import asyncio
import base64
import hashlib
import io
import json
import math
import os
import re
import struct
import time
from collections import OrderedDict
from collections.abc import Mapping
from concurrent.futures import ThreadPoolExecutor
from http import HTTPStatus
from pathlib import Path
from typing import Any

import numpy as np
import soundfile as sf
import torch
from fastapi import HTTPException, Request, UploadFile
from fastapi.responses import Response, StreamingResponse
from transformers.utils.hub import cached_file
from vllm.entrypoints.generate.base.serving import GenerateBaseServing as OpenAIServing
from vllm.entrypoints.launcher import terminate_if_errored
from vllm.entrypoints.openai.engine.protocol import (
    ErrorResponse,
    RequestResponseMetadata,
)
from vllm.inputs import tokens_input
from vllm.logger import init_logger
from vllm.multimodal.media import MediaConnector
from vllm.utils import random_uuid
from vllm.utils.async_utils import make_async
from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError

from vllm_omni.entrypoints.openai.audio_utils_mixin import AudioMixin
from vllm_omni.entrypoints.openai.protocol.audio import (
    AudioResponse,
    BatchSpeechRequest,
    BatchSpeechResponse,
    CreateAudio,
    OpenAICreateSpeechRequest,
    SpeechBatchItem,
    SpeechBatchItemResult,
    SpeechInputTokenDetails,
    SpeechTokenUsage,
)
from vllm_omni.entrypoints.openai.speech_usage import (
    SpeechOutputTokenCounter,
    build_speech_usage,
    qwen3_tts_input_token_details,
)
from vllm_omni.entrypoints.openai.tts_adapters import (
    SpeechServingContext,
    all_tts_stage_keys,
    detect_tts_model_type,
    resolve_adapter,
    tts_entry_stage_archs,
)
from vllm_omni.entrypoints.utils import coerce_param_message_types
from vllm_omni.model_executor.models.fish_speech.prompt_utils import (
    build_fish_text_only_prompt_ids,
    estimate_fish_voice_clone_prompt_len_from_normalized,
    normalize_fish_voice_clone_texts,
)
from vllm_omni.model_executor.models.ming_flash_omni.prompt_utils import (
    DEFAULT_PROMPT as MING_DEFAULT_PROMPT,
)
from vllm_omni.model_executor.models.ming_flash_omni.prompt_utils import (
    create_instruction as ming_create_instruction,
)
from vllm_omni.model_executor.models.ming_tts.constants import SPEAKER_EMBEDDING_DIM
from vllm_omni.outputs import OmniRequestOutput
from vllm_omni.utils.speaker_cache import (
    get_speaker_cache,
    iter_custom_voice_profiles,
    load_validated_profile_tensors,
)

logger = init_logger(__name__)

_SPEECH_USAGE_INPUT_TOKENS_HEADER = "X-VLLM-OMNI-INPUT-TOKENS"
_SPEECH_USAGE_OUTPUT_TOKENS_HEADER = "X-VLLM-OMNI-OUTPUT-TOKENS"
_SPEECH_USAGE_TOTAL_TOKENS_HEADER = "X-VLLM-OMNI-TOTAL-TOKENS"
_SPEECH_USAGE_INPUT_TEXT_TOKENS_HEADER = "X-VLLM-OMNI-INPUT-TEXT-TOKENS"
_SPEECH_USAGE_INPUT_AUDIO_TOKENS_HEADER = "X-VLLM-OMNI-INPUT-AUDIO-TOKENS"

# TTS Configuration
#
# The stage-key -> model-type mapping is NOT declared here: it is derived from
# the ``stage_keys`` / ``model_archs`` each adapter declares, via
# ``tts_adapters.detect_tts_model_type``. Adding a TTS model must not require an
# edit to this module.
#
# CosyVoice3 talker expects its reference transcript wrapped in the model
# instruction template; without the delimiter the talker re-speaks the
# reference (issue #4644). Matches the offline example/test and upstream demo.
_COSYVOICE3_PROMPT_DELIMITER = "<|endofprompt|>"
_COSYVOICE3_PROMPT_PREFIX = f"You are a helpful assistant.{_COSYVOICE3_PROMPT_DELIMITER}"

# Audex contract: zero-codec / invalid generations arrive as empty terminal
# payloads and must fail the request, never serialize as a successful empty
# WAV. Covers both the TTS ("audex") and TTA ("audex_tta") pipelines.
_AUDEX_NO_AUDIO_GUARD_MODEL_TYPES = frozenset({"audex", "audex_tta"})
_TTS_LANGUAGES = frozenset(
    {
        "Auto",
        "Chinese",
        "English",
        "Japanese",
        "Korean",
        "German",
        "French",
        "Russian",
        "Portuguese",
        "Spanish",
        "Italian",
    }
)
_REF_AUDIO_MIN_DURATION = 1.0  # seconds
_REF_AUDIO_MAX_DURATION = 30.0  # seconds
_REF_AUDIO_RESOLVE_CACHE_MAX_ENTRIES = 256
_REF_AUDIO_RESOLVE_CACHE_MAX_BYTES = 256 * 1024 * 1024
_HIGGS_V3_REF_CODE_CACHE_MAX_ENTRIES = 256
_HIGGS_V3_REF_CODE_CACHE_MAX_BYTES = 64 * 1024 * 1024
_QWEN3_TTS_REF_AUDIO_CACHE_KEY = "_qwen3_tts_ref_audio_cache_key"
_TTS_MAX_INSTRUCTIONS_LENGTH = 500
_TTS_MAX_NEW_TOKENS_MAX = 4096
_MING_DEFAULT_PROMPT = MING_DEFAULT_PROMPT
_DEFAULT_VOICE_NAME = "default"


def _is_default_voice(voice, supported_speakers):
    """Check if a lowercased voice name is the placeholder default and not
    an actual registered/built-in speaker."""
    return voice == _DEFAULT_VOICE_NAME and voice not in supported_speakers


def _create_wav_header(sample_rate: int, num_channels: int = 1, bits_per_sample: int = 16) -> bytes:
    """Create a WAV header with placeholder size values for streaming.

    Uses 0xFFFFFFFF as placeholder for data size fields, which is accepted
    by most audio clients and matches OpenAI's streaming WAV implementation.

    Args:
        sample_rate: Audio sample rate in Hz
        num_channels: Number of audio channels (1 for mono, 2 for stereo)
        bits_per_sample: Bits per sample (typically 16)

    Returns:
        44-byte WAV header as bytes
    """
    byte_rate = sample_rate * num_channels * bits_per_sample // 8
    block_align = num_channels * bits_per_sample // 8

    # Use 0xFFFFFFFF as placeholder for unknown size (streaming)
    placeholder_size = 0xFFFFFFFF

    # ref https://docs.fileformat.com/audio/wav/
    header = struct.pack(
        "<4sI4s4sIHHIIHH4sI",
        b"RIFF",  # ChunkID
        placeholder_size,  # ChunkSize (placeholder)
        b"WAVE",  # Format
        b"fmt ",  # Subchunk1ID
        16,  # Subchunk1Size (16 for PCM)
        1,  # AudioFormat (1 for PCM)
        num_channels,  # NumChannels
        sample_rate,  # SampleRate
        byte_rate,  # ByteRate
        block_align,  # BlockAlign
        bits_per_sample,  # BitsPerSample
        b"data",  # Subchunk2ID
        placeholder_size,  # Subchunk2Size (placeholder)
    )

    return header


def _infer_audio_num_channels(audio: np.ndarray) -> int:
    """Infer channel count before streaming PCM bytes are wrapped as WAV."""
    if audio.ndim == 3 and audio.shape[0] == 1:
        audio = audio[0]
    if audio.ndim == 2:
        if audio.shape[0] in (1, 2):
            return int(audio.shape[0])
        if audio.shape[1] in (1, 2):
            return int(audio.shape[1])
    return 1


def _sanitize_filename(filename: str) -> str:
    """Sanitize filename to prevent path traversal attacks.

    Only allows alphanumeric characters, underscores, hyphens, and dots.
    Replaces any other characters with underscores.
    """
    # Remove any path components
    filename = os.path.basename(filename)
    # Replace any non-alphanumeric, underscore, hyphen, or dot with underscore
    sanitized = re.sub(r"[^a-zA-Z0-9_.\-]", "_", filename)
    # Ensure filename is not empty
    if not sanitized:
        sanitized = "file"
    # Limit length to prevent potential issues
    if len(sanitized) > 255:
        sanitized = sanitized[:255]
    return sanitized


def _validate_speaker_name(name: str) -> str:
    """Trim and reject empty / path-separator / NUL / reserved voice names."""
    trimmed = (name or "").strip()
    if not trimmed or trimmed in (".", "..") or any(c in trimmed for c in "/\\\x00"):
        raise ValueError(f"Invalid voice name {name!r}: must be non-empty, no path separators or NUL")
    return trimmed


def _validate_path_within_directory(file_path: Path, directory: Path) -> bool:
    """Validate that file_path is within the specified directory.

    Prevents path traversal attacks by ensuring the resolved path
    is within the target directory.
    """
    try:
        # Resolve both paths to absolute paths
        file_path_resolved = file_path.resolve()
        directory_resolved = directory.resolve()
        # Check if file_path is within directory
        return directory_resolved in file_path_resolved.parents or directory_resolved == file_path_resolved
    except Exception:
        return False


def _conditioning_cache_salt(request, tts_params: dict | None = None) -> str:
    """Stable hash of the real Stage 0 conditioning for the prefix cache.

    The talker's vLLM prompt is placeholder token ids; the real inputs are
    rebuilt from text / ref_audio / ref_text into inputs_embeds. vLLM hashes
    token ids (folded with cache_salt) for prefix caching, so without a salt
    every request collides and a hit could reuse KV from a semantically
    different input. Tying the salt to the conditioning keeps a hit safe:
    identical conditioning may share the prefix, any difference never does.

    Raw request fields alone are not enough for uploaded voices: the request
    only carries the voice *name* (ref_audio/ref_text/task_type are resolved
    from stored voice data into ``tts_params``). Delete + re-upload under the
    same name leaves every raw field identical, so the resolved conditioning
    must also be folded in. ``voice_created_at`` bumps on every (re-)upload,
    which uniquely identifies the resolved reference artifact together with
    the voice name; the decoded ref_audio array itself need not be hashed.
    """
    h = hashlib.sha256()
    for part in (
        request.input,
        request.task_type,
        request.language,
        request.voice,
        request.ref_text,
        request.ref_audio,
        request.instructions,
        request.x_vector_only_mode,
        request.speaker_embedding,
    ):
        h.update(b"\x00")
        if part is not None:
            h.update(repr(part).encode("utf-8"))
    # Fold resolved conditioning that is auto-derived for uploaded voices and
    # absent from the raw request.
    for key in (
        "voice_created_at",
        "task_type",
        "speaker",
        "ref_text",
        "x_vector_only_mode",
    ):
        h.update(b"\x00")
        value = tts_params.get(key) if tts_params is not None else None
        if value is not None:
            h.update(repr(value).encode("utf-8"))
    return h.hexdigest()[:32]


class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
    _diffusion_mode: bool = False
    _tts_executor: ThreadPoolExecutor | None = None

    def _init_speaker_storage(self) -> None:
        """Initialize speaker storage + cache, restoring any persisted uploads."""
        speaker_samples_dir = os.environ.get("SPEAKER_SAMPLES_DIR", os.path.expanduser("~/.cache/vllm-omni/speakers"))
        self.uploaded_speakers_dir = Path(speaker_samples_dir).expanduser()
        self.uploaded_speakers_dir.mkdir(parents=True, exist_ok=True)
        _raw_cap = os.environ.get("SPEAKER_MAX_UPLOADED", "")
        try:
            self._max_uploaded_speakers = int(_raw_cap) if _raw_cap else 1000
        except ValueError:
            logger.warning("Invalid SPEAKER_MAX_UPLOADED=%r; using default 1000", _raw_cap)
            self._max_uploaded_speakers = 1000
        self.uploaded_speakers: dict[str, dict] = {}
        self.precomputed_speakers: dict[str, dict[str, Any]] = {}
        self.supported_speakers: set[str] = set()
        self._ref_audio_data_url_cache: dict[str, str] = {}
        self._ref_audio_resolve_cache: OrderedDict[str, tuple[list[float], int, int, str]] = OrderedDict()
        self._ref_audio_resolve_cache_bytes = 0
        self._ref_audio_resolve_cache_max_entries = _REF_AUDIO_RESOLVE_CACHE_MAX_ENTRIES
        self._ref_audio_resolve_cache_max_bytes = _REF_AUDIO_RESOLVE_CACHE_MAX_BYTES
        # Readiness is keyed by (artifact_key, x_vector_only). An x-vector-only
        # request caches a speaker embedding but no ref_code, so its artifact
        # must not satisfy a later ICL request that needs ref_code (#5049).
        self._ref_audio_model_artifact_ready: set[tuple[str, bool]] = set()
        self._request_ref_audio_artifact_keys: dict[str, tuple[str, bool]] = {}
        self._higgs_audio_v3_ref_code_cache: OrderedDict[str, tuple[torch.Tensor, int]] = OrderedDict()
        self._higgs_audio_v3_ref_code_cache_bytes = 0
        self._higgs_audio_v3_ref_code_inflight: dict[str, asyncio.Task[torch.Tensor]] = {}
        self._speaker_cache = get_speaker_cache()
        self._last_upload_ts = 0
        self._upload_lock = asyncio.Lock()
        self._restore_uploaded_speakers()
        logger.info(
            "Speaker storage: dir=%s, max_speakers=%d, restored=%d",
            self.uploaded_speakers_dir,
            self._max_uploaded_speakers,
            len(self.uploaded_speakers),
        )

    def _next_upload_timestamp(self) -> int:
        ts = max(int(time.time()), self._last_upload_ts + 1)
        self._last_upload_ts = ts
        return ts

    _META_SCALAR_INT_KEYS: tuple[str, ...] = (
        "created_at",
        "file_size",
        "sample_rate",
        "embedding_dim",
    )

    @classmethod
    def _speaker_metadata_to_header(cls, speaker_data: dict[str, Any]) -> dict[str, str]:
        """Serialize a speaker_data dict into safetensors' ``dict[str, str]`` header."""
        header: dict[str, str] = {}
        for k, v in speaker_data.items():
            if v is None:
                continue
            # file_path is re-derived from the path on load; don't persist it.
            if k == "file_path":
                continue
            header[k] = str(v)
        return header

    @classmethod
    def _speaker_metadata_from_header(cls, header: dict[str, str], file_path: str) -> dict[str, Any]:
        """Reverse of :meth:`_speaker_metadata_to_header`: coerce ints back and re-inject file_path."""
        data: dict[str, Any] = dict(header)
        for k in cls._META_SCALAR_INT_KEYS:
            if k in data:
                try:
                    data[k] = int(data[k])
                except ValueError:
                    logger.warning(
                        "Speaker metadata %r in %s is not a valid int (got %r); leaving as string",
                        k,
                        file_path,
                        data[k],
                    )
        data["file_path"] = file_path
        return data

    def _restore_uploaded_speakers(self) -> None:
        """Scan ``uploaded_speakers_dir`` for safetensors files and rebuild state."""
        try:
            from safetensors import safe_open
        except ImportError:
            logger.warning("safetensors unavailable; uploaded voices will not persist across restarts")
            return

        restored = 0
        for path in sorted(self.uploaded_speakers_dir.glob("*.safetensors")):
            try:
                with safe_open(str(path), framework="pt") as f:
                    header = dict(f.metadata() or {})
            except Exception as e:
                logger.warning("Could not read voice file %s: %s", path, e)
                continue
            voice_name_lower = header.get("voice_name_lower") or header.get("name", "").lower()
            if not voice_name_lower:
                logger.warning("Voice file %s has no voice name in metadata; skipping", path)
                continue
            speaker_data = self._speaker_metadata_from_header(header, str(path))
            speaker_data.setdefault("name", voice_name_lower)
            speaker_data.setdefault("file_size", int(path.stat().st_size))
            self.uploaded_speakers[voice_name_lower] = speaker_data
            self.supported_speakers.add(voice_name_lower)
            self._last_upload_ts = max(self._last_upload_ts, int(speaker_data.get("created_at", 0)))
            restored += 1
        if restored:
            logger.info("Restored %d uploaded voice(s) from %s", restored, self.uploaded_speakers_dir)

    @classmethod
    def for_diffusion(
        cls,
        diffusion_engine: "Any",
        model_name: str,
        stage_configs: "list[Any] | None" = None,
    ) -> "OmniOpenAIServingSpeech":
        """Create a speech serving instance for pure diffusion TTS models.

        Bypasses OpenAIServing.__init__ which requires a fully configured
        engine client that pure diffusion engines don't provide.
        """
        instance = cls.__new__(cls)
        instance._diffusion_mode = True
        instance._diffusion_engine = diffusion_engine
        instance._diffusion_model_name = model_name
        instance._diffusion_stage_configs = stage_configs
        instance._tts_model_type = "omnivoice"
        instance._is_tts = False
        # Diffusion-only instances don't have a TTS stage; set None so any
        # ``_is_tts_model()`` / ``_tts_stage`` access doesn't raise AttributeError.
        instance._tts_stage = None
        instance._init_speaker_storage()
        return instance

    def __init__(self, *args, **kwargs):
        self.model_name = kwargs.pop("model_name", None)
        # True when the server was launched with --forced-aligner (a pooling
        # aligner stage is appended to the pipeline). Gates word_timestamps.
        self.forced_aligner_enabled: bool = bool(kwargs.pop("forced_aligner_enabled", False))
        super().__init__(*args, **kwargs)
        self._init_speaker_storage()

        # Find and cache the TTS stage (if any) during initialization
        self._tts_stage = self._find_tts_stage()
        self._is_tts = self._tts_stage is not None
        self._fish_speech_tokenizer = None
        self._covo_audio_tokenizer = None
        # Cached per process: the CosyVoice3 Qwen tokenizer + resolved model
        # path used for dynamic-token sizing. Without this, every request
        # re-ran snapshot_download + reloaded the tokenizer (~100 ms on the
        # TTFP critical path) in the CosyVoice3 adapter's sampling override.
        self._cosyvoice3_tokenizer = None

        # Determine TTS model type or None
        self._tts_model_type = self._detect_tts_model_type()
        self.precomputed_speakers = self._load_precomputed_speakers()

        # Sub-variant inside the full MOSS-TTS family. We collapse all five
        # variants onto the same _tts_model_type="moss_tts" because they share
        # the same stage layout, but request validation + param building
        # differ per HF repo (voice-clone vs dialogue vs ambient-sound vs
        # instruction vs streaming voice-clone).
        self._moss_variant = self._detect_moss_variant() if self._tts_model_type == "moss_tts" else None

        # GLM-TTS lazy-cached resources (populated on first GLM-TTS request)
        self._glm_tts_text_tokenizer: object | None = None
        self._glm_tts_text_frontend: object | None = None

        # Cache TTS configuration values (computed once, reused per request)
        self._max_instructions_length = self._compute_max_instructions_length()

        # Merge built-in speakers into the set initialized by _init_speaker_storage.
        self.supported_speakers |= self._load_supported_speakers()
        self.supported_speakers |= set(self.precomputed_speakers)

        self.supported_languages = self._load_supported_languages()

        self._tts_tokenizer = None
        self._voxcpm2_tokenizer = None
        self._audex_tokenizer = None
        self._audex_tta_rvq = None
        self._voxcpm2_split_map: dict[int, list[int]] = {}

        if self.supported_speakers:
            logger.info(
                "Loaded %d supported speakers: %s", len(self.supported_speakers), sorted(self.supported_speakers)
            )
        else:
            logger.info(
                "No built-in speakers configured; only '%s' and uploaded voices are available", _DEFAULT_VOICE_NAME
            )

        # Batch configuration
        self._batch_max_items: int = getattr(self.engine_client, "tts_batch_max_items", 32)

        # Load speech tokenizer codec parameters for prompt length estimation
        self._codec_frame_rate: float | None = self._load_codec_frame_rate()

        # Shared thread pool executor for blocking TTS preprocessing
        # operations. max_workers=1 serializes tokenizer access to avoid
        # Rust RefCell "Already borrowed" errors from concurrent use.
        self._tts_executor = ThreadPoolExecutor(max_workers=1)
        self._build_voxtral_prompt_async = make_async(self._build_voxtral_prompt, executor=self._tts_executor)
        self._build_fish_speech_prompt_async = make_async(self._build_fish_speech_prompt, executor=self._tts_executor)
        self._estimate_prompt_len_async = make_async(self._estimate_prompt_len, executor=self._tts_executor)

        # Resolve the per-model serving adapter (RFC #4327), keyed on the
        # detected model-type. Every dedicated TTS model has an adapter; the
        # adapter owns request validation, prompt/param building, and sampling
        # overrides; the model-type label remains available for compatibility.
        self._adapter = None
        if self._tts_stage is not None:
            adapter_cls = resolve_adapter(self._tts_model_type)
            if adapter_cls is not None:
                ctx = SpeechServingContext(server=self, engine_client=self.engine_client)
                self._adapter = adapter_cls(ctx)
                logger.info("Resolved TTS serving adapter: %s", adapter_cls.__name__)

    def _get_tts_adapter(self):
        """Return the per-model serving adapter for the current ``_tts_model_type``.

        Pure-diffusion speech uses its dedicated request path and does not
        resolve adapters for AR-stage TTS models.

        Resolved lazily (rebuilt if ``_tts_model_type`` changed since the cached
        instance was built) so callers that set ``_tts_model_type`` after
        construction still dispatch to the matching adapter. In production
        ``_tts_model_type`` is fixed at init, so the cached instance is reused.
        """
        if self._diffusion_mode:
            return None

        adapter_cls = resolve_adapter(self._tts_model_type)
        if adapter_cls is None:
            self._adapter = None
            return None
        if self._adapter is None or type(self._adapter) is not adapter_cls:
            ctx = SpeechServingContext(server=self, engine_client=self.engine_client)
            self._adapter = adapter_cls(ctx)
        return self._adapter

    def _uses_native_speed_control(self) -> bool:
        adapter = self._get_tts_adapter()
        return bool(adapter is not None and adapter.native_speed_control)

    def _audio_encode_speed(self, request: OpenAICreateSpeechRequest) -> float:
        if self._uses_native_speed_control():
            return 1.0
        return float(request.speed or 1.0)

    async def warmup(self) -> None:
        """Run a synthetic speech request to trigger all first-request warmup.

        Unlike qwen3-tts, whose CUDA Graph warmup targets a standalone tokenizer
        decoder (no vLLM dependencies) and can complete entirely at model-init
        time, VoxCPM2 needs to warm up PagedAttention scaffold/residual LLMs.
        Their CUDA Graph capture requires a vLLM ``ForwardContext``
        (attn_metadata, slot_mapping, etc.) that only exists during real
        inference steps.  The same request also pays the one-time torch.compile
        JIT tax for the LocDiT estimator, feat_encoder, AudioVAE decoder, and
        projection helpers.

        For VoxCPM2 this shifts ~15s of torch.compile + CUDA Graph capture from
        the first user request to server startup.
        """
        if self._tts_model_type != "voxcpm2":
            return

        t0 = time.time()
        logger.info("Running warmup speech request for model_type=%s", self._tts_model_type)
        # VoxCPM2 has no predefined speaker presets — "default" means zero-shot
        # mode (no voice cloning).  The voice field is required by the OpenAI
        # API schema but semantically ignored by the model.
        warmup_req = OpenAICreateSpeechRequest(
            input="Warmup.",
            voice="default",
            response_format="wav",
            speed=1.0,
            stream=False,
            model=self.model_name,
        )
        try:
            _audio_bytes, _media_type = await self._generate_audio_bytes(warmup_req, request_id="speech-warmup")
        except Exception as exc:
            logger.warning("Speech warmup failed (non-fatal): %s", exc)
            return

        elapsed = time.time() - t0
        logger.info("Speech warmup complete in %.1fs", elapsed)

    def _get_qwen_tts_expected_speaker_embedding_dim(self) -> int | None:
        """Return the loaded Qwen3-TTS speaker embedding dim, if known.

        The user-provided speaker embedding is concatenated directly with
        talker codec embeddings, so the real compatibility requirement is the
        talker hidden size.
        """
        if self._tts_model_type != "qwen3_tts":
            return None
        hf_config = self.engine_client.model_config.hf_config
        talker_config = hf_config.talker_config
        return int(talker_config.hidden_size)

    def _validate_qwen_tts_speaker_embedding_dim(self, emb_dim: int) -> str | None:
        expected_dim = self._get_qwen_tts_expected_speaker_embedding_dim()
        if expected_dim is None:
            return None
        if emb_dim != expected_dim:
            return f"speaker_embedding has {emb_dim} dimensions; expected {expected_dim} for the loaded Qwen3-TTS model"
        return None

    def _load_codec_frame_rate(self) -> float | None:
        """Load codec frame rate from speech tokenizer config for prompt length estimation."""
        if self._tts_model_type == "ming_tts":
            try:
                from vllm_omni.model_executor.models.ming_tts.config_ming_tts import MingTTSConfig

                hf_config = self.engine_client.model_config.hf_config
                ming_cfg = MingTTSConfig.from_hf_config(hf_config)
                patch_size = int(ming_cfg.patch_size)
                audio_frame_hop = int(ming_cfg.audio_frame_hop)
                sample_rate = int(ming_cfg.sample_rate)
                if patch_size <= 0 or audio_frame_hop <= 0 or sample_rate <= 0:
                    raise ValueError(
                        "Ming config has invalid tokenizer timing values: "
                        f"patch_size={patch_size}, audio_frame_hop={audio_frame_hop}, sample_rate={sample_rate}"
                    )
                rate = float(sample_rate) / float(audio_frame_hop * patch_size)
                logger.info(
                    "Derived Ming codec frame rate: %.1f Hz (sample_rate=%s, audio_frame_hop=%s, patch_size=%s)",
                    rate,
                    sample_rate,
                    audio_frame_hop,
                    patch_size,
                )
                return rate
            except Exception as e:
                logger.warning(f"Failed to derive Ming codec frame rate from hf_config: {e}")

        try:
            model_path = self.engine_client.model_config.model
            st_config_path = os.path.join(model_path, "speech_tokenizer", "config.json")
            if not os.path.exists(st_config_path):
                st_config_path = cached_file(model_path, "speech_tokenizer/config.json")
            if st_config_path is not None and os.path.exists(st_config_path):
                with open(st_config_path) as f:
                    st_config = json.load(f)
                output_sr = st_config.get("output_sample_rate")
                downsample = st_config.get("encode_downsample_rate")
                if output_sr and downsample and downsample > 0:
                    rate = float(output_sr) / float(downsample)
                    logger.info(
                        "Loaded codec frame rate: %.1f Hz (output_sample_rate=%s, encode_downsample_rate=%s)",
                        rate,
                        output_sr,
                        downsample,
                    )
                    return rate
        except Exception as e:
            logger.warning("Failed to load codec frame rate from speech tokenizer config: %s", e)

        # Fallback: try codec_frame_rate_hz from hf_config
        try:
            hf_config = self.engine_client.model_config.hf_config
            rate = getattr(hf_config, "codec_frame_rate_hz", None)
            if rate is not None:
                logger.info("Using codec frame rate from hf_config: %s Hz", rate)
                return float(rate)
        except Exception:
            pass
        return None

    def shutdown(self) -> None:
        """Shut down the TTS thread pool executor."""
        if self._tts_executor is not None:
            self._tts_executor.shutdown(wait=False, cancel_futures=True)
            self._tts_executor = None
        for name in list(self.uploaded_speakers.keys()):
            self._speaker_cache.clear(name)

    def _find_tts_stage(self):
        """Find and return the TTS stage config, or None if not found."""
        tts_stage_keys = all_tts_stage_keys()
        entry_stage_archs = tts_entry_stage_archs()
        all_stages = frozenset(
            getattr(stage.engine_args, "model_stage", None) for stage in self.engine_client.stage_configs
        )
        for stage in self.engine_client.stage_configs:
            engine_args = stage.engine_args
            model_stage = engine_args.model_stage
            model_arch = getattr(engine_args, "model_arch", None)
            worker_type = getattr(engine_args, "worker_type", None)
            if model_stage in tts_stage_keys:
                # Owning the stage key is not always enough: a model may be
                # speech-capable only in some deployment topologies. Ask the
                # adapter. Stages that resolve to no adapter keep the legacy
                # behaviour of being accepted here (and detected as ``None``).
                adapter_cls = resolve_adapter(detect_tts_model_type(model_stage, model_arch))
                if adapter_cls is not None and not adapter_cls.stage_serves_speech(model_stage, all_stages):
                    continue
                return stage
            # Models with no dedicated TTS model_stage value identify their AR
            # entry stage by architecture (Ming dense).
            if model_arch in entry_stage_archs and worker_type == "ar":
                return stage
        return None

    def _detect_tts_model_type(self) -> str | None:
        """Detect TTS model type from the resolved stage's deployment metadata.

        The mapping lives on the adapters (``stage_keys`` / ``model_archs``);
        this only supplies the stage under inspection.
        """
        if self._tts_stage is None:
            return None
        return detect_tts_model_type(
            getattr(self._tts_stage.engine_args, "model_stage", None),
            getattr(self._tts_stage.engine_args, "model_arch", None),
        )

    def _get_custom_voice_dir(self) -> str | None:
        try:
            value = getattr(self.engine_client.model_config.hf_config, "custom_voice_dir", None)
        except AttributeError:
            return None
        if isinstance(value, os.PathLike):
            return os.fspath(value)
        if isinstance(value, str) and value:
            return value
        return None

    def _load_precomputed_speakers(self) -> dict[str, dict[str, Any]]:
        """Load precomputed voice names from ``custom_voice_dir`` for API validation."""
        if self._tts_model_type not in ("qwen3_tts", "voxcpm2"):
            return {}
        custom_voice_dir = self._get_custom_voice_dir()
        if not custom_voice_dir:
            return {}

        profiles: dict[str, dict[str, Any]] = {}
        qwen3_embedding_dim = self._get_qwen_tts_expected_speaker_embedding_dim()
        for profile in iter_custom_voice_profiles(custom_voice_dir, expected_model_type=self._tts_model_type):
            tensors = load_validated_profile_tensors(
                profile,
                expected_model_type=self._tts_model_type,
                qwen3_embedding_dim=qwen3_embedding_dim,
            )
            if tensors is None:
                continue
            profiles[profile["voice_name_lower"]] = profile
        if profiles:
            logger.info(
                "Loaded %d precomputed %s voice profile(s) from %s",
                len(profiles),
                self._tts_model_type,
                custom_voice_dir,
            )
        return profiles

    def _compute_max_instructions_length(self) -> int:
        """Compute max instructions length with precedence: CLI > stage config > default.

        Called once during initialization; result is cached in self._max_instructions_length.
        """
        # 1. CLI override takes highest priority (stored in engine_client)
        cli_override = getattr(self.engine_client, "tts_max_instructions_length", None)
        if cli_override is not None:
            return cli_override

        # 2. Try to get from TTS stage config
        if self._tts_stage is not None:
            tts_args = getattr(self._tts_stage, "tts_args", {})
            if "max_instructions_length" in tts_args:
                return tts_args["max_instructions_length"]

        # 3. Default fallback
        return _TTS_MAX_INSTRUCTIONS_LENGTH

    def _get_available_voices(self) -> set[str]:
        """Get all voice names accepted by the API, including the placeholder default."""
        return self.supported_speakers | self.uploaded_speakers.keys() | {_DEFAULT_VOICE_NAME}

    def _load_supported_speakers(self) -> set[str]:
        """Load supported speakers (case-insensitive) from the model configuration."""
        if self._tts_model_type == "ming_flash_omni_tts":
            # Ming-flash-omni drives speaker selection via the caption JSON
            # (audio_sequence[0]["说话人"]) rather than a spk_id table, so there
            # is no static speaker list to surface here.
            return set()
        try:
            if self._tts_model_type == "glm_tts":
                return set()
            if self._tts_model_type == "ming_tts":
                return set()
            if self._tts_model_type == "voxcpm2":
                return {"default"}
            if self._tts_model_type == "voxtral_tts":
                config = self.engine_client.model_config.hf_config.audio_config
            else:
                # Default is qwen3_tts path
                config = getattr(self.engine_client.model_config.hf_config, "talker_config", None)
                if config is None:
                    return set()

            # Check for speakers in either spk_id or speaker_id
            for attr_name in ["spk_id", "speaker_id"]:
                if isinstance(config, dict):
                    speakers_dict = config.get(attr_name)
                else:
                    speakers_dict = getattr(config, attr_name, None)
                if speakers_dict and isinstance(speakers_dict, dict):
                    return {speaker.lower() for speaker in speakers_dict.keys()}

            logger.warning("No speakers found in config (checked spk_id and speaker_id)")
        except Exception as e:
            logger.warning("Could not load speakers from model config: %s", e)

        return set()

    def _load_supported_languages(self) -> frozenset[str]:
        """Load supported languages (title-cased) from the model configuration"""
        if self._tts_model_type != "qwen3_tts":
            return _TTS_LANGUAGES
        try:
            config = self.engine_client.model_config.hf_config.talker_config

            if isinstance(config, dict):
                codec_language_id = config.get("codec_language_id")
            else:
                codec_language_id = getattr(config, "codec_language_id", None)

            if codec_language_id and isinstance(codec_language_id, Mapping):
                return frozenset(str(language).title() for language in codec_language_id) | {"Auto"}

            logger.warning("No codec_language_id found in talker_config; falling back to default languages")
        except Exception as e:
            logger.warning("Could not load languages from model config: %s", e)
        return _TTS_LANGUAGES

    def _estimate_ref_code_len(self, ref_audio: object) -> int | None:
        """Estimate ref_code length from ref_audio waveform without running the codec.

        The codec produces one frame per (output_sample_rate / encode_downsample_rate)
        audio samples, so ref_code_len = ceil(duration_seconds * codec_frame_rate).
        """
        if self._codec_frame_rate is None:
            return None
        try:
            # ref_audio comes from tts_params as [[wav_array, sr]] or similar nested structure
            item = ref_audio
            while isinstance(item, list) and item:
                if len(item) == 2 and isinstance(item[1], (int, float)):
                    break
                item = item[0]
            if isinstance(item, list) and len(item) == 2:
                wav, sr = item
            elif isinstance(item, tuple) and len(item) == 2:
                wav, sr = item
            else:
                return None
            sr = int(sr)
            if hasattr(wav, "__len__"):
                n_samples = len(wav)
            elif hasattr(wav, "shape"):
                n_samples = wav.shape[-1] if wav.ndim > 1 else wav.shape[0]
            else:
                return None
            if sr <= 0 or n_samples <= 0:
                return None
            duration = n_samples / sr
            return math.ceil(duration * self._codec_frame_rate)
        except Exception:
            return None

    def _estimate_prompt_len(self, tts_params: dict[str, Any]) -> int:
        """Estimate prompt length so the placeholder matches model-side embeddings."""
        try:
            from vllm_omni.model_executor.models.qwen3_tts.prompt_embeds_builder import (
                Qwen3TTSPromptEmbedsBuilder,
            )

            if self._tts_tokenizer is None:
                from transformers import AutoTokenizer

                model_name = self.engine_client.model_config.model
                self._tts_tokenizer = AutoTokenizer.from_pretrained(
                    model_name,
                    trust_remote_code=True,
                    padding_side="left",
                )
            hf_config = self.engine_client.model_config.hf_config
            talker_config = hf_config.talker_config
            task_type = (tts_params.get("task_type") or ["CustomVoice"])[0]
            return Qwen3TTSPromptEmbedsBuilder.estimate_prompt_len_from_additional_information(
                additional_information=tts_params,
                task_type=task_type,
                tokenize_prompt=lambda t: self._tts_tokenizer(t, padding=False)["input_ids"],
                codec_language_id=getattr(talker_config, "codec_language_id", None),
                spk_is_dialect=getattr(talker_config, "spk_is_dialect", None),
                estimate_ref_code_len=self._estimate_ref_code_len,
            )
        except Exception as e:
            logger.warning("Failed to estimate TTS prompt length, using fallback 2048: %s", e)
            return 2048

    def _get_usage_text_tokenizer(self):
        """Return a text tokenizer for counting input-text usage tokens.

        Prefer the per-model tokenizer already loaded for prompt-length
        estimation (`_tts_tokenizer`, which is the *correct* tokenizer for the
        active model). Fall back to a lazily-loaded, cached generic tokenizer
        for models that never populate `_tts_tokenizer`. Returns None if no
        tokenizer can be obtained (usage then reports text_tokens=0).
        """
        if self._tts_tokenizer is not None:
            return self._tts_tokenizer
        if getattr(self, "_usage_text_tokenizer", None) is None:
            try:
                from transformers import AutoTokenizer

                self._usage_text_tokenizer = AutoTokenizer.from_pretrained(
                    self.engine_client.model_config.model, trust_remote_code=True
                )
            except Exception as e:  # pragma: no cover - environment dependent
                logger.warning("Usage: could not load a text tokenizer (%s); text_tokens will be 0", e)
                self._usage_text_tokenizer = None
        return self._usage_text_tokenizer

    def _count_usage_text_tokens(self, text: str) -> int:
        """Token count of `text` using the model's text tokenizer (0 on failure)."""
        if not text:
            return 0
        tok = self._get_usage_text_tokenizer()
        if tok is None:
            return 0
        try:
            return len(tok(text, padding=False)["input_ids"])
        except Exception:
            return 0

    def _compute_speech_input_details(
        self, request: OpenAICreateSpeechRequest, tts_params: dict[str, Any]
    ) -> SpeechInputTokenDetails:
        """Input-token breakdown (text + reference-audio) for a speech request.

        Counts `input` (+ `instructions`) as text tokens, and reference-audio
        codec frames as audio tokens *only* when in-context voice cloning is
        active (see `qwen3_tts_input_token_details` / `gate_audio_tokens`). The
        audio gating reads Qwen3-TTS `tts_params` conventions; other TTS models
        do not set those keys, so they degrade cleanly to text-only counts.
        """
        return qwen3_tts_input_token_details(
            input_text=request.input,
            instructions=request.instructions,
            tts_params=tts_params or {},
            count_text_tokens=self._count_usage_text_tokens,
        )

    def _build_speech_usage(
        self,
        request: OpenAICreateSpeechRequest,
        tts_params: dict[str, Any],
        output_tokens: int,
    ) -> SpeechTokenUsage:
        """Assemble the full usage object (input breakdown + generated tokens)."""
        details = self._compute_speech_input_details(request, tts_params)
        return build_speech_usage(details, output_tokens)

    @staticmethod
    def _build_speech_usage_headers(usage: SpeechTokenUsage | None) -> dict[str, str]:
        """Map speech usage into non-streaming response headers.

        Returns an empty dict when usage is unavailable, allowing callers to
        merge these headers with other optional response headers.
        """
        if usage is None:
            return {}

        return {
            _SPEECH_USAGE_INPUT_TOKENS_HEADER: str(usage.input_tokens),
            _SPEECH_USAGE_OUTPUT_TOKENS_HEADER: str(usage.output_tokens),
            _SPEECH_USAGE_TOTAL_TOKENS_HEADER: str(usage.total_tokens),
            _SPEECH_USAGE_INPUT_TEXT_TOKENS_HEADER: str(usage.input_token_details.text_tokens),
            _SPEECH_USAGE_INPUT_AUDIO_TOKENS_HEADER: str(usage.input_token_details.audio_tokens),
        }

    def _estimate_fish_ref_code_len(self, ref_audio: object) -> int | None:
        """Estimate Fish Speech semantic token length from raw reference audio."""
        from vllm_omni.model_executor.models.fish_speech.dac_utils import (
            DAC_HOP_LENGTH,
            DAC_SAMPLE_RATE,
        )

        if not isinstance(ref_audio, (list, tuple)) or len(ref_audio) != 2:
            return None
        wav, sr = ref_audio
        sr = int(sr)
        n_samples = len(wav)
        if sr <= 0 or n_samples <= 0:
            return None
        resampled_len = max(1, math.ceil(n_samples * DAC_SAMPLE_RATE / sr))
        return max(1, math.ceil(resampled_len / DAC_HOP_LENGTH))

    def _estimate_fish_prompt_len(self, text: str, ref_text: str, ref_audio: object) -> int:
        """Estimate Fish Speech clone prompt length without encoding reference audio."""
        try:
            from transformers import AutoTokenizer

            if self._fish_speech_tokenizer is None:
                model_name = self.engine_client.model_config.model
                self._fish_speech_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

            tokenizer = self._fish_speech_tokenizer
            semantic_len = self._estimate_fish_ref_code_len(ref_audio)
            if semantic_len is None:
                raise ValueError("Failed to estimate Fish Speech semantic token length")
            return estimate_fish_voice_clone_prompt_len_from_normalized(tokenizer, text, ref_text, semantic_len)
        except Exception as e:
            logger.warning("Failed to estimate Fish Speech prompt length, using fallback 2048: %s", e)
            return 2048

    def _voice_created_at(self, voice_lower: str) -> int:
        """Return the upload timestamp of an uploaded voice, or 0 for built-ins.

        Plumbed through to the model-side cache key so that delete + re-upload
        of the same name yields a fresh cache slot.
        """
        info = self.uploaded_speakers.get(voice_lower)
        return int(info.get("created_at", 0)) if info else 0

    async def _build_voxcpm2_prompt(
        self,
        request: OpenAICreateSpeechRequest,
        *,
        uploaded_ref: tuple[np.ndarray, int] | None = None,
    ) -> dict[str, Any]:
        """Build prefill prompt for VoxCPM2 TTS (`prompt_token_ids` padded to full prefill length).

        ``uploaded_ref`` supplies the audio for uploaded voices (no explicit
        ``ref_audio`` in the request) so prefill length includes it.
        """
        from vllm_omni.model_executor.models.voxcpm2.voxcpm2_talker import build_voxcpm2_prompt

        self._voxcpm2_encode("")  # lazy-init tokenizer + split_map
        ref_audio = None
        ref_sr = None
        voice_profile = None
        if request.ref_audio is not None:
            ref_audio, ref_sr = await self._resolve_ref_audio(request.ref_audio)
        elif uploaded_ref is not None:
            wav_np, ref_sr = uploaded_ref
            ref_audio = wav_np.tolist()
        elif request.voice is not None:
            voice_profile = self.precomputed_speakers.get(request.voice.lower())
        return build_voxcpm2_prompt(
            hf_config=self.engine_client.model_config.hf_config,
            tokenizer=self._voxcpm2_tokenizer,
            split_map=self._voxcpm2_split_map,
            text=request.input,
            ref_audio=ref_audio,
            ref_sr=ref_sr,
            ref_text=request.ref_text,
            voice_profile=voice_profile,
        )

    def _load_uploaded_audio(self, voice_name: str) -> tuple[np.ndarray, int] | None:
        """Load decoded audio samples + sample rate from an uploaded voice's safetensors."""
        voice_name_lower = voice_name.lower()
        info = self.uploaded_speakers.get(voice_name_lower)
        if info is None or info.get("embedding_source") != "audio":
            return None
        file_path = Path(info["file_path"])
        if not file_path.exists():
            logger.warning("Voice file not found for %s: %s", voice_name, file_path)
            return None
        try:
            from safetensors import safe_open
        except ImportError:
            logger.error("The 'safetensors' package is required to load uploaded voices")
            return None
        try:
            with safe_open(str(file_path), framework="pt") as f:
                if "audio" not in f.keys():
                    return None
                samples = f.get_tensor("audio").numpy()
                sr = int((f.metadata() or {}).get("sample_rate", info.get("sample_rate", 0)))
        except Exception as e:
            logger.error("Could not load audio for voice %s: %s", voice_name, e)
            return None
        if sr <= 0:
            return None
        return samples, sr

    def _get_uploaded_audio_data(self, voice_name: str) -> str | None:
        """Return a base64-encoded WAV data URL for an uploaded voice.

        Memoized so the WAV re-encode runs once per voice per process.
        """
        voice_name_lower = voice_name.lower()
        cached = self._ref_audio_data_url_cache.get(voice_name_lower)
        if cached is not None:
            return cached

        data = self._load_uploaded_audio(voice_name)
        if data is None:
            return None
        samples, sr = data
        try:
            buf = io.BytesIO()
            sf.write(buf, samples, sr, format="WAV")
            audio_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
            data_url = f"data:audio/wav;base64,{audio_b64}"
        except Exception as e:
            logger.error("Could not encode voice %s as WAV: %s", voice_name, e)
            return None
        self._ref_audio_data_url_cache[voice_name_lower] = data_url
        return data_url

    def _get_uploaded_speaker_embedding(self, voice_name: str) -> list[float] | None:
        """Load a pre-computed speaker embedding from an uploaded voice's safetensors.

        Returns ``None`` if the voice has audio (not a direct embedding)."""
        voice_name_lower = voice_name.lower()
        info = self.uploaded_speakers.get(voice_name_lower)
        if info is None or info.get("embedding_source") != "direct":
            return None
        file_path = Path(info["file_path"])
        if not file_path.exists():
            logger.warning("Embedding file not found for voice %s: %s", voice_name, file_path)
            return None
        if not _validate_path_within_directory(file_path, self.uploaded_speakers_dir):
            logger.error("File path traversal detected for voice %s: %s", voice_name, file_path)
            return None
        try:
            from safetensors.torch import load_file
        except ImportError:
            logger.error("The 'safetensors' package is required to load speaker embeddings")
            return None
        try:
            tensors = load_file(str(file_path))
            if "speaker_embedding" not in tensors:
                logger.warning("Key 'speaker_embedding' missing in %s", file_path)
                return None
            return tensors["speaker_embedding"].squeeze().tolist()
        except Exception as e:
            logger.error("Could not load embedding for voice %s: %s", voice_name, e)
            return None

    def _apply_uploaded_speaker(self, request: OpenAICreateSpeechRequest) -> str | None:
        """Resolve ``request.voice`` against uploaded speakers, mutating
        ``request.ref_audio`` / ``request.ref_text`` in place. Returns an
        error string if the voice is invalid, else ``None``.
        """
        if request.voice is None or request.ref_audio is not None:
            return None

        voice_lower = request.voice.lower()
        if voice_lower not in self.uploaded_speakers:
            if self._tts_model_type in (
                "cosyvoice3",
                "fish_tts",
                "omnivoice",
                "moss_tts_nano",
                "glm_tts",
                "higgs_audio_v2",
                "higgs_audio_v3",
            ):
                label = {
                    "cosyvoice3": "CosyVoice3",
                    "fish_tts": "Fish Speech",
                    "omnivoice": "OmniVoice",
                    "moss_tts_nano": "MOSS-TTS-Nano",
                    "higgs_audio_v2": "Higgs-Audio V2",
                    "higgs_audio_v3": "Higgs-Audio V3",
                    "glm_tts": "GLM-TTS",
                }.get(self._tts_model_type, self._tts_model_type)
                return (
                    f"Unknown voice '{request.voice}'. {label} has no "
                    f"built-in speakers. Upload a voice first via "
                    f"POST /v1/audio/voices, or use ref_audio + ref_text."
                )
            return None

        speaker_info = self.uploaded_speakers[voice_lower]
        if speaker_info.get("embedding_source") == "direct":
            return (
                f"Uploaded voice '{request.voice}' uses a speaker embedding "
                f"(Qwen3-only). Re-upload with an audio file for this model."
            )

        audio_data = self._get_uploaded_audio_data(request.voice)
        if not audio_data:
            return f"Audio file for uploaded voice '{request.voice}' is missing"

        request.ref_audio = audio_data
        if not request.ref_text or not request.ref_text.strip():
            stored_ref_text = speaker_info.get("ref_text")
            if stored_ref_text:
                request.ref_text = stored_ref_text

        logger.info("Resolved uploaded voice '%s' for %s", voice_lower, self._tts_model_type)
        return None

    def _check_upload_cap(self) -> None:
        if len(self.uploaded_speakers) >= self._max_uploaded_speakers:
            raise ValueError(
                f"Uploaded voice limit reached ({self._max_uploaded_speakers}). "
                f"Delete an existing voice before registering a new one, or raise "
                f"the cap via SPEAKER_MAX_UPLOADED."
            )

    def _evict_existing_upload(self, voice_name_lower: str, name: str) -> None:
        """Drop an existing upload with this name so the caller can re-register it."""
        if voice_name_lower not in self.uploaded_speakers:
            return
        old = self.uploaded_speakers.pop(voice_name_lower)
        self.supported_speakers.discard(voice_name_lower)
        self._ref_audio_data_url_cache.pop(voice_name_lower, None)
        old_path = old.get("file_path")
        if old_path:
            try:
                Path(old_path).unlink(missing_ok=True)
            except Exception as e:
                logger.warning("Failed to remove previous file for '%s': %s", name, e)
        self._speaker_cache.clear(voice_name_lower)
        logger.info("Speaker '%s' re-uploaded; previous cache and file overwritten", name)

    async def upload_voice(
        self,
        audio_file: UploadFile,
        consent: str,
        name: str,
        *,
        ref_text: str | None = None,
        speaker_description: str | None = None,
    ) -> dict:
        """Upload a new voice sample."""
        name = _validate_speaker_name(name)
        # Normalize optional strings: treat whitespace-only as absent
        if ref_text is not None:
            ref_text = ref_text.strip() or None
        if speaker_description is not None:
            speaker_description = speaker_description.strip() or None
        # Validate file size (max 10MB)
        MAX_FILE_SIZE = 10 * 1024 * 1024  # 10MB
        audio_file.file.seek(0, 2)  # Seek to end
        file_size = audio_file.file.tell()
        audio_file.file.seek(0)  # Reset to beginning

        if file_size > MAX_FILE_SIZE:
            raise ValueError(f"File size exceeds maximum limit of 10MB. Got {file_size} bytes.")

        # Detect MIME type from filename if content_type is generic
        mime_type = audio_file.content_type
        if mime_type == "application/octet-stream":
            # Simple MIME type detection based on file extension
            filename_lower = audio_file.filename.lower()
            if filename_lower.endswith(".wav"):
                mime_type = "audio/wav"
            elif filename_lower.endswith((".mp3", ".mpeg")):
                mime_type = "audio/mpeg"
            elif filename_lower.endswith(".flac"):
                mime_type = "audio/flac"
            elif filename_lower.endswith(".ogg"):
                mime_type = "audio/ogg"
            elif filename_lower.endswith(".aac"):
                mime_type = "audio/aac"
            elif filename_lower.endswith(".webm"):
                mime_type = "audio/webm"
            elif filename_lower.endswith(".mp4"):
                mime_type = "audio/mp4"
            else:
                mime_type = "audio/wav"  # Default

        # Validate MIME type
        allowed_mime_types = {
            "audio/mpeg",
            "audio/wav",
            "audio/x-wav",
            "audio/ogg",
            "audio/aac",
            "audio/flac",
            "audio/webm",
            "audio/mp4",
        }

        if mime_type not in allowed_mime_types:
            raise ValueError(f"Unsupported MIME type: {mime_type}. Allowed: {allowed_mime_types}")

        # Read content before acquiring the lock; decode happens inside.
        content = await audio_file.read()

        async with self._upload_lock:
            voice_name_lower = name.lower()
            self._evict_existing_upload(voice_name_lower, name)
            self._check_upload_cap()

            sanitized_name = _sanitize_filename(name)
            sanitized_consent = _sanitize_filename(consent)
            timestamp = self._next_upload_timestamp()
            file_suffix = Path(audio_file.filename).suffix
            file_ext = file_suffix[1:] if file_suffix and len(file_suffix) > 1 else "wav"
            sanitized_ext = _sanitize_filename(file_ext)
            if not sanitized_ext or sanitized_ext == "file":
                sanitized_ext = "wav"

            filename = f"{sanitized_name}_{sanitized_consent}_{timestamp}.safetensors"
            file_path = self.uploaded_speakers_dir / filename
            if not _validate_path_within_directory(file_path, self.uploaded_speakers_dir):
                raise ValueError("Invalid file path: potential path traversal attack detected")

            try:
                wav_np, sr = sf.read(io.BytesIO(content))
            except Exception as e:
                raise ValueError(f"Could not decode audio file: {e}")
            duration = len(wav_np) / sr if sr > 0 else 0.0
            if duration < _REF_AUDIO_MIN_DURATION:
                raise ValueError(
                    f"Reference audio too short ({duration:.1f}s). "
                    f"At least {_REF_AUDIO_MIN_DURATION:.0f}s of clear speech is required."
                )
            if duration > _REF_AUDIO_MAX_DURATION:
                raise ValueError(
                    f"Reference audio too long ({duration:.1f}s). "
                    f"Maximum {_REF_AUDIO_MAX_DURATION:.0f}s supported — use a shorter clip."
                )

            speaker_data: dict[str, Any] = {
                "name": name,
                "voice_name_lower": voice_name_lower,
                "consent": consent,
                "file_path": str(file_path),
                "created_at": timestamp,
                "mime_type": mime_type,
                "original_filename": audio_file.filename,
                "file_size": file_size,
                "sample_rate": int(sr),
                "ref_text": ref_text,
                "embedding_source": "audio",
            }
            if speaker_description:
                speaker_data["speaker_description"] = speaker_description

            try:
                from safetensors.torch import save_file
            except ImportError as exc:
                raise ValueError("safetensors is required for voice upload") from exc
            try:
                audio_tensor = torch.from_numpy(np.asarray(wav_np, dtype=np.float32)).contiguous()
                save_file(
                    {"audio": audio_tensor},
                    str(file_path),
                    metadata=self._speaker_metadata_to_header(speaker_data),
                )
            except Exception as e:
                raise ValueError(f"Failed to save voice file: {e}")

            self.uploaded_speakers[voice_name_lower] = speaker_data
            self.supported_speakers.add(voice_name_lower)

        logger.info("Uploaded new voice '%s' with consent ID '%s'", name, consent)

        # Return voice information without exposing the server file path
        result = {
            "name": name,
            "consent": consent,
            "created_at": timestamp,
            "mime_type": mime_type,
            "file_size": file_size,
        }
        if speaker_data.get("ref_text"):
            result["ref_text"] = speaker_data["ref_text"]
        if speaker_data.get("speaker_description"):
            result["speaker_description"] = speaker_data["speaker_description"]
        return result

    async def upload_voice_embedding(self, embedding_json: str, consent: str, name: str) -> dict:
        """Upload a voice from a pre-computed speaker embedding.

        Stores the embedding as a safetensors file and marks it immediately
        ready (no audio processing needed).

        Args:
            embedding_json: JSON-encoded list of floats (1024 or 2048 dim).
            consent: Consent recording ID.
            name: Name for the new voice.

        Returns:
            dict with voice information.
        """
        name = _validate_speaker_name(name)
        try:
            embedding = json.loads(embedding_json)
        except (json.JSONDecodeError, TypeError) as exc:
            raise ValueError(f"'speaker_embedding' must be valid JSON: {exc}") from exc

        if not isinstance(embedding, list) or not embedding:
            raise ValueError("'speaker_embedding' must be a non-empty list of numbers")

        if len(embedding) > 4096:
            raise ValueError("'speaker_embedding' exceeds maximum length (4096 elements)")

        if not all(isinstance(x, (int, float)) for x in embedding):
            raise ValueError("'speaker_embedding' must contain only numeric values")

        if not all(math.isfinite(x) for x in embedding):
            raise ValueError("'speaker_embedding' values must be finite (no NaN or Inf)")

        emb_dim = len(embedding)
        if self._tts_model_type == "ming_tts":
            if emb_dim != SPEAKER_EMBEDDING_DIM:
                raise ValueError(f"Ming speaker embedding must have {SPEAKER_EMBEDDING_DIM} dims, got {emb_dim}")
        else:
            dim_err = self._validate_qwen_tts_speaker_embedding_dim(emb_dim)
            if dim_err is not None:
                raise ValueError(dim_err)

        async with self._upload_lock:
            voice_name_lower = name.lower()
            self._evict_existing_upload(voice_name_lower, name)
            self._check_upload_cap()

            sanitized_name = _sanitize_filename(name)
            sanitized_consent = _sanitize_filename(consent)
            timestamp = self._next_upload_timestamp()

            tensor = torch.tensor(embedding, dtype=torch.float32)
            filename = f"{sanitized_name}_{sanitized_consent}_{timestamp}.safetensors"
            file_path = self.uploaded_speakers_dir / filename
            if not _validate_path_within_directory(file_path, self.uploaded_speakers_dir):
                raise ValueError("Invalid file path: potential path traversal attack detected")

            speaker_data: dict[str, Any] = {
                "name": name,
                "voice_name_lower": voice_name_lower,
                "consent": consent,
                "file_path": str(file_path),
                "created_at": timestamp,
                "mime_type": "application/x-safetensors",
                "original_filename": filename,
                "embedding_source": "direct",
                "embedding_dim": emb_dim,
            }
            try:
                from safetensors.torch import save_file
            except ImportError as exc:
                raise ValueError("safetensors is required for embedding upload") from exc
            save_file(
                {"speaker_embedding": tensor},
                str(file_path),
                metadata=self._speaker_metadata_to_header(speaker_data),
            )
            speaker_data["file_size"] = file_path.stat().st_size

            self.uploaded_speakers[voice_name_lower] = speaker_data
            self.supported_speakers.add(voice_name_lower)

        logger.info("Uploaded voice '%s' from speaker embedding (%d-dim)", name, emb_dim)

        return {
            "name": name,
            "consent": consent,
            "created_at": timestamp,
            "embedding_source": "direct",
            "embedding_dim": emb_dim,
        }

    async def delete_voice(self, name: str) -> bool:
        """
        Delete an uploaded voice.

        Args:
            name: Voice name to delete

        Returns:
            bool: True if successful, False if voice doesn't exist
        """
        async with self._upload_lock:
            voice_name_lower = name.lower()

            if voice_name_lower not in self.uploaded_speakers:
                logger.warning("Voice '%s' not found", name)
                return False

            speaker_info = self.uploaded_speakers.pop(voice_name_lower)
            self.supported_speakers.discard(voice_name_lower)
            self._ref_audio_data_url_cache.pop(voice_name_lower, None)

            file_path = speaker_info.get("file_path")
            if file_path:
                try:
                    Path(file_path).unlink(missing_ok=True)
                except Exception as e:
                    logger.warning("Failed to delete audio file for '%s': %s", name, e)

            self._speaker_cache.clear(voice_name_lower)

        logger.info("Deleted voice '%s'", name)
        return True

    def _is_tts_model(self) -> bool:
        """Check if the current model is a supported TTS model."""
        return self._find_tts_stage() is not None

    def _validate_tts_request(self, request: OpenAICreateSpeechRequest) -> str | None:
        """Validate TTS request parameters. Returns error message or None."""
        adapter = self._get_tts_adapter()
        if adapter is not None:
            return adapter.validate(request)

        adapter_cls = resolve_adapter("qwen3_tts")
        if adapter_cls is None:
            raise ValueError("Qwen3-TTS adapter is not registered")

        ctx = SpeechServingContext(
            server=self,
            engine_client=self.engine_client,
        )
        return adapter_cls(ctx).validate(request)

    def _voxcpm2_encode(self, text: str) -> list[int]:
        """Tokenize text for VoxCPM2, splitting multichar Chinese tokens."""
        from vllm_omni.model_executor.models.voxcpm2.voxcpm2_talker import (
            build_cjk_split_map,
            split_multichar_chinese,
        )

        if self._voxcpm2_tokenizer is None:
            from transformers import AutoTokenizer

            model_name = self.engine_client.model_config.model
            tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
            self._voxcpm2_split_map = build_cjk_split_map(tokenizer)
            self._voxcpm2_tokenizer = tokenizer
            logger.info("VoxCPM2 serving: built multichar split map (%d entries)", len(self._voxcpm2_split_map))

        ids = self._voxcpm2_tokenizer.encode(text, add_special_tokens=True)
        return split_multichar_chinese(ids, self._voxcpm2_split_map)

    def _validate_ref_audio_format(self, ref_audio: str) -> str | None:
        """Validate ref_audio is a supported URI format. Returns error or None."""
        if not isinstance(ref_audio, str):
            return "ref_audio must be a URL (http/https), base64 data URL (data:...), or file URI (file://...)"
        if not (
            ref_audio.startswith(("http://", "https://"))
            or ref_audio.startswith("data:")
            or ref_audio.startswith("file://")
        ):
            return "ref_audio must be a URL (http/https), base64 data URL (data:...), or file URI (file://...)"
        return None

    def _detect_moss_variant(self) -> str:
        """Sub-classify a ``moss_tts``-stage server into the actual MOSS-TTS
        variant family (tts, ttsd, sound_effect, voice_generator, realtime).

        Detection key is the HF repo path / model_name; matches
        ``_try_resolve_omni_model_type`` in entrypoints/utils.py so users get
        consistent behaviour no matter how they launched the server (--model
        OpenMOSS-Team/MOSS-TTSD-v1.0 vs --deploy-config moss_ttsd.yaml).
        """
        try:
            name = (self.engine_client.model_config.model or "").lower().replace("-", "").replace("_", "")
        except Exception:
            name = ""
        if "realtime" in name:
            return "realtime"
        if "local" in name:
            return "local"
        if "ttsd" in name:
            return "ttsd"
        if "soundeffect" in name:
            return "sound_effect"
        if "voicegenerator" in name:
            return "voice_generator"
        return "tts"

    def _get_moss_processor(self):
        """Lazily load the upstream MOSS-TTS processor once per server.

        Cached on ``self._moss_processor_cache``. The processor owns its own
        audio_tokenizer (~1.6 B params); we keep it on CPU so it doesn't
        compete with the talker (~8 GiB) and codec (~7 GiB) for our 96 GiB
        GPU — per-request ref-audio encoding is fast enough on CPU.
        """
        cached = getattr(self, "_moss_processor_cache", None)
        if cached is not None:
            return cached
        from transformers import AutoProcessor

        model_id = self.engine_client.model_config.model
        proc = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
        if hasattr(proc, "audio_tokenizer"):
            proc.audio_tokenizer = proc.audio_tokenizer.to("cpu").eval()
        self._moss_processor_cache = proc
        return proc

    async def _build_moss_tts_params(self, request: OpenAICreateSpeechRequest) -> dict[str, Any]:
        """Build the talker prompt + ``additional_information`` payload for any
        MOSS-TTS-family request (nano + 5 full variants).

        For the legacy ``moss_tts_nano`` model_type, keeps the original nano
        contract (``{text, mode=voice_clone, prompt_audio_array}``); the
        caller still uses a ``[1]`` placeholder prompt.

        For the full MOSS-TTS family (``MossTTSDelay*`` / ``MossTTSRealtime*``)
        we **call the upstream processor server-side** to produce the unified
        ``(text_ids, audio_codes)`` shape the talker actually consumes — same
        flow as ``examples/.../moss_tts/end2end.py:_build_unified_codes``.
        Returns ``{prompt_token_ids: list[int], codes.ref: torch.LongTensor,
        max_new_frames, ...}``. The caller treats ``prompt_token_ids`` as the
        prompt and forwards the rest as ``additional_information``.
        """
        import torch  # local to avoid pulling torch at module import time

        v = self._moss_variant

        # ---- Legacy nano path (unchanged) ----
        if v is None:  # moss_tts_nano
            params: dict[str, Any] = {
                "text": [request.input or ""],
                "mode": ["voice_clone"],
            }
            if request.max_new_tokens is not None:
                params["max_new_frames"] = [request.max_new_tokens]
            wav_list, sr = await self._resolve_ref_audio(request.ref_audio)
            params["prompt_audio_array"] = [[wav_list, sr]]
            return params

        # ---- MOSS-TTS-Realtime: keep the old prompt_audio_array path ----
        # ``AutoProcessor.from_pretrained`` doesn't auto-discover
        # ``MossTTSRealtimeProcessor`` (no ``processor_config.json`` in the
        # snapshot), and Realtime's prompt format diverges from MossTTSDelay
        # (16-channel grid, separate per-step text feed). The
        # ``prompt_audio_array`` shape lines up well enough with what the
        # talker reads for short prompts; full Realtime support needs a
        # separate processor.from_module path which we don't wire here.
        if v == "realtime":
            params: dict[str, Any] = {
                "text": [request.input or ""],
                "mode": ["voice_clone"],
            }
            if request.max_new_tokens is not None:
                params["max_new_frames"] = [request.max_new_tokens]
            wav_list, sr = await self._resolve_ref_audio(request.ref_audio)
            params["prompt_audio_array"] = [[wav_list, sr]]
            return params

        # ---- MossTTSDelay family (tts/ttsd/sound_effect/voice_generator)
        # and MOSS-TTS-Local-Transformer-v1.5: call the upstream processor
        # server-side to produce unified codes. Local-v1.5 ships its own
        # AutoProcessor (processor_config.json + processing_moss_tts.py) and
        # reuses this exact build_user_message/encode_audios_from_wav path in
        # the offline example (examples/.../moss_tts/end2end.py:
        # _build_unified_codes) -- it is NOT in the same boat as Realtime
        # (no processor_config.json there), so it must not fall back to the
        # prompt_audio_array path above (which the talker's preprocess()
        # never reads -- info_dict["codes"]["ref"] is the only thing it
        # consumes, so skipping this path silently drops all voice-clone
        # conditioning and produces unconditioned/garbage audio online). ----
        proc = self._get_moss_processor()
        n_vq = int(getattr(proc.model_config, "n_vq", 32))
        # Local-v1.5 encodes reference audio at a fixed 24 kHz working rate
        # regardless of its 48 kHz stereo *output* codec -- mirrors the
        # offline example's hardcoded encode_audios_from_wav(sampling_rate=24000)
        # for this variant; proc.model_config.sampling_rate there is the
        # output rate (48000), the wrong value to resample the reference into.
        sr_target = 24000 if v == "local" else int(getattr(proc.model_config, "sampling_rate", 24000))

        # Reference-audio encoding + speaker caching lives in the model package
        # (moss_tts.reference_encoder), mirroring Fish Speech / CosyVoice3 /
        # Qwen3-TTS which keep reference handling with the model rather than in
        # this shared serving file. Imported lazily so the API-server process
        # only pulls it on the delay-family path (alongside the upstream proc).
        from vllm_omni.model_executor.models.moss_tts.reference_encoder import encode_reference_codes

        _voice = getattr(request, "voice", None)
        _voice = _voice.strip() if isinstance(_voice, str) else ""
        _voice_created = self._voice_created_at(_voice.lower()) if _voice else 0

        async def _encode_ref(ref_str: str) -> torch.Tensor:
            return await encode_reference_codes(
                ref_str,
                processor=proc,
                resolve_ref_audio=self._resolve_ref_audio,
                speaker_cache=self._speaker_cache,
                variant=v,
                n_vq=n_vq,
                sr_target=sr_target,
                voice_name=_voice or None,
                voice_created_at=_voice_created,
            )

        user_kwargs: dict[str, Any] = {"text": request.input or ""}
        if v in ("tts", "local"):
            user_kwargs["reference"] = [await _encode_ref(request.ref_audio)]
        elif v == "ttsd":
            refs = [await _encode_ref(request.ref_audio)]
            if request.ref_audio_2:
                refs.append(await _encode_ref(request.ref_audio_2))
            user_kwargs["reference"] = refs
        elif v == "sound_effect":
            user_kwargs["text"] = request.input or ""  # may be empty
            user_kwargs["ambient_sound"] = request.ambient_sound or ""
            if request.duration_seconds is not None:
                user_kwargs["tokens"] = max(1, int(float(request.duration_seconds) * 12.5))
            elif request.max_new_tokens is not None:
                user_kwargs["tokens"] = int(request.max_new_tokens)
        elif v == "voice_generator":
            user_kwargs["instruction"] = request.instructions or ""

        # Optional language tag for the spoken-text variants. MOSS-TTS-v1.5's
        # headline improvement is multilingual synthesis when the language is
        # given (build_user_message(..., language=...)); 1.0 ignores it
        # gracefully. Sound-effect output is non-verbal, so skip it there.
        if v in ("tts", "local", "ttsd", "voice_generator") and getattr(request, "language", None):
            user_kwargs["language"] = request.language

        # Build the unified-codes prompt: (L, 1+n_vq) where col 0 is text/special
        # tokens and cols 1..n_vq are the delay-pattern audio code grid (mostly
        # audio_pad_code outside the reference block).
        user_msg = proc.build_user_message(**user_kwargs)
        batch = proc(conversations=[[user_msg]], mode="generation")
        unified = batch["input_ids"][0]  # torch.LongTensor (L, 1+n_vq)
        text_ids: list[int] = unified[:, 0].tolist()
        audio_codes: torch.Tensor = unified[:, 1:].contiguous().to(torch.int64)

        params: dict[str, Any] = {
            "prompt_token_ids": text_ids,
            "codes": {"ref": audio_codes},
        }
        if request.max_new_tokens is not None:
            params["max_new_frames"] = [request.max_new_tokens]
        return params

    async def _build_higgs_audio_v2_params(self, request: OpenAICreateSpeechRequest):
        """Build prompt_token_ids for higgs_audio_v2 via the upstream processor.

        Plain-text path: runs ``build_plain_text_prompt`` and returns the
        token-only prompt. Voice-clone path (``ref_audio`` + ``ref_text``):
        resolves the reference clip via ``_resolve_ref_audio``, runs
        ``build_voice_clone_prompt`` (which encodes the clip through HF's
        ``HiggsAudioV2TokenizerModel`` loaded from the k2-fsa/OmniVoice
        ``audio_tokenizer/`` subdirectory), and attaches the encoded
        ``audio_input_ids`` + ``audio_input_ids_mask`` tensors via
        ``additional_information`` so the talker substitutes them at the
        prompt-side audio placeholders.
        """
        from vllm_omni.model_executor.models.higgs_audio_v2.higgs_audio_v2_tokenizer import (
            build_plain_text_prompt,
            build_voice_clone_prompt,
            input_ids_to_python_list,
        )

        processor = await self._resolve_higgs_audio_v2_processor()

        if request.ref_audio is None:
            inputs = await asyncio.to_thread(build_plain_text_prompt, processor, request.input)
            prompt_token_ids = input_ids_to_python_list(inputs)
            return tokens_input(prompt_token_ids=prompt_token_ids)

        wav_list, sr = await self._resolve_ref_audio(request.ref_audio)
        wav = np.asarray(wav_list, dtype=np.float32)
        out = await asyncio.to_thread(
            build_voice_clone_prompt,
            processor,
            request.input,
            wav,
            int(sr),
            request.ref_text or "",
        )
        prompt = tokens_input(prompt_token_ids=out["prompt_token_ids"])
        # Pass tensors at the top level of additional_information (NOT list-
        # wrapped). ``vllm_omni.data_entry_keys.serialize_payload`` routes
        # bare ``torch.Tensor`` values through ``_serialize_tensor``; a list
        # containing tensors would fall into the ``list_data`` field which
        # msgspec cannot serialize and the tensors would be dropped over the
        # process boundary (silent voice-clone failure).
        prompt["additional_information"] = {
            "audio_input_ids": out["audio_input_ids"],
            "audio_input_ids_mask": out["audio_input_ids_mask"],
        }
        return prompt

    async def _resolve_higgs_audio_v2_processor(self):
        """Lazy-load the AutoProcessor for higgs_audio_v2 (once per serving instance)."""
        cached = getattr(self, "_higgs_audio_v2_processor", None)
        if cached is not None:
            return cached

        from transformers import AutoProcessor

        model_path = None
        for stage in self.engine_client.stage_configs:
            model_path = getattr(getattr(stage, "engine_args", None), "model", None)
            if model_path:
                break
        if model_path is None:
            # Fallback: the orchestrator stores the served model id on the engine
            # itself (set by AsyncOmniEngine.__init__). Stage-level engine_args
            # may not surface ``model`` when the deploy yaml doesn't set it per
            # stage (the CLI-passed model id is the single source of truth).
            model_path = getattr(self.engine_client, "model", None)
        if model_path is None:
            raise RuntimeError("higgs_audio_v2 serving could not resolve the model path from the engine stage configs")
        processor = AutoProcessor.from_pretrained(model_path)
        self._higgs_audio_v2_processor = processor
        return processor

    # ---- higgs-audio v3 ----

    async def _build_higgs_audio_v3_params(self, request: OpenAICreateSpeechRequest):
        """Build prompt_token_ids for higgs_audio_v3.

        Plain-text path: builds ``[tts, text, tokens, audio]``.
        Voice-clone path: encodes reference audio, applies delay pattern,
        builds ``[tts, (ref_text, tokens,) ref_audio, -100xN, text, tokens, audio]``.
        """
        adapter = await self._resolve_higgs_audio_v3_adapter()

        if request.ref_audio is None:
            prompt_ids = adapter.build_prompt(request.input)
            return tokens_input(prompt_token_ids=prompt_ids)

        # Voice clone
        from vllm_omni.model_executor.models.higgs_audio_v3.higgs_audio_v3_tokenizer import (
            apply_delay_pattern,
            encode_reference_audio,
        )

        wav_list, sr = await self._resolve_ref_audio(request.ref_audio)
        artifact_key = self._get_resolved_ref_audio_artifact_key(request.ref_audio)
        wav = np.asarray(wav_list, dtype=np.float32)
        ref_codes_delayed, cache_hit, inflight_wait = await self._resolve_higgs_audio_v3_ref_codes(
            artifact_key,
            wav,
            int(sr),
            encode_reference_audio,
            apply_delay_pattern,
        )
        del cache_hit, inflight_wait

        prompt_ids = adapter.build_prompt(
            request.input,
            num_ref_tokens=int(ref_codes_delayed.shape[0]),
            reference_text=request.ref_text or None,
        )
        prompt = tokens_input(prompt_token_ids=prompt_ids)
        import torch

        prompt["additional_information"] = {
            "audio_input_ids": ref_codes_delayed.to(torch.long),
            "audio_input_ids_mask": torch.ones(ref_codes_delayed.shape[0], dtype=torch.bool),
        }
        return prompt

    async def _resolve_higgs_audio_v3_ref_codes(
        self,
        artifact_key: str | None,
        wav: np.ndarray,
        sr: int,
        encode_reference_audio,
        apply_delay_pattern,
    ) -> tuple[torch.Tensor, bool, bool]:
        ref_codes_delayed = self._get_higgs_audio_v3_ref_codes(artifact_key)
        if ref_codes_delayed is not None:
            return ref_codes_delayed, True, False
        if not artifact_key:
            ref_codes_raw = await asyncio.to_thread(encode_reference_audio, wav, sr)
            return apply_delay_pattern(ref_codes_raw), False, False

        task = self._higgs_audio_v3_ref_code_inflight.get(artifact_key)
        if task is not None:
            return (await task).clone(), False, True

        async def _encode_and_cache() -> torch.Tensor:
            ref_codes_raw = await asyncio.to_thread(encode_reference_audio, wav, sr)
            delayed = apply_delay_pattern(ref_codes_raw)
            self._put_higgs_audio_v3_ref_codes(artifact_key, delayed)
            cached = self._get_higgs_audio_v3_ref_codes(artifact_key)
            return cached if cached is not None else delayed.detach().to("cpu", dtype=torch.long).contiguous()

        task = asyncio.create_task(_encode_and_cache())
        self._higgs_audio_v3_ref_code_inflight[artifact_key] = task
        try:
            return (await task).clone(), False, False
        finally:
            if self._higgs_audio_v3_ref_code_inflight.get(artifact_key) is task:
                self._higgs_audio_v3_ref_code_inflight.pop(artifact_key, None)

    def _get_higgs_audio_v3_ref_codes(self, artifact_key: str | None) -> torch.Tensor | None:
        if not artifact_key:
            return None
        cached = self._higgs_audio_v3_ref_code_cache.get(artifact_key)
        if cached is None:
            return None
        self._higgs_audio_v3_ref_code_cache.move_to_end(artifact_key)
        return cached[0].clone()

    def _put_higgs_audio_v3_ref_codes(self, artifact_key: str, codes: torch.Tensor) -> None:
        if _HIGGS_V3_REF_CODE_CACHE_MAX_ENTRIES <= 0 or _HIGGS_V3_REF_CODE_CACHE_MAX_BYTES <= 0 or not artifact_key:
            return
        cached_codes = codes.detach().to("cpu", dtype=torch.long).contiguous()
        size = int(cached_codes.numel() * cached_codes.element_size())
        if size > _HIGGS_V3_REF_CODE_CACHE_MAX_BYTES:
            return
        previous = self._higgs_audio_v3_ref_code_cache.pop(artifact_key, None)
        if previous is not None:
            self._higgs_audio_v3_ref_code_cache_bytes -= previous[1]
        self._higgs_audio_v3_ref_code_cache[artifact_key] = (cached_codes, size)
        self._higgs_audio_v3_ref_code_cache_bytes += size
        while len(self._higgs_audio_v3_ref_code_cache) > _HIGGS_V3_REF_CODE_CACHE_MAX_ENTRIES:
            _, (_, old_size) = self._higgs_audio_v3_ref_code_cache.popitem(last=False)
            self._higgs_audio_v3_ref_code_cache_bytes -= old_size
        while self._higgs_audio_v3_ref_code_cache_bytes > _HIGGS_V3_REF_CODE_CACHE_MAX_BYTES:
            _, (_, old_size) = self._higgs_audio_v3_ref_code_cache.popitem(last=False)
            self._higgs_audio_v3_ref_code_cache_bytes -= old_size

    async def _resolve_higgs_audio_v3_adapter(self):
        """Lazy-load the tokenizer adapter for higgs_audio_v3."""
        cached = getattr(self, "_higgs_audio_v3_adapter", None)
        if cached is not None:
            return cached

        from transformers import AutoTokenizer

        from vllm_omni.model_executor.models.higgs_audio_v3.higgs_audio_v3_tokenizer import (
            HiggsAudioV3TokenizerAdapter,
        )

        model_path = None
        for stage in self.engine_client.stage_configs:
            model_path = getattr(getattr(stage, "engine_args", None), "model", None)
            if model_path:
                break
        if model_path is None:
            model_path = getattr(self.engine_client, "model", None)
        if model_path is None:
            raise RuntimeError("higgs_audio_v3 serving could not resolve model path")
        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
        adapter = HiggsAudioV3TokenizerAdapter(tokenizer)
        self._higgs_audio_v3_adapter = adapter
        return adapter

    async def _resolve_ref_audio(self, ref_audio_str: str) -> tuple[list[float], int]:
        """Resolve ref_audio to (wav_samples, sample_rate).

        Delegates to upstream vLLM's MediaConnector which handles http(s)
        URLs, ``data:`` base64 URIs, and ``file:`` local paths (the latter
        gated by ``--allowed-local-media-path``).
        """
        cache_key = hashlib.sha1(ref_audio_str.encode("utf-8")).hexdigest()
        cached = self._ref_audio_resolve_cache.get(cache_key)
        if cached is not None:
            self._ref_audio_resolve_cache.move_to_end(cache_key)
            wav_list, sr, _, _ = cached
            logger.debug(
                "Resolved ref_audio from cache: samples=%d sr=%d duration_s=%.3f",
                len(wav_list),
                sr,
                len(wav_list) / sr if sr > 0 else 0.0,
            )
            return wav_list, sr

        # In diffusion mode, model_config may not be available
        if self._diffusion_mode:
            connector = MediaConnector()
        else:
            model_config = self.model_config
            connector = MediaConnector(
                allowed_local_media_path=model_config.allowed_local_media_path,
                allowed_media_domains=model_config.allowed_media_domains,
            )
        fetch_start_s = time.perf_counter()
        wav_np, sr = await connector.fetch_audio_async(ref_audio_str)
        fetch_decode_ms = (time.perf_counter() - fetch_start_s) * 1000.0
        wav_np = np.asarray(wav_np, dtype=np.float32)
        if wav_np.ndim > 1:
            wav_np = np.mean(wav_np, axis=-1)
        sr = int(sr)
        artifact_key = self._make_ref_audio_artifact_cache_key(wav_np, sr)
        duration = len(wav_np) / sr if sr > 0 else 0.0
        if duration < _REF_AUDIO_MIN_DURATION:
            raise ValueError(
                f"Reference audio too short ({duration:.1f}s). "
                f"At least {_REF_AUDIO_MIN_DURATION:.0f}s of clear speech is required."
            )
        if duration > _REF_AUDIO_MAX_DURATION:
            raise ValueError(
                f"Reference audio too long ({duration:.1f}s). "
                f"Maximum {_REF_AUDIO_MAX_DURATION:.0f}s supported — use a shorter clip."
            )
        tolist_start_s = time.perf_counter()
        wav_list = wav_np.tolist()
        tolist_ms = (time.perf_counter() - tolist_start_s) * 1000.0
        logger.debug(
            "Resolved ref_audio: fetch_decode_ms=%.3f tolist_ms=%.3f samples=%d sr=%d duration_s=%.3f",
            fetch_decode_ms,
            tolist_ms,
            len(wav_np),
            sr,
            duration,
        )
        self._put_resolved_ref_audio(cache_key, wav_list, sr, artifact_key)
        return wav_list, sr

    @staticmethod
    def _make_ref_audio_artifact_cache_key(wav: np.ndarray, sr: int) -> str:
        wav_f32 = wav.astype(np.float32, copy=False).reshape(-1)
        h = hashlib.sha1()
        h.update(int(sr).to_bytes(4, byteorder="little", signed=False))
        h.update(int(wav_f32.size).to_bytes(8, byteorder="little", signed=False))
        h.update(wav_f32.tobytes(order="C"))
        return h.hexdigest()

    def _get_resolved_ref_audio_artifact_key(self, ref_audio_str: str) -> str | None:
        source_key = hashlib.sha1(ref_audio_str.encode("utf-8")).hexdigest()
        cached = self._ref_audio_resolve_cache.get(source_key)
        if cached is None:
            return None
        self._ref_audio_resolve_cache.move_to_end(source_key)
        return cached[3]

    def _put_resolved_ref_audio(self, cache_key: str, wav_list: list[float], sr: int, artifact_key: str) -> None:
        if self._ref_audio_resolve_cache_max_entries <= 0 or self._ref_audio_resolve_cache_max_bytes <= 0:
            return
        # Approximate list[float] storage. CPython float objects add per-element
        # overhead, so max_entries remains the hard cache cap.
        size = len(wav_list) * 40
        if size > self._ref_audio_resolve_cache_max_bytes:
            return
        previous = self._ref_audio_resolve_cache.pop(cache_key, None)
        if previous is not None:
            self._ref_audio_resolve_cache_bytes -= previous[2]
            if previous[3] != artifact_key:
                self._discard_ref_audio_artifact_ready_if_unreferenced(previous[3])
        self._ref_audio_resolve_cache[cache_key] = (wav_list, int(sr), size, artifact_key)
        self._ref_audio_resolve_cache_bytes += size
        while len(self._ref_audio_resolve_cache) > self._ref_audio_resolve_cache_max_entries:
            _, (_, _, old_size, old_artifact_key) = self._ref_audio_resolve_cache.popitem(last=False)
            self._ref_audio_resolve_cache_bytes -= old_size
            self._discard_ref_audio_artifact_ready_if_unreferenced(old_artifact_key)
        while self._ref_audio_resolve_cache_bytes > self._ref_audio_resolve_cache_max_bytes:
            _, (_, _, old_size, old_artifact_key) = self._ref_audio_resolve_cache.popitem(last=False)
            self._ref_audio_resolve_cache_bytes -= old_size
            self._discard_ref_audio_artifact_ready_if_unreferenced(old_artifact_key)

    def _discard_ref_audio_artifact_ready_if_unreferenced(self, artifact_key: str) -> None:
        if artifact_key and all(entry[3] != artifact_key for entry in self._ref_audio_resolve_cache.values()):
            self._ref_audio_model_artifact_ready = {
                (key, mode) for (key, mode) in self._ref_audio_model_artifact_ready if key != artifact_key
            }

    @staticmethod
    def _tts_x_vector_only(tts_params: dict[str, Any]) -> bool:
        return bool((tts_params.get("x_vector_only_mode") or [False])[0])

    def _qwen3_tts_can_use_ref_audio_artifact_only(self, tts_params: dict[str, Any], artifact_key: str | None) -> bool:
        if self._tts_model_type != "qwen3_tts":
            return False
        x_vector_only = self._tts_x_vector_only(tts_params)
        if not artifact_key or (artifact_key, x_vector_only) not in self._ref_audio_model_artifact_ready:
            return False
        return (tts_params.get("task_type") or ["CustomVoice"])[0] == "Base"

    def _track_ref_audio_artifact_warmup(
        self, request_id: str, artifact_key: str | None, x_vector_only: bool = False
    ) -> None:
        if artifact_key:
            self._request_ref_audio_artifact_keys[request_id] = (artifact_key, bool(x_vector_only))

    def _mark_ref_audio_artifact_ready_for_request(self, request_id: str) -> None:
        tracked = self._request_ref_audio_artifact_keys.pop(request_id, None)
        if tracked is None:
            return
        artifact_key, x_vector_only = tracked
        if artifact_key and any(entry[3] == artifact_key for entry in self._ref_audio_resolve_cache.values()):
            self._ref_audio_model_artifact_ready.add((artifact_key, x_vector_only))

    def _discard_ref_audio_artifact_warmup(self, request_id: str) -> None:
        self._request_ref_audio_artifact_keys.pop(request_id, None)

    async def _resolve_ref_audio_many(self, ref_audio_list: list[str]) -> list[tuple[list[float], int]]:
        resolved = []
        for ref_audio in ref_audio_list:
            resolved.append(await self._resolve_ref_audio(ref_audio))
        return resolved

    # ---- Ming TTS helpers ----

    def _coerce_ming_prompt_waveform(self, wav_samples, sample_rate):
        from torchaudio.functional import resample as resample_audio

        from vllm_omni.model_executor.models.ming_tts.config_ming_tts import SAMPLE_RATE

        waveform = torch.as_tensor(wav_samples, dtype=torch.float32).reshape(1, -1)
        if int(sample_rate) != SAMPLE_RATE:
            waveform = resample_audio(waveform, int(sample_rate), SAMPLE_RATE)
        return waveform

    def _build_ming_reference_waveforms(
        self,
        ref_audio_data: tuple[list[float], int] | list[tuple[list[float], int]] | None,
    ) -> tuple[list[torch.Tensor], list[int]]:
        if ref_audio_data is None:
            return [], []
        items = ref_audio_data if isinstance(ref_audio_data, list) else [ref_audio_data]
        waveforms = [torch.as_tensor(samples, dtype=torch.float32).reshape(1, -1) for samples, _ in items]
        sample_rates = [int(sample_rate) for _, sample_rate in items]
        if any(sample_rate <= 0 for sample_rate in sample_rates):
            raise ValueError(f"Ming reference audio sample rates must be positive, got {sample_rates}")
        return waveforms, sample_rates

    def _parse_ming_instruction_fields(
        self,
        request,
        *,
        include_voice=False,
        plain_text_passthrough=False,
    ):
        instruction_text = request.instructions.strip() if isinstance(request.instructions, str) else None
        instruction_dict: dict[str, Any] = {}

        voice_lower = request.voice.lower() if isinstance(request.voice, str) else None
        if include_voice and request.voice and not (voice_lower and voice_lower in self.uploaded_speakers):
            instruction_dict["IP"] = request.voice

        if instruction_text:
            try:
                parsed = json.loads(instruction_text)
            except json.JSONDecodeError:
                parsed = None
            if isinstance(parsed, dict):
                instruction_dict.update(parsed)
            elif instruction_dict or not plain_text_passthrough:
                instruction_dict["风格"] = instruction_text
            else:
                return instruction_text

        return instruction_dict or None

    def _parse_ming_instruction(self, request: OpenAICreateSpeechRequest) -> Any:
        """Build a Ming instruction payload from OpenAI speech fields."""
        return self._parse_ming_instruction_fields(
            request,
            include_voice=True,
            plain_text_passthrough=True,
        )

    def _build_ming_dense_prompt(
        self,
        request: OpenAICreateSpeechRequest,
        *,
        ref_audio_data: tuple[list[float], int] | list[tuple[list[float], int]] | None = None,
        voice_name: str | None = None,
        voice_created_at: int = 0,
    ) -> dict[str, Any]:
        """Build a Ming dense prompt directly from the OpenAI speech request."""
        from transformers import AutoTokenizer

        from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
            KEY_MAX_DECODE_STEPS,
            KEY_SPEAKER_SAMPLE_RATES,
            KEY_SPEAKER_WAVEFORM,
            KEY_SPEAKER_WAVEFORM_LENGTHS,
        )
        from vllm_omni.model_executor.models.ming_tts.prompt_assembly import build_ming_dense_prompt

        if self._tts_tokenizer is None:
            model_name = self.engine_client.model_config.model
            trust_remote_code = bool(getattr(self.engine_client.model_config, "trust_remote_code", False))
            self._tts_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)

        ref_text = request.ref_text
        reference_waveforms, reference_sample_rates = self._build_ming_reference_waveforms(ref_audio_data)
        reference_waveform = torch.cat(reference_waveforms, dim=-1) if reference_waveforms else None
        prompt_waveforms = (
            [
                self._coerce_ming_prompt_waveform(waveform, sample_rate)
                for waveform, sample_rate in zip(reference_waveforms, reference_sample_rates, strict=True)
            ]
            if ref_text is not None
            else []
        )
        prompt_waveform = torch.cat(prompt_waveforms, dim=-1) if prompt_waveforms else None
        speaker_embedding = request.speaker_embedding
        pending_speaker_extraction = speaker_embedding is None and reference_waveform is not None
        use_zero_spk_emb = not pending_speaker_extraction and prompt_waveform is None and speaker_embedding is None

        runtime_controls = {}
        if request.max_new_tokens is not None:
            runtime_controls[KEY_MAX_DECODE_STEPS] = request.max_new_tokens

        prompt_dict = build_ming_dense_prompt(
            self._tts_tokenizer,
            # bgm / music-prompt mode not supported online;
            # requires prompt_mode API extension (deferred).
            prompt=_MING_DEFAULT_PROMPT,
            text=request.input,
            runtime_controls=runtime_controls or None,
            instruction=self._parse_ming_instruction(request),
            prompt_text=ref_text,
            prompt_waveform=prompt_waveform,
            speaker_embedding=speaker_embedding,
            speaker_count=len(reference_waveforms) if pending_speaker_extraction else None,
            use_zero_spk_emb=use_zero_spk_emb,
        )
        additional_information = prompt_dict["additional_information"]
        if pending_speaker_extraction:
            additional_information[KEY_SPEAKER_WAVEFORM] = reference_waveform
            additional_information[KEY_SPEAKER_WAVEFORM_LENGTHS] = torch.tensor(
                [int(waveform.shape[-1]) for waveform in reference_waveforms],
                dtype=torch.int32,
            )
            additional_information[KEY_SPEAKER_SAMPLE_RATES] = torch.tensor(
                reference_sample_rates,
                dtype=torch.int32,
            )
            if voice_name:
                additional_information["voice_name"] = voice_name
                additional_information["voice_created_at"] = int(voice_created_at)
        prompt = tokens_input(prompt_token_ids=prompt_dict["prompt_token_ids"])
        prompt["prompt"] = prompt_dict["prompt"]
        prompt["text"] = prompt_dict["text"]
        prompt["additional_information"] = prompt_dict["additional_information"]
        return prompt

    async def _generate_audio_chunks(
        self,
        generator,
        request_id: str,
        response_format: str = "pcm",
        raw_request: Request | None = None,
        request_start_s: float | None = None,
        include_sample_rate: bool = False,
        usage_acc: SpeechOutputTokenCounter | None = None,
        collect: dict | None = None,
    ):
        """Generate audio chunks for streaming response.

        Handles two audio output modes from the engine:
        - Cumulative mode (list): Engine returns growing list of chunks;
        we emit only the new tail on each iteration.
        - Per-step mode (tensor): Engine returns single tensor per iteration;
        we emit it directly.

        Args:
            generator: Async generator from the engine
            request_id: Request identifier for logging
            response_format: Audio format (pcm or wav)

        Yields:
            Raw audio bytes for each chunk (with WAV header for first chunk if wav format)
        """
        prev_count = 0
        sample_rate_val = 24000
        first_chunk = True
        first_audio_chunk_s: float | None = None
        stream_start_s = request_start_s if request_start_s is not None else time.perf_counter()
        artifact_ready = False

        try:
            async for res in generator:
                # Tally generated codec tokens for usage (reads per-stage metrics
                # off the final output; a cheap early-return on every other res).
                if usage_acc is not None:
                    usage_acc.observe(res)
                audio_output, audio_key = self._extract_audio_output(res)
                if audio_key is None:
                    # Stash the aligner's timestamps output for streaming callers.
                    if collect is not None and self._is_timestamps_output(res):
                        collect["aligner_res"] = res
                    continue

                sr_raw = audio_output.get("sr")
                if sr_raw is not None:
                    sr_val = sr_raw[-1] if isinstance(sr_raw, list) and sr_raw else sr_raw
                    sample_rate_val = sr_val.item() if hasattr(sr_val, "item") else int(sr_val)

                audio_val = audio_output[audio_key]
                if isinstance(audio_val, list):
                    # Cumulative mode: each update grows the list; emit only new tail.
                    new_chunks = audio_val[prev_count:]
                    prev_count = len(audio_val)
                else:
                    # Per-step mode: each update is a single tensor; emit directly.
                    if audio_val is not None:
                        new_chunks = [audio_val]
                        prev_count += 1
                    else:
                        new_chunks = []

                for chunk_tensor in new_chunks:
                    chunk_np = (
                        chunk_tensor.float().detach().cpu().numpy() if hasattr(chunk_tensor, "float") else chunk_tensor
                    )
                    if chunk_np.ndim > 1:
                        chunk_np = chunk_np.squeeze()
                    if self._tts_model_type in _AUDEX_NO_AUDIO_GUARD_MODEL_TYPES and int(np.size(chunk_np)) == 0:
                        # Zero-size chunks must not emit a WAV header or count
                        # as first audio; the post-loop guard below needs to
                        # see an audio-less stream to fail the request.
                        continue
                    # For WAV format, emit header before first audio chunk
                    if response_format == "wav" and first_chunk:
                        # Assert that sample rate has been set from chunk metadata (not just default)
                        # This ensures the WAV header contains the correct sample rate
                        assert sr_raw is not None, (
                            "First audio chunk must include sample rate metadata for WAV streaming"
                        )
                        num_channels = _infer_audio_num_channels(np.asarray(chunk_np))
                        wav_header = _create_wav_header(
                            sample_rate=sample_rate_val,
                            num_channels=num_channels,
                            bits_per_sample=16,
                        )
                        yield wav_header
                        first_chunk = False

                    # Convert audio to PCM bytes
                    audio_obj = CreateAudio(
                        audio_tensor=chunk_np,
                        sample_rate=sample_rate_val,
                        response_format="pcm",
                        speed=1.0,
                        base64_encode=False,
                    )
                    if first_audio_chunk_s is None:
                        first_audio_chunk_s = time.perf_counter()
                    audio_bytes = self.create_audio(audio_obj).audio_data
                    if include_sample_rate:
                        yield audio_bytes, sample_rate_val
                    else:
                        yield audio_bytes
            if self._tts_model_type in _AUDEX_NO_AUDIO_GUARD_MODEL_TYPES and first_audio_chunk_s is None:
                # Audex contract: zero codec tokens must abort the stream, not
                # complete it cleanly with zero audio bytes.
                raise ValueError("Audex produced no audio (the thinker emitted zero or invalid codec tokens)")
            self._mark_ref_audio_artifact_ready_for_request(request_id)
            artifact_ready = True
            total_ms = (time.perf_counter() - stream_start_s) * 1000.0
            if first_audio_chunk_s is not None:
                first_chunk_ms = (first_audio_chunk_s - stream_start_s) * 1000.0
                logger.info(
                    "[SpeechE2E] request_id=%s stream=true status=ok total_ms=%.2f first_chunk_ms=%.2f",
                    request_id,
                    total_ms,
                    first_chunk_ms,
                )
            else:
                logger.info(
                    "[SpeechE2E] request_id=%s stream=true status=ok total_ms=%.2f first_chunk_ms=NA",
                    request_id,
                    total_ms,
                )
        except asyncio.CancelledError:
            total_ms = (time.perf_counter() - stream_start_s) * 1000.0
            logger.info(
                "[SpeechE2E] request_id=%s stream=true status=cancelled total_ms=%.2f",
                request_id,
                total_ms,
            )
            logger.info("Streaming request %s cancelled by client", request_id)
            raise
        except EngineDeadError as e:
            total_ms = (time.perf_counter() - stream_start_s) * 1000.0
            logger.error(
                "[SpeechE2E] request_id=%s stream=true status=engine_dead total_ms=%.2f",
                request_id,
                total_ms,
            )
            logger.error(
                "EngineDeadError during streaming speech for %s: %s",
                request_id,
                e,
            )
            # Actively signal shutdown rather than relying on the watchdog.
            if raw_request is not None:
                terminate_if_errored(
                    server=raw_request.app.state.server,
                    engine=self.engine_client,
                )
            raise
        except Exception as e:
            total_ms = (time.perf_counter() - stream_start_s) * 1000.0
            logger.exception(
                "[SpeechE2E] request_id=%s stream=true status=error total_ms=%.2f error=%s",
                request_id,
                total_ms,
                e,
            )
            logger.exception("Streaming speech generation failed for %s: %s", request_id, e)
            raise
        finally:
            if not artifact_ready:
                self._discard_ref_audio_artifact_warmup(request_id)

    async def _generate_audio_sse_events(
        self,
        generator,
        request_id: str,
        response_format: str = "pcm",
        raw_request: Request | None = None,
        request_start_s: float | None = None,
        request: OpenAICreateSpeechRequest | None = None,
        tts_params: dict[str, Any] | None = None,
    ):
        """Generate OpenAI-style SSE events with base64 audio deltas.

        Field naming follows the OpenAI ``speech.audio.delta`` schema, which
        carries the base64 chunk in ``audio`` (not ``delta`` — that is the
        Realtime API ``response.audio.delta`` convention, a different event).
        See https://platform.openai.com/docs/api-reference/audio-streaming.

        The terminal ``speech.audio.done`` event carries a ``usage`` object
        (``input_tokens``/``output_tokens``/``total_tokens`` + a per-modality
        ``input_token_details`` breakdown), matching OpenAI's documented
        ``speech.audio.done`` schema. ``output_tokens`` is accumulated from the
        stage-0 deltas as they stream (see ``SpeechOutputTokenCounter``);
        ``input_tokens`` is computed from the request text + reference audio.
        """
        usage_acc = SpeechOutputTokenCounter()
        try:
            async for chunk in self._generate_audio_chunks(
                generator,
                request_id,
                response_format,
                raw_request=raw_request,
                request_start_s=request_start_s,
                usage_acc=usage_acc,
            ):
                payload = {
                    "type": "speech.audio.delta",
                    "audio": base64.b64encode(chunk).decode("ascii"),
                    "response_format": response_format,
                }
                data = json.dumps(payload, separators=(",", ":"))
                yield f"event: speech.audio.delta\ndata: {data}\n\n"
            done_payload: dict[str, Any] = {"type": "speech.audio.done"}
            if request is not None:
                # Streaming path: output_tokens = sum of stage-0 deltas.
                usage = self._build_speech_usage(request, tts_params or {}, usage_acc.total())
                done_payload["usage"] = usage.model_dump()
            done = json.dumps(done_payload, separators=(",", ":"))
            yield f"event: speech.audio.done\ndata: {done}\n\n"
        except asyncio.CancelledError:
            raise
        except Exception as e:
            payload = {
                "type": "speech.audio.error",
                "error": {
                    "message": str(e),
                    "type": "server_error",
                    "param": None,
                    "code": HTTPStatus.INTERNAL_SERVER_ERROR.value,
                },
            }
            data = json.dumps(payload, separators=(",", ":"))
            yield f"event: speech.audio.error\ndata: {data}\n\n"

    @staticmethod
    def _is_timestamps_output(res) -> bool:
        """True when ``res`` is the forced-aligner stage's terminal timestamps output."""
        from vllm_omni.model_executor.stage_input_processors.forced_aligner import TIMESTAMPS_MODALITY

        return getattr(res, "final_output_type", None) == TIMESTAMPS_MODALITY

    @staticmethod
    def _extract_audio_output(res) -> tuple[dict | None, str | None]:
        """Return (audio_output dict, audio key) or (None, None).

        Returns the raw dict so callers can apply their own extraction strategy:
        streaming needs per-chunk delta slicing; non-streaming needs full concatenation.
        """
        mm = getattr(res, "multimodal_output", None)
        ro = None
        if not mm:
            ro = res
            mm = getattr(ro, "multimodal_output", None) if ro else None
        if not mm:
            # MultimodalOutputProcessor attaches mm_accumulated on per-completion outputs.
            container = res if hasattr(res, "outputs") else ro
            outputs = getattr(container, "outputs", None) if container is not None else None
            if outputs:
                for completion_output in outputs:
                    completion_mm = getattr(completion_output, "multimodal_output", None)
                    if completion_mm:
                        mm = completion_mm
                        break
        if not mm:
            return None, None
        key = "audio" if "audio" in mm else ("model_outputs" if "model_outputs" in mm else None)
        return mm, key

    def _build_tts_params(self, request: OpenAICreateSpeechRequest) -> dict[str, Any]:
        """Build TTS parameters from request.

        Processes each parameter if present, skips if not.
        Values are wrapped in lists as required by the model.
        """
        params: dict[str, Any] = {}

        # Text content (always required)
        params["text"] = [request.input]

        # Task type
        if request.task_type is not None:
            params["task_type"] = [request.task_type]
        else:
            params["task_type"] = ["CustomVoice"]

        # Language
        if request.language is not None:
            params["language"] = [request.language]
        else:
            params["language"] = ["Auto"]

        # Speaker (voice)
        if request.voice is not None:
            voice_lower = request.voice.lower()
            params["speaker"] = [request.voice]
            params["voice_created_at"] = [self._voice_created_at(voice_lower)]

            # Uploaded voices use task_type="Base" (CustomVoice requires built-in spk_id).
            # If ref_text was provided at upload time, use in-context cloning; otherwise x_vector only.
            if voice_lower in self.uploaded_speakers and request.ref_audio is None:
                speaker_info = self.uploaded_speakers[voice_lower]

                # Check if this voice was uploaded with a pre-computed embedding.
                # Populate request.speaker_embedding so the existing code path
                # (below) handles voice_clone_prompt and x_vector_only_mode.
                embedding = self._get_uploaded_speaker_embedding(request.voice)
                if embedding is not None:
                    request.speaker_embedding = embedding
                    params["speaker"] = [voice_lower]
                    params["task_type"] = ["Base"]
                    logger.info("Auto-set speaker_embedding for uploaded voice: %s", request.voice)
                else:
                    audio_data = self._get_uploaded_audio_data(request.voice)
                    if not audio_data:
                        raise ValueError(f"Audio file for uploaded voice '{request.voice}' is missing or corrupted")
                    stored_ref_text = speaker_info.get("ref_text")
                    params["speaker"] = [voice_lower]
                    params["ref_audio"] = [audio_data]
                    params["task_type"] = ["Base"]
                    if stored_ref_text:
                        params["ref_text"] = [stored_ref_text]
                        params["x_vector_only_mode"] = [False]
                    else:
                        params["x_vector_only_mode"] = [True]
                    logger.info(
                        "Auto-set ref_audio for uploaded voice: %s (icl=%s)", request.voice, bool(stored_ref_text)
                    )
            elif voice_lower in self.precomputed_speakers and request.ref_audio is None:
                profile = self.precomputed_speakers[voice_lower]
                mode = str(profile.get("mode") or "xvec").lower()
                params["speaker"] = [voice_lower]
                params["task_type"] = ["Base"]
                params["x_vector_only_mode"] = [mode != "icl"]
                ref_text = request.ref_text or profile.get("ref_text")
                if isinstance(ref_text, str) and ref_text.strip():
                    params["ref_text"] = [ref_text]
                ref_code_length = profile.get("ref_code_length")
                if mode == "icl" and ref_code_length:
                    params["ref_code_length"] = [int(ref_code_length)]
                logger.info("Using precomputed Qwen3-TTS custom voice profile: %s (mode=%s)", voice_lower, mode)

        elif params["task_type"][0] == "CustomVoice":
            params["speaker"] = ["Vivian"]  # Default for CustomVoice

        # Instructions for style/emotion control
        if request.instructions is not None:
            params["instruct"] = [request.instructions]
        else:
            params["instruct"] = [""]

        # Voice clone: ref_audio resolved in create_speech(), not here.
        if request.ref_text is not None:
            params["ref_text"] = [request.ref_text]
        if request.speaker_embedding is not None:
            # Store as plain float list (not tensor) so it survives msgspec
            # serialization through the EngineCore IPC boundary.  The talker's
            # _build_prompt_embeds converts it back to a tensor on the GPU.
            params["voice_clone_prompt"] = [
                {
                    "ref_spk_embedding": list(request.speaker_embedding),
                }
            ]
            # speaker_embedding implies x_vector_only_mode
            params["x_vector_only_mode"] = [True]
        elif request.x_vector_only_mode is not None:
            params["x_vector_only_mode"] = [request.x_vector_only_mode]

        # Generation parameters
        if request.max_new_tokens is not None:
            params["max_new_tokens"] = [request.max_new_tokens]
        else:
            params["max_new_tokens"] = [2048]

        if request.initial_codec_chunk_frames is not None:
            params["initial_codec_chunk_frames"] = [request.initial_codec_chunk_frames]

        if request.non_streaming_mode is not None:
            params["non_streaming_mode"] = [request.non_streaming_mode]
        # Preserve the legacy VoiceDesign fallback when the request omits an
        # explicit override. CustomVoice and Base rely on model defaults
        # (True and False respectively).
        elif params["task_type"][0] == "VoiceDesign":
            params["non_streaming_mode"] = [True]

        return params

    # ---- Voxtral TTS helpers ----

    def _build_voxtral_prompt(self, request: OpenAICreateSpeechRequest) -> dict[str, Any]:
        """Build Voxtral TTS engine prompt, supporting both preset voices and inline
        ``ref_audio`` (base64 or data URI)."""
        from mistral_common.protocol.speech.request import SpeechRequest

        text = request.input
        voice = request.voice
        ref_audio = request.ref_audio
        if not voice and not ref_audio:
            raise ValueError("Voxtral requires either a voice name or ref_audio.")
        # mistral_common expects raw base64 (no data: prefix)
        if ref_audio is not None and isinstance(ref_audio, str) and ref_audio.startswith("data:"):
            _, _, ref_audio = ref_audio.partition(",")
        if self._tts_tokenizer is None:
            from vllm.tokenizers import cached_tokenizer_from_config

            mistral_tokenizer = cached_tokenizer_from_config(self.engine_client.model_config)
            self._tts_tokenizer = mistral_tokenizer.instruct
        if voice is not None:
            tokens = self._tts_tokenizer.encode_speech_request(SpeechRequest(input=text, voice=voice)).tokens
            prompt = tokens_input(prompt_token_ids=tokens)
            prompt["additional_information"] = {"voice": [voice]}
            return prompt
        else:
            tokenized = self._tts_tokenizer.encode_speech_request(SpeechRequest(input=text, ref_audio=ref_audio))
            audio = tokenized.audios[0]
            return {
                "prompt_token_ids": tokenized.tokens,
                "multi_modal_data": {"audio": [(audio.audio_array, audio.sampling_rate)]},
            }

    # ---- Step-Audio2 helpers ----

    def _build_step_audio2_prompt(
        self,
        request: OpenAICreateSpeechRequest,
    ) -> dict[str, Any]:
        """Build prompt for Step-Audio2 TTS.

        Constructs the chat prompt with ``<tts_start>`` as the last token
        of the assistant turn (without ``<|im_end|>``), so the thinker
        continues generating audio tokens.

        Prompt format::
            <|im_start|>system\\n{system_prompt}<|im_end|>\\n
            <|im_start|>user\\n{input_text}<|im_end|>\\n
            <|im_start|>assistant\\n<tts_start>
        """
        system_prompt = getattr(request, "instructions", None) or "You are a voice assistant. Read the text aloud."
        text = request.input

        raw_prompt = (
            f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
            f"<|im_start|>user\n{text}<|im_end|>\n"
            f"<|im_start|>assistant\n<tts_start>"
        )
        return {"prompt": raw_prompt}

    # ---- Fish Speech helpers ----

    def _build_fish_speech_prompt(
        self,
        request: OpenAICreateSpeechRequest,
        ref_audio_data: tuple[list[float], int] | None = None,
        *,
        has_inline_ref_audio: bool = False,
    ) -> dict[str, Any]:
        """Build prompt for Fish Speech S2 Pro.

        Without voice cloning:
          <|im_start|>system\\nconvert the provided text to speech<|im_end|>
          <|im_start|>user\\n{text}<|im_end|>\\n<|im_start|>assistant\\n<|voice|>

        With voice cloning (ref_audio + ref_text):
          <|im_start|>system\\nconvert the provided text to speech reference to the following...
          <|im_end|>\\n<|im_start|>user\\n{text}<|im_end|>\\n<|im_start|>assistant\\n<|voice|>
        """
        from transformers import AutoTokenizer

        if self._fish_speech_tokenizer is None:
            model_name = self.engine_client.model_config.model
            self._fish_speech_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

        tokenizer = self._fish_speech_tokenizer

        if ref_audio_data is None or not request.ref_text:
            prompt_ids, normalized_text = build_fish_text_only_prompt_ids(tokenizer, request.input)

            # Keep the prompt-dict metadata shape aligned with the existing text-only
            # TTS entrypoints: scalar values are wrapped in single-item lists before
            # EngineCore serialization. Structured clone below is different because
            # model-side preprocess consumes concrete per-request scalar fields.
            additional_information: dict[str, Any] = {
                "text": [normalized_text],
            }
            if request.max_new_tokens is not None:
                additional_information["max_new_tokens"] = [request.max_new_tokens]
            prompt = tokens_input(prompt_token_ids=prompt_ids)
            prompt["additional_information"] = additional_information
            return prompt

        wav_samples, sr = ref_audio_data
        normalized_text, normalized_ref_text = normalize_fish_voice_clone_texts(request.input, request.ref_text)
        ph_len = self._estimate_fish_prompt_len(normalized_text, normalized_ref_text, ref_audio_data)

        # Structured clone: scalars (not list-wrapped) because model-side
        # preprocess() consumes per-request fields directly.
        additional_information: dict[str, Any] = {
            "text": normalized_text,
            "ref_text": normalized_ref_text,
            "ref_audio_wav": torch.from_numpy(np.asarray(wav_samples, dtype=np.float32)),
            "ref_audio_sr": int(sr),
            "fish_structured_voice_clone": True,
        }
        # Pass voice identity for model-side DAC code caching.
        if request.voice is not None:
            voice_lower = request.voice.lower()
            if voice_lower in self.uploaded_speakers and not has_inline_ref_audio:
                additional_information["voice_name"] = voice_lower
                additional_information["voice_created_at"] = self._voice_created_at(voice_lower)
        if request.max_new_tokens is not None:
            additional_information["max_new_tokens"] = request.max_new_tokens
        prompt = tokens_input(prompt_token_ids=[1] * ph_len)
        prompt["additional_information"] = additional_information
        return prompt

    # ---- CosyVoice3 helpers ----

    async def _build_cosyvoice3_prompt(
        self,
        request: OpenAICreateSpeechRequest,
        *,
        has_inline_ref_audio: bool = False,
    ) -> dict[str, Any]:
        """Build prompt for CosyVoice3.

        CosyVoice3 uses multimodal input with reference audio for voice cloning.
        The prompt format matches the offline example: text prompt + audio data
        + mm_processor_kwargs with prompt_text.
        """
        # Resolve reference audio
        wav_samples, sr = await self._resolve_ref_audio(request.ref_audio)
        audio_data = (np.asarray(wav_samples, dtype=np.float32), sr)

        # Wrap the reference transcript in the CosyVoice3 instruction template
        # so the talker emits target-only speech (see _COSYVOICE3_PROMPT_PREFIX).
        # Skip if the caller already supplied a formatted prompt_text.
        ref_text = request.ref_text or ""
        if _COSYVOICE3_PROMPT_DELIMITER not in ref_text:
            ref_text = f"{_COSYVOICE3_PROMPT_PREFIX}{ref_text}"
        mm_kwargs: dict[str, Any] = {
            "prompt_text": ref_text,
            "sample_rate": sr,
        }
        # Pass voice metadata for caching in the processor
        if request.voice:
            voice_lower = request.voice.lower()
            if voice_lower in self.uploaded_speakers and not has_inline_ref_audio:
                mm_kwargs["voice_name"] = voice_lower
                mm_kwargs["voice_created_at"] = self._voice_created_at(voice_lower)

        return {
            "prompt": request.input,
            "multi_modal_data": {
                "audio": audio_data,
            },
            "mm_processor_kwargs": mm_kwargs,
        }

    # ---- Covo-Audio helpers ----

    def _build_covo_audio_prompt(
        self,
        request: OpenAICreateSpeechRequest,
    ) -> dict[str, Any]:
        """Build a chat-style prompt for Covo-Audio-Chat.

        Covo-Audio requires a specific system prompt that instructs the model
        to interleave text and audio tokens in its output.  We render the
        messages through the chat template and pass prompt_token_ids so that
        the engine does not need to re-tokenize.
        """
        from transformers import AutoTokenizer

        from vllm_omni.model_executor.models.covo_audio.prompt_utils import (
            build_covo_audio_prompt_token_ids,
        )

        if self._covo_audio_tokenizer is None:
            model_name = self.engine_client.model_config.model
            try:
                self._covo_audio_tokenizer = AutoTokenizer.from_pretrained(
                    model_name,
                    trust_remote_code=True,
                )
            except Exception as exc:
                raise RuntimeError(f"Failed to load Covo-Audio tokenizer from '{model_name}': {exc}") from exc

        prompt_ids = build_covo_audio_prompt_token_ids(
            self._covo_audio_tokenizer,
            request.input,
        )
        return {"prompt_token_ids": prompt_ids}

    def _get_audex_tokenizer(self):
        """Load (once per process) the Audex thinker tokenizer for CFG/TTA prompts.

        The folder follows the serving stage: the full-pipeline thinker
        (``audex_omni``) tokenizes with ``checkpoint_folder_full``; the TTS
        and TTA thinkers use ``checkpoint_folder_audiogen``.
        """
        if self._audex_tokenizer is None:
            from transformers import AutoTokenizer

            from vllm_omni.model_executor.models.audex.checkpoint import ensure_audex_snapshot

            stage = getattr(getattr(self._tts_stage, "engine_args", None), "model_stage", None)
            if stage == "audex_omni":
                profile, folder = "full", "checkpoint_folder_full"
            elif stage == "audex_tta_thinker":
                profile, folder = "tta", "checkpoint_folder_audiogen"
            else:
                profile, folder = "tts", "checkpoint_folder_audiogen"
            # The serving model_config may already hold the RESOLVED per-stage
            # checkpoint folder (stage init joins model_subdir onto the
            # snapshot root); joining again would produce a non-existent path
            # that transformers then mistakes for a repo id.
            model_path = os.path.normpath(self.engine_client.model_config.model)
            if os.path.basename(model_path).startswith("checkpoint_folder"):
                root = os.path.dirname(model_path)
            else:
                root = ensure_audex_snapshot(model_path, profile=profile)
            self._audex_tokenizer = AutoTokenizer.from_pretrained(os.path.join(root, folder))
        return self._audex_tokenizer

    # ---- GLM-TTS helpers ----

    async def _build_glm_tts_prompt(
        self,
        request: OpenAICreateSpeechRequest,
        *,
        has_inline_ref_audio: bool = False,
    ) -> dict[str, Any]:
        """Build prompt for GLM-TTS.

        Uses the multimodal processor path (same as CosyVoice3):
        - prompt: synthesis text
        - multi_modal_data["audio"]: (wav_samples, sr) reference audio
        - mm_processor_kwargs["prompt_text"]: reference text transcript

        AR preprocess() builds [PromptText | Text | BOA | PromptSpeechTokens + ATS].
        DiT receives prompt_token, prompt_feat, embedding for conditioning.
        """
        # Voice cloning requires ref_audio + ref_text
        if request.ref_audio is not None and request.ref_text:
            wav_samples, sr = await self._resolve_ref_audio(request.ref_audio)
            audio_data = (np.asarray(wav_samples, dtype=np.float32), int(sr))

            mm_kwargs: dict[str, Any] = {
                "prompt_text": request.ref_text,
            }
            if request.voice:
                voice_lower = request.voice.lower()
                if voice_lower in self.uploaded_speakers and not has_inline_ref_audio:
                    mm_kwargs["voice_name"] = voice_lower
                    mm_kwargs["voice_created_at"] = self._voice_created_at(voice_lower)

            return {
                "prompt": request.input,
                "multi_modal_data": {
                    "audio": audio_data,
                },
                "mm_processor_kwargs": mm_kwargs,
                "additional_information": self._build_glm_tts_prefill_metadata(
                    request.input,
                    request.ref_text,
                ),
            }

        raise ValueError("GLM-TTS requires ref_audio and ref_text for voice cloning.")

    def _glm_tts_text_tokenizer_and_frontend(self):
        from vllm_omni.model_executor.models.glm_tts.glm_tts import (
            load_glm_tts_tokenizer,
            resolve_glm_tts_tokenizer_path,
        )
        from vllm_omni.model_executor.models.glm_tts.text_frontend import GLMTTSTextFrontend

        cached = self._glm_tts_text_tokenizer
        if cached is None:
            model_name_or_path = self.engine_client.model_config.model
            tokenizer_path = getattr(self.engine_client.model_config, "tokenizer", None)
            if tokenizer_path is None:
                tokenizer_path = resolve_glm_tts_tokenizer_path(model_name_or_path)
            cached = load_glm_tts_tokenizer(
                tokenizer_path,
                model_name_or_path=model_name_or_path,
                trust_remote_code=bool(getattr(self.engine_client.model_config, "trust_remote_code", False)),
            )
            self._glm_tts_text_tokenizer = cached

        frontend = self._glm_tts_text_frontend
        if frontend is None:
            frontend = GLMTTSTextFrontend()
            self._glm_tts_text_frontend = frontend
        return cached, frontend

    def _estimate_glm_tts_text_token_len(self, text: str | None, *, add_trailing_space: bool = False) -> int:
        """Estimate GLM-TTS normalized text length with the model tokenizer."""
        cached, frontend = self._glm_tts_text_tokenizer_and_frontend()
        text = text or ""
        normalized = frontend.text_normalize(text) or text
        normalized = normalized.strip()
        if add_trailing_space and normalized:
            normalized = f"{normalized} "
        return max(1, len(cached.encode(normalized)))

    def _build_glm_tts_prefill_metadata(self, text: str, prompt_text: str | None) -> dict[str, Any]:
        """Build GLM-TTS processor length metadata for additional_information.

        The model preprocess hook runs before postprocess can mirror MM kwargs
        into additional_information, so these scalar fields must originate from
        the request payload rather than runner-level mm_features.
        """
        text_len = self._estimate_glm_tts_text_token_len(text)
        prompt_text_len = (
            self._estimate_glm_tts_text_token_len(prompt_text, add_trailing_space=True) if prompt_text else 0
        )
        return {
            "glm_tts_text_token_len": [text_len],
            "glm_tts_prompt_text_token_len": [prompt_text_len],
            "input_len": [prompt_text_len + text_len + 1],
        }

    # ---- Ming-flash-omni standalone-talker (TTS) helpers ----

    def _build_ming_flash_omni_prompt(self, request: OpenAICreateSpeechRequest) -> dict[str, Any]:
        # request.instructions accepts two forms:
        # 1. Plain text: mapped to the caption's 风格 (style) field
        # 2. JSON object: parsed and splatted into the caption. Unlocks
        #       Unknown keys are dropped by `ming_create_instruction`.
        caption_fields = self._parse_ming_instruction_fields(request) or {}

        has_spk_emb = request.speaker_embedding is not None

        # TTS path applies ming task type `instruct`.
        # voice_name enables talker-side voice preset resolution (e.g. "DB30").
        additional_information: dict[str, Any] = {
            "ming_task": "instruct",
            "prompt": MING_DEFAULT_PROMPT,
            "text": request.input,
            "instruction": ming_create_instruction(caption_fields),
            "voice_name": request.voice or None,
            "use_zero_spk_emb": not has_spk_emb,
            "max_decode_steps": request.max_new_tokens or _TTS_MAX_NEW_TOKENS_MAX,
            "cfg": 2.0,
            "sigma": 0.25,
            "temperature": 0.0,
        }
        if has_spk_emb:
            # Passed as plain float list
            additional_information["spk_emb"] = list(request.speaker_embedding)
        prompt = tokens_input(prompt_token_ids=[0])
        prompt["additional_information"] = additional_information
        return prompt

    # ---- Common speech generation helpers ----

    async def _build_qwen3_tts_request(
        self,
        request: OpenAICreateSpeechRequest,
    ) -> tuple[dict[str, Any], dict[str, Any], str | None]:
        """Build prompt + tts_params for Qwen3-TTS.

        Called from ``Qwen3TTSAdapter.build``. Returns
        ``(prompt, tts_params, warmup_artifact_key)`` where the warmup key is the
        Qwen3-TTS ref-audio artifact tracked after ``generate()``.
        """
        qwen3_ref_audio_warmup_artifact_key: str | None = None
        tts_params = self._build_tts_params(request)
        # Resolve ref_audio (explicit or auto-set for uploaded voices)
        # to [[wav_list, sr]] so the model doesn't re-decode base64.
        ref_audio_source = request.ref_audio
        if ref_audio_source is None and isinstance(tts_params.get("ref_audio"), list):
            # Uploaded voice: ref_audio was auto-set as [base64_data_url]
            ref_audio_source = tts_params["ref_audio"][0]
        if ref_audio_source is not None and isinstance(ref_audio_source, str):
            wav_list, sr = await self._resolve_ref_audio(ref_audio_source)
            artifact_key = self._get_resolved_ref_audio_artifact_key(ref_audio_source)
            if artifact_key:
                tts_params[_QWEN3_TTS_REF_AUDIO_CACHE_KEY] = [artifact_key]
            ref_code_length = self._estimate_ref_code_len([wav_list, sr])
            if ref_code_length is not None:
                tts_params["ref_code_length"] = [int(ref_code_length)]
            if self._qwen3_tts_can_use_ref_audio_artifact_only(tts_params, artifact_key):
                logger.debug("Using Qwen3-TTS ref_audio artifact-only path: %s", artifact_key)
            else:
                tts_params["ref_audio"] = [[wav_list, sr]]
                qwen3_ref_audio_warmup_artifact_key = artifact_key

        ph_len = await self._estimate_prompt_len_async(tts_params)
        prompt = tokens_input(prompt_token_ids=[1] * ph_len)
        prompt["additional_information"] = tts_params
        prompt["cache_salt"] = _conditioning_cache_salt(request, tts_params)
        return prompt, tts_params, qwen3_ref_audio_warmup_artifact_key

    async def _prepare_speech_generation(
        self,
        request: OpenAICreateSpeechRequest,
        request_id: str | None = None,
        has_inline_ref_audio: bool | None = None,
    ) -> tuple[str, Any, dict[str, Any]]:
        if self.engine_client.errored:
            raise self.engine_client.dead_error

        request_id = request_id or f"speech-{random_uuid()}"
        qwen3_ref_audio_warmup_artifact_key: str | None = None

        # If this is a streaming request with real async chunks, we need to
        # coerce cumulative outputs to delta outputs; this ensures we don't
        # emit redundant MM data & drain after emitting. Qwen3-TTS full-payload
        # (async_chunk=False) has no incremental audio chunks, so keep
        # FINAL_ONLY semantics and let the streaming response send the final
        # waveform once. Scoped to qwen3_tts: other async_chunk=False models
        # keep the DELTA coercion they stream with today.
        # list() makes a copy to avoid mutating the params.
        sampling_params_list = list(self.engine_client.default_sampling_params_list)
        async_chunk = getattr(self.model_config, "async_chunk", True)
        qwen3_full_payload = self._tts_model_type == "qwen3_tts" and not bool(async_chunk)
        is_streaming_request = request.is_streaming() and not qwen3_full_payload
        sampling_params_list = coerce_param_message_types(sampling_params_list, is_streaming_request)

        # Build prompt + tts_params via the per-model adapter (RFC #4327). Every
        # dedicated TTS model resolves to an adapter that owns its validation,
        # uploaded-speaker handling, prompt/param building, and sampling
        # overrides. The model-type label remains available for compatibility.
        # Non-TTS deployments (no adapter) fall through to the rejection below.
        # Capture inline-ref-audio status BEFORE validate(): several adapters
        # apply uploaded speakers inside validate(), which sets request.ref_audio
        # in place. The builders need to know whether the caller supplied audio
        # inline vs. via an uploaded voice.
        model_type: str | None = None
        has_inline_ref_audio = (request.ref_audio is not None) if has_inline_ref_audio is None else has_inline_ref_audio
        if (adapter := self._get_tts_adapter()) is not None:
            validation_error = adapter.validate(request)
            if validation_error:
                raise ValueError(validation_error)
            prepared = await adapter.build(request, sampling_params_list, has_inline_ref_audio)
            prompt = prepared.prompt
            tts_params = prepared.tts_params
            model_type = prepared.model_type
            qwen3_ref_audio_warmup_artifact_key = prepared.warmup_artifact_key
        else:
            # Qwen omni models (Qwen3-Omni, Qwen2.5-Omni) use a "talker"
            # stage whose preprocess requires chat-templated tokens.  The
            # async-chunk orchestrator prewarms the talker via
            # compute_talker_prompt_ids_length(), which scans for Qwen
            # chat-template markers (im_start_token_id 151644).  A raw-text
            # prompt produces a 1-token placeholder that crashes the talker's
            # prefill/decode handoff.  Reject early with an actionable message.
            stage_names = {
                getattr(getattr(s, "engine_args", None), "model_stage", None) for s in self.engine_client.stage_configs
            }
            if "talker" in stage_names:
                raise ValueError(
                    "The /v1/audio/speech endpoint is only supported for "
                    "dedicated TTS models (e.g., Qwen3-TTS, Voxtral, Fish "
                    "Speech, CosyVoice3, OmniVoice, VoxCPM2). For omni "
                    "models like Qwen3-Omni, use /v1/chat/completions with "
                    '\'"modalities": ["audio"]\' instead.'
                )
            tts_params = {}
            prompt = {"prompt": request.input}

        if model_type is None:
            if self._is_tts:
                model_type = tts_params.get("task_type", ["unknown"])[0]
            else:
                model_type = "generic"
        logger.info("TTS speech request %s: model=%s", request_id, model_type)
        _rl = getattr(self, "request_logger", None)
        if _rl:
            base_len = len(f"TTS speech request {request_id}: text=")
            raw_max = getattr(_rl, "max_log_len", None)
            cap = raw_max if isinstance(raw_max, int) else 200
            text = request.input[: max(cap - base_len, 0)]
            logger.debug("TTS speech request %s: text=%r", request_id, text)

        # Apply model-specific extra parameters
        if request.extra_params is not None and sampling_params_list:
            if not isinstance(request.extra_params, dict):
                raise HTTPException(
                    status_code=HTTPStatus.BAD_REQUEST.value,
                    detail="extra_params must be a JSON object/dict.",
                )
            import copy

            sampling_params_list = copy.deepcopy(sampling_params_list)
            for name in ("temperature", "top_p", "top_k"):
                if (value := request.extra_params.get(name)) is not None:
                    setattr(sampling_params_list[0], name, value)
            if sampling_params_list[0].extra_args is None:
                sampling_params_list[0].extra_args = {}
            sampling_params_list[0].extra_args.update(request.extra_params)
            logger.info("Applied extra_params: %s", request.extra_params)

        # Apply adapter-owned sampling overrides, including request-level token
        # limits and model-specific dynamic token or stop-token configuration.
        if sampling_params_list and (adapter := self._get_tts_adapter()) is not None:
            sampling_params_list = adapter.apply_sampling_overrides(sampling_params_list, request, prompt, request_id)

        if request.seed is not None and sampling_params_list:
            import copy

            sampling_params_list = copy.deepcopy(sampling_params_list)
            stage0_params = sampling_params_list[0]
            stage0_params.seed = request.seed
            if stage0_params.extra_args is None:
                stage0_params.extra_args = {}
            stage0_params.extra_args["tts_local_seed"] = request.seed

        if self._tts_model_type == "qwen3_tts" and sampling_params_list:
            stage0_params = sampling_params_list[0]
            default_seed = getattr(stage0_params, "seed", None)
            if default_seed is not None:
                import copy

                sampling_params_list = copy.deepcopy(sampling_params_list)
                stage0_params = sampling_params_list[0]
                if stage0_params.extra_args is None:
                    stage0_params.extra_args = {}
                stage0_params.extra_args.setdefault("tts_local_seed", int(default_seed))

        # When word_timestamps is requested, also ask for the aligner stage's
        # output so the orchestrator drives the request through the forced-aligner
        # stage (final_stage_id extends to it). Harmless if no aligner stage exists.
        output_modalities = ["audio"]
        if getattr(request, "word_timestamps", False):
            from vllm_omni.model_executor.stage_input_processors.forced_aligner import TIMESTAMPS_MODALITY

            output_modalities.append(TIMESTAMPS_MODALITY)

        generator = self.engine_client.generate(
            prompt=prompt,
            request_id=request_id,
            sampling_params_list=sampling_params_list,
            output_modalities=output_modalities,
        )
        self._track_ref_audio_artifact_warmup(
            request_id,
            qwen3_ref_audio_warmup_artifact_key,
            x_vector_only=self._tts_x_vector_only(tts_params),
        )
        return request_id, generator, tts_params

    async def _generate_pcm_chunks(
        self, generator, request_id: str, *, include_sample_rate: bool = False, collect: dict | None = None
    ):
        """Yield raw PCM byte chunks from the engine generator.

        Delegates to ``_generate_audio_chunks`` with ``response_format="pcm"``.
        Used by the WebSocket streaming handler and ``_iter_pcm_audio_bytes``.
        ``collect`` (when given) receives the forced-aligner stage's pooling
        output under ``"aligner_res"`` for downstream word-timestamp extraction.
        """
        async for chunk in self._generate_audio_chunks(
            generator,
            request_id,
            response_format="pcm",
            include_sample_rate=include_sample_rate,
            collect=collect,
        ):
            yield chunk

    async def _iter_pcm_audio_bytes(self, request: OpenAICreateSpeechRequest):
        """Yield raw PCM bytes for a speech request as soon as chunks are decoded."""
        request_id, generator, _ = await self._prepare_speech_generation(request)
        try:
            async for chunk in self._generate_pcm_chunks(generator, request_id):
                yield chunk
        finally:
            self._discard_ref_audio_artifact_warmup(request_id)

    async def _generate_audio_bytes(
        self,
        request: OpenAICreateSpeechRequest,
        base64_encode: bool = False,
        request_id: str | None = None,
        usage_out: list[SpeechTokenUsage] | None = None,
        has_inline_ref_audio: bool | None = None,
        collect: dict | None = None,
    ) -> tuple[bytes | str, str]:
        # ``usage_out`` is an opt-in output channel: when a list is passed, the
        # computed SpeechTokenUsage is appended to it. The return stays a
        # 2-tuple so existing callers (and their test mocks) are unaffected;
        # batch and non-streaming response-header paths opt in when surfacing
        # usage outside the raw audio body.
        request_id, generator, bytes_tts_params = await self._prepare_speech_generation(
            request, request_id=request_id, has_inline_ref_audio=has_inline_ref_audio
        )
        artifact_ready = False

        try:
            # MOSS-TTS-Nano emits delta chunks per yield (single-stage,
            # async_chunk=false). The engine surfaces each yield as its own
            # RequestOutput, so we need to accumulate across the async-for loop —
            # final_output alone only carries the last (often empty) sentinel.
            is_moss = self._tts_model_type == "moss_tts_nano"
            moss_chunks: list[Any] = []
            moss_sample_rate: int | None = None

            final_output: OmniRequestOutput | None = None
            # Non-streaming is FINAL_ONLY, so the stage-0 output carries the full
            # token sequence; the counter records its length for output_tokens.
            usage_acc = SpeechOutputTokenCounter()
            audio_res: OmniRequestOutput | None = None
            aligner_res: OmniRequestOutput | None = None
            async for res in generator:
                final_output = res
                usage_acc.observe(res)
                # The generator yields both the audio output (Code2Wav) and, with
                # a forced-aligner stage, a timestamps output. Keep the audio res
                # for the WAV and the aligner res for word timestamps.
                if self._is_timestamps_output(res):
                    aligner_res = res
                else:
                    _, audio_key = self._extract_audio_output(res)
                    if audio_key is not None:
                        audio_res = res
                if not is_moss:
                    continue
                try:
                    step_audio, step_key = self._extract_audio_output(res)
                except Exception:
                    continue
                if step_key is None:
                    continue
                chunk = step_audio[step_key]
                candidates = chunk if isinstance(chunk, list) else [chunk]
                for cand in candidates:
                    if hasattr(cand, "numel") and cand.numel() > 0:
                        moss_chunks.append(cand)
                sr_step = step_audio.get("sr")
                if sr_step is not None:
                    sr_val_step = sr_step[-1] if isinstance(sr_step, list) and sr_step else sr_step
                    moss_sample_rate = int(sr_val_step.item()) if hasattr(sr_val_step, "item") else int(sr_val_step)

            if final_output is None:
                raise ValueError("No output generated from the model.")

            # Extract audio from the audio-bearing res (not necessarily the last
            # yielded one, which may be the aligner's timestamps output).
            audio_source = audio_res if audio_res is not None else final_output
            audio_output, audio_key = self._extract_audio_output(audio_source)
            if audio_key is None:
                raise ValueError("TTS model did not produce audio output.")

            # Surface forced-aligner word timestamps to the caller (set as a
            # response header) when requested and an aligner stage produced them.
            if collect is not None and getattr(request, "word_timestamps", False):
                from vllm_omni.utils.forced_aligner import extract_word_timestamps

                ts = (
                    extract_word_timestamps(aligner_res, request.input, getattr(request, "language", None))
                    if aligner_res is not None
                    else None
                )
                if ts is not None:
                    collect["word_timestamps"] = ts

            audio_tensor = audio_output[audio_key]
            sr_raw = audio_output.get("sr", 24000)
            sr_val = sr_raw[-1] if isinstance(sr_raw, list) and sr_raw else sr_raw
            sample_rate = sr_val.item() if hasattr(sr_val, "item") else int(sr_val)

            if is_moss:
                # Prefer the engine's own consolidated audio when present. After the
                # vllm 0.20 rebase non-stream requests resolve to FINAL_ONLY, so
                # final_output already carries the full concatenated waveform; the
                # delta-accumulator below is kept as a fallback for DELTA-style
                # engines that surface chunks one yield at a time.
                if isinstance(audio_tensor, list):
                    non_empty_final = [c for c in audio_tensor if hasattr(c, "numel") and c.numel() > 0]
                    final_audio = torch.cat(non_empty_final, dim=-1) if non_empty_final else None
                elif hasattr(audio_tensor, "numel") and audio_tensor.numel() > 0:
                    final_audio = audio_tensor
                else:
                    final_audio = None

                if final_audio is not None:
                    audio_tensor = final_audio
                elif moss_chunks:
                    audio_tensor = torch.cat(moss_chunks, dim=-1)
                else:
                    audio_tensor = np.zeros((0,), dtype=np.float32)
                if moss_sample_rate is not None:
                    sample_rate = moss_sample_rate
            elif isinstance(audio_tensor, list):
                async_chunk = bool(getattr(self.engine_client.model_config, "async_chunk", False))
                if async_chunk:
                    non_empty_chunks = [candidate for candidate in audio_tensor if candidate.numel() > 0]
                    audio_tensor = (
                        torch.cat(non_empty_chunks, dim=-1) if non_empty_chunks else np.zeros((0,), dtype=np.float32)
                    )
                else:
                    audio_history = audio_tensor
                    audio_tensor = np.zeros((0,), dtype=np.float32)
                    # Non-async Qwen3-TTS returns cumulative history snapshots, so keep the latest non-empty tensor.
                    for candidate in reversed(audio_history):
                        if candidate.numel() > 0:
                            audio_tensor = candidate
                            break
            if hasattr(audio_tensor, "float"):
                audio_tensor = audio_tensor.float().detach().cpu().numpy()

            if audio_tensor.ndim > 1:
                audio_tensor = audio_tensor.squeeze()

            if self._tts_model_type in _AUDEX_NO_AUDIO_GUARD_MODEL_TYPES and int(np.size(audio_tensor)) == 0:
                # Audex contract: zero codec tokens must fail the request, not
                # serialize as an empty-but-successful WAV.
                raise ValueError("Audex produced no audio (the thinker emitted zero or invalid codec tokens)")

            audio_obj = CreateAudio(
                audio_tensor=audio_tensor,
                sample_rate=sample_rate,
                response_format=request.response_format or "wav",
                speed=self._audio_encode_speed(request),
                base64_encode=base64_encode,
            )
            audio_response: AudioResponse = self.create_audio(audio_obj)
            self._mark_ref_audio_artifact_ready_for_request(request_id)
            artifact_ready = True
            if usage_out is not None:
                usage_out.append(self._build_speech_usage(request, bytes_tts_params or {}, usage_acc.total()))
            return audio_response.audio_data, audio_response.media_type
        finally:
            if not artifact_ready:
                self._discard_ref_audio_artifact_warmup(request_id)

    def _get_normalized_voice(self, voice: str | None) -> str | None:
        """Get the normalized voice to be used; currently this means that
        the voice is a:
            - lowercase str if it's a valid supported/uploaded speaker
            - None if the voice is the placeholder default or not provided
        """
        if voice is not None:
            voice = voice.lower()
            if voice not in self.uploaded_speakers and voice not in self.supported_speakers:
                raise ValueError(
                    f"Invalid voice '{voice}'. Supported: {', '.join(sorted(self._get_available_voices()))}"
                )
        return voice

    async def _create_diffusion_speech(
        self,
        request: OpenAICreateSpeechRequest,
    ) -> Response:
        """Handle speech generation for pure diffusion TTS models (e.g. OmniVoice)."""
        from vllm_omni.outputs import OmniRequestOutput

        try:
            if not request.input or not request.input.strip():
                raise ValueError("Input text cannot be empty")

            if request.ref_audio is not None:
                fmt_err = self._validate_ref_audio_format(request.ref_audio)
                if fmt_err:
                    return self._diffusion_error_response(fmt_err, status_code=400)

            request.voice = self._get_normalized_voice(request.voice)

            has_inline_ref_audio = request.ref_audio is not None
            err = self._apply_uploaded_speaker(request)
            if err:
                raise ValueError(err)

            request_id = f"speech-{random_uuid()}"
            prompt: dict[str, Any] = {"input": request.input}
            if request.ref_audio:
                wav, sr = await self._resolve_ref_audio(request.ref_audio)
                prompt["ref_audio"] = (np.asarray(wav, dtype=np.float32), sr)
            if request.ref_text:
                prompt["ref_text"] = request.ref_text
            if request.voice:
                if request.voice in self.uploaded_speakers and not has_inline_ref_audio:
                    prompt["voice_name"] = request.voice
                    prompt["voice_created_at"] = self._voice_created_at(request.voice)
            if request.language:
                prompt["lang"] = request.language
            if request.instructions:
                prompt["instruct"] = request.instructions

            logger.info(
                "Diffusion TTS speech request %s: voice_clone=%s",
                request_id,
                "ref_audio" in prompt,
            )
            _rl = getattr(self, "request_logger", None)
            if _rl:
                base_len = len(f"Diffusion TTS speech request {request_id}: text=")
                raw_max = getattr(_rl, "max_log_len", None)
                cap = raw_max if isinstance(raw_max, int) else 200
                text = request.input[: max(cap - base_len, 0)]
                logger.debug("Diffusion TTS speech request %s: text=%r", request_id, text)
            if request.extra_params is not None and not isinstance(request.extra_params, dict):
                raise ValueError("extra_params must be a JSON object/dict.")
            extra = dict(request.extra_params or {})
            if request.seed is not None:
                extra["seed"] = request.seed
            # Apply extra_params from the request to sampling params
            sampling_params_list = self._diffusion_engine.default_sampling_params_list
            if extra:
                import copy

                sampling_params_list = copy.deepcopy(sampling_params_list)
                if sampling_params_list[0].extra_args is None:
                    sampling_params_list[0].extra_args = {}
                sampling_params_list[0].extra_args.update(extra)
                logger.info("Applied extra_params to diffusion: %s", extra)

            generator = self._diffusion_engine.generate(
                prompt=prompt,
                request_id=request_id,
                sampling_params_list=sampling_params_list,
                output_modalities=["audio"],
            )

            final_output: OmniRequestOutput | None = None
            async for res in generator:
                final_output = res

            if final_output is None:
                raise ValueError("No output generated from the model.")

            audio_output, audio_key = self._extract_audio_output(final_output)
            if audio_key is None:
                raise ValueError("TTS model did not produce audio output.")

            audio_tensor = audio_output[audio_key]
            sr_raw = audio_output.get("sr", 24000)
            sr_val = sr_raw[-1] if isinstance(sr_raw, list) and sr_raw else sr_raw
            sample_rate = sr_val.item() if hasattr(sr_val, "item") else int(sr_val)

            if isinstance(audio_tensor, list):
                non_empty = [c for c in audio_tensor if c.numel() > 0]
                audio_tensor = torch.cat(non_empty, dim=-1) if non_empty else np.zeros((0,), dtype=np.float32)
            if hasattr(audio_tensor, "float"):
                audio_tensor = audio_tensor.float().detach().cpu().numpy()
            if audio_tensor.ndim > 1:
                audio_tensor = audio_tensor.squeeze()

            audio_obj = CreateAudio(
                audio_tensor=audio_tensor,
                sample_rate=sample_rate,
                response_format=request.response_format or "wav",
                speed=self._audio_encode_speed(request),
                base64_encode=False,
            )
            audio_response: AudioResponse = self.create_audio(audio_obj)
            return Response(content=audio_response.audio_data, media_type=audio_response.media_type)

        except asyncio.CancelledError:
            return self._diffusion_error_response("Client disconnected")
        except (EngineGenerateError, EngineDeadError):
            raise  # Propagate to the global Omni exception handler
        except ValueError as e:
            return self._diffusion_error_response(str(e), status_code=400)
        except Exception as e:
            logger.exception("Diffusion speech generation failed: %s", e)
            return self._diffusion_error_response(f"Speech generation failed: {e}")

    @staticmethod
    def _diffusion_error_response(message: str, status_code: int = 500) -> Response:
        """Create a JSON error response without depending on OpenAIServing.

        Args:
            message: Error message to surface to the client.
            status_code: HTTP status code; defaults to 500. Pass a 4xx code for
                client-input validation failures so the response semantics match
                the OpenAI-compatible behavior used by ``create_speech``.
        """
        err_type = "BadRequestError" if 400 <= status_code < 500 else "server_error"
        error_body = json.dumps({"error": {"message": message, "type": err_type, "param": None, "code": status_code}})
        return Response(content=error_body, media_type="application/json", status_code=status_code)

    def _validate_speech_streaming_request(
        self,
        request: OpenAICreateSpeechRequest,
        *,
        mode_label: str,
    ) -> tuple[str, Response | None]:
        """Validate pcm/wav + speed constraints for streaming speech responses."""
        response_format = (request.response_format or "wav").lower()
        if response_format not in ("pcm", "wav"):
            return response_format, self.create_error_response(
                f"{mode_label} is only supported for 'pcm' and 'wav' formats. Got '{response_format}'."
            )
        if request.speed is not None and request.speed != 1.0 and not self._uses_native_speed_control():
            return response_format, self.create_error_response(
                f"{mode_label} is not supported with speed adjustment. "
                "Use a non-streaming request or remove the speed parameter."
            )
        return response_format, None

    async def create_speech(
        self,
        request: OpenAICreateSpeechRequest,
        raw_request: Request | None = None,
    ):
        """
        Create Speech API similar to OpenAI's API.

        See https://platform.openai.com/docs/api-reference/audio/createSpeech
        for the API specification. This API mimics the OpenAI
        Create Speech API.

        For Qwen3-TTS models, additional parameters are supported:
        - task_type: "CustomVoice", "VoiceDesign", or "Base"
        - language: Language code (e.g., "Chinese", "English", "Auto")
        - voice: Speaker name (e.g., "Vivian", "Ryan") for CustomVoice
        - instructions: Voice style/emotion instructions
        - ref_audio: Reference audio for voice cloning (Base task)
        - ref_text: Transcript of reference audio (Base task)
        - x_vector_only_mode: Use speaker embedding only (Base task)

        Streaming is supported via the ``stream=True`` switch or ``stream_format='sse'``,
        which return OpenAI ``speech.audio.*`` SSE events. ``stream_format='audio'``
        opts into raw audio streaming with ``response_format='pcm'`` or ``'wav'``.
        Raw audio streaming yields each Code2Wav chunk as raw bytes as soon as it is
        decoded. Raw WAV streaming emits a header with placeholder size values first.
        """
        if request.voice is not None:
            if _is_default_voice(request.voice.lower(), self.supported_speakers):
                request.voice = None

        if self._diffusion_mode:
            return await self._create_diffusion_speech(request)

        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            logger.error("Error with model %s", error_check_ret)
            return error_check_ret

        request_id = f"speech-{random_uuid()}"
        request_start_s = time.perf_counter()
        if raw_request:
            raw_request.state.request_metadata = RequestResponseMetadata(
                request_id=request_id,
            )

        try:
            if request.is_streaming() and request.word_timestamps:
                return self.create_error_response(
                    "word_timestamps=true is currently supported by the WebSocket "
                    "/v1/audio/speech/stream path. Use session.config with "
                    "stream_audio=true and response_format='pcm'."
                )
            if request.word_timestamps and not self.forced_aligner_enabled:
                # Fail loud instead of silently returning 200 with no timestamps header.
                return self.create_error_response(
                    "word_timestamps=true requires the server to be launched with --forced-aligner."
                )

            if request.is_raw_audio_stream():
                response_format, error = self._validate_speech_streaming_request(
                    request,
                    mode_label="Streaming",
                )
                if error is not None:
                    return error

                media_type = "audio/wav" if response_format == "wav" else "audio/pcm"
                _, generator, _ = await self._prepare_speech_generation(request, request_id=request_id)
                return StreamingResponse(
                    self._generate_audio_chunks(
                        generator,
                        request_id,
                        response_format,
                        raw_request=raw_request,
                        request_start_s=request_start_s,
                    ),
                    media_type=media_type,
                )

            if request.is_sse_stream():
                response_format, error = self._validate_speech_streaming_request(
                    request,
                    mode_label="SSE streaming",
                )
                if error is not None:
                    return error

                _, generator, sse_tts_params = await self._prepare_speech_generation(request, request_id=request_id)
                return StreamingResponse(
                    self._generate_audio_sse_events(
                        generator,
                        request_id,
                        response_format,
                        raw_request=raw_request,
                        request_start_s=request_start_s,
                        request=request,
                        tts_params=sse_tts_params,
                    ),
                    media_type="text/event-stream",
                )

            collect: dict = {}
            usage_box: list[SpeechTokenUsage] = []
            audio_bytes, media_type = await self._generate_audio_bytes(
                request, request_id=request_id, usage_out=usage_box, collect=collect
            )
            total_ms = (time.perf_counter() - request_start_s) * 1000.0
            logger.info(
                "[SpeechE2E] request_id=%s stream=false status=ok total_ms=%.2f response_bytes=%d",
                request_id,
                total_ms,
                len(audio_bytes) if isinstance(audio_bytes, (bytes, bytearray)) else len(str(audio_bytes)),
            )
            headers = self._build_speech_usage_headers(usage_box[0] if usage_box else None)
            if collect.get("word_timestamps") is not None:
                # Default ensure_ascii keeps the header latin-1 encodable (non-ASCII words \uXXXX-escaped).
                ts_json = json.dumps(collect["word_timestamps"])
                # Cap at 4 KB: oversized headers turn into opaque 502s at common reverse-proxy defaults.
                if len(ts_json) <= 4096:
                    headers["X-Word-Timestamps"] = ts_json
                else:
                    # Marker header so clients can tell an oversized alignment from no alignment.
                    headers["X-Word-Timestamps-Omitted"] = f"oversize; bytes={len(ts_json)}; limit=4096"
                    logger.warning(
                        "X-Word-Timestamps header omitted: %d bytes exceeds the 4 KB budget "
                        "(use the WebSocket streaming path for long transcripts)",
                        len(ts_json),
                    )
            return Response(content=audio_bytes, media_type=media_type, headers=headers)

        except asyncio.CancelledError:
            total_ms = (time.perf_counter() - request_start_s) * 1000.0
            logger.info(
                "[SpeechE2E] request_id=%s stream=%s status=cancelled total_ms=%.2f",
                request_id,
                bool(request.stream),
                total_ms,
            )
            return self.create_error_response("Client disconnected")
        except (EngineGenerateError, EngineDeadError):
            total_ms = (time.perf_counter() - request_start_s) * 1000.0
            logger.error(
                "[SpeechE2E] request_id=%s stream=%s status=engine_error total_ms=%.2f",
                request_id,
                bool(request.stream),
                total_ms,
            )
            raise  # Propagate to the global Omni exception handler
        except ValueError as e:
            total_ms = (time.perf_counter() - request_start_s) * 1000.0
            logger.warning(
                "[SpeechE2E] request_id=%s stream=%s status=bad_request total_ms=%.2f error=%s",
                request_id,
                bool(request.stream),
                total_ms,
                e,
            )
            return self.create_error_response(e)
        except Exception as e:
            total_ms = (time.perf_counter() - request_start_s) * 1000.0
            logger.exception(
                "[SpeechE2E] request_id=%s stream=%s status=error total_ms=%.2f error=%s",
                request_id,
                bool(request.stream),
                total_ms,
                e,
            )
            logger.exception("Speech generation failed: %s", e)
            return self.create_error_response(f"Speech generation failed: {e}")

    @staticmethod
    def _merge_batch_item(
        batch: BatchSpeechRequest,
        item: SpeechBatchItem,
    ) -> OpenAICreateSpeechRequest:
        """Merge batch-level defaults with per-item overrides into a full request."""

        def _pick(field: str):
            """Return item-level value if set, else batch-level value."""
            item_val = getattr(item, field, None)
            return item_val if item_val is not None else getattr(batch, field, None)

        picked_speed = _pick("speed")
        return OpenAICreateSpeechRequest(
            input=item.input,
            model=batch.model,
            voice=_pick("voice"),
            instructions=_pick("instructions"),
            response_format=_pick("response_format") or "wav",
            speed=picked_speed if picked_speed is not None else 1.0,
            stream=False,
            task_type=_pick("task_type"),
            language=_pick("language"),
            ref_audio=_pick("ref_audio"),
            ref_text=_pick("ref_text"),
            x_vector_only_mode=_pick("x_vector_only_mode"),
            max_new_tokens=_pick("max_new_tokens"),
            initial_codec_chunk_frames=_pick("initial_codec_chunk_frames"),
            non_streaming_mode=_pick("non_streaming_mode"),
        )

    async def create_speech_batch(
        self,
        batch_request: BatchSpeechRequest,
    ) -> BatchSpeechResponse | ErrorResponse:
        """Generate speech for multiple items concurrently."""
        if self._diffusion_mode:
            raise ValueError("Batch speech is not supported in diffusion mode")
        if len(batch_request.items) > self._batch_max_items:
            raise ValueError(
                f"Batch contains {len(batch_request.items)} items, exceeding the maximum of {self._batch_max_items}."
            )

        error_check_ret = await self._check_model(batch_request)
        if error_check_ret is not None:
            return error_check_ret

        if self.engine_client.errored:
            raise self.engine_client.dead_error

        batch_id = f"speech-batch-{random_uuid()}"

        merged_requests = [self._merge_batch_item(batch_request, item) for item in batch_request.items]

        async def _run_item(idx: int, req: OpenAICreateSpeechRequest) -> SpeechBatchItemResult:
            has_inline_ref_audio = req.ref_audio is not None
            validation_error = self._validate_tts_request(req)
            if validation_error is not None:
                return SpeechBatchItemResult(index=idx, status="error", error=validation_error)
            usage_box: list[SpeechTokenUsage] = []
            try:
                audio_data, media_type = await self._generate_audio_bytes(
                    req, base64_encode=True, usage_out=usage_box, has_inline_ref_audio=has_inline_ref_audio
                )
            except Exception as e:
                logger.exception("Batch item %d failed: %s", idx, e)
                return SpeechBatchItemResult(index=idx, status="error", error=str(e))
            return SpeechBatchItemResult(
                index=idx,
                status="success",
                audio_data=audio_data,
                media_type=media_type,
                usage=usage_box[0] if usage_box else None,
            )

        results = await asyncio.gather(
            *[_run_item(i, req) for i, req in enumerate(merged_requests)],
            return_exceptions=True,
        )

        final_results: list[SpeechBatchItemResult] = []
        for i, r in enumerate(results):
            if isinstance(r, BaseException):
                logger.exception("Batch item %d raised unexpected exception: %s", i, r)
                final_results.append(SpeechBatchItemResult(index=i, status="error", error=str(r)))
            else:
                final_results.append(r)

        succeeded = sum(1 for r in final_results if r.status == "success")
        return BatchSpeechResponse(
            id=batch_id,
            results=final_results,
            total=len(final_results),
            succeeded=succeeded,
            failed=len(final_results) - succeeded,
        )


ServingSpeech = OmniOpenAIServingSpeech