[Model] Aura support - Non async chunk path

docs/user_guide/examples/online_serving/aura_omni.md

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+# AURA Omni Pipeline
+
+`aura_omni` wires ASR, AURA, and Qwen3-TTS into one vLLM-Omni pipeline:
+
+```text
+ASR -> AURA -> Qwen3-TTS Talker -> Code2Wav
+```
+
+Qwen3-TTS remains two engine stages so the pipeline reuses the existing native
+Talker and Code2Wav implementation.
+
+```bash
+vllm serve aurateam/AURA \
+  --omni \
+  --deploy-config vllm_omni/deploy/aura_omni.yaml \
+  --served-model-name aurateam/AURA \
+  --trust-remote-code
+```
+
+Configure local checkpoints by editing per-stage `model` values in
+`vllm_omni/deploy/aura_omni.yaml`. The deploy file sets
+`pipeline: aura_omni`, so the four-stage topology is used even if the
+command-line `--model` points at one of the component checkpoints.
+
+Send requests with `"model": "aurateam/AURA"`. The ASR, AURA, and Qwen3-TTS
+checkpoint paths are internal stage models from the deploy YAML, not the
+OpenAI-facing served model name.
+
+The AURA stage can emit `<|silent|>`. Silent outputs are treated as a gate:
+they produce no Qwen3-TTS Talker input, so no audio is synthesized for that
+turn.
+
+## GPU Utilization Recommendation
+
+`gpu_memory_utilization` in `vllm_omni/deploy/aura_omni.yaml` controls how much
+VRAM each stage can reserve. Start with this split for a single GPU:
+
+- Stage 0 (ASR): `0.10`
+- Stage 1 (AURA): `0.40`
+- Stage 2 (Qwen3-TTS Talker): `0.20`
+- Stage 3 (Qwen3-TTS Code2Wav): `0.20`
+
+## TTS Modes
+
+`aura_omni` can pass AURA text to Qwen3-TTS in two task modes:
+
+- `Base`: voice clone from `tts_ref_audio` with ICL enabled in the AURA
+  pipeline. Provide both `tts_ref_audio` and `tts_ref_text`. Set
+  `tts_x_vector_only_mode=true` to disable ICL and use speaker embedding only.
+- `CustomVoice`: predefined speaker mode. Use a Qwen3-TTS CustomVoice
+  checkpoint for stages 2 and 3 in `aura_omni.yaml`, then pass
+  `tts_task_type=CustomVoice` and `tts_speaker`.
+
+By default, AURA responses are passed to Qwen3-TTS as text. Set
+`additional_information.tts_pass_token_ids=true` to pass AURA-generated
+assistant token ids directly instead. Even when token passthrough is disabled,
+the stage processor uses AURA token ids when available to estimate the Talker
+prefill length, so scheduling does not rely on raw character length.
+
+The example client exposes this as:
+
+```bash
+python examples/online_serving/aura_omni/openai_chat_completion_client.py \
+  --tts-pass-token-ids
+```

examples/offline_inference/aura_omni/README.md

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+# AURA Omni
+
+This example runs the native AURA Omni pipeline offline:
+
+```text
+Qwen3-ASR -> AURA/Qwen3-VL -> Qwen3-TTS Talker -> Qwen3-TTS Code2Wav
+```
+
+The first stage consumes speech and produces a transcript. The AURA stage then combines the transcript with the video frames from the original request and returns text or `<|silent|>`. Non-silent responses are passed to Qwen3-TTS as AURA-generated token ids.
+
+## Run
+
+```bash
+cd examples/offline_inference/aura_omni
+bash run_single_prompt.sh
+```
+
+Use local media:
+
+```bash
+python end2end.py \
+  --audio-path /path/to/input.wav \
+  --video-path /path/to/video.mp4 \
+  --modalities text,audio
+```
+
+Base voice clone mode is the default. It uses the AURA reference audio unless
+you override it:
+
+```bash
+python end2end.py \
+  --tts-task-type Base \
+  --tts-ref-audio /data/yrr/rein_test/shuhan.mp3 \
+  --tts-x-vector-only-mode
+```
+
+CustomVoice mode requires stages 2 and 3 in `vllm_omni/deploy/aura_omni.yaml`
+to point at a Qwen3-TTS CustomVoice checkpoint:
+
+```bash
+python end2end.py \
+  --tts-task-type CustomVoice \
+  --tts-speaker Vivian
+```
+
+For local checkpoints, edit the stage `model` entries in
+`vllm_omni/deploy/aura_omni.yaml` or pass a copied deploy config with
+`--deploy-config`.
+
+## GPU Utilization Recommendation
+
+Set `gpu_memory_utilization` in `vllm_omni/deploy/aura_omni.yaml` by stage.
+Suggested starting point:
+
+- Stage 0 (ASR): `0.10`
+- Stage 1 (AURA): `0.4`
+- Stage 2 (Qwen3-TTS Talker): `0.20`
+- Stage 3 (Qwen3-TTS Code2Wav): `0.20`
+
+Generated text and audio are written to `--output-dir`
+(default: `output_aura_omni`).

examples/offline_inference/aura_omni/end2end.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline AURA Omni example.
+
+The native AURA pipeline is:
+
+    Qwen3-ASR -> AURA/Qwen3-VL -> Qwen3-TTS Talker -> Code2Wav
+
+Stage 0 consumes audio and emits a transcript. The stage input processor then
+combines that transcript with the original visual payload for AURA.
+"""
+
+from __future__ import annotations
+
+import os
+from typing import NamedTuple
+
+import numpy as np
+import soundfile as sf
+import vllm
+from vllm import SamplingParams
+from vllm.assets.audio import AudioAsset
+from vllm.assets.video import VideoAsset, video_to_ndarrays
+from vllm.multimodal.media.audio import load_audio
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
+from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.model_executor.stage_input_processors.aura_omni import (
+    DEFAULT_QWEN3_TTS_REF_TEXT,
+    default_qwen3_tts_ref_audio_path,
+)
+
+SEED = 42
+DEFAULT_MODEL = "aurateam/AURA"
+DEFAULT_DEPLOY_CONFIG = "vllm_omni/deploy/aura_omni.yaml"
+
+
+class QueryResult(NamedTuple):
+    inputs: dict
+    limit_mm_per_prompt: dict[str, int]
+
+
+def _load_audio(audio_path: str | None, sampling_rate: int) -> tuple[np.ndarray, int]:
+    if audio_path:
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"Audio file not found: {audio_path}")
+        audio_signal, sr = load_audio(audio_path, sr=sampling_rate)
+        return audio_signal.astype(np.float32), int(sr)
+    audio_signal, sr = AudioAsset("mary_had_lamb").audio_and_sample_rate
+    return audio_signal.astype(np.float32), int(sr)
+
+
+def _load_video(video_path: str | None, num_frames: int) -> list[np.ndarray]:
+    if video_path:
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        return video_to_ndarrays(video_path, num_frames=num_frames)
+    return VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
+
+
+def build_query(args) -> QueryResult:
+    prompt = (
+        "<|im_start|>user\n"
+        "<|audio_start|><|audio_pad|><|audio_end|>"
+        "Please transcribe this speech and use the video context to decide whether a reply is needed.<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+    audio_data = _load_audio(args.audio_path, args.sampling_rate)
+    video_frames = _load_video(args.video_path, args.num_frames)
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "audio": audio_data,
+                "video": video_frames,
+            },
+            "additional_information": {
+                "aura_system_prompt": args.aura_system_prompt,
+                "tts_task_type": args.tts_task_type,
+                "tts_language": args.tts_language,
+                "tts_speaker": args.tts_speaker,
+                "tts_instruct": args.tts_instruct,
+                "tts_ref_audio": args.tts_ref_audio,
+                "tts_ref_text": args.tts_ref_text,
+                "tts_x_vector_only_mode": args.tts_x_vector_only_mode,
+                "tts_pass_token_ids": args.tts_pass_token_ids,
+            },
+        },
+        limit_mm_per_prompt={"audio": 1, "video": 1},
+    )
+
+
+def build_sampling_params() -> list[SamplingParams]:
+    asr_sampling = SamplingParams(
+        temperature=0.0,
+        top_p=1.0,
+        top_k=-1,
+        max_tokens=256,
+        detokenize=True,
+        seed=SEED,
+    )
+    aura_sampling = SamplingParams(
+        temperature=0.5,
+        top_p=1.0,
+        top_k=-1,
+        max_tokens=256,
+        detokenize=True,
+        repetition_penalty=1.0,
+        seed=SEED,
+    )
+    talker_sampling = SamplingParams(
+        temperature=0.9,
+        top_k=50,
+        max_tokens=4096,
+        detokenize=False,
+        repetition_penalty=1.05,
+        stop_token_ids=[2150],
+        seed=SEED,
+    )
+    code2wav_sampling = SamplingParams(
+        temperature=0.0,
+        top_p=1.0,
+        top_k=-1,
+        max_tokens=65536,
+        detokenize=True,
+        repetition_penalty=1.0,
+        seed=SEED,
+    )
+    return [asr_sampling, aura_sampling, talker_sampling, code2wav_sampling]
+
+
+def save_stage_output(stage_outputs, output_dir: str) -> None:
+    output = stage_outputs.request_output
+    request_id = output.request_id
+    if stage_outputs.final_output_type == "text":
+        text_output = output.outputs[0].text
+        out_txt = os.path.join(output_dir, f"{request_id}.txt")
+        with open(out_txt, "w", encoding="utf-8") as f:
+            f.write(str(text_output).strip() + "\n")
+        print(f"Request ID: {request_id}, text saved to {out_txt}")
+        print(text_output)
+        return
+
+    if stage_outputs.final_output_type == "audio":
+        mm = output.outputs[0].multimodal_output
+        if not mm or "audio" not in mm:
+            print(f"Request ID: {request_id}, no audio output")
+            return
+        audio = mm["audio"]
+        if isinstance(audio, list):
+            import torch
+
+            audio = torch.cat([(item if hasattr(item, "detach") else torch.tensor(item)).flatten() for item in audio])
+        sample_rate = int(mm.get("sr", 24000))
+        audio_numpy = audio.float().detach().cpu().numpy().flatten()
+        out_wav = os.path.join(output_dir, f"{request_id}.wav")
+        sf.write(out_wav, audio_numpy, samplerate=sample_rate, format="WAV")
+        print(f"Request ID: {request_id}, audio saved to {out_wav}")
+
+
+def main(args) -> None:
+    print("=" * 20, "\n", f"vllm version: {vllm.__version__}", "\n", "=" * 20)
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    query = build_query(args)
+    prompts = [query.inputs for _ in range(args.num_prompts)]
+    if args.modalities:
+        modalities = [item.strip() for item in args.modalities.split(",") if item.strip()]
+        for prompt in prompts:
+            prompt["modalities"] = modalities
+
+    omni_kwargs = vars(args).copy()
+    omni_kwargs["model"] = args.model
+    omni = Omni(**omni_kwargs)
+    sampling_params_list = build_sampling_params()[: omni.num_stages]
+
+    try:
+        for stage_outputs in omni.generate(prompts, sampling_params_list):
+            save_stage_output(stage_outputs, args.output_dir)
+    finally:
+        omni.close()
+
+
+def parse_args():
+    parser = FlexibleArgumentParser(description="AURA Omni offline inference example")
+    parser.add_argument("--model", default=DEFAULT_MODEL, help="Top-level model name/path.")
+    parser.add_argument("--deploy-config", default=DEFAULT_DEPLOY_CONFIG, help="AURA Omni deploy config.")
+    parser.add_argument("--audio-path", "-a", default=None, help="Local audio file. Uses a built-in asset by default.")
+    parser.add_argument("--video-path", "-v", default=None, help="Local video file. Uses a built-in asset by default.")
+    parser.add_argument("--num-frames", type=int, default=16, help="Number of video frames to sample.")
+    parser.add_argument("--sampling-rate", type=int, default=16000, help="Audio sampling rate for ASR input.")
+    parser.add_argument("--num-prompts", type=int, default=1, help="Number of repeated prompts.")
+    parser.add_argument("--modalities", default="text,audio", help="Comma-separated final modalities.")
+    parser.add_argument("--output-dir", default="output_aura_omni", help="Directory for generated files.")
+    parser.add_argument(
+        "--aura-system-prompt",
+        default=(
+            "You are receiving a live video stream where the final frame is the present moment. "
+            "Respond only when a response is needed. Otherwise output '<|silent|>'. Respond in Chinese."
+        ),
+        help="System prompt for the AURA stage.",
+    )
+    parser.add_argument("--tts-task-type", default="Base", choices=["Base", "CustomVoice"], help="Qwen3-TTS task.")
+    parser.add_argument("--tts-language", default="Chinese", help="Qwen3-TTS language.")
+    parser.add_argument("--tts-speaker", default="Vivian", help="Qwen3-TTS speaker.")
+    parser.add_argument("--tts-instruct", default="", help="Optional Qwen3-TTS style instruction.")
+    parser.add_argument(
+        "--tts-ref-audio",
+        default=default_qwen3_tts_ref_audio_path(),
+        help="Base-mode reference audio path/URL.",
+    )
+    parser.add_argument(
+        "--tts-ref-text",
+        default=DEFAULT_QWEN3_TTS_REF_TEXT,
+        help="Base-mode reference audio transcript.",
+    )
+    parser.add_argument(
+        "--tts-x-vector-only-mode",
+        action="store_true",
+        help="Use speaker embedding only for Base mode (disable ICL ref_text conditioning).",
+    )
+    parser.add_argument(
+        "--tts-pass-token-ids",
+        action="store_true",
+        help="Pass AURA-generated assistant token ids directly to Qwen3-TTS. Defaults to sending text.",
+    )
+    parser.add_argument("--init-timeout", type=int, default=2000, help="Overall initialization timeout.")
+    parser.add_argument("--stage-init-timeout", type=int, default=2000, help="Per-stage initialization timeout.")
+    parser.add_argument("--log-stats", action="store_true", default=False, help="Enable stats logging.")
+    nullify_stage_engine_defaults(parser)
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    main(parse_args())

examples/offline_inference/aura_omni/run_single_prompt.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+python end2end.py \
+  --model aurateam/AURA \
+  --deploy-config vllm_omni/deploy/aura_omni.yaml \
+  --modalities text,audio \
+  "$@"