IndexTTS-2.5

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. - **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](LICENSE) - **Repository:** [github.com/index-tts/index-tts](https://github.com/index-tts/index-tts) - **Paper:** [arXiv:2601.03888](https://arxiv.org/abs/2601.03888) Requires Python 3.10–3.11, an NVIDIA GPU, and roughly 6 GB of VRAM for inference. git clone https://github.com/index-tts/index-tts.git && cd index-tts pip install -U uv uv sync --all-extras uv tool install "huggingface-hub" hf download IndexTeam/IndexTTS-2.5 --local-dir=checkpoints 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. from indextts.infer_v2_5 import IndexTTS2 tts = IndexTTS2(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", use_bf16=True) tts.infer( spk_audio_prompt="prompt.wav", text="Hello, this is a voice cloning demo.", lang="EN", output_path="output.wav", ) 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], ) 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", ) 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, ) uv run webui.py - 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. @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}, }