[Model] Add Ming-omni-tts dense 0.5B pipeline (#2906)

Signed-off-by: akshatvishu <akshatnayak197@gmail.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

examples/offline_inference/text_to_speech/README.md

@@ -17,6 +17,7 @@ list of supported architectures across all modalities, see
 | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | 2 (talker + code2wav) | ✓ | ✓ | — | 24 kHz |
 | Fish Speech S2 Pro | `fishaudio/s2-pro` | dual-AR | ✓ | ✓ | — | 44.1 kHz |
 | GLM-TTS | `zai-org/GLM-TTS` | 2 (AR + DiT) | ✓ (required) | ✓ | — | 24 kHz |
+| Ming-omni-tts | `inclusionAI/Ming-omni-tts-0.5B` | 2 (AR + audio VAE) | ✓ | ✓ | style / IP / dialect / TTA / podcast | 44.1 kHz |
 | Ming-flash-omni-TTS | `Jonathan1909/Ming-flash-omni-2.0` | single (talker only) | — (caption-controlled) | — | style / IP / basic captions | 44.1 kHz |
 | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | single (AR + codec) | ✓ (required) | ✓ | voice_clone, continuation | 48 kHz |
 | OmniVoice | `k2-fsa/OmniVoice` | 2 (gen + dec) | ✓ | — | voice design, language hint | 24 kHz |
@@ -159,6 +160,46 @@ Streaming requires `async_chunk: true` in the stage config.
 
 ---
 
+## Ming-omni-tts
+
+Dense 0.5B two-stage TTS pipeline (`AR + flow` + audio VAE) at 44.1 kHz. The example covers style, IP voice, music-only generation, text-to-audio events, emotion, dialect, zero-shot cloning, podcast, speech+BGM, and speech+environment-sound cases.
+
+### Quick start
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case style \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+### Voice cloning
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case zero_shot \
+    --ref-audio /path/to/reference.wav \
+    --ref-text "在此奉劝大家别乱打美白针。" \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+### Streaming
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case basic \
+    --ref-audio /path/to/reference.wav \
+    --streaming \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+### Notes
+- `style`, `ip`, `bgm`, and `tta` do not require reference audio.
+- Reference-audio cases use `--ref-audio`; `zero_shot` also requires `--ref-text`.
+- `podcast` uses multiple references via `--ref-audio-paths`.
+- Full case details live in [`ming_tts/README.md`](ming_tts/README.md).
+
+---
+
 ## Ming-flash-omni-TTS
 
 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.

examples/offline_inference/text_to_speech/ming_tts/README.md

@@ -0,0 +1,248 @@
+# Ming-omni-tts Offline Inference
+
+`end2end.py` runs Ming dense 0.5B end to end with vLLM-Omni. It uses the in-repo Ming prompt assembly helper directly, so the example request shape matches the real integration instead of a simplified wrapper.
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `end2end.py` | Driver: CLI, case loading, prompt construction, orchestration (~150 lines) |
+| `cases.yaml` | All 11 built-in case definitions (prompt, text, instruction, ref-audio flags, flow controls) |
+| `runner.py` | Engine management and audio output (streaming + blocking paths) |
+
+## Model Overview
+
+Ming dense 0.5B is exposed here as a two-stage offline pipeline:
+
+- **Stage 0**: Qwen2-based AR generation with Ming prompt formatting and inline flow controls
+- **Stage 1**: audio VAE decode to mono 44.1 kHz waveform
+
+`config_ming_tts.py` adapts the checkpoint's HuggingFace config fields
+(LLM, DiT, aggregator, AudioVAE, token ids). `vllm_omni/deploy/ming_tts.yaml`
+selects the vLLM-Omni pipeline and stage runtime topology, including
+connectors, async chunking, memory limits, and sampling defaults.
+
+The example supports both:
+
+- **Blocking eager** via `vllm_omni/deploy/ming_tts.yaml`
+- **Async chunk eager** via `vllm_omni/deploy/ming_tts.yaml` (default `async_chunk: true`)
+
+## Setup
+
+Install vLLM-Omni with the platform requirements for your accelerator:
+
+```bash
+uv pip install -e .
+```
+
+The Ming offline example does not require a separate upstream Ming package.
+Reference-audio cases use the repo dependencies for audio loading,
+resampling, and CampPlus speaker extraction, including `soundfile`,
+`torchaudio`, and `onnxruntime-rocm` in the documented ROCm environment.
+
+The tested ROCm environment is summarized in the
+[Ming recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/inclusionAI/Ming-omni-tts-0.5B.md).
+
+## Supported Cases
+
+These cases cover the upstream dense 0.5B cookbook surface that maps cleanly onto the current vLLM-Omni example:
+
+- `style`: zero-speaker style-conditioned speech
+- `ip`: zero-speaker IP voice generation
+- `bgm`: music-only generation
+- `tta`: text-to-audio event generation with FlowLoss controls
+- `emotion`: reference-audio speech with emotion control
+- `basic`: reference-audio speech with speed / pitch / volume control
+- `dialect`: reference-audio speech with dialect control
+- `zero_shot`: reference-audio cloning with explicit transcript
+- `podcast`: multi-reference dialogue generation with automatic speaker embedding extraction
+- `speech_bgm`: speech with background music conditioning
+- `speech_sound`: speech with environmental sound conditioning
+
+## Quick Start
+
+Run the zero-speaker style example:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case style \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+Run zero-shot cloning with a transcript:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case zero_shot \
+    --ref-audio /path/to/10002287-00000094.wav \
+    --ref-text "在此奉劝大家别乱打美白针。" \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+Run emotion-controlled speech:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case emotion \
+    --ref-audio /path/to/emotion_prompt.wav \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+Run podcast generation with two reference clips:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case podcast \
+    --ref-audio-paths /path/to/CTS-CN-F2F-2019-11-11-423-012-A.wav /path/to/CTS-CN-F2F-2019-11-11-423-012-B.wav \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+The script automatically extracts one 192-d speaker embedding per reference WAV using the Ming model's `campplus.onnx`.
+
+If you already have precomputed multi-speaker embeddings, you can override extraction with:
+
+```bash
+--speaker-embedding /path/to/podcast_speaker_embeddings.json
+```
+
+where the JSON is a list of speaker embeddings, one 192-d vector per speaker.
+
+Run text-to-audio event generation:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case tta \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+Use async_chunk streaming:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case basic \
+    --ref-audio /path/to/10002287-00000095.wav \
+    --streaming \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager
+```
+
+`--streaming` uses `AsyncOmni` and the async_chunk stage config. It currently
+supports one prompt per process invocation; use blocking mode for
+`--num-prompts > 1`.
+
+Collect runtime stats and a manifest:
+
+```bash
+python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --case style \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --enforce-eager \
+    --enable-stats \
+    --stats-log-file output_audio/ming_style_pipeline.log \
+    --metadata-json output_audio/ming_style_manifest.json
+```
+
+## Reference Fixtures
+
+The upstream Ming cookbook uses these public audio fixtures from `inclusionAI/Ming-omni-tts/data/wavs`:
+
+- `10002287-00000094.wav` for zero-shot cloning
+- `10002287-00000095.wav` for `basic`
+- `emotion_prompt.wav` for `emotion`
+- `yue_prompt.wav` for `dialect`
+- `00000309-00000300.wav` for `speech_bgm` and `speech_sound`
+- `CTS-CN-F2F-2019-11-11-423-012-A.wav` and `CTS-CN-F2F-2019-11-11-423-012-B.wav` for `podcast`
+
+## Validation Matrix
+
+The repo-facing example is intended to cover the same dense TTS workflows used
+by the local Ming validation script:
+
+| Case | Blocking `deploy/ming_tts.yaml` | Async chunk `deploy/ming_tts.yaml` | Extra inputs |
+|---|---:|---:|---|
+| `style` | Yes | Optional smoke test | none |
+| `ip` | Yes | Optional smoke test | none |
+| `bgm` | Yes | Optional smoke test | none |
+| `tta` | Yes | Optional smoke test | none |
+| `emotion` | Yes | Yes | `--ref-audio emotion_prompt.wav` |
+| `basic` | Yes | Yes | `--ref-audio 10002287-00000095.wav` |
+| `dialect` | Yes | Yes | `--ref-audio yue_prompt.wav` |
+| `zero_shot` | Yes | Yes | `--ref-audio 10002287-00000094.wav --ref-text ...` |
+| `podcast` | Yes | Yes | two `--ref-audio-paths` |
+| `speech_bgm` | Yes | Yes | `--ref-audio 00000309-00000300.wav` |
+| `speech_sound` | Yes | Yes | `--ref-audio 00000309-00000300.wav` |
+
+## Validated Outputs
+
+The following measurements are retained from an earlier L4 CUDA validation;
+they are not ROCm benchmark results. Default async_chunk matched blocking
+output frame counts and Stage-1 patch counts for every case:
+
+| Case | Blocking frames / patches / sec | Async chunk frames / patches / sec |
+|---|---:|---:|
+| `style` | 409248 / 29 / 9.28 | 409248 / 29 / 9.28 |
+| `ip` | 183456 / 13 / 4.16 | 183456 / 13 / 4.16 |
+| `bgm` | 1326528 / 94 / 30.08 | 1326528 / 94 / 30.08 |
+| `tta` | 465696 / 33 / 10.56 | 465696 / 33 / 10.56 |
+| `emotion` | 324576 / 23 / 7.36 | 324576 / 23 / 7.36 |
+| `basic` | 211680 / 15 / 4.80 | 211680 / 15 / 4.80 |
+| `dialect` | 239904 / 17 / 5.44 | 239904 / 17 / 5.44 |
+| `zero_shot` | 409248 / 29 / 9.28 | 409248 / 29 / 9.28 |
+| `podcast` | 437472 / 31 / 9.92 | 437472 / 31 / 9.92 |
+| `speech_bgm` | 296352 / 21 / 6.72 | 296352 / 21 / 6.72 |
+| `speech_sound` | 352800 / 25 / 8.00 | 352800 / 25 / 8.00 |
+
+## Key Arguments
+
+| Argument | Description |
+|---|---|
+| `--model` | Hugging Face repo or local Ming checkpoint path |
+| `--deploy-config` | Deploy config YAML. Use `vllm_omni/deploy/ming_tts.yaml` |
+| `--case` | Built-in demo case |
+| `--ref-audio` | Single reference wav path for cloning-style cases |
+| `--ref-audio-paths` | Multiple reference wav paths, used by `podcast` |
+| `--ref-text` | Reference transcript. Required for `zero_shot` |
+| `--instructions` | Free-form Ming instruction string |
+| `--instruction-json` | Structured Ming instruction JSON |
+| `--speaker-embedding` | JSON file containing a 192-d speaker embedding |
+| `--extract-speaker-embeddings` | Force CampPlus speaker extraction from the provided reference audio paths |
+| `--max-decode-steps` | Override `ming_max_decode_steps` |
+| `--num-prompts` | Repeat the same case N times. Outputs are indexed when `N > 1` |
+| `--streaming` | Use `AsyncOmni` and async_chunk transport |
+| `--enforce-eager` | Recommended for Ming dense; non-eager is out of scope |
+| `--enable-stats` / `--log-stats` | Enable vLLM-Omni per-request stats logging |
+| `--stats-log-file` | Optional path for the stats log |
+| `--metadata-json` | Optional path for the run manifest JSON |
+| `--stage-init-timeout` | Per-stage initialization timeout in seconds |
+| `--init-timeout` | Total initialization timeout in seconds |
+| `--batch-timeout` | Batch timeout in seconds |
+| `--worker-backend` | `multi_process` or `ray` |
+| `--ray-address` | Ray cluster address when using `--worker-backend ray` |
+
+## Output
+
+- The script writes one mono 44.1 kHz WAV file per run
+- Default output directory: `output_audio/`
+- Default filename: `ming_<case>.wav`
+- When `--num-prompts > 1`, outputs are indexed as `ming_<case>_00000.wav`, `..._00001.wav`, etc.
+- When stats are enabled, the script can also write:
+  - a stats log file such as `ming_style_pipeline.log`
+  - a manifest JSON with per-output metadata, stage durations, peak memory info,
+    and streaming client latency metrics when `--streaming` is used
+
+## Notes
+
+- `style` and `ip` are zero-speaker paths and do not require a reference clip
+- `emotion`, `basic`, `dialect`, `speech_bgm`, and `speech_sound` require one reference clip
+- `zero_shot` requires both `--ref-audio` and `--ref-text`
+- `podcast` requires at least two reference clips via `--ref-audio-paths`
+- `podcast` automatically extracts one speaker embedding per reference clip
+- `--speaker-embedding` may contain either one 192-d vector or a list of 192-d vectors
+- `--enforce-eager` was used for the validated runs
+- The earlier L4 validation used SDPA for the Ming audio VAE instead of
+  FlashAttention2, which is the preferred default when available.

examples/offline_inference/text_to_speech/ming_tts/cases.yaml

@@ -0,0 +1,110 @@
+style:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "我会一直在这里陪着你，直到你慢慢、慢慢地沉入那个最温柔的梦里……好吗？"
+  instruction:
+    风格: >-
+      这是一种ASMR耳语，属于一种旨在引发特殊感官体验的创意风格。这个女性使用轻柔的普通话进行耳语，声音气音成分重。音量极低，紧贴麦克风，语速极慢，旨在制造触发听者颅内快感的声学刺激。
+  use_zero_spk_emb: true
+  max_decode_steps: 200
+
+ip:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "这款产品的名字，叫变态坑爹牛肉丸。"
+  instruction:
+    IP: "灵小甄"
+  use_zero_spk_emb: true
+  max_decode_steps: 200
+
+bgm:
+  prompt: "Please generate music based on the following description.\n"
+  text: " Genre: 电子舞曲. Mood: 自信 / 坚定. Instrument: 架子鼓. Theme: 节日. Duration: 30s."
+  instruction: null
+  use_zero_spk_emb: false
+  max_decode_steps: 400
+
+tta:
+  prompt: "Please generate audio events based on given text.\n"
+  text: "Thunder and a gentle rain"
+  instruction: null
+  use_zero_spk_emb: false
+  max_decode_steps: 200
+  cfg: 4.5
+  sigma: 0.3
+  temperature: 2.5
+
+emotion:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "我竟然抢到了陈奕迅的演唱会门票！太棒了！终于可以现场听一听他的歌声了！"
+  instruction:
+    情感: "高兴"
+  requires_ref_audio: true
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200
+
+basic:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "简单地说，这相当于惠普把消费领域市场拱手相让了。"
+  instruction:
+    语速: "快速"
+    基频: "中"
+    音量: "中"
+  requires_ref_audio: true
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200
+
+dialect:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "我觉得社会企业同个人都有责任"
+  instruction:
+    方言: "广粤话"
+  requires_ref_audio: true
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200
+
+zero_shot:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "我们的愿景是构建未来服务业的数字化基础设施，为世界带来更多微小而美好的改变。"
+  instruction: null
+  requires_ref_audio: true
+  requires_ref_text: true
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200
+
+podcast:
+  prompt: "Please generate speech based on the following description.\n"
+  text: " speaker_1:你可以说一下，就大概说一下，可能虽然我也不知道，我看过那部电影没有。\n speaker_2:就是那个叫什么，变相一节课的嘛。\n speaker_1:嗯。\n speaker_2:一部搞笑的电影。\n speaker_1:一部搞笑的。\n"
+  instruction: null
+  prompt_text: " speaker_1:并且我们还要进行每个月还要考核 笔试的话还要进行笔试，做个，当服务员还要去笔试了\n speaker_2:对啊，这真的很奇怪，就是 单纯的因，单纯自己工资不高，只是因为可能人家那个店比较出名一点，就对你苛刻要求\n"
+  requires_ref_audio_count: 2
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200
+
+speech_bgm:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "此次业绩下滑原因，可归结为企业停止服务某些品牌，而带来的负面影响。"
+  instruction:
+    BGM:
+      Genre: "当代古典音乐."
+      Mood: "温暖 / 友善."
+      Instrument: "电吉他"
+      Theme: "节日."
+      SNR: 10.0
+      ENV: null
+  requires_ref_audio: true
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200
+
+speech_sound:
+  prompt: "Please generate speech based on the following description.\n"
+  text: "此次业绩下滑原因，可归结为企业停止服务某些品牌，而带来的负面影响。"
+  instruction:
+    BGM:
+      ENV: "Birds chirping"
+      SNR: 10.0
+      Genre: null
+      Mood: null
+      Instrument: null
+      Theme: null
+  requires_ref_audio: true
+  auto_extract_speaker_embeddings: true
+  max_decode_steps: 200

examples/offline_inference/text_to_speech/ming_tts/end2end.py

@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline inference demo for Ming-omni-tts via vLLM Omni."""
+
+import json
+import os
+from pathlib import Path
+
+import soundfile as sf
+import torch
+import torchaudio
+import yaml
+from transformers import AutoTokenizer
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
+    KEY_CFG,
+    KEY_MAX_DECODE_STEPS,
+    KEY_SIGMA,
+    KEY_TEMPERATURE,
+    SAMPLE_RATE,
+)
+from vllm_omni.model_executor.models.ming_tts.prompt_assembly import build_ming_dense_prompt
+from vllm_omni.model_executor.models.ming_tts.speaker_extractor import MingSpeakerEmbeddingExtractor
+
+try:
+    from .runner import (
+        build_manifest,
+        build_sampling_params,
+        resolve_metadata_json,
+        resolve_stats_log_file,
+        run_generation,
+    )
+except ImportError:
+    from runner import (
+        build_manifest,
+        build_sampling_params,
+        resolve_metadata_json,
+        resolve_stats_log_file,
+        run_generation,
+    )
+
+_DEFAULT_MODEL = "inclusionAI/Ming-omni-tts-0.5B"
+_REPO_ROOT = Path(__file__).resolve().parents[4]
+_DEFAULT_DEPLOY_CONFIG = str(_REPO_ROOT / "vllm_omni/deploy/ming_tts.yaml")
+_CASES_FILE = Path(__file__).with_name("cases.yaml")
+
+CASE_DEFAULTS = yaml.safe_load(_CASES_FILE.read_text(encoding="utf-8")) or {}
+if not CASE_DEFAULTS:
+    raise RuntimeError(f"Empty or missing case definitions in {_CASES_FILE}")
+
+
+def _build_parser() -> FlexibleArgumentParser:
+    p = FlexibleArgumentParser(description="Offline Ming-omni-tts example")
+    p.add_argument("--model", default=_DEFAULT_MODEL, help="Model name or local path")
+    p.add_argument("--deploy-config", default=None, help="Deploy config YAML; auto-selected when omitted")
+    p.add_argument("--case", choices=sorted(CASE_DEFAULTS), default="style", help="Built-in demo case")
+    p.add_argument("--text", default=None, help="Override case text")
+    p.add_argument("--prompt", default=None, help="Override the system prompt prefix")
+    p.add_argument("--instructions", default=None, help="Free-form Ming instruction string")
+    p.add_argument(
+        "--instruction-json", default=None, help='Structured Ming instruction JSON, e.g. \'{"方言":"广粤话"}\''
+    )
+    p.add_argument("--ref-audio", default=None, help="Single reference audio path for cloning-style cases")
+    p.add_argument("--ref-audio-paths", nargs="+", default=None, help="Multiple reference audio paths (podcast)")
+    p.add_argument("--ref-text", default=None, help="Reference transcript; required for zero_shot")
+    p.add_argument("--speaker-embedding", default=None, help="Path to a JSON speaker embedding file")
+    p.add_argument(
+        "--extract-speaker-embeddings",
+        action="store_true",
+        help="Extract speaker embeddings from ref audio via campplus.onnx",
+    )
+    p.add_argument("--max-decode-steps", type=int, default=None, help="Override ming_max_decode_steps")
+    p.add_argument("--output-dir", default="output_audio", help="Directory for output wav files")
+    p.add_argument("--output-name", default=None, help="Output wav filename")
+    p.add_argument("--num-prompts", type=int, default=1, help="Repeat the same prompt N times")
+    p.add_argument("--streaming", action="store_true", help="Use AsyncOmni with async_chunk streaming")
+    p.add_argument("--trust-remote-code", action="store_true")
+    p.add_argument("--enforce-eager", action="store_true")
+    p.add_argument(
+        "--log-stats", "--enable-stats", dest="log_stats", action="store_true", help="Enable Omni stats logging"
+    )
+    p.add_argument("--stats-log-file", default=None, help="Path for the Omni stats log file")
+    p.add_argument("--metadata-json", default=None, help="Path for the run manifest JSON")
+    p.add_argument("--stage-init-timeout", type=int, default=300, help="Per-stage init timeout (s)")
+    p.add_argument("--init-timeout", type=int, default=600, help="Total init timeout (s)")
+    p.add_argument("--batch-timeout", type=int, default=5, help="Batch timeout (s)")
+    p.add_argument("--shm-threshold-bytes", type=int, default=65536)
+    p.add_argument("--worker-backend", default="multi_process", choices=["multi_process", "ray"])
+    p.add_argument("--ray-address", default=None, help="Ray cluster address (--worker-backend ray)")
+    return p
+
+
+def _finalize_args(args) -> None:
+    if args.instructions is not None and args.instruction_json is not None:
+        raise RuntimeError("Use either --instructions or --instruction-json, not both")
+    if args.num_prompts < 1:
+        raise RuntimeError("--num-prompts must be at least 1")
+    if args.streaming and args.num_prompts != 1:
+        raise RuntimeError("--streaming currently supports exactly one prompt")
+    if args.deploy_config is None:
+        args.deploy_config = _DEFAULT_DEPLOY_CONFIG
+
+
+def _load_waveform(path: str) -> torch.Tensor:
+    samples, sr = sf.read(path, dtype="float32")
+    wav = torch.as_tensor(samples, dtype=torch.float32)
+    if wav.ndim == 2:
+        wav = wav.mean(dim=1)
+    wav = wav.reshape(1, -1)
+    if int(sr) != SAMPLE_RATE:
+        wav = torchaudio.functional.resample(wav, int(sr), SAMPLE_RATE)
+    return wav
+
+
+def _load_speaker_embedding(path: str) -> torch.Tensor:
+    return torch.as_tensor(json.loads(Path(path).read_text(encoding="utf-8")), dtype=torch.float32)
+
+
+def _ref_audio_paths(args) -> list[str]:
+    if args.ref_audio is not None and args.ref_audio_paths is not None:
+        raise RuntimeError("Use either --ref-audio or --ref-audio-paths, not both")
+    if args.ref_audio_paths is not None:
+        return list(args.ref_audio_paths)
+    return [args.ref_audio] if args.ref_audio else []
+
+
+def _resolve_reference_inputs(args, case: dict, paths: list[str]):
+    required = int(case.get("requires_ref_audio_count", 0))
+    if required > 0 and len(paths) < required:
+        raise RuntimeError(f"Case '{args.case}' needs {required} ref audio paths via --ref-audio-paths")
+    if required <= 0 and case.get("requires_ref_audio") and not paths:
+        raise RuntimeError(f"--ref-audio required for case '{args.case}'")
+    if not paths:
+        return None
+    wavs = [_load_waveform(p) for p in paths]
+    return wavs[0] if len(wavs) == 1 else wavs
+
+
+def _resolve_speaker_embedding(args, case: dict, paths: list[str]):
+    if args.speaker_embedding:
+        return _load_speaker_embedding(args.speaker_embedding)
+    if not (case.get("auto_extract_speaker_embeddings") or args.extract_speaker_embeddings) or not paths:
+        return None
+    embs = MingSpeakerEmbeddingExtractor(args.model).extract_many(paths)
+    if not embs:
+        raise RuntimeError("Speaker extraction produced no embeddings")
+    return embs[0] if len(embs) == 1 else torch.stack(embs, dim=0)
+
+
+def _build_prompt_payload(tokenizer, args):
+    case = CASE_DEFAULTS[args.case]
+    paths = _ref_audio_paths(args)
+    prompt_text = args.ref_text if args.ref_text is not None else case.get("prompt_text")
+    if case.get("requires_ref_text") and not prompt_text:
+        raise RuntimeError(f"--ref-text required for case '{args.case}'")
+    prompt_waveform = _resolve_reference_inputs(args, case, paths) if prompt_text is not None else None
+    speaker_embedding = _resolve_speaker_embedding(args, case, paths)
+    use_zero_spk_emb = bool(case.get("use_zero_spk_emb")) and prompt_waveform is None and speaker_embedding is None
+    runtime_controls = {KEY_MAX_DECODE_STEPS: args.max_decode_steps or case["max_decode_steps"]}
+    for key, field in [(KEY_CFG, "cfg"), (KEY_SIGMA, "sigma"), (KEY_TEMPERATURE, "temperature")]:
+        if field in case:
+            runtime_controls[key] = case[field]
+    instruction = (
+        json.loads(args.instruction_json) if args.instruction_json else (args.instructions or case.get("instruction"))
+    )
+    return build_ming_dense_prompt(
+        tokenizer,
+        prompt=args.prompt or case["prompt"],
+        text=args.text or case["text"],
+        runtime_controls=runtime_controls,
+        instruction=instruction,
+        prompt_text=prompt_text,
+        prompt_waveform=prompt_waveform,
+        speaker_embedding=speaker_embedding,
+        use_zero_spk_emb=use_zero_spk_emb,
+    )
+
+
+def main():
+    args = _build_parser().parse_args()
+    _finalize_args(args)
+    tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=False)
+    prompt_payload = _build_prompt_payload(tokenizer, args)
+    case = CASE_DEFAULTS[args.case]
+    sampling_params_list = build_sampling_params(args.max_decode_steps or case["max_decode_steps"])
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    stats_log_file = resolve_stats_log_file(args)
+    summaries = run_generation(args, prompt_payload, sampling_params_list, output_dir, stats_log_file)
+    metadata_json = resolve_metadata_json(args)
+    manifest = build_manifest(args, prompt_payload, stats_log_file, summaries)
+    if metadata_json is not None:
+        Path(metadata_json).write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
+        print(f"Saved run manifest to {metadata_json}")
+
+
+if __name__ == "__main__":
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    main()

examples/offline_inference/text_to_speech/ming_tts/runner.py

@@ -0,0 +1,277 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import json
+import time
+import uuid
+import wave
+from pathlib import Path
+
+import torch
+from vllm import SamplingParams
+
+from vllm_omni import AsyncOmni, Omni
+from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
+    KEY_SPEAKER_EMBEDDING,
+    SAMPLE_RATE,
+    TEXT_EOS_TOKEN_ID,
+)
+
+
+def coerce_audio_tensor(audio, *, async_chunk: bool) -> torch.Tensor:
+    if isinstance(audio, list):
+        parts = []
+        for item in audio:
+            tensor = torch.as_tensor(item, dtype=torch.float32).reshape(-1)
+            if tensor.numel() > 0:
+                parts.append(tensor)
+        if not parts:
+            return torch.zeros((0,), dtype=torch.float32)
+        return torch.cat(parts, dim=0)
+
+    return torch.as_tensor(audio, dtype=torch.float32).reshape(-1)
+
+
+def resolve_sr(sr) -> int:
+    if isinstance(sr, list):
+        sr = sr[-1]
+    if hasattr(sr, "item"):
+        return int(sr.item())
+    return int(sr)
+
+
+def extract_sample_rate(multimodal_output: dict) -> int:
+    sr = multimodal_output.get("sr")
+    if sr is None:
+        raise RuntimeError("Expected multimodal_output['sr']")
+    return resolve_sr(sr)
+
+
+def write_wav(path: str, audio: torch.Tensor, sample_rate: int) -> None:
+    audio = audio.clamp(-1.0, 1.0)
+    pcm16 = (audio * 32767.0).round().to(torch.int16).cpu().numpy()
+    with wave.open(path, "wb") as wav_file:
+        wav_file.setnchannels(1)
+        wav_file.setsampwidth(2)
+        wav_file.setframerate(int(sample_rate))
+        wav_file.writeframes(pcm16.tobytes())
+
+
+def request_index(request_id: str | None, fallback: int) -> int:
+    try:
+        return int(request_id)
+    except (TypeError, ValueError):
+        if isinstance(request_id, str):
+            head = request_id.split("_", 1)[0]
+            if head.isdigit():
+                return int(head)
+    return fallback
+
+
+def audio_summary(audio: torch.Tensor, sample_rate: int) -> dict:
+    waveform = audio.detach().cpu().reshape(-1).to(torch.float32)
+    return {
+        "sample_rate": int(sample_rate),
+        "num_samples": int(waveform.numel()),
+        "duration_seconds": float(waveform.numel()) / float(sample_rate),
+        "max_abs_amplitude": float(waveform.abs().max().item()) if waveform.numel() > 0 else 0.0,
+    }
+
+
+def resolve_output_name(output_name: str | None, case: str, index: int, total: int) -> str:
+    if total == 1:
+        return output_name or f"ming_{case}.wav"
+    base = Path(output_name or f"ming_{case}.wav")
+    return f"{base.stem}_{index:05d}{base.suffix or '.wav'}"
+
+
+def resolve_stats_log_file(args) -> str | None:
+    if not args.log_stats:
+        return None
+    if args.stats_log_file:
+        return args.stats_log_file
+    base = Path(args.output_name or f"ming_{args.case}.wav").stem
+    return str(Path(args.output_dir) / f"{base}_pipeline.log")
+
+
+def resolve_metadata_json(args) -> str | None:
+    if args.metadata_json:
+        return args.metadata_json
+    if args.log_stats:
+        base = Path(args.output_name or f"ming_{args.case}.wav").stem
+        return str(Path(args.output_dir) / f"{base}_manifest.json")
+    return None
+
+
+def build_manifest(args, prompt_payload, stats_log_file: str | None, outputs: list[dict]) -> dict:
+    additional_information = {}
+    if isinstance(prompt_payload, dict):
+        additional_information = dict(prompt_payload.get("additional_information", {}))
+    return {
+        "model": args.model,
+        "case": args.case,
+        "streaming": bool(args.streaming),
+        "deploy_config": args.deploy_config,
+        "enforce_eager": bool(args.enforce_eager),
+        "num_prompts": int(args.num_prompts),
+        "log_stats": bool(args.log_stats),
+        "stats_log_file": stats_log_file,
+        "prompt_text": additional_information.get("prompt_text"),
+        "instruction": additional_information.get("instruction"),
+        "speaker_embedding_shape": (
+            list(additional_information[KEY_SPEAKER_EMBEDDING].shape)
+            if KEY_SPEAKER_EMBEDDING in additional_information
+            and hasattr(additional_information[KEY_SPEAKER_EMBEDDING], "shape")
+            else None
+        ),
+        "outputs": outputs,
+        "generated_at_unix": time.time(),
+    }
+
+
+def build_engine_kwargs(args, stats_log_file: str | None) -> dict:
+    kwargs = {
+        "model": args.model,
+        "deploy_config": args.deploy_config,
+        "enforce_eager": args.enforce_eager,
+        "trust_remote_code": args.trust_remote_code,
+        "log_stats": args.log_stats,
+        "stage_init_timeout": args.stage_init_timeout,
+        "init_timeout": args.init_timeout,
+        "batch_timeout": args.batch_timeout,
+        "shm_threshold_bytes": args.shm_threshold_bytes,
+        "worker_backend": args.worker_backend,
+    }
+    if stats_log_file is not None:
+        kwargs["log_file"] = stats_log_file
+    if args.ray_address is not None:
+        kwargs["ray_address"] = args.ray_address
+    return kwargs
+
+
+def build_sampling_params(max_decode_steps: int) -> list[SamplingParams]:
+    return [
+        SamplingParams(
+            temperature=0.0,
+            max_tokens=max_decode_steps + 1,
+            stop_token_ids=[int(TEXT_EOS_TOKEN_ID)],
+        ),
+        SamplingParams(temperature=0.0, max_tokens=1),
+    ]
+
+
+async def run_streaming(args, prompt_payload, sampling_params_list, output_dir: Path, stats_log_file: str | None):
+    engine = AsyncOmni(**build_engine_kwargs(args, stats_log_file))
+    try:
+        all_audio_chunks = []
+        accumulated_samples = 0
+        chunk_idx = 0
+        start_time = time.time()
+        chunk_times = []
+        ttfp_seconds = None
+        final_stage_output = None
+        async for stage_output in engine.generate(
+            prompt=prompt_payload,
+            request_id=str(uuid.uuid4()),
+            sampling_params_list=sampling_params_list,
+        ):
+            final_stage_output = stage_output
+            multimodal_output = stage_output.multimodal_output or {}
+            audio = multimodal_output.get("audio")
+            if audio is None:
+                continue
+
+            finished = stage_output.finished
+            if isinstance(audio, torch.Tensor):
+                if finished:
+                    audio_chunk = audio[accumulated_samples:].float().detach().cpu()
+                else:
+                    audio_chunk = audio.float().detach().cpu()
+            elif isinstance(audio, list):
+                audio_chunk = torch.as_tensor(audio[chunk_idx], dtype=torch.float32).reshape(-1).cpu()
+            else:
+                audio_chunk = torch.as_tensor(audio, dtype=torch.float32).reshape(-1).cpu()
+
+            accumulated_samples += int(audio_chunk.numel())
+            chunk_idx += 1
+            if audio_chunk.numel() > 0:
+                now = time.time()
+                if ttfp_seconds is None:
+                    ttfp_seconds = now - start_time
+                chunk_times.append(now)
+                all_audio_chunks.append(audio_chunk)
+
+        if not all_audio_chunks:
+            raise RuntimeError("Streaming Ming example produced no audio chunks")
+
+        waveform = torch.cat(all_audio_chunks, dim=0)
+        output_name = resolve_output_name(args.output_name, args.case, 0, 1)
+        output_path = str(output_dir / output_name)
+        write_wav(output_path, waveform, SAMPLE_RATE)
+        summary = {
+            "request_id": getattr(final_stage_output, "request_id", None),
+            "stage_id": getattr(final_stage_output, "stage_id", None),
+            "output_path": output_path,
+            "stage_durations": getattr(final_stage_output, "stage_durations", {}),
+            "peak_memory_mb": getattr(final_stage_output, "peak_memory_mb", 0.0),
+            "ttfp_seconds": ttfp_seconds,
+            "mean_inter_chunk_seconds": (
+                sum(t1 - t0 for t0, t1 in zip(chunk_times, chunk_times[1:])) / (len(chunk_times) - 1)
+                if len(chunk_times) > 1
+                else None
+            ),
+        }
+        summary.update(audio_summary(waveform, SAMPLE_RATE))
+        print(f"Saved streaming output to {output_path}")
+        print(json.dumps(summary, ensure_ascii=False, indent=2))
+        return [summary]
+    finally:
+        engine.shutdown()
+
+
+def run_non_streaming(args, prompt_payload, sampling_params_list, output_dir: Path, stats_log_file: str | None):
+    engine = Omni(**build_engine_kwargs(args, stats_log_file))
+    try:
+        outputs = engine.generate(
+            prompts=[prompt_payload for _ in range(args.num_prompts)],
+            sampling_params_list=sampling_params_list,
+            py_generator=False,
+        )
+        summaries = []
+        for fallback_index, output in enumerate(outputs):
+            if output.final_output_type != "audio":
+                continue
+            multimodal_output = output.multimodal_output or {}
+            waveform = coerce_audio_tensor(multimodal_output.get("audio"), async_chunk=False)
+            sample_rate = extract_sample_rate(multimodal_output)
+            output_name = resolve_output_name(
+                args.output_name,
+                args.case,
+                request_index(output.request_id, fallback_index),
+                args.num_prompts,
+            )
+            output_path = str(output_dir / output_name)
+            write_wav(output_path, waveform, sample_rate)
+            summary = {
+                "request_id": output.request_id,
+                "stage_id": output.stage_id,
+                "output_path": output_path,
+                "stage_durations": output.stage_durations,
+                "peak_memory_mb": output.peak_memory_mb,
+            }
+            summary.update(audio_summary(waveform, sample_rate))
+            summaries.append(summary)
+            print(f"Saved output to {output_path}")
+            print(json.dumps(summary, ensure_ascii=False, indent=2))
+        if not summaries:
+            raise RuntimeError("Non-streaming Ming example produced no audio outputs")
+        return summaries
+    finally:
+        engine.close()
+
+
+def run_generation(args, prompt_payload, sampling_params_list, output_dir: Path, stats_log_file: str | None):
+    if args.streaming:
+        return asyncio.run(run_streaming(args, prompt_payload, sampling_params_list, output_dir, stats_log_file))
+    return run_non_streaming(args, prompt_payload, sampling_params_list, output_dir, stats_log_file)