`--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.
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).
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, create an overlay based on `vllm_omni/deploy/qwen3_tts.yaml` and pass it with `--deploy-config`:
Not yet — PR1 is zero-shot only (the learned `null_prefix` default voice). Reference-audio cloning (`ref_compressor` speaker prefix) is a follow-up PR.
### Notes
- Output: 22.05 kHz mono WAV.
- First run downloads two checkpoints, not one: the model itself, and the NanoCodec decoder it names in `codec_id` (`nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps` by default). Both are fetched automatically; expect the first startup to be correspondingly slower.
- Offline only for now; the `/v1/audio/speech` adapter is a follow-up PR.
- Deploy config: `vllm_omni/deploy/gepard.yaml` (the example's default; copy it and pass `--deploy-config` to change it).
- Generation length and reproducibility are stage settings, not script flags. One output token is one audio frame, so `max_tokens` in the YAML is the frame budget; `seed` makes the in-model 32-head sampling reproducible. The script deliberately passes no `SamplingParams`: one supplied by a caller replaces the stage defaults wholesale rather than merging, which would drop the pipeline's stop token and run every request to `max_tokens`.
- Text length: short texts are repeated internally to match the training layout (the checkpoint's `text_repetition` block); the upper bound is the stage's `max_model_len`, enforced by the engine. Empty text is rejected rather than voiced.
-`VLLM_GEPARD_GREEDY=1` swaps the 32-head Gumbel-max sampling for argmax, for reproducible comparisons that do not depend on a seed.
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`).
Single-stage native AR TTS at 48 kHz (rednote-hilab). Pipeline: `Qwen2.5-1.5B base LM → DiT (10-step Euler flow matching) → patch_encoder AR loopback → AudioVAE (streaming decode)`. Same "vLLM-native base LM + side-path computation" pattern as VoxCPM2, with a plain Qwen2 backbone instead of MiniCPM4 and no FSQ / residual LM stage.
--text "Hello, this is a test of dots TTS running on vLLM Omni."
```
### Voice cloning
Not wired in this release — generation is zero-shot only. The CAM++ x-vector speaker encoder weights load, but `end2end.py` has no `--ref-audio`/`--ref-text` flags yet.
### Streaming
The AudioVAE decoder has an internal streaming path (`init_stream_state` / `stream_step` / `stream_flush`) used to avoid boundary artifacts between 160 ms patches, but it is not yet exposed through an online serving endpoint or example.
### Notes
- Output: 48 kHz mono WAV.
- Deploy config: `vllm_omni/deploy/dots_tts.yaml` (auto-loaded by HF `model_type`).
- Checkpoints: `rednote-hilab/dots.tts-soar` is the validated default. `dots.tts-base` shares the same architecture but is unvalidated in this repo. `dots.tts-mf` (MeanFlow, 2-4 step) is not supported yet.
- Known limitation: no CUDA graph capture and no batched side-path computation yet, so concurrent requests do not currently scale (each request's DiT Euler steps run serially). See `recipes/rednote-hilab/dots.tts.md` for details and the roadmap.
Both versions use a 2-stage TTS pipeline and produce 22.05 kHz mono audio. Stage 0 is a GPT AR talker. IndexTTS-2 Stage 1 uses RepCodec semantic embeddings, GPT latent, S2Mel CFM/DiT, and BigVGAN. IndexTTS-2.5 replaces RepCodec with EnhancedCodec and uses the official code-only path (`use_gpt_latent=false`). Every request requires reference audio for zero-shot voice cloning.
IndexTTS-2 uses its existing frontend and does not require the IndexTTS-2.5 text dependencies. Before running IndexTTS-2.5, install its optional frontend dependencies:
```bash
pip install 'vllm-omni[indextts2]'
```
IndexTTS-2.5 with a native repository/lab bundle whose model root contains `checkpoints/`:
-`--ref-audio` is **required** — neither version provides text-only synthesis.
- IndexTTS-2.5 Stage 0 (AR Talker) generates mel codes from text + reference audio using plain vLLM sampling. It does not reproduce the official default `num_beams=3` beam search. For parity comparisons, run upstream with `num_beams=1`; output quality can differ from the official beam-search result.
- IndexTTS-2.5 accepts language codes such as `zh`, `en`, `zhen` (mixed Chinese/English), `ja`, and `yue`. `Mandarin` is a vLLM-Omni convenience alias for `zh`. Japanese (`ja`) uses `fugashi` tokenization and produces audio, but it does not automatically expand numbers, dates, or percentages; write those inputs as readable Japanese text before calling the model. Use `--no-text-normalization` only when the input is already normalized.
- A seed controls Stage 0 AR sampling and per-request CFM noise. Different concurrent batch compositions do not guarantee a bit-identical waveform.
- Stage 1 (semantic codec + S2Mel + BigVGAN): CFM/DiT converts the complete semantic-code sequence to waveform.
- Deploy configs: `vllm_omni/deploy/indextts2.yaml` and `indextts2_5.yaml`.
-`async_chunk=false` is intentional for IndexTTS-2.5 correctness: S2Mel consumes the completed semantic sequence.
Singing voice synthesis (SVS) and conversion (SVC) at 24 kHz. Script: `soulxsinger/end2end.py`. Deploy: `vllm_omni/deploy/soulxsinger_svs.yaml` or `soulxsinger_svc.yaml`.
### Prerequisites
Download DiT and preprocess weights, then set up separate SVS / SVC view directories. Copy `soulxsinger/utils/phoneme/phone_set.json` from upstream [SoulX-Singer](https://github.com/Soul-AILab/SoulX-Singer) into the model weights dir as `phoneme/phone_set.json` — HuggingFace does not ship it.
`config.yaml` hyper-parameters live under `$BASE`; each view's `config.json``architectures` field is the single source of truth for SVS vs SVC. Point `--model` at the matching directory (`$BASE` for SVS, `$SVC_DIR` for SVC). Deploy YAML is chosen automatically from `config.json`; optional `--svs` / `--svc` only assert the mode matches.
**Online preprocess** is the default: pass `--prompt-audio` and `--target-audio`, and the worker runs vocal separation, F0, and (for SVS) lyrics/MIDI before DiT. Install only what your run needs:
```bash
pip install "BS-RoFormer"# vocal sep + F0 on GPU — SVS and SVC
```
Mandarin SVS also needs FunASR and Chinese G2P; `ffmpeg` must be on `PATH`:
```bash
# install optional dependencies:
pip install -e ".[soulx-svs]"
```
English SVS adds NeMo ASR and NLTK data; pass `--language English`:
**Precomputed metadata** is the alternative: pass both `--prompt-metadata-path` and `--target-metadata-path` and skip online ASR/ROSVOT — none of the packages above are required. JSON can be produced by integrated preprocess on a prior run, or by upstream [SoulX-Singer](https://github.com/Soul-AILab/SoulX-Singer) `preprocess/` scripts if you prefer to run that outside vLLM-Omni.
`SOULX_PREPROCESS_WEIGHTS_DIR` makes `--preprocess-weights-dir` optional. Long SVS targets are handled in one request. See `end2end.py --help` for `--pitch-shift`, `--vocal-sep`, `--auto-shift`, and language/control options.
### Notes
- Output: 24 kHz mono WAV; batch only.
- Defaults match upstream: `--guidance-scale 3.0`, `--seed 42`, `--auto-shift` on.
