vllm-omni/docs/user_guide/examples/offline_inference/qwen3_tts.md

# Qwen3-TTS

Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen3_tts>.


This directory contains an offline demo for running Qwen3 TTS models with vLLM Omni. It builds task-specific inputs and generates WAV files locally.

## Model Overview

Qwen3 TTS provides multiple task variants for speech generation:

- **CustomVoice**: Generate speech with a known speaker identity (speaker ID) and optional instruction.
- **VoiceDesign**: Generate speech from text plus a descriptive instruction that designs a new voice.
- **Base**: Voice cloning using a reference audio + reference transcript, with optional mode selection. The `ref_audio` field accepts a local file path, HTTP URL, or base64 data URL.

## Setup
Please refer to the [stage configuration documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/configuration/stage_configs/) to configure memory allocation appropriately for your hardware setup.

### ROCm Dependencies

You will need to install the dependency `onnxruntime-rocm`.

```
pip uninstall onnxruntime # should be removed before we can install onnxruntime-rocm
pip install onnxruntime-rocm
```

## Quick Start

Run a single sample for a task:

```
python end2end.py --query-type CustomVoice
```

Generated audio files are saved to `output_audio/` by default.

## Task Usage

### CustomVoice

Single sample:

```
python end2end.py --query-type CustomVoice
```

Batch sample (multiple prompts in one run):

```
python end2end.py --query-type CustomVoice --use-batch-sample
```

### VoiceDesign

Single sample:

```
python end2end.py --query-type VoiceDesign
```

Batch sample:

```
python end2end.py --query-type VoiceDesign --use-batch-sample
```

### Base (Voice Clone)

Single sample:

```
python end2end.py --query-type Base
```

Batch sample:

```
python end2end.py --query-type Base --use-batch-sample
```

Mode selection for Base:

- `--mode-tag icl` (default): standard mode
- `--mode-tag xvec_only`: enable `x_vector_only_mode` in the request

Examples:

```
python end2end.py --query-type Base --mode-tag icl
```

## Voice and Language Control

### Supported Voices (CustomVoice)

Predefined speaker voices are set via the `speaker` (or `voice_type`) field in `additional_information`. Available speakers depend on the loaded checkpoint; check `talker_config.spk_id` in the model config for the full list. Common voices include `vivian`, `ryan`, `aiden`, `ethan`, `serena` (case-insensitive).

Pass the speaker name in your request:

```python
additional_information = {
    "text": ["你好，我是通义千问"],
    "task_type": ["CustomVoice"],
    "speaker": ["Vivian"],  
    "language": ["Chinese"],
}
```

### Supported Languages

The `language` field controls the codec-level language tag. Use `"Auto"` (default) for automatic detection.

Supported values: `Auto`, `Chinese`, `English`, `Japanese`, `Korean`, `German`, `French`, `Russian`, `Portuguese`, `Spanish`, `Italian`.

```python
additional_information = {
    "text": ["Hello, nice to meet you."],
    "task_type": ["CustomVoice"],
    "speaker": ["Aiden"],
    "language": ["English"],
}
```

### VoiceDesign and Base

- **VoiceDesign**: Use `instruct` for natural-language voice description; no `speaker` needed.
- **Base**: Use `ref_audio` and `ref_text` for voice cloning; `language` is optional.

## Streaming Mode

Add `--streaming` to stream audio chunks progressively via `AsyncOmni` (requires `async_chunk: true` in the stage config):

```bash
python end2end.py --query-type CustomVoice --streaming --output-dir /tmp/out_stream
```

Each Code2Wav chunk is logged as it arrives (default 25 frames; configurable via `codec_chunk_frames`
in the stage config). The initial chunk size is dynamically selected based on server load for reduced
TTFA, and can be overridden per-request via the `initial_codec_chunk_frames` API field. The final WAV file is written once generation
completes. This demonstrates that audio data is available progressively rather than only at the end.

> **Note:** Streaming uses `AsyncOmni` internally. The non-streaming path (`Omni`) is unchanged.

## Batched Decoding

The Code2Wav stage (stage 1) supports batched decoding, where multiple requests are decoded in a single forward pass through the SpeechTokenizer. To use it, provide a stage config with `max_num_seqs > 1` and pass multiple prompts via `--txt-prompts` with a matching `--batch-size`.

```
python end2end.py --query-type CustomVoice \
    --txt-prompts benchmark_prompts.txt \
    --batch-size 4 \
    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts_batch.yaml
```

**Important:** `--batch-size` must match a CUDA graph capture size (1, 2, 4, 8, 16...) because the Talker's code predictor KV cache is sized to `max_num_seqs`, and CUDA graphs pad the batch to the next capture size. Both stages need `max_num_seqs >= batch_size` in the stage config for batching to take effect. If only stage 1 has a higher `max_num_seqs`, it won't help — stage 1 can only batch chunks from requests that are in-flight simultaneously, which requires stage 0 to also process multiple requests concurrently.

## Notes

- The script uses the model paths embedded in `end2end.py`. Update them if your local cache path differs.
- Use `--output-dir` to change the output folder.

## Example materials

??? abstract "benchmark_prompts.txt"
    ``````txt
    --8<-- "examples/offline_inference/qwen3_tts/benchmark_prompts.txt"
    ``````
??? abstract "end2end.py"
    ``````py
    --8<-- "examples/offline_inference/qwen3_tts/end2end.py"
    ``````