IndexTeam/IndexTTS-2.5

Overview

IndexTTS-2.5 is a two-stage text-to-speech system: an autoregressive talker produces semantic codes, then EnhancedCodec, S2Mel CFM/DiT, and BigVGAN synthesize 22.05 kHz mono WAV audio. The vLLM-Omni integration exposes zero-shot voice cloning, reusable named voices, multilingual text processing, text normalization, model-native speed control, and three emotion-conditioning modes through /v1/audio/speech.

The model is available from IndexTeam/IndexTTS-2.5 on Hugging Face.

Prerequisites

• Download access to IndexTeam/IndexTTS-2.5 on Hugging Face. vLLM-Omni also accepts an already-downloaded native bundle and its nested upstream checkpoints/ layout.
• Use a CUDA GPU with enough memory for both stages. The standard deploy config reserves up to 0.4 GPU-memory utilization per stage on one GPU; lower-memory profiles have not been validated.
• Follow the merged vLLM-Omni IndexTTS-2.5 recipe for the upstream serving requirements.

Installation

uv venv && source .venv/bin/activate
uv pip install "vllm-omni[indextts2] @ git+https://github.com/vllm-project/vllm-omni.git"

Launch the server

IndexTTS-2.5's standard two-stage deploy config is selected automatically:

vllm-omni serve IndexTeam/IndexTTS-2.5 \
  --omni \
  --trust-remote-code \
  --served-model-name IndexTeam/IndexTTS-2.5 \
  --port 8092

Quick start: upload and reuse a voice

Upload a reference recording once. The voice-storage API requires a consent identifier:

curl -X POST http://localhost:8092/v1/audio/voices \
  -F "audio_sample=@/path/to/reference.wav" \
  -F "consent=user-consent-id" \
  -F "name=demo_voice" \
  -F "speaker_description=IndexTTS-2.5 demo voice"

Reuse that speaker by name:

curl -X POST http://localhost:8092/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "IndexTeam/IndexTTS-2.5",
    "input": "你好，这是复用已上传音色的语音合成测试。",
    "voice": "demo_voice",
    "response_format": "wav",
    "extra_params": {"lang": "zh", "text_normalization": true}
  }' --output named_voice.wav

Use ref_audio instead of voice to clone directly from a URL, data URL, or permitted file:// URI. There are no built-in text-only preset speakers.

Language and text controls

IndexTTS-2.5 officially supports zh (Chinese), en (English), ja (Japanese), es (Spanish), and ar (Arabic). Pass the corresponding code through extra_params.lang. vLLM-Omni also provides zhen as an additional Chinese-English mixed-text preprocessing mode; it is not a separate model language. Text normalization is enabled by default:

{
  "input": "Hello，欢迎使用 IndexTTS 二点五。",
  "voice": "demo_voice",
  "extra_params": {
    "lang": "zhen",
    "text_normalization": true
  }
}

Spanish and Arabic requests use "lang": "es" and "lang": "ar", respectively. Cantonese (yue) is not listed as an officially supported IndexTTS-2.5 language.

For Japanese, write numbers, dates, and percentages as readable Japanese text because the Japanese tokenizer does not automatically expand them.

Native speed control

speed accepts values from 0.5 to 2.0. Values above 1.0 produce shorter, faster speech. vLLM-Omni maps the public API convention to the model-native duration_factor = 1 / speed, so the audio waveform is not post-resampled:

curl -X POST http://localhost:8092/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "IndexTeam/IndexTTS-2.5",
    "input": "这是使用模型原生语速控制的测试。",
    "voice": "demo_voice",
    "speed": 1.5,
    "extra_params": {"lang": "zh"}
  }' --output speed_1_5.wav

Emotion control

IndexTTS-2.5 supports an eight-value emotion vector, an emotion description, or a separate emotion-reference recording. emo_alpha controls conditioning strength from 0 to 1.

Explicit vector order: happy, angry, sad, afraid, disgusted, melancholic, surprised, calm:

{
  "input": "今天真是令人开心的一天！",
  "voice": "demo_voice",
  "extra_params": {
    "lang": "zh",
    "emo_vector": [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
    "emo_alpha": 0.8
  }
}

Infer emotion from text:

{
  "input": "我们终于完成了这项工作！",
  "voice": "demo_voice",
  "extra_params": {
    "lang": "zh",
    "use_emo_text": true,
    "emo_text": "开心、兴奋而且充满活力",
    "emo_alpha": 0.8
  }
}

Transfer expression from another recording while preserving the uploaded speaker identity:

{
  "input": "请用情绪参考音频中的表达方式朗读这句话。",
  "voice": "demo_voice",
  "extra_params": {
    "lang": "zh",
    "emo_audio": "file:///path/to/emotion_reference.wav",
    "emo_alpha": 0.8
  }
}

When several emotion sources are supplied, precedence is use_emo_text > emo_vector > emo_audio > the speaker-reference emotion.

Validation notes

The integration has completed end-to-end generation with voice cloning and Whisper-small transcript checks. In the fixed-seed Chinese validation, speed=1.0 produced 5.503 s and speed=2.0 produced 2.752 s; both matched the requested sentence. These measurements are functional evidence, not a general throughput claim.

References

• IndexTTS-2.5 model page
• vLLM-Omni IndexTTS-2.5 recipe
• vLLM-Omni repository