`--ref-audio` and `--ref-text` are optional (text-only synthesis works without them) and must be provided together for voice cloning. The exotic scripts — Qwen3-TTS, Voxtral TTS, CosyVoice3 — accept additional model-specific flags documented in their per-model section below. Qwen3-TTS in particular uses its own argparse surface (`--query-type`, `--audio-path`, etc.) and does not follow the common shape; see its section.
---
## CosyVoice3
2-stage TTS pipeline (`talker` + `code2wav`) at 22.05 kHz.
### Prerequisites
```bash
uv pip install -e .
# Includes soundfile, onnxruntime, x-transformers, einops via requirements.
If your downloaded checkpoint lacks `config.json`, add it:
```json
{
"model_type":"cosyvoice3",
"architectures":["CosyVoice3Model"]
}
```
This is required because `AutoConfig.register("cosyvoice3", CosyVoice3Config)` only registers the class mapping; the loader still reads `model_type` from `config.json` to select the class.
If `--ref-audio` is omitted, the script downloads the upstream
[`zero_shot_prompt.wav`](https://github.com/FunAudioLLM/CosyVoice/blob/main/asset/zero_shot_prompt.wav) from the CosyVoice repo into the current directory.
To use your own clip, pass `--ref-audio /path/to/reference.wav`, and modify `--prompt-text` correspondingly.
Streaming is enabled by default via `async_chunk: true` in `vllm_omni/deploy/cosyvoice3.yaml`. Pass `--no-async-chunk` on `vllm serve` to switch to the legacy synchronous path.
Standalone talker-only deployment of Ming-flash-omni-2.0 at 44.1 kHz. Voice is controlled through caption fields (`风格` / `IP` / `语速`/`基频`/`音量`) rather than reference audio.
### Prerequisites
The example calls into `vllm_omni.model_executor.models.ming_flash_omni.prompt_utils` for the default prompt and instruction builder; no extra pip install on top of the base vLLM-Omni install.
- Decode defaults from the Ming cookbook: `max_decode_steps=200`, `cfg=2.0`, `sigma=0.25`, `temperature=0.0`, `use_zero_spk_emb=True`.
---
## MOSS-TTS-Nano
Single-stage 0.1B AR LM + MOSS-Audio-Tokenizer-Nano codec at 48 kHz mono (mixed down from upstream stereo). ZH / EN / JA. Every request requires a reference clip via `--ref-audio`.
> **No built-in speaker presets.** `--ref-audio` is required on every call. Default `--mode voice_clone` matches upstream's recommended workflow; `--mode continuation` is exposed for completeness but upstream's continuation-with-prompt path emits very short / near-silent output, so it is rarely useful in practice. Sample reference clips ship in the upstream repo under [`assets/audio/`](https://github.com/OpenMOSS/MOSS-TTS-Nano/tree/main/assets/audio) (e.g. `zh_1.wav`, `en_2.wav`, `jp_2.wav`).
### Quick start
```bash
# Fetch a sample reference clip (one-off, user-scoped cache).
Streaming requires `async_chunk: true` in the stage config.
### Batched decoding
The Code2Wav stage supports batched decoding through the SpeechTokenizer. Pass multiple prompts via `--txt-prompts` and set `--batch-size` accordingly. To raise `max_num_seqs` on either stage, point `--stage-configs-path` at a stage configs YAML with the desired values (see `vllm_omni/model_executor/stage_configs/` for templates):
`--batch-size` must match a CUDA-graph capture size (1, 2, 4, 8, 16…).
### Notes
- Run `--help` for the full argument surface.
- See `qwen3_tts/end2end.py` for the prompt-length-estimation logic the Talker uses.
---
## VoxCPM
Split-stage TTS. The hub example covers single TTS, single voice cloning, and streaming. Use `benchmarks/voxcpm/` for warmup, batch JSONL prompts, profiler injection, and offline TTFP / RTF measurement.
The engine auto-patches a missing `model_type` in the checkpoint's `config.json` in-memory at startup (see `vllm_omni/engine/arg_utils.py`), so no manual setup is required.
If you'd rather maintain a persistent patched config dir (e.g. to share across runs or to add fields the auto-patcher doesn't set), export `VLLM_OMNI_VOXCPM_HF_CONFIG_PATH` to its location — both the bundled deploy and async-chunk yamls read it via `${oc.env:VLLM_OMNI_VOXCPM_HF_CONFIG_PATH,}`.
- Non-streaming auto-loads `vllm_omni/deploy/voxcpm.yaml` from the model directory name / HF `model_type`. Streaming uses the legacy `voxcpm_async_chunk.yaml` passed via `--stage-configs-path`.
-`--ref-text` must be the real transcript of `--ref-audio`; mismatched text degrades quality.
- For online serving, see the [VoxCPM section in the online hub](../../online_serving/text_to_speech/README.md#voxcpm). For benchmark reporting, see [`benchmarks/voxcpm`](../../../benchmarks/voxcpm/README.md).
---
## VoxCPM2
Single-stage native AR TTS at 48 kHz. Pipeline: `feat_encoder → MiniCPM4 → FSQ → residual_lm → LocDiT → AudioVAE`.
Streaming is exposed through the online OpenAI Speech API (`stream=true`). See [`examples/online_serving/text_to_speech/voxcpm2/gradio_demo.py`](../../online_serving/text_to_speech/voxcpm2/gradio_demo.py) for an AudioWorklet-based gapless streaming player; the offline `end2end.py` script does not expose a streaming path.
### Notes
- Output: 48 kHz mono WAV.
- Deploy config: `vllm_omni/deploy/voxcpm2.yaml` (auto-loaded by HF `model_type`).
---
## Voxtral TTS
Voxtral-4B-TTS (Mistral). Has its own argparse surface; uses voice presets and the `mistral_common``SpeechRequest` protocol.
### Prerequisites
Latest `mistral_common` with `SpeechRequest` support:
```bash
pip install -e /path/to/mistral-common # or upgrade from PyPI when available
