launched with `vllm serve <model> --omni`. Each TTS model has its own
subdirectory containing client snippets, gradio demos, and helper
scripts; this README is the single doc entry point for all of them.
For offline inference, see [`examples/offline_inference/text_to_speech`](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_speech/README.md).
For the full list of supported architectures across all modalities, see
For offline inference of any of these models, see the [offline TTS hub](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_speech/README.md).
For full request-shape documentation (all parameters, response formats, error codes), see the [Speech API reference](https://github.com/vllm-project/vllm-omni/tree/main/docs/serving/speech_api.md).
AuK accepts the complete instruction after you fill in the [local recipe template](../../../../recipes/Tencent/AuK-H100.md#supported-model-contract) or the [official AuK cookbook](https://github.com/Tencent-Hunyuan/AuK/blob/main/docs/COOKBOOK.md). Pass it in `instructions` and leave `input` empty. For instruct TTS, fill in `Generate speech based on the following description: "{voice description}". The content to speak is: "{text}".`; for zero-shot TTS, editing, enhancement and separation, use the corresponding complete instruction from the cookbook. Set `duration_seconds` for text-only generation; with `ref_audio`, omitting it keeps the source length.
For compatibility with clients that can only send `input`, AuK treats an input-only request as the complete instruction and logs a recommendation to move it to `instructions` with `input=""`.
`extra_params` accepts `num_inference_steps`, `guidance_scale`, `sway`, `t_grid`, and `vae_sample`; `seed` is a top-level field.
--instructions 'Generate speech based on the following description: "A calm young woman speaking warmly and slowly.". The content to speak is: "Welcome back, how was your day?".'\
--duration-seconds 3 --seed 7
```
Complete-instruction editing also does not need a separate text argument:
--model ckpts/auk-omni --instructions "Keep pure speech voice, remove noise and reverberation."\
--ref-audio noisy.wav --seed 7 --output clean.wav
```
The client accepts local reference files, URLs, and data URLs. `--stream` writes signed 16-bit 24 kHz PCM after the complete waveform is ready. See `speech_client.py --help` for editing and sampling options.
2-stage TTS (`talker` + flow-matching `code2wav`) at 24 kHz. Voice cloning only — every request needs `ref_audio` + `ref_text`; there are no built-in voice presets.
If your downloaded checkpoint lacks `config.json`, add one with `{"model_type": "cosyvoice3", "architectures": ["CosyVoice3Model"]}` (the loader reads `model_type` to select the class).
Streaming is on by default. On CUDA Hopper GPUs with at least 140 GiB of device memory (H200), serving automatically selects `cosyvoice3_packed_streaming_optimized_standard.yaml` when `nvidia-cuda-mps-control` is available on `PATH`: AR32 / codec32, standard sampling, packed Flow, cached ISTFT, HiFT decode graphs, and local CUDA MPS. No `--hf-overrides` is needed. Other devices and environments without the MPS control tool use `cosyvoice3.yaml` with RAS sampling and AR8 / codec8.
The optimized profile is not a universal hardware configuration. Its packed kernels are Hopper-only; the talker's Model Runner V2 is unsupported on NPU/XPU, and CUDA MPS requires a local NVIDIA GPU and its control tool. Larger batches, 25-frame chunks, and graph allocations also require per-device validation on smaller or slower GPUs. Automatic selection retains the generic profile outside the validated device class; an explicit deploy configuration takes precedence.
The H200 default targets streaming throughput at C32/C64. It changes sampling from RAS to standard (temperature 0.7, top-p 0.8, top-k 20, repetition penalty 1.21); output lengths and quality can differ. HiFT captures a recurring exact shape on its third use, which adds latency to that request, and its graph pool needs memory headroom beyond the engine reservations. This profile is not a claim of optimal first-audio latency at low concurrency. Pass `--deploy-config cosyvoice3.yaml` to retain the previous configuration, or `--deploy-config <path>` to select another profile.
Pass `--no-async-chunk` (or `NO_ASYNC_CHUNK=1 ./cosyvoice3/run_server.sh`) for the legacy synchronous path; use `--deploy-config cosyvoice3.yaml` alongside it when selecting that path on H200.
Cross-request Stage-1 flow batching is enabled in the H200 profile. For the generic configuration, enable it in the server environment when concurrent requests should share a flow-estimator call:
Batching preserves output lengths and streaming cache alignment, but the different GEMM shapes can produce small waveform differences compared with processing each request separately. Use `--deploy-config cosyvoice3.yaml` and leave `COSYVOICE3_BATCH_FLOW` unset (or set it to `0`) when request-independent numerical behavior is required. Set `COSYVOICE3_BATCH_FLOW_DEBUG=1` to log the observed group-size distribution and enable detailed Stage-1 profiler scopes; diagnostics are disabled by default to avoid per-step profiling overhead.
- For offline inference and the end-to-end script, see the [offline CosyVoice3 section](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_speech/README.md#cosyvoice3).
Single-stage native AR TTS at 22.05 kHz mono. Zero-shot only: omit `voice` or pass `"default"`. Voice cloning from reference audio is not available yet.
### Prerequisites
Same NeMo NanoCodec install as the [offline Gepard section](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_speech/README.md#gepard-10). On a host whose CUDA toolkit cannot build kernels — no `nvcc`/`ninja`, or a consumer Blackwell (`sm_120`) card — also `export VLLM_USE_FLASHINFER_SAMPLER=0` before launch. That env is not a deploy-YAML field.
`--stage-init-timeout 900` matches the online e2e fixture and the offline example. Serve defaults to 300s, which is often too short for a cold download of the talker plus NanoCodec.
The packaged `vllm_omni/deploy/gepard.yaml` must be passed with `--deploy-config`. The checkpoint self-identifies as `qwen3_5_text`, so omitting the YAML launches a diffusion fallback instead of the Gepard pipeline. The YAML sets `async_chunk: false`, `max_num_seqs: 4`, and currently pins `seed: 42`, so serving is deterministic by default until that YAML seed is removed. Pass an explicit per-request `seed` in tests and clients rather than depending on either default.
- Concurrent requests at `max_num_seqs: 4` are supported. Native-AR recompute preemption is a known limitation of this architecture (a request that is preempted mid-generation can resume incorrectly); keep concurrency at or below `max_num_seqs` and treat preemption as out of scope until the platform fix lands.
- Optional comparison against the upstream Gepard reference server needs Blackwell/Hopper + CUDA 13 + Postgres and is not part of CI.
- For the request-field table, see the [Speech API Gepard section](https://github.com/vllm-project/vllm-omni/blob/main/docs/serving/speech_api.md#gepard-10).
2-stage TTS at 24 kHz: a vLLM-native Llama-3.2-3B talker with a DualFFN audio expert (Stage 0) feeding a HiggsAudio codec decoder (Stage 1) that streams chunks back to the client.
### Prerequisites
Voice clone uses HF's `HiggsAudioV2TokenizerModel` loaded from `k2-fsa/OmniVoice/audio_tokenizer/` (~806 MB subdir; the boson-ai standalone tokenizer Hub repo's `model.safetensors` is the 3B talker LM, not the codec):
Zero-shot multilingual TTS (600+ languages). Online serving currently exposes **auto voice** only; voice cloning and voice design are available offline.
- Voice cloning and voice design require offline inference; see the [offline OmniVoice section](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_speech/README.md#omnivoice).
---
## Qwen3-TTS
Three model variants exposed via separate checkpoints:
Single-GPU serves now default to the uniproc executor (lower IPC overhead, the Base cloning use case from [#2603](https://github.com/vllm-project/vllm-omni/issues/2603) / [#2604](https://github.com/vllm-project/vllm-omni/pull/2604)). `vllm_omni/deploy/qwen3_tts.yaml` is the only Qwen3-TTS deploy config; pass `--deploy-config <path>` to override.
The bundled `qwen3_tts.yaml` ships stage 1 (Code2Wav) at `max_num_seqs: 10`, tuned for Base voice cloning: stage-1 lifetimes are long (~3 s/req), so admitting up to 10 concurrent codec sequences lets requests progress in parallel in the scheduler — ~2× TTFA p95 at c=4 / c=8 (1× H100, 1.7B-Base, seed-tts) at an 8–12 % audio-throughput cost.
CustomVoice / VoiceDesign have much shorter stage-1 lifetimes (~50–200 ms) and are TTFA-optimal at `max_num_seqs: 1`. Override the default when serving those task types:
`--mode icl` stores both `speaker_embedding` and `ref_code`; `--mode xvec` stores only the speaker embedding. Add the output directory to a deploy config:
Raw PCM streaming requires `stream_format="audio"`, `response_format="pcm"`, and `async_chunk: true` in the deploy config (default in `qwen3_tts.yaml`). `speed` is not supported when streaming.
- Base voice cloning has uniproc-vs-mp tradeoffs depending on per-request reference audio cost; see the executor-backend section above.
- With async chunking, Qwen3-TTS Base voice cloning sends the full reference context in the first Code2Wav packet, then caches that prefix on the Code2Wav stage for follow-up chunks in the same request.
-`vllm_omni/deploy/qwen3_tts.yaml` is the default deploy config (loaded by HF `model_type`); per-stage runtime overrides are available via `--stage-N-<field> <value>`.
- Under vocoder-bound overload (single-stream `rtf_p99 ≥ 1` at the target concurrency), set `active_stream_window: 2` at the top of the deploy yaml to cap simultaneously active Stage 1 streams. Off by default; trades TTFP for streaming continuity. See [#3592](https://github.com/vllm-project/vllm-omni/pull/3592) for the mechanism and tradeoff numbers.
Deploy config auto-loads from `vllm_omni/deploy/voxcpm2.yaml`. Pass `--deploy-config <path>` to override or `--stage-N-<field> <value>` for per-stage runtime tweaks.
Uses an AudioWorklet-based player adapted from the Qwen3-TTS demo for gap-free playback. Raw PCM audio is streamed from the OpenAI Speech endpoint with `stream=true` and `stream_format="audio"`.
The demo handles voice-preset selection and reference-audio upload. `voxtral_tts/text_preprocess.py` provides the text-normalization helpers used by the demo (also available for other clients).
