural duration generation, and the method is scalable for any autoregressive large-scale TTS model.
- The emotional and speaker-related features are decoupled from the prompts, and a feature fusion strategy is designed to maintain semantic fluency and pronunciation clarity during emotionally rich expressions. Furthermore, a tool was developed for emotion control, utilizing natural language descriptions for the benefit of users.
- To address the lack of highly expressive speech data, we propose an effective training strategy, significantly enhancing the emotional expressiveness of zeroshot TTS to State-of-the-Art (SOTA) level.
- We will publicly release the code and pre-trained weights to facilitate future research and practical applications.
## Model Download
| **HuggingFace** | **ModelScope** |
|----------------------------------------------------------|----------------------------------------------------------|
| [😁 IndexTTS-2](https://huggingface.co/IndexTeam/IndexTTS-2) | [IndexTTS-2](https://modelscope.cn/models/IndexTeam/IndexTTS-2) |
| [IndexTTS-1.5](https://huggingface.co/IndexTeam/IndexTTS-1.5) | [IndexTTS-1.5](https://modelscope.cn/models/IndexTeam/IndexTTS-1.5) |
| [IndexTTS](https://huggingface.co/IndexTeam/Index-TTS) | [IndexTTS](https://modelscope.cn/models/IndexTeam/Index-TTS) |
## Usage Instructions
### ⚙️ Environment Setup
1. Ensure that you have both [git](https://git-scm.com/downloads)
and [git-lfs](https://git-lfs.com/) on your system.
The Git-LFS plugin must also be enabled on your current user account:
```bash
git lfs install
```
2. Download this repository:
```bash
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](https://docs.astral.sh/uv/getting-started/installation/).
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:
>
> ```bash
> 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](https://github.com/astral-sh/uv/blob/main/BENCHMARKS.md)
> 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 install the correct versions of all dependencies into your `.venv` directory:
```bash
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):
```bash
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](https://developer.nvidia.com/cuda-toolkit)
> version **12.8** (or newer) on your system.
5. Download the required models:
Download via `huggingface-cli`:
```bash
uv tool install "huggingface_hub[cli]"
hf download IndexTeam/IndexTTS-2 --local-dir=checkpoints
```
Or download via `modelscope`:
```bash
uv tool install "modelscope"
modelscope download --model IndexTeam/IndexTTS-2 --local_dir checkpoints
```
> [!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:
>
> 除了以上模型外，项目初次运行时还会自动下载一些小模型，如果您的网络环境访问HuggingFace的速度较慢，推荐执行：
>
> ```bash
> 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:
```bash
uv run tools/gpu_check.py
```
### 🔥 IndexTTS2 Quickstart
#### 🌐 Web Demo
```bash
uv run webui.py
```
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 FP16 inference (lower
VRAM usage), DeepSpeed acceleration, compiled CUDA kernels for speed, etc. All
available options can be seen via the following command:
```bash
uv run webui.py -h
```
Have fun!
> [!IMPORTANT]
> It can be very helpful to use **FP16** (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.
#### 📝 Using IndexTTS2 in Python
To run scripts, you *must* use the `uv run ` 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`:
```bash
PYTHONPATH="$PYTHONPATH:." uv run indextts/infer_v2.py
```
Here are several examples of how to use IndexTTS2 in your own scripts:
1. Synthesize new speech with a single reference audio file (voice cloning):
```python
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)
text = "Translate for me, what is a surprise!"
tts.infer(spk_audio_prompt='examples/voice_01.wav', text=text, output_path="gen.wav", verbose=True)
```
2. Using a separate, emotional reference audio file to condition the speech synthesis:
```python
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)
text = "酒楼丧尽天良，开始借机竞拍房间，哎，一群蠢货。"
tts.infer(spk_audio_prompt='examples/voice_07.wav', text=text, 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%):
```python
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)
text = "酒楼丧尽天良，开始借机竞拍房间，哎，一群蠢货。"
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)
```
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.
```python
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)
text = "哇塞！这个爆率也太高了！欧皇附体了！"
tts.infer(spk_audio_prompt='examples/voice_10.wav', text=text, output_path="gen.wav", emo_vector=[0, 0, 0, 0, 0, 0, 0.45, 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):
```python
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)
text = "快躲起来！是他要来了！他要来抓我们了！"
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)
```
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:
```python
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)
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？"
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)
```
### Legacy: IndexTTS1 User Guide
You can also use our previous IndexTTS1 model by importing a different module:
```python
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](archive/README_INDEXTTS_1_5.md),
or visit the IndexTTS1 repository at index-tts:v1.5.0.
## Our Releases and Demos
### IndexTTS2: [[Paper]](https://arxiv.org/abs/2506.21619); [[Demo]](https://index-tts.github.io/index-tts2.github.io/); [[HuggingFace]](https://huggingface.co/spaces/IndexTeam/IndexTTS-2-Demo)
### IndexTTS1: [[Paper]](https://arxiv.org/abs/2502.05512); [[Demo]](https://index-tts.github.io/); [[ModelScope]](https://modelscope.cn/studios/IndexTeam/IndexTTS-Demo); [[HuggingFace]](https://huggingface.co/spaces/IndexTeam/IndexTTS)
## Acknowledgements
1. [tortoise-tts](https://github.com/neonbjb/tortoise-tts)
2. [XTTSv2](https://github.com/coqui-ai/TTS)
3. [BigVGAN](https://github.com/NVIDIA/BigVGAN)
4. [wenet](https://github.com/wenet-e2e/wenet/tree/main)
5. [icefall](https://github.com/k2-fsa/icefall)
6. [maskgct](https://github.com/open-mmlab/Amphion/tree/main/models/tts/maskgct)
7. [seed-vc](https://github.com/Plachtaa/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}
}
``` 
