IndexTTS-2.5 is a zero-shot text-to-speech model that clones a voice from a single reference audio clip. It supports Chinese, English, Japanese, Spanish and Arabic, with cross-lingual voice transfer and emotion control disentangled from timbre.

Compared with IndexTTS-2, it adds Japanese, Spanish and Arabic, infers faster, adds speaking speed control, and improves controllability of Chinese Pinyin, English CMU phonemes and Japanese Kana.

Model Details

Developed by: IndexTeam, Bilibili
Model type: Autoregressive zero-shot TTS — GPT backbone, flow-matching speech-to-mel decoder, BigVGAN vocoder
Parameters: ~0.8B (GPT backbone)
Languages: Chinese, English, Japanese, Spanish, Arabic
Output: 22.05 kHz waveform
License: bilibili Model Use License Agreement
Repository: github.com/index-tts/index-tts
Paper: arXiv:2601.03888

Getting Started

Requires Python 3.10–3.11, an NVIDIA GPU, and roughly 6 GB of VRAM for inference.

Install

bash

git clone https://github.com/index-tts/index-tts.git && cd index-tts
pip install -U uv
uv sync --all-extras

Download the weights

bash

# HuggingFace
uv tool install "huggingface-hub"
hf download IndexTeam/IndexTTS-2.5 --local-dir=checkpoints

# or ModelScope
uv tool install "modelscope"
modelscope download --model IndexTeam/IndexTTS-2.5 --local_dir checkpoints

Auxiliary models (w2v-bert-2.0, MaskGCT semantic codec, CAMPPlus, BigVGAN) are not part of this repository; they are downloaded into checkpoints/hf_cache/ on first run.

Inference

python

from indextts.infer_v2_5 import IndexTTS2

tts = IndexTTS2(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", use_bf16=True)

# Voice cloning
tts.infer(
    spk_audio_prompt="prompt.wav",
    text="Hello, this is a voice cloning demo.",
    lang="EN",
    output_path="output.wav",
)

# Emotion control with an 8-float vector, in the order
# [happy, angry, sad, afraid, disgusted, melancholic, surprised, calm]
tts.infer(
    spk_audio_prompt="prompt.wav",
    text="快躲起来！是他要来了！",
    lang="ZH",
    output_path="output.wav",
    emo_vector=[0, 0, 0.8, 0, 0, 0, 0, 0],
)

# Pronunciation control: Pinyin, CMU phonemes, or Kana in <word|reading> form
tts.infer(
    spk_audio_prompt="prompt.wav",
    text="他在银<行|XING2>里<行|HANG2>走了半天。",
    lang="ZH",
    output_path="output.wav",
)

# Speaking speed: >1.0 slows down, <1.0 speeds up (valid range 0.5–2.0)
tts.infer(
    spk_audio_prompt="prompt.wav",
    text="大家好，欢迎来到IndexTTS。",
    lang="ZH",
    output_path="output.wav",
    duration_factor=1.2,
)

Web UI

uv run webui.py

Limitations

Long text is split into segments and the pieces are concatenated with a short silence, so prosody is not modelled across a segment boundary.
Emotion control from a text description needs the QwenEmotion model, which is loaded only when IndexTTS2 is constructed with use_qwen_emo=True. Passing use_emo_text=True without it raises at inference time.
Enabling random sampling for emotion (use_random=True) reduces voice cloning fidelity.
The model does not verify that the speaker in a reference clip consented to being cloned. Obtaining that consent is the user's responsibility, and all use is subject to the license terms.

Citation

bibtex

@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 Yining Wang and Yaogen Yang and Zhetao Hu and Shiyao Duan and Jiacheng Xu and Bin Xia and Jingchen Shu},
      year={2026},
      eprint={2601.03888},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2601.03888},
}