# IndexTeam/IndexTTS-2.5 — vLLM serving recipe

> Multilingual zero-shot voice-cloning TTS with native speed, emotion, and text-normalization controls, served through vLLM-Omni's OpenAI-compatible speech API.

Configuration: hardware `h200` (8× H200) · variant `default` (BF16) · task `t2a`

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/IndexTeam/IndexTTS-2.5
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/IndexTeam/IndexTTS-2.5.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `task`: `t2a`

## Install

Requires vLLM >= 0.27.0.

IndexTTS-2.5 support is included in vLLM-Omni; see the upstream IndexTTS-2.5 recipe for deployment details.

```bash
uv pip install "vllm-omni[indextts2] @ git+https://github.com/vllm-project/vllm-omni.git"
```

## Serve

vLLM-Omni recipe — the generation endpoints and request examples are in the guide below.

```bash
vllm serve IndexTeam/IndexTTS-2.5 \
  --trust-remote-code \
  --port 8092 \
  --omni
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### 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](https://huggingface.co/IndexTeam/IndexTTS-2.5).

### Prerequisites

- Download access to [IndexTeam/IndexTTS-2.5 on Hugging Face](https://huggingface.co/IndexTeam/IndexTTS-2.5).
  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](https://github.com/vllm-project/vllm-omni/blob/main/recipes/IndexTeam/IndexTTS-2_5.md)
  for the upstream serving requirements.

### Installation

```bash
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:

```bash
vllm serve IndexTeam/IndexTTS-2.5 \
  --omni \
  --trust-remote-code \
  --port 8092
```

### Quick start: upload and reuse a voice

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

```bash
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:

```bash
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:

```json
{
  "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:

```bash
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`:

```json
{
  "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:

```json
{
  "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:

```json
{
  "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](https://huggingface.co/IndexTeam/IndexTTS-2.5)
- [vLLM-Omni IndexTTS-2.5 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/IndexTeam/IndexTTS-2_5.md)
- [vLLM-Omni repository](https://github.com/vllm-project/vllm-omni)