diff --git a/tests/entrypoints/openai_api/test_audex_serving_guards.py b/tests/entrypoints/openai_api/test_audex_serving_guards.py
index 27ed11356..87524825e 100644
--- a/tests/entrypoints/openai_api/test_audex_serving_guards.py
+++ b/tests/entrypoints/openai_api/test_audex_serving_guards.py
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Serving-layer regressions for the Audex zero-codec-token contract.
 
 A zero-codec-token (or phase-invalid TTA) request reaches the serving layer
@@ -171,7 +171,7 @@ class TestAudexCfgValidation:
 class TestAudexCfgInjection:
     def _serving_with_fake_tokenizer(self):
         serving = _serving()
-        serving._get_audex_tokenizer = lambda: _MarkerTokenizer()
+        serving._adapter._get_tokenizer = lambda: _MarkerTokenizer()
         return serving
 
     def _cond_prompt(self, text: str = "Hello world.") -> str:
@@ -333,8 +333,8 @@ class TestAdapterRouting:
 class TestAudexTTAInjection:
     def _serving(self):
         serving = _serving(model_type="audex_tta")
-        serving._get_audex_tokenizer = lambda: _TTAVocabTokenizer()
-        serving._audex_tta_rvq = None
+        serving._adapter._get_tokenizer = lambda: _TTAVocabTokenizer()
+        serving._adapter._tta_rvq = None
         return serving
 
     def _prompt(self):
diff --git a/tests/entrypoints/openai_api/test_serving_speech.py b/tests/entrypoints/openai_api/test_serving_speech.py
index 47c11d740..7e1a9ccec 100644
--- a/tests/entrypoints/openai_api/test_serving_speech.py
+++ b/tests/entrypoints/openai_api/test_serving_speech.py
@@ -53,7 +53,7 @@ from vllm_omni.entrypoints.openai.tts_adapters.base import (
 )
 from vllm_omni.entrypoints.openai.tts_adapters.capabilities import load_supported_speakers
 from vllm_omni.entrypoints.openai.tts_adapters.ming_tts import MingTTSAdapter
-from vllm_omni.entrypoints.openai.tts_adapters.qwen3_tts import Qwen3TTSCodecLimitError
+from vllm_omni.entrypoints.openai.tts_adapters.qwen3_tts import Qwen3TTSAdapter, Qwen3TTSCodecLimitError
 from vllm_omni.entrypoints.openai.tts_adapters.voxtral import VoxtralTTSAdapter
 from vllm_omni.model_executor.models.fish_speech.prompt_utils import (
     FISH_TEXT_ONLY_SYSTEM_PROMPT,
@@ -812,6 +812,15 @@ class TestTTSMethods:
             models=mock_models,
             request_logger=mocker.MagicMock(),
         )
+        qwen3_adapter = Qwen3TTSAdapter(
+            SpeechServingContext(
+                server=server,
+                engine_client=mock_engine_client,
+            )
+        )
+        server._build_tts_params = qwen3_adapter._build_tts_params
+        server._estimate_prompt_len = qwen3_adapter._estimate_prompt_len
+
         yield server
         server.shutdown()
 
@@ -1663,12 +1672,12 @@ class TestTTSMethods:
     def test_conditioning_cache_salt_changes_with_ref_audio_cache_key(self):
         """Prefix-cache salt must fold the content-aware resolve key so a
         same-path file rewrite cannot reuse KV from the previous reference."""
-        from vllm_omni.entrypoints.openai.serving_speech import _conditioning_cache_salt
+        from vllm_omni.entrypoints.openai.tts_adapters.base import conditioning_cache_salt
 
         req = OpenAICreateSpeechRequest(input="hello", ref_audio="file:///data/spk.wav")
-        salt_a = _conditioning_cache_salt(req, {"ref_audio_cache_key": "key_aaa"})
-        salt_b = _conditioning_cache_salt(req, {"ref_audio_cache_key": "key_bbb"})
-        salt_locator_only = _conditioning_cache_salt(req, {})
+        salt_a = conditioning_cache_salt(req, {"ref_audio_cache_key": "key_aaa"})
+        salt_b = conditioning_cache_salt(req, {"ref_audio_cache_key": "key_bbb"})
+        salt_locator_only = conditioning_cache_salt(req, {})
         assert salt_a != salt_b
         assert salt_a != salt_locator_only
 
@@ -1689,20 +1698,22 @@ class TestTTSMethods:
         }
         adapter = HiggsAudioV3TokenizerAdapter(tokenizer)
         mocker.patch.object(adapter, "build_prompt", return_value=[1, -100, -100, 2])
-        speech_server._resolve_higgs_audio_v3_adapter = mocker.AsyncMock(return_value=adapter)
+        speech_server._tts_model_type = "higgs_audio_v3"
+        speech_server._adapter = speech_server._get_tts_adapter()
+        speech_server._adapter._resolve_higgs_audio_v3_adapter = mocker.AsyncMock(return_value=adapter)
         codes = torch.arange(16, dtype=torch.long).view(2, 8)
-        speech_server._resolve_higgs_audio_v3_ref_codes = mocker.AsyncMock(return_value=(codes, False, False))
-        speech_server._get_resolved_ref_audio_artifact_key = mocker.MagicMock(return_value="art")
+        speech_server._adapter._resolve_higgs_audio_v3_ref_codes = mocker.AsyncMock(return_value=(codes, False, False))
+        speech_server._adapter._get_resolved_ref_audio_artifact_key = mocker.MagicMock(return_value="art")
 
         req = OpenAICreateSpeechRequest(
             input="hello",
             ref_audio="file:///data/spk.wav",
             ref_text="transcript",
         )
-        speech_server._resolve_ref_audio = mocker.AsyncMock(return_value=([0.1] * 48000, 24000, "key_aaa"))
-        prompt_a = await speech_server._build_higgs_audio_v3_params(req)
-        speech_server._resolve_ref_audio = mocker.AsyncMock(return_value=([0.9] * 48000, 24000, "key_bbb"))
-        prompt_b = await speech_server._build_higgs_audio_v3_params(req)
+        speech_server._adapter._resolve_ref_audio = mocker.AsyncMock(return_value=([0.1] * 48000, 24000, "key_aaa"))
+        prompt_a = await speech_server._adapter._build_higgs_audio_v3_params(req)
+        speech_server._adapter._resolve_ref_audio = mocker.AsyncMock(return_value=([0.9] * 48000, 24000, "key_bbb"))
+        prompt_b = await speech_server._adapter._build_higgs_audio_v3_params(req)
 
         assert prompt_a["prompt_token_ids"] == [1, 151700, 151700, 2]
         assert prompt_a["prompt_token_ids"] == prompt_b["prompt_token_ids"]
@@ -1830,7 +1841,9 @@ class TestTTSMethods:
     @pytest.mark.asyncio
     async def test_ttsd_second_reference_does_not_use_named_voice(self, speech_server, mocker):
         """Uploaded voice names speaker 1 only; speaker 2 must stay anonymous."""
-        speech_server._moss_variant = "ttsd"
+        speech_server._tts_model_type = "moss_tts"
+        speech_server._adapter = speech_server._get_tts_adapter()
+        speech_server._adapter._moss_variant = "ttsd"
         speech_server.uploaded_speakers = {"alice": {}}
         mocker.patch.object(speech_server, "_voice_created_at", return_value=42)
 
@@ -1839,7 +1852,7 @@ class TestTTSMethods:
         proc.model_config.sampling_rate = 24000
         proc.build_user_message.return_value = "msg"
         proc.return_value = {"input_ids": [torch.zeros((4, 9), dtype=torch.int64)]}
-        mocker.patch.object(speech_server, "_get_moss_processor", return_value=proc)
+        mocker.patch.object(speech_server._adapter, "_get_moss_processor", return_value=proc)
 
         seen: list[tuple[str, str | None]] = []
 
@@ -1848,7 +1861,7 @@ class TestTTSMethods:
                 seen.append((ref_str, kwargs.get("voice_name")))
                 return torch.zeros(3, dtype=torch.int64), f"rk:{ref_str}"
 
-        mocker.patch.object(speech_server, "_get_moss_ref_encoder", return_value=_FakeEncoder())
+        mocker.patch.object(speech_server._adapter, "_get_moss_ref_encoder", return_value=_FakeEncoder())
 
         req = OpenAICreateSpeechRequest(
             input="hello",
@@ -1856,7 +1869,7 @@ class TestTTSMethods:
             ref_audio="data:audio/wav;base64,aaa",
             ref_audio_2="data:audio/wav;base64,bbb",
         )
-        params = await speech_server._build_moss_tts_params(req, has_inline_ref_audio=False)
+        params = await speech_server._adapter._build_moss_tts_params(req, has_inline_ref_audio=False)
         assert sorted(seen) == [
             ("data:audio/wav;base64,aaa", "alice"),
             ("data:audio/wav;base64,bbb", None),
@@ -1887,7 +1900,7 @@ class TestTTSMethods:
         assert speech_server._validate_tts_request(req) is None
         assert req.task_type == "Base"
 
-        params = speech_server._build_tts_params(req)
+        params = speech_server._adapter._build_tts_params(req)
         assert params["task_type"] == ["Base"]
         assert params["speaker"] == ["alice"]
         assert params["x_vector_only_mode"] == [False]
@@ -2036,14 +2049,14 @@ class TestTTSMethods:
         )
         speech_server.engine_client.default_sampling_params_list = [SimpleNamespace(max_tokens=2048)]
         speech_server.engine_client.generate = mocker.MagicMock(return_value="generator")
-        speech_server._build_voxcpm2_prompt = mocker.AsyncMock(
+        speech_server._adapter._build_prompt = mocker.AsyncMock(
             return_value={"prompt_token_ids": [1], "additional_information": {}}
         )
 
         with pytest.raises(ValueError, match="Invalid voice 'bob'"):
             asyncio.run(speech_server._prepare_speech_generation(OpenAICreateSpeechRequest(input="Hello", voice="Bob")))
 
-        speech_server._build_voxcpm2_prompt.assert_not_awaited()
+        speech_server._adapter._build_prompt.assert_not_awaited()
         speech_server.engine_client.generate.assert_not_called()
 
     def test_prepare_voxcpm2_accepts_default_voice(self, speech_server, mocker):
@@ -2057,13 +2070,13 @@ class TestTTSMethods:
         )
         speech_server.engine_client.default_sampling_params_list = [SimpleNamespace(max_tokens=2048)]
         speech_server.engine_client.generate = mocker.MagicMock(return_value=iter(()))
-        speech_server._build_voxcpm2_prompt = mocker.AsyncMock(
+        speech_server._adapter._build_prompt = mocker.AsyncMock(
             return_value={"prompt_token_ids": [1], "additional_information": {}}
         )
 
         asyncio.run(speech_server._prepare_speech_generation(OpenAICreateSpeechRequest(input="Hello", voice="default")))
 
-        speech_server._build_voxcpm2_prompt.assert_awaited_once()
+        speech_server._adapter._build_prompt.assert_awaited_once()
         speech_server.engine_client.generate.assert_called_once()
 
     def test_prepare_voxcpm2_precomputed_voice_sets_model_cache_key(self, speech_server, mocker):
@@ -2084,7 +2097,7 @@ class TestTTSMethods:
         )
         speech_server.engine_client.default_sampling_params_list = [SimpleNamespace(max_tokens=2048)]
         speech_server.engine_client.generate = mocker.MagicMock(return_value=iter(()))
-        speech_server._build_voxcpm2_prompt = mocker.AsyncMock(
+        speech_server._adapter._build_prompt = mocker.AsyncMock(
             return_value={
                 "prompt_token_ids": [1],
                 "additional_information": {
@@ -2478,13 +2491,14 @@ class TestTTSMethods:
             speech_server, "_get_uploaded_speaker_embedding", return_value=fake_embedding
         )
         mock_get_audio = mocker.patch.object(speech_server, "_get_uploaded_audio_data")
+        adapter = MingTTSAdapter(SpeechServingContext(server=speech_server))
+        speech_server._adapter = adapter
         mock_prompt = mocker.patch.object(
-            speech_server,
+            speech_server._adapter,
             "_build_ming_dense_prompt",
             return_value={"additional_information": {"speaker_count": 1}},
         )
 
-        adapter = MingTTSAdapter(SpeechServingContext(server=speech_server))
         req = OpenAICreateSpeechRequest(input="Hello", voice="emb_voice")
         prepared = asyncio.run(adapter.build(req, [], False))
 
@@ -2522,13 +2536,14 @@ class TestTTSMethods:
             "_resolve_ref_audio",
             new=mocker.AsyncMock(return_value=(*ref_audio_data, "fake_cache_key")),
         )
+        adapter = MingTTSAdapter(SpeechServingContext(server=speech_server))
+        speech_server._adapter = adapter
         mock_prompt = mocker.patch.object(
-            speech_server,
+            speech_server._adapter,
             "_build_ming_dense_prompt",
             return_value={"additional_information": {"speaker_count": 1}},
         )
 
-        adapter = MingTTSAdapter(SpeechServingContext(server=speech_server))
         req = OpenAICreateSpeechRequest(input="Hello", voice="audio_voice")
         prepared = asyncio.run(adapter.build(req, [], False))
 
@@ -2552,6 +2567,8 @@ class TestTTSMethods:
             def convert_tokens_to_ids(self, token):
                 return 888 if token == "<audioPatch>" else self.unk_token_id
 
+        speech_server._tts_model_type = "ming_tts"
+        speech_server._adapter = speech_server._get_tts_adapter()
         speech_server._tts_tokenizer = FakeTokenizer()
         request = OpenAICreateSpeechRequest(
             input="Hello",
@@ -2562,7 +2579,7 @@ class TestTTSMethods:
             ([0.4, 0.5], 24000),
         ]
 
-        prompt = speech_server._build_ming_dense_prompt(
+        prompt = speech_server._adapter._build_ming_dense_prompt(
             request,
             ref_audio_data=ref_audio_data,
             voice_name="uploaded_voice",
@@ -2592,10 +2609,12 @@ class TestTTSMethods:
             def convert_tokens_to_ids(self, token):
                 return 888 if token == "<audioPatch>" else self.unk_token_id
 
+        speech_server._tts_model_type = "ming_tts"
+        speech_server._adapter = speech_server._get_tts_adapter()
         speech_server._tts_tokenizer = FakeTokenizer()
         request = OpenAICreateSpeechRequest(input="Hello")
 
-        prompt = speech_server._build_ming_dense_prompt(
+        prompt = speech_server._adapter._build_ming_dense_prompt(
             request,
             ref_audio_data=([0.1, 0.2, 0.3], 16000),
         )
@@ -2617,6 +2636,10 @@ class TestTTSMethods:
         from safetensors.torch import save_file
 
         speech_server._tts_model_type = "ming_tts"
+        speech_server._adapter = speech_server._get_tts_adapter()
+        speech_server._adapter.ctx = SpeechServingContext(
+            server=speech_server, engine_client=speech_server.engine_client
+        )
         speech_server.uploaded_speakers_dir = tmp_path
         speech_server._speaker_cache.clear()
         file_path = tmp_path / "legacy_audio_voice.safetensors"
@@ -2640,16 +2663,15 @@ class TestTTSMethods:
             new=mocker.AsyncMock(return_value=(*ref_audio_data, "fake_cache_key")),
         )
         mock_prompt = mocker.patch.object(
-            speech_server,
+            speech_server._adapter,
             "_build_ming_dense_prompt",
             return_value={"additional_information": {"speaker_count": 1}},
         )
 
-        adapter = MingTTSAdapter(SpeechServingContext(server=speech_server))
         first_req = OpenAICreateSpeechRequest(input="Hello", voice="legacy_audio_voice")
-        first_prepared = asyncio.run(adapter.build(first_req, [], False))
+        first_prepared = asyncio.run(speech_server._adapter.build(first_req, [], False))
         second_req = OpenAICreateSpeechRequest(input="Hello again", voice="legacy_audio_voice")
-        second_prepared = asyncio.run(adapter.build(second_req, [], False))
+        second_prepared = asyncio.run(speech_server._adapter.build(second_req, [], False))
 
         assert first_req.speaker_embedding is None
         assert second_req.speaker_embedding is None
@@ -2921,7 +2943,7 @@ class TestTTSMethods:
         )
         speech_server.engine_client.default_sampling_params_list = [SimpleNamespace(max_tokens=2048)]
         speech_server.engine_client.generate = mocker.MagicMock(return_value=iter(()))
-        speech_server._build_voxcpm2_prompt = mocker.AsyncMock(
+        speech_server._adapter._build_prompt = mocker.AsyncMock(
             return_value={"prompt_token_ids": [1], "additional_information": {}}
         )
 
@@ -4182,13 +4204,13 @@ def fish_speech_server(mocker: MockerFixture):
 class TestFishSpeechServing:
     def test_build_fish_prompt_normalizes_legacy_speaker_tags(self, fish_speech_server):
         tokenizer = _FakeFishTokenizer()
-        fish_speech_server._fish_speech_tokenizer = tokenizer
+        fish_speech_server._adapter._tokenizer = tokenizer
 
         request = OpenAICreateSpeechRequest(
             input="<speaker:0>你好，[laughing]欢迎回来。<speaker:1>我也来了。",
         )
 
-        prompt = fish_speech_server._build_fish_speech_prompt(request)
+        prompt = fish_speech_server._adapter._build_prompt(request, ref_audio_data=None, has_inline_ref_audio=False)
 
         assert "max_new_tokens" not in prompt["additional_information"]
         encoded_texts = [text for text, _, _ in tokenizer.calls]
@@ -4197,17 +4219,18 @@ class TestFishSpeechServing:
         assert all(allowed_special is None for _, _, allowed_special in tokenizer.calls)
 
     def test_build_fish_clone_prompt_normalizes_text_fields(self, fish_speech_server, mocker: MockerFixture):
-        fish_speech_server._fish_speech_tokenizer = _FakeFishTokenizer()
-        fish_speech_server._estimate_fish_prompt_len = mocker.MagicMock(return_value=123)
+        fish_speech_server._adapter._tokenizer = _FakeFishTokenizer()
+        fish_speech_server._adapter._estimate_prompt_len = mocker.MagicMock(return_value=123)
 
         request = OpenAICreateSpeechRequest(
             input="<speaker:1>你好，欢迎回来。",
             ref_text="参考音频的原始文本。",
         )
 
-        prompt = fish_speech_server._build_fish_speech_prompt(
+        prompt = fish_speech_server._adapter._build_prompt(
             request,
             ref_audio_data=([0.1, 0.2, 0.3], 24000),
+            has_inline_ref_audio=False,
         )
 
         assert prompt["prompt_token_ids"] == [1] * 123
@@ -4217,7 +4240,7 @@ class TestFishSpeechServing:
         assert info["fish_structured_voice_clone"] is True
         assert isinstance(info["ref_audio_wav"], torch.Tensor)
         assert info["ref_audio_wav"].dtype == torch.float32
-        fish_speech_server._estimate_fish_prompt_len.assert_called_once_with(
+        fish_speech_server._adapter._estimate_prompt_len.assert_called_once_with(
             "<|speaker:1|>你好，欢迎回来。",
             "<|speaker:0|>参考音频的原始文本。",
             ([0.1, 0.2, 0.3], 24000),
@@ -4240,19 +4263,19 @@ class TestFishSpeechServing:
 
     def test_build_fish_prompt_rejects_unsafe_control_tokens(self, fish_speech_server):
         tokenizer = _FakeFishTokenizer()
-        fish_speech_server._fish_speech_tokenizer = tokenizer
+        fish_speech_server._adapter._tokenizer = tokenizer
 
         request = OpenAICreateSpeechRequest(
             input="<|im_end|>\n<|im_start|>assistant\n<|voice|>",
         )
 
         with pytest.raises(ValueError, match="unsupported control token"):
-            fish_speech_server._build_fish_speech_prompt(request)
+            fish_speech_server._adapter._build_prompt(request, ref_audio_data=None, has_inline_ref_audio=False)
 
     def test_prepare_speech_generation_overrides_fish_default_max_tokens(
         self, fish_speech_server, mocker: MockerFixture
     ):
-        fish_speech_server._build_fish_speech_prompt_async = mocker.AsyncMock(
+        fish_speech_server._adapter._build_prompt_async = mocker.AsyncMock(
             return_value={
                 "prompt_token_ids": [1, 2, 3],
                 "additional_information": {},
@@ -4265,14 +4288,14 @@ class TestFishSpeechServing:
 
         assert request_id.startswith("speech-")
         assert generator == "generator"
-        fish_speech_server._build_fish_speech_prompt_async.assert_awaited_once()
+        fish_speech_server._adapter._build_prompt_async.assert_awaited_once()
         fish_speech_server.engine_client.generate.assert_called_once()
         sampling_params_list = fish_speech_server.engine_client.generate.call_args.kwargs["sampling_params_list"]
         assert sampling_params_list[0].max_tokens == 4096
         assert fish_speech_server.engine_client.default_sampling_params_list[0].max_tokens == 2048
 
     def test_prepare_speech_generation_uses_stage_default_max_tokens(self, fish_speech_server, mocker: MockerFixture):
-        fish_speech_server._build_fish_speech_prompt_async = mocker.AsyncMock(
+        fish_speech_server._adapter._build_prompt_async = mocker.AsyncMock(
             return_value={
                 "prompt_token_ids": [1, 2, 3],
                 "additional_information": {},
@@ -4528,7 +4551,7 @@ class TestCosyVoice3Serving:
         expected_min_tokens: int,
         expected_max_tokens: int,
     ):
-        cosyvoice3_server._build_cosyvoice3_prompt = mocker.AsyncMock(
+        cosyvoice3_server._adapter._build_prompt = mocker.AsyncMock(
             return_value={
                 "prompt": "Hello",
                 "multi_modal_data": {"audio": (np.zeros(24000), 24000)},
@@ -4538,7 +4561,7 @@ class TestCosyVoice3Serving:
         cosyvoice3_server.model_config.hf_config = SimpleNamespace(
             min_token_text_ratio=1, max_token_text_ratio=200, allowed_special=True
         )
-        cosyvoice3_server._cosyvoice3_tokenizer = mocker.MagicMock()
+        cosyvoice3_server._adapter._tokenizer = mocker.MagicMock()
         mocker.patch("vllm_omni.model_executor.models.cosyvoice3.utils.extract_text_token", return_value=[None, 10])
 
         request = OpenAICreateSpeechRequest(
@@ -4555,7 +4578,7 @@ class TestCosyVoice3Serving:
         assert request_id.startswith("speech-")
         assert generator == "generator"
         assert tts_params == {}
-        cosyvoice3_server._build_cosyvoice3_prompt.assert_awaited_once()
+        cosyvoice3_server._adapter._build_prompt.assert_awaited_once()
 
 
 # ---- GLM-TTS Serving Tests ----
@@ -4635,13 +4658,13 @@ class TestGLMTTSServing:
             return_value="normalized target",
         )
 
-        text_token_len = glm_tts_server._estimate_glm_tts_text_token_len("abcdef")
+        text_token_len = glm_tts_server._adapter._estimate_text_token_len("abcdef")
 
         assert text_token_len == 3
         load_tokenizer.assert_called_once()
 
     def test_prepare_speech_generation_glm_tts(self, glm_tts_server, mocker: MockerFixture):
-        glm_tts_server._build_glm_tts_prompt = mocker.AsyncMock(
+        glm_tts_server._adapter._build_prompt = mocker.AsyncMock(
             return_value={
                 "prompt": "Hello",
                 "multi_modal_data": {"audio": (np.zeros(24000), 24000)},
@@ -4654,7 +4677,7 @@ class TestGLMTTSServing:
             ref_text="Reference text",
         )
         glm_tts_server.model_config.hf_config = SimpleNamespace(min_token_text_ratio=1, max_token_text_ratio=200)
-        glm_tts_server._estimate_glm_tts_text_token_len = mocker.MagicMock(return_value=10)
+        glm_tts_server._adapter._estimate_text_token_len = mocker.MagicMock(return_value=10)
         request_id, generator, tts_params = asyncio.run(glm_tts_server._prepare_speech_generation(request))
         sampling_params = glm_tts_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]
 
@@ -4663,7 +4686,7 @@ class TestGLMTTSServing:
         assert request_id.startswith("speech-")
         assert generator == "generator"
         assert tts_params == {}
-        glm_tts_server._build_glm_tts_prompt.assert_awaited_once()
+        glm_tts_server._adapter._build_prompt.assert_awaited_once()
 
 
 @pytest.fixture
@@ -4782,16 +4805,14 @@ class TestMingFlashOmniTTSServing:
         assert error is not None
         assert "ref_audio" in error
 
-    def test_ming_flash_omni_tts_adapter_builds_prompt(self, ming_flash_omni_tts_server, mocker: MockerFixture):
-        ming_flash_omni_tts_server._build_ming_flash_omni_prompt = mocker.MagicMock(
-            return_value={
-                "prompt_token_ids": [1, 2, 3],
-                "additional_information": {"voice": ["test"]},
-            }
-        )
+    def test_ming_flash_omni_tts_adapter_builds_prompt(self, ming_flash_omni_tts_server):
         request = OpenAICreateSpeechRequest(input="Hello", voice="test")
         asyncio.run(ming_flash_omni_tts_server._prepare_speech_generation(request))
-        ming_flash_omni_tts_server._build_ming_flash_omni_prompt.assert_called_once()
+
+        prompt = ming_flash_omni_tts_server.engine_client.generate.call_args.kwargs["prompt"]
+        assert prompt["prompt_token_ids"] == [0]
+        assert prompt["additional_information"]["text"] == "Hello"
+        assert prompt["additional_information"]["voice_name"] == "test"
 
 
 @pytest.fixture
@@ -4880,10 +4901,6 @@ class TestDotsTTSServing:
 class TestTTSAsyncOffloading:
     """Tests for event-loop-safe offloading of blocking TTS operations."""
 
-    def test_build_voxtral_prompt_is_sync(self):
-        """_build_voxtral_prompt should be a regular function, not a coroutine."""
-        assert not asyncio.iscoroutinefunction(OmniOpenAIServingSpeech._build_voxtral_prompt)
-
     @pytest.fixture
     def voxtral_server(self, mocker: MockerFixture):
         mocker.patch(
@@ -4977,7 +4994,7 @@ class TestTTSAsyncOffloading:
 
     def test_prepare_speech_generation_awaits_voxtral_async(self, voxtral_server, mocker: MockerFixture):
         """Voxtral path in _prepare_speech_generation should call the async wrapper."""
-        voxtral_server._build_voxtral_prompt_async = mocker.AsyncMock(
+        voxtral_server._adapter._build_prompt_async = mocker.AsyncMock(
             return_value={
                 "prompt_token_ids": [1, 2, 3],
                 "additional_information": {"voice": ["test"]},
@@ -4985,19 +5002,19 @@ class TestTTSAsyncOffloading:
         )
         request = OpenAICreateSpeechRequest(input="hello", voice="test")
         asyncio.run(voxtral_server._prepare_speech_generation(request))
-        voxtral_server._build_voxtral_prompt_async.assert_awaited_once()
+        voxtral_server._adapter._build_prompt_async.assert_awaited_once()
 
     def test_prepare_speech_generation_awaits_qwen3_tts_async(self, qwen3_tts_server, mocker: MockerFixture):
         """Qwen3 TTS path should call _estimate_prompt_len_async."""
         qwen3_tts_server._adapter.validate = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._build_tts_params = mocker.MagicMock(
+        qwen3_tts_server._adapter._build_tts_params = mocker.MagicMock(
             return_value={"text": ["hello"], "task_type": ["CustomVoice"], "speaker": ["Vivian"]}
         )
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
         request = OpenAICreateSpeechRequest(input="hello")
         asyncio.run(qwen3_tts_server._prepare_speech_generation(request))
-        qwen3_tts_server._build_tts_params.assert_called_once()
-        qwen3_tts_server._estimate_prompt_len_async.assert_awaited_once()
+        qwen3_tts_server._adapter._build_tts_params.assert_called_once()
+        qwen3_tts_server._adapter._estimate_prompt_len_async.assert_awaited_once()
 
     def test_prepare_speech_generation_qwen3_default_seed_sets_tts_local_seed(
         self, qwen3_tts_server, mocker: MockerFixture
@@ -5007,10 +5024,10 @@ class TestTTSAsyncOffloading:
             SimpleNamespace(max_tokens=2048, seed=42, extra_args=None)
         ]
         qwen3_tts_server._adapter.validate = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._build_tts_params = mocker.MagicMock(
+        qwen3_tts_server._adapter._build_tts_params = mocker.MagicMock(
             return_value={"text": ["hello"], "task_type": ["CustomVoice"], "speaker": ["Vivian"]}
         )
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
         request = OpenAICreateSpeechRequest(input="hello")
 
         asyncio.run(qwen3_tts_server._prepare_speech_generation(request))
@@ -5058,10 +5075,10 @@ class TestTTSAsyncOffloading:
     def test_prepare_speech_generation_treats_sse_as_streaming(self, qwen3_tts_server, mocker: MockerFixture):
         """stream_format=sse should request delta-style multimodal outputs."""
         qwen3_tts_server._adapter.validate = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._build_tts_params = mocker.MagicMock(
+        qwen3_tts_server._adapter._build_tts_params = mocker.MagicMock(
             return_value={"text": ["hello"], "task_type": ["CustomVoice"], "speaker": ["Vivian"]}
         )
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
         mock_coerce = mocker.patch(
             "vllm_omni.entrypoints.openai.serving_speech.coerce_param_message_types",
             return_value=qwen3_tts_server.engine_client.default_sampling_params_list,
@@ -5075,10 +5092,10 @@ class TestTTSAsyncOffloading:
     def test_prepare_speech_generation_treats_stream_true_as_streaming(self, qwen3_tts_server, mocker: MockerFixture):
         """stream=True should request delta-style multimodal outputs for SSE streaming."""
         qwen3_tts_server._adapter.validate = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._build_tts_params = mocker.MagicMock(
+        qwen3_tts_server._adapter._build_tts_params = mocker.MagicMock(
             return_value={"text": ["hello"], "task_type": ["CustomVoice"], "speaker": ["Vivian"]}
         )
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
         mock_coerce = mocker.patch(
             "vllm_omni.entrypoints.openai.serving_speech.coerce_param_message_types",
             return_value=qwen3_tts_server.engine_client.default_sampling_params_list,
@@ -5092,10 +5109,10 @@ class TestTTSAsyncOffloading:
     def test_prepare_speech_generation_treats_audio_as_streaming(self, qwen3_tts_server, mocker: MockerFixture):
         """stream_format=audio should request delta-style multimodal outputs."""
         qwen3_tts_server._adapter.validate = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._build_tts_params = mocker.MagicMock(
+        qwen3_tts_server._adapter._build_tts_params = mocker.MagicMock(
             return_value={"text": ["hello"], "task_type": ["CustomVoice"], "speaker": ["Vivian"]}
         )
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
         mock_coerce = mocker.patch(
             "vllm_omni.entrypoints.openai.serving_speech.coerce_param_message_types",
             return_value=qwen3_tts_server.engine_client.default_sampling_params_list,
@@ -5112,10 +5129,10 @@ class TestTTSAsyncOffloading:
         """Full-payload TTS streaming should not request delta multimodal outputs."""
         qwen3_tts_server.engine_client.model_config.async_chunk = False
         qwen3_tts_server._adapter.validate = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._build_tts_params = mocker.MagicMock(
+        qwen3_tts_server._adapter._build_tts_params = mocker.MagicMock(
             return_value={"text": ["hello"], "task_type": ["CustomVoice"], "speaker": ["Vivian"]}
         )
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
         mock_coerce = mocker.patch(
             "vllm_omni.entrypoints.openai.serving_speech.coerce_param_message_types",
             return_value=qwen3_tts_server.engine_client.default_sampling_params_list,
@@ -5132,7 +5149,7 @@ class TestTTSAsyncOffloading:
         """FINAL_ONLY streaming for async_chunk=False is scoped to qwen3_tts only."""
         voxtral_server.engine_client.model_config.async_chunk = False
         mocker.patch.object(voxtral_server._get_tts_adapter(), "validate", return_value=None)
-        voxtral_server._build_voxtral_prompt_async = mocker.AsyncMock(
+        voxtral_server._adapter._build_prompt_async = mocker.AsyncMock(
             return_value={
                 "prompt_token_ids": [1, 2, 3],
                 "additional_information": {"voice": ["test"]},
@@ -5153,7 +5170,7 @@ class TestTTSAsyncOffloading:
     ):
         """VoiceDesign explicit false should reach the model prompt additional_information."""
         qwen3_tts_server._validate_tts_request = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
 
         request = OpenAICreateSpeechRequest(
             input="hello",
@@ -5176,7 +5193,7 @@ class TestTTSAsyncOffloading:
         qwen3_tts_server._validate_tts_request = mocker.MagicMock(return_value=None)
         qwen3_tts_server._resolve_ref_audio = mocker.AsyncMock(return_value=([0.0] * 48000, 24000, "fake_cache_key"))
         qwen3_tts_server._get_resolved_ref_audio_artifact_key = mocker.MagicMock(return_value=None)
-        qwen3_tts_server._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
+        qwen3_tts_server._adapter._estimate_prompt_len_async = mocker.AsyncMock(return_value=512)
 
         request = OpenAICreateSpeechRequest(
             input="hello",
@@ -5491,7 +5508,7 @@ class TestTTSAsyncOffloading:
         qwen3_tts_server._mark_ref_audio_artifact_ready_for_request("req-xvec")
 
         icl_params = {"task_type": ["Base"], "x_vector_only_mode": [False]}
-        assert qwen3_tts_server._qwen3_tts_can_use_ref_audio_artifact_only(icl_params, "artifact-a") is False
+        assert qwen3_tts_server._adapter._qwen3_tts_can_use_ref_audio_artifact_only(icl_params, "artifact-a") is False
 
     def test_qwen3_xvector_ready_artifact_still_reusable_by_xvector_request(self, qwen3_tts_server):
         qwen3_tts_server._put_resolved_ref_audio("ref-a", [0.0] * 8, 24000, "artifact-a")
@@ -5499,7 +5516,7 @@ class TestTTSAsyncOffloading:
         qwen3_tts_server._mark_ref_audio_artifact_ready_for_request("req-xvec")
 
         xvec_params = {"task_type": ["Base"], "x_vector_only_mode": [True]}
-        assert qwen3_tts_server._qwen3_tts_can_use_ref_audio_artifact_only(xvec_params, "artifact-a") is True
+        assert qwen3_tts_server._adapter._qwen3_tts_can_use_ref_audio_artifact_only(xvec_params, "artifact-a") is True
 
     def test_qwen3_icl_ready_artifact_enables_icl_artifact_only(self, qwen3_tts_server):
         qwen3_tts_server._put_resolved_ref_audio("ref-a", [0.0] * 8, 24000, "artifact-a")
@@ -5507,7 +5524,7 @@ class TestTTSAsyncOffloading:
         qwen3_tts_server._mark_ref_audio_artifact_ready_for_request("req-icl")
 
         icl_params = {"task_type": ["Base"], "x_vector_only_mode": [False]}
-        assert qwen3_tts_server._qwen3_tts_can_use_ref_audio_artifact_only(icl_params, "artifact-a") is True
+        assert qwen3_tts_server._adapter._qwen3_tts_can_use_ref_audio_artifact_only(icl_params, "artifact-a") is True
 
     def test_shutdown_is_idempotent(self, mocker: MockerFixture):
         """Calling shutdown() twice should not raise."""
diff --git a/tests/entrypoints/openai_api/test_tts_adapter.py b/tests/entrypoints/openai_api/test_tts_adapter.py
index d1a0174c3..a2c0ace0b 100644
--- a/tests/entrypoints/openai_api/test_tts_adapter.py
+++ b/tests/entrypoints/openai_api/test_tts_adapter.py
@@ -22,12 +22,14 @@ from vllm_omni.entrypoints.openai.tts_adapters import (
     detect_tts_model_type,
     resolve_adapter,
 )
+from vllm_omni.entrypoints.openai.tts_adapters.covo_audio import CovoAudioAdapter
 from vllm_omni.entrypoints.openai.tts_adapters.higgs_audio_v2 import HiggsAudioV2Adapter
 from vllm_omni.entrypoints.openai.tts_adapters.indextts2 import (
     IndexTTS2Adapter,
     IndexTTS25Adapter,
     indextts2_conditioning_cache_salt,
 )
+from vllm_omni.entrypoints.openai.tts_adapters.ming_flash_omni_tts import MingFlashOmniTTSAdapter
 from vllm_omni.entrypoints.openai.tts_adapters.moss_tts import (
     MossTTSAdapter,
     MossTTSNanoAdapter,
@@ -37,6 +39,8 @@ from vllm_omni.entrypoints.openai.tts_adapters.qwen3_tts import (
     Qwen3TTSAdapter,
     Qwen3TTSCodecLimitError,
 )
+from vllm_omni.entrypoints.openai.tts_adapters.step_audio2 import StepAudio2Adapter
+from vllm_omni.entrypoints.openai.tts_adapters.voxtral import VoxtralTTSAdapter
 from vllm_omni.model_executor.models.indextts2 import prompt_utils
 from vllm_omni.model_executor.models.indextts2.tokenizer_v2_5 import (
     INDEXTTS25_TOKENIZER_FILE,
@@ -125,8 +129,8 @@ def test_moss_tts_applies_request_max_new_tokens(adapter_cls):
 
 def _build_moss_tts_request(adapter_cls, mocker, *, request_seed):
     server = mocker.Mock()
-    server._build_moss_tts_params = mocker.AsyncMock(return_value={})
     adapter = adapter_cls(SpeechServingContext(server=server))
+    adapter._build_moss_tts_params = mocker.AsyncMock(return_value={})
     request = OpenAICreateSpeechRequest(input="hello", seed=request_seed)
 
     return asyncio.run(
@@ -167,6 +171,81 @@ def test_qwen3_tts_metadata():
     assert issubclass(Qwen3TTSAdapter, ARTTSAdapter)
 
 
+def test_qwen3_tts_build_constructs_prepared_request():
+    server = SimpleNamespace(_tts_executor=None, _tts_tokenizer=None, uploaded_speakers={})
+    engine_client = SimpleNamespace(model_config=SimpleNamespace())
+    adapter = Qwen3TTSAdapter(SimpleNamespace(server=server, engine_client=engine_client))
+
+    async def estimate_prompt_len(_tts_params):
+        return 3
+
+    adapter._estimate_prompt_len_async = estimate_prompt_len
+    request = OpenAICreateSpeechRequest(input="hello")
+
+    prepared = asyncio.run(adapter.build(request, [], False))
+
+    assert prepared.prompt["prompt_token_ids"] == [1, 1, 1]
+    assert prepared.prompt["additional_information"] is prepared.tts_params
+    assert prepared.tts_params["text"] == ["hello"]
+    assert prepared.tts_params["speaker"] == ["Vivian"]
+    assert prepared.model_type == "CustomVoice"
+
+
+def test_covo_audio_build_constructs_tokenized_prompt():
+    class FakeTokenizer:
+        def apply_chat_template(self, messages, *, tokenize, add_generation_prompt):
+            assert messages[-1] == {"role": "user", "content": "hello"}
+            assert tokenize is True
+            assert add_generation_prompt is True
+            return [11, 12, 13]
+
+    adapter = CovoAudioAdapter(SimpleNamespace(server=SimpleNamespace(), engine_client=SimpleNamespace()))
+    adapter._tokenizer = FakeTokenizer()
+
+    prepared = asyncio.run(adapter.build(OpenAICreateSpeechRequest(input="hello"), [], False))
+
+    assert prepared.prompt == {"prompt_token_ids": [11, 12, 13]}
+    assert prepared.tts_params == {}
+    assert prepared.model_type == "covo_audio"
+
+
+def test_ming_flash_omni_build_constructs_prepared_request():
+    server = SimpleNamespace(uploaded_speakers={})
+    adapter = MingFlashOmniTTSAdapter(SimpleNamespace(server=server, engine_client=SimpleNamespace()))
+    request = OpenAICreateSpeechRequest(input="hello", voice="test")
+
+    prepared = asyncio.run(adapter.build(request, [], False))
+
+    assert prepared.prompt["prompt_token_ids"] == [0]
+    info = prepared.prompt["additional_information"]
+    assert info["ming_task"] == "instruct"
+    assert info["text"] == "hello"
+    assert info["voice_name"] == "test"
+    assert prepared.model_type == "ming_flash_omni_tts"
+
+
+def test_step_audio2_build_constructs_open_assistant_turn():
+    adapter = StepAudio2Adapter(SimpleNamespace(server=SimpleNamespace(), engine_client=SimpleNamespace()))
+    request = OpenAICreateSpeechRequest(input="hello", instructions="Read warmly.")
+
+    prepared = asyncio.run(adapter.build(request, [], False))
+
+    assert "<|im_start|>system\nRead warmly.<|im_end|>" in prepared.prompt["prompt"]
+    assert prepared.prompt["prompt"].endswith("<|im_start|>assistant\n<tts_start>")
+    assert prepared.tts_params == {}
+    assert prepared.model_type == "step_audio2"
+
+
+def test_voxtral_prompt_builder_is_sync():
+    ctx = SimpleNamespace(
+        server=SimpleNamespace(_tts_executor=None),
+        engine_client=SimpleNamespace(),
+    )
+    adapter = VoxtralTTSAdapter(ctx)
+
+    assert not asyncio.iscoroutinefunction(adapter._build_prompt)
+
+
 @pytest.mark.parametrize(
     ("task_type", "text_tokens", "request_cap", "expected_cap"),
     [
diff --git a/tests/model_executor/models/higgs_audio_v3/test_higgs_audio_v3.py b/tests/model_executor/models/higgs_audio_v3/test_higgs_audio_v3.py
index 3fba046dc..3d43078ce 100755
--- a/tests/model_executor/models/higgs_audio_v3/test_higgs_audio_v3.py
+++ b/tests/model_executor/models/higgs_audio_v3/test_higgs_audio_v3.py
@@ -11,13 +11,15 @@ import asyncio
 import json
 import os
 import time
-from collections import OrderedDict, defaultdict
+from collections import defaultdict
 from types import SimpleNamespace
 
 import pytest
 import torch
 
 from tests.helpers.stage_config import get_deploy_config_path
+from vllm_omni.entrypoints.openai.tts_adapters.base import SpeechServingContext
+from vllm_omni.entrypoints.openai.tts_adapters.higgs_audio_v3 import HiggsAudioV3Adapter
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
@@ -1683,31 +1685,21 @@ class TestVoiceCloneReferenceCache:
         assert tok._resolve_audio_tokenizer_dir() is None
 
     def test_higgs_v3_ref_code_cache_returns_clone(self):
-        from vllm_omni.entrypoints.openai.serving_speech import OmniOpenAIServingSpeech
-
-        serving = object.__new__(OmniOpenAIServingSpeech)
-        serving._higgs_audio_v3_ref_code_cache = OrderedDict()
-        serving._higgs_audio_v3_ref_code_cache_bytes = 0
-        serving._higgs_audio_v3_ref_code_inflight = {}
+        adapter = HiggsAudioV3Adapter(SpeechServingContext(server=SimpleNamespace()))
 
         codes = torch.arange(16, dtype=torch.long).reshape(2, 8)
-        serving._put_higgs_audio_v3_ref_codes("ref-a", codes)
+        adapter._put_higgs_audio_v3_ref_codes("ref-a", codes)
 
-        cached = serving._get_higgs_audio_v3_ref_codes("ref-a")
+        cached = adapter._get_higgs_audio_v3_ref_codes("ref-a")
         assert cached is not None
         cached.fill_(0)
 
-        cached_again = serving._get_higgs_audio_v3_ref_codes("ref-a")
+        cached_again = adapter._get_higgs_audio_v3_ref_codes("ref-a")
         assert cached_again is not None
         assert torch.equal(cached_again, codes)
 
     def test_higgs_v3_ref_code_inflight_deduplicates_concurrent_encode(self):
-        from vllm_omni.entrypoints.openai.serving_speech import OmniOpenAIServingSpeech
-
-        serving = object.__new__(OmniOpenAIServingSpeech)
-        serving._higgs_audio_v3_ref_code_cache = OrderedDict()
-        serving._higgs_audio_v3_ref_code_cache_bytes = 0
-        serving._higgs_audio_v3_ref_code_inflight = {}
+        adapter = HiggsAudioV3Adapter(SpeechServingContext(server=SimpleNamespace()))
         calls = 0
 
         def encode_reference_audio(_wav, _sr):
@@ -1722,7 +1714,7 @@ class TestVoiceCloneReferenceCache:
         async def run():
             return await asyncio.gather(
                 *[
-                    serving._resolve_higgs_audio_v3_ref_codes(
+                    adapter._resolve_higgs_audio_v3_ref_codes(
                         "ref-a",
                         object(),
                         24000,
@@ -1741,7 +1733,7 @@ class TestVoiceCloneReferenceCache:
             assert torch.equal(codes, torch.arange(16, dtype=torch.long).reshape(2, 8) + 1)
 
         cached, cache_hit, inflight_wait = asyncio.run(
-            serving._resolve_higgs_audio_v3_ref_codes(
+            adapter._resolve_higgs_audio_v3_ref_codes(
                 "ref-a",
                 object(),
                 24000,
diff --git a/tests/model_executor/models/ming_tts/test_ming_shared_modules.py b/tests/model_executor/models/ming_tts/test_ming_shared_modules.py
index 21c8dfd14..3ec6662cc 100644
--- a/tests/model_executor/models/ming_tts/test_ming_shared_modules.py
+++ b/tests/model_executor/models/ming_tts/test_ming_shared_modules.py
@@ -1,12 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from types import SimpleNamespace
 
 import pytest
 import torch
 
-from vllm_omni.entrypoints.openai.serving_speech import OmniOpenAIServingSpeech
+from vllm_omni.entrypoints.openai.tts_adapters.base import SpeechServingContext
+from vllm_omni.entrypoints.openai.tts_adapters.ming_tts import (
+    MingTTSAdapter,
+    parse_ming_instruction_fields,
+)
 from vllm_omni.model_executor.models.common.ming.aggregator import Aggregator
 from vllm_omni.model_executor.models.common.ming.audio_vae import AudioVAEConfig
 from vllm_omni.model_executor.models.common.ming.cfm_cudagraph import CFMGraphExecutor, CFMSampler
@@ -174,19 +178,19 @@ def test_ming_dense_validation_rejects_semantic_audio_vae_config():
 
 def test_ming_instruction_parser_preserves_dense_and_flash_defaults():
     """Ming dense and Ming flash keep distinct instruction defaults."""
-    serving = object.__new__(OmniOpenAIServingSpeech)
-    serving.uploaded_speakers = {"uploaded": {}}
+    serving = SimpleNamespace(uploaded_speakers={"uploaded": {}})
+    adapter = MingTTSAdapter(SpeechServingContext(server=serving))
 
-    dense_plain = serving._parse_ming_instruction(SimpleNamespace(instructions="calm", language=None, voice=None))
+    dense_plain = adapter._parse_ming_instruction(SimpleNamespace(instructions="calm", language=None, voice=None))
     assert dense_plain == "calm"
 
-    dense_with_fields = serving._parse_ming_instruction(
+    dense_with_fields = adapter._parse_ming_instruction(
         SimpleNamespace(instructions="calm", language="Auto", voice="灵小甄")
     )
     assert dense_with_fields == {"IP": "灵小甄", "风格": "calm"}
 
-    flash_fields = serving._parse_ming_instruction_fields(
-        SimpleNamespace(instructions="calm", language="粤语", voice="灵小甄")
+    flash_fields = parse_ming_instruction_fields(
+        serving, SimpleNamespace(instructions="calm", language="粤语", voice="灵小甄")
     )
     assert flash_fields == {"风格": "calm"}
 
diff --git a/tools/pre_commit/check_tts_adapter.py b/tools/pre_commit/check_tts_adapter.py
index d46928662..199c46a30 100644
--- a/tools/pre_commit/check_tts_adapter.py
+++ b/tools/pre_commit/check_tts_adapter.py
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Ratchet on the TTS adapter migration (RFC #4327, #4855).
 
 Per-model behaviour is being moved out of ``serving_speech.py`` and into
@@ -28,7 +29,7 @@ REPO_ROOT = Path(__file__).resolve().parents[2]
 SERVING_SPEECH = Path("vllm_omni/entrypoints/openai/serving_speech.py")
 
 # Budgets. These may only go DOWN. See the module docstring.
-MAX_MODEL_TYPE_BRANCHES = 9
+MAX_MODEL_TYPE_BRANCHES = 6
 
 _GUIDANCE = """
 Move the per-model behaviour into its adapter under
diff --git a/vllm_omni/entrypoints/openai/serving_speech.py b/vllm_omni/entrypoints/openai/serving_speech.py
index 813d916fe..50f459a58 100644
--- a/vllm_omni/entrypoints/openai/serving_speech.py
+++ b/vllm_omni/entrypoints/openai/serving_speech.py
@@ -30,11 +30,9 @@ 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, StreamingAudioResampler
@@ -63,17 +61,6 @@ from vllm_omni.entrypoints.openai.tts_adapters import (
     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.outputs import OmniRequestOutput
 from vllm_omni.utils.speaker_cache import get_speaker_cache
 
@@ -93,12 +80,6 @@ _SPEECH_USAGE_INPUT_AUDIO_TOKENS_HEADER = "X-VLLM-OMNI-INPUT-AUDIO-TOKENS"
 # ``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.
@@ -109,12 +90,7 @@ _REF_AUDIO_METADATA_FETCH_ATTEMPTS = 3
 _REMOTE_REF_AUDIO_SCHEMES = frozenset({"http", "https", "data"})
 _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"
 
 
@@ -220,64 +196,6 @@ def _validate_path_within_directory(file_path: Path, directory: Path) -> bool:
         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.
-
-    Local ``ref_audio`` locators are the same class of gap: the raw request
-    hashes the path/URI string, so an on-disk rewrite is invisible to the
-    salt. Callers stash the content-aware key from ``_resolve_ref_audio`` as
-    ``ref_audio_cache_key`` (and ``ref_audio_2_cache_key`` / emotion keys
-    when those clips exist) so a file edit cannot reuse KV from the previous
-    reference.
-    """
-    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 (or that a locator string cannot see).
-    for key in (
-        "voice_created_at",
-        "task_type",
-        "speaker",
-        "ref_text",
-        "x_vector_only_mode",
-        "ref_audio_cache_key",
-        "ref_audio_2_cache_key",
-    ):
-        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
     _media_connector: MediaConnector | None = None
@@ -306,9 +224,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         # 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()
@@ -438,16 +353,14 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         # 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()
+
+        # Shared executor for blocking adapter preprocessing. It must exist
+        # before adapter construction so adapters can create their make_async
+        # wrappers during their own lifecycle initialization.
+        self._tts_executor = ThreadPoolExecutor(max_workers=1)
         # 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, capability
@@ -467,37 +380,14 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         available_speakers = self._get_available_speakers()
         logger.info("Loaded %d supported speakers: %s", len(available_speakers), sorted(available_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()
 
         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]] = {}
 
         # Batch configuration
         self._batch_max_items: int = getattr(self.engine_client, "tts_batch_max_items", 32)
 
-        # 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)
-
     def _get_tts_adapter(self):
         """Return the per-model serving adapter for the current ``_tts_model_type``.
 
@@ -622,73 +512,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         """Get all voice names accepted by the API, including the placeholder default."""
         return self._get_available_speakers() | {_DEFAULT_VOICE_NAME}
 
-    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).
-        """
-        codec_frame_rate = self._adapter.capabilities.codec_frame_rate
-        if 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 * 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.
 
@@ -784,41 +607,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
             _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.
 
@@ -828,41 +616,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         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._adapter.capabilities.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()
@@ -1330,25 +1083,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
             return "sample_rate is not supported by the current TTS model"
         return None
 
-    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):
@@ -1358,445 +1092,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
             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
-
-    def _get_moss_ref_encoder(self):
-        """Lazily build the per-server MOSS reference-audio encoder once."""
-        cached = getattr(self, "_moss_ref_encoder", None)
-        if cached is not None:
-            return cached
-        from vllm_omni.model_executor.models.moss_tts.reference_encoder import build_reference_encoder
-
-        # The variant's encode geometry (n_vq, working sample rate) is derived
-        # inside the model package; this layer only supplies the processor and
-        # the process-wide speaker cache.
-        encoder = build_reference_encoder(
-            self._get_moss_processor(),
-            variant=self._moss_variant,
-            speaker_cache=self._speaker_cache,
-        )
-        self._moss_ref_encoder = encoder
-        return encoder
-
-    async def _build_moss_tts_params(
-        self,
-        request: OpenAICreateSpeechRequest,
-        *,
-        has_inline_ref_audio: bool = False,
-    ) -> 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, cache_key = await self._resolve_ref_audio(request.ref_audio)
-            params["prompt_audio_array"] = [[wav_list, sr]]
-            params["ref_audio_cache_key"] = cache_key
-            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, cache_key = await self._resolve_ref_audio(request.ref_audio)
-            params["prompt_audio_array"] = [[wav_list, sr]]
-            params["ref_audio_cache_key"] = cache_key
-            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()
-
-        user_kwargs: dict[str, Any] = {"text": request.input or ""}
-        resolve_keys: dict[int, str] = {}
-        if v in ("tts", "local", "ttsd"):
-            user_kwargs["reference"], resolve_keys = await self._encode_moss_references(
-                request,
-                has_inline_ref_audio=has_inline_ref_audio,
-                two_speaker=(v == "ttsd"),
-            )
-        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]
-        if 0 in resolve_keys:
-            params["ref_audio_cache_key"] = resolve_keys[0]
-        if 1 in resolve_keys:
-            params["ref_audio_2_cache_key"] = resolve_keys[1]
-        return params
-
-    async def _encode_moss_references(
-        self,
-        request: OpenAICreateSpeechRequest,
-        *,
-        has_inline_ref_audio: bool,
-        two_speaker: bool,
-    ) -> tuple[list, dict[int, str]]:
-        """Encode the request's reference clip(s) into MOSS RVQ code tensors.
-
-        Reference 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. This method only resolves the serving-side
-        inputs the model helper needs: the generic audio resolver/artifact-key
-        lookups and whether the request may use the named-voice cache.
-
-        Returns ``(codes_per_speaker, resolve_keys)`` where ``resolve_keys``
-        maps the reference slot (0 = ref_audio, 1 = ref_audio_2) to its
-        content-aware resolve key, for salting the KV prefix cache.
-        """
-        from vllm_omni.model_executor.models.moss_tts.reference_encoder import encode_request_references
-
-        # Named-voice caching is only valid for uploaded speakers without an
-        # inline ref_audio: ``request.voice`` plus a file/URL would otherwise
-        # key on (name, created_at=0) and skip the content-aware resolve.
-        raw_voice = getattr(request, "voice", None)
-        raw_voice = raw_voice.strip() if isinstance(raw_voice, str) else ""
-        voice_lower = raw_voice.lower()
-        use_named_voice = bool(voice_lower) and voice_lower in self.uploaded_speakers and not has_inline_ref_audio
-        voice = voice_lower if use_named_voice else ""
-        voice_created = self._voice_created_at(voice) if voice else 0
-
-        return await encode_request_references(
-            self._get_moss_ref_encoder(),
-            request.ref_audio,
-            request.ref_audio_2 if two_speaker else None,
-            resolve_ref_audio=self._resolve_ref_audio,
-            get_artifact_key=self._get_resolved_ref_audio_artifact_key,
-            voice_name=voice or None,
-            voice_created_at=voice_created,
-        )
-
-    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, and
-        records reference-audio prompt positions while submitting valid token IDs.
-        """
-        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, cache_key = await self._resolve_ref_audio(request.ref_audio)
-        artifact_key = self._get_resolved_ref_audio_artifact_key(cache_key)
-        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_ids, audio_placeholder_positions = adapter.prepare_prompt_for_engine(prompt_ids)
-        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),
-            "audio_placeholder_positions": audio_placeholder_positions,
-            "ref_audio_cache_key": cache_key,
-        }
-        # Placeholder prompt ids do not change when a same-path file is
-        # rewritten at the same duration; fold the content-aware resolve key
-        # into cache_salt so prefix caching cannot reuse the previous KV.
-        prompt["cache_salt"] = _conditioning_cache_salt(request, prompt["additional_information"])
-        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
-
     @staticmethod
     def _local_ref_audio_stat_path(ref_audio_str: str) -> str | None:
         """Filesystem path to stat for a local locator, else ``None``.
@@ -2043,14 +1338,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
     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:
@@ -2075,145 +1362,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
             resolved.append((wav_list, sr))
         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,
@@ -2542,534 +1690,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         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()
-            precomputed_speakers = self._adapter.capabilities.precomputed_speakers if self._adapter is not None else {}
-            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 precomputed_speakers and request.ref_audio is None:
-                profile = 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, cache_key = await self._resolve_ref_audio(ref_audio_source)
-            tts_params["ref_audio_cache_key"] = cache_key
-            artifact_key = self._get_resolved_ref_audio_artifact_key(cache_key)
-            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,
@@ -3189,18 +1809,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
                 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.
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/audex.py b/vllm_omni/entrypoints/openai/tts_adapters/audex.py
index ab7c7c25e..0e31b633f 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/audex.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/audex.py
@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Audex (Nemotron-Labs-Audex-2B) TTS serving adapter."""
 
+import os
 from typing import TYPE_CHECKING, Any
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
@@ -29,6 +31,30 @@ class AudexAdapter(ARTTSAdapter):
     stage_keys = frozenset({"audex_thinker", "audex_omni"})
     name = "audex"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._tokenizer = None
+
+    def _get_tokenizer(self):
+        """Load and cache the tokenizer used only by the Audex TTS adapter."""
+        if self._tokenizer is None:
+            from transformers import AutoTokenizer
+
+            from vllm_omni.model_executor.models.audex.checkpoint import ensure_audex_snapshot
+
+            stage = getattr(getattr(self.ctx.server._tts_stage, "engine_args", None), "model_stage", None)
+            profile, folder = (
+                ("full", "checkpoint_folder_full") if stage == "audex_omni" else ("tts", "checkpoint_folder_audiogen")
+            )
+            model_path = os.path.normpath(self.ctx.engine_client.model_config.model)
+            root = (
+                os.path.dirname(model_path)
+                if os.path.basename(model_path).startswith("checkpoint_folder")
+                else ensure_audex_snapshot(model_path, profile=profile)
+            )
+            self._tokenizer = AutoTokenizer.from_pretrained(os.path.join(root, folder))
+        return self._tokenizer
+
     @classmethod
     def stage_serves_speech(cls, model_stage: str | None, all_stage_keys: frozenset[str]) -> bool:
         """``audex_omni`` covers the audex_s2s deployment, whose TTS pass uses
@@ -102,7 +128,6 @@ class AudexAdapter(ARTTSAdapter):
         element of the engine's shared ``default_sampling_params_list`` —
         see ``_inject_audex_tta_args``).
         """
-        server = self.ctx.server
         import copy
 
         stage0_params = copy.deepcopy(stage0_params)
@@ -121,7 +146,7 @@ class AudexAdapter(ARTTSAdapter):
         cond_prompt = prompt.get("prompt") if isinstance(prompt, dict) else prompt
         if not isinstance(cond_prompt, str) or not cond_prompt:
             raise ValueError("Audex CFG requires the adapter-built text prompt")
-        null_prompt = build_null_prompt(cond_prompt, server._get_audex_tokenizer())
+        null_prompt = build_null_prompt(cond_prompt, self._get_tokenizer())
         extra_args.update(
             {
                 "cfg_scale": cfg_scale,
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/audex_tta.py b/vllm_omni/entrypoints/openai/tts_adapters/audex_tta.py
index 33dd2294a..b0af5cdca 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/audex_tta.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/audex_tta.py
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Audex (Nemotron-Labs-Audex-2B) text-to-audio serving adapter.
 
 Caption → general audio through the ``audex_tta`` pipeline.
@@ -8,6 +9,7 @@ completes the sampling parameters with the RVQ phase-mask contract
 scale 3.0 — guidance is effectively mandatory for TTA quality).
 """
 
+import os
 from typing import TYPE_CHECKING, Any
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
@@ -32,6 +34,27 @@ class AudexTTAAdapter(ARTTSAdapter):
     stage_keys = frozenset({"audex_tta_thinker"})
     name = "audex_tta"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._tokenizer = None
+        self._tta_rvq = None
+
+    def _get_tokenizer(self):
+        """Load and cache the tokenizer used only by the Audex TTA adapter."""
+        if self._tokenizer is None:
+            from transformers import AutoTokenizer
+
+            from vllm_omni.model_executor.models.audex.checkpoint import ensure_audex_snapshot
+
+            model_path = os.path.normpath(self.ctx.engine_client.model_config.model)
+            root = (
+                os.path.dirname(model_path)
+                if os.path.basename(model_path).startswith("checkpoint_folder")
+                else ensure_audex_snapshot(model_path, profile="tta")
+            )
+            self._tokenizer = AutoTokenizer.from_pretrained(os.path.join(root, "checkpoint_folder_audiogen"))
+        return self._tokenizer
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         if not request.input or not request.input.strip():
             return "Audex TTA requires a non-empty caption"
@@ -95,7 +118,6 @@ class AudexTTAAdapter(ARTTSAdapter):
         state (``cfg_pair_id``/``cfg_null_prompt``) into it would race
         concurrent requests and leak stale CFG metadata into later ones.
         """
-        server = self.ctx.server
         import copy
 
         from vllm_omni.model_executor.models.audex.prompt import build_tta_null_prompt
@@ -106,10 +128,10 @@ class AudexTTAAdapter(ARTTSAdapter):
             raise ValueError("Audex TTA requires the adapter-built caption prompt")
 
         stage0_params = copy.deepcopy(stage0_params)
-        tokenizer = server._get_audex_tokenizer()
-        if server._audex_tta_rvq is None:
+        tokenizer = self._get_tokenizer()
+        if self._tta_rvq is None:
             phase_token_ids, start_tid, end_tid = build_tta_phase_token_ids(tokenizer)
-            server._audex_tta_rvq = {
+            self._tta_rvq = {
                 "phase_token_ids": phase_token_ids,
                 "start_tid": start_tid,
                 "end_tid": end_tid,
@@ -123,7 +145,7 @@ class AudexTTAAdapter(ARTTSAdapter):
         if extra_args is None:
             extra_args = {}
             stage0_params.extra_args = extra_args
-        extra_args["tta_rvq"] = server._audex_tta_rvq
+        extra_args["tta_rvq"] = self._tta_rvq
 
         cfg_scale = extra_args.get("cfg_scale")
         cfg_scale = 3.0 if cfg_scale is None else float(cfg_scale)
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/base.py b/vllm_omni/entrypoints/openai/tts_adapters/base.py
index d4316a2fe..ff43b27a5 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/base.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/base.py
@@ -11,8 +11,9 @@ instead of editing the shared serving module in ~10 scattered places.
 See the RFC for the full design (issue #4327).
 """
 
+import hashlib
 from abc import ABC, abstractmethod
-from collections.abc import Callable, Mapping
+from collections.abc import Mapping
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, ClassVar
 
@@ -37,22 +38,52 @@ DEFAULT_TTS_LANGUAGES = frozenset(
     }
 )
 
-_conditioning_cache_salt_fn: "Callable[..., str] | None" = None
 
+def conditioning_cache_salt(request: "OpenAICreateSpeechRequest", tts_params: dict | None = None) -> str:
+    """Stable hash of the real Stage 0 conditioning for the prefix cache.
 
-def conditioning_cache_salt(request: "OpenAICreateSpeechRequest", tts_params: dict) -> str:
-    """Return the conditioning cache salt for ``request`` + ``tts_params``.
+    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.
 
-    Lazily imports and caches ``serving_speech._conditioning_cache_salt`` on first
-    use: the import is deferred to break the adapters<->serving_speech import
-    cycle, and cached so it resolves once instead of on every ``build()`` call.
+    Raw request fields alone are not enough for uploaded voices: resolved
+    conditioning such as ``voice_created_at`` and content-aware ref-audio
+    cache keys must also be folded in. This distinguishes delete/re-upload of
+    the same voice and same-path local audio rewrites without hashing decoded
+    waveform arrays.
     """
-    global _conditioning_cache_salt_fn
-    if _conditioning_cache_salt_fn is None:
-        from vllm_omni.entrypoints.openai.serving_speech import _conditioning_cache_salt
-
-        _conditioning_cache_salt_fn = _conditioning_cache_salt
-    return _conditioning_cache_salt_fn(request, tts_params)
+    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 conditioning derived by adapters and absent from the raw request.
+    for key in (
+        "voice_created_at",
+        "task_type",
+        "speaker",
+        "ref_text",
+        "x_vector_only_mode",
+        "ref_audio_cache_key",
+        "ref_audio_2_cache_key",
+    ):
+        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]
 
 
 def apply_max_new_tokens(
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/cosyvoice3.py b/vllm_omni/entrypoints/openai/tts_adapters/cosyvoice3.py
index ec1c145c7..25993ed6e 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/cosyvoice3.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/cosyvoice3.py
@@ -1,9 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """CosyVoice3 TTS serving adapter."""
 
 import os
 from typing import TYPE_CHECKING, Any
 
+import numpy as np
 from vllm.logger import init_logger
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
@@ -14,12 +16,46 @@ if TYPE_CHECKING:
 
 logger = init_logger(__name__)
 
+# Without this delimiter the talker re-speaks the reference transcript instead
+# of emitting target-only speech (issue #4644). Matches the offline example and
+# upstream demo.
+_PROMPT_DELIMITER = "<|endofprompt|>"
+_PROMPT_PREFIX = f"You are a helpful assistant.{_PROMPT_DELIMITER}"
+
 
 @register_tts_adapter
 class CosyVoice3Adapter(ARTTSAdapter):
     stage_keys = frozenset({"cosyvoice3_talker"})
     name = "cosyvoice3"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        # Cache the resolved Qwen tokenizer used for dynamic-token sizing.
+        # Rebuilding it would repeat snapshot resolution and add ~100 ms to the
+        # TTFP-critical sampling override on every request.
+        self._tokenizer = None
+
+    async def _build_prompt(
+        self, request: "OpenAICreateSpeechRequest", *, has_inline_ref_audio: bool = False
+    ) -> dict[str, Any]:
+        """Build the multimodal CosyVoice3 voice-cloning prompt."""
+        server = self.ctx.server
+        wav_samples, sr, _ = await server._resolve_ref_audio(request.ref_audio)
+        ref_text = request.ref_text or ""
+        if _PROMPT_DELIMITER not in ref_text:
+            ref_text = f"{_PROMPT_PREFIX}{ref_text}"
+        mm_kwargs: dict[str, Any] = {"prompt_text": ref_text, "sample_rate": sr}
+        if request.voice:
+            voice_lower = request.voice.lower()
+            if voice_lower in server.uploaded_speakers and not has_inline_ref_audio:
+                mm_kwargs["voice_name"] = voice_lower
+                mm_kwargs["voice_created_at"] = server._voice_created_at(voice_lower)
+        return {
+            "prompt": request.input,
+            "multi_modal_data": {"audio": (np.asarray(wav_samples, dtype=np.float32), sr)},
+            "mm_processor_kwargs": mm_kwargs,
+        }
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         """Validate CosyVoice3 request parameters. Returns error message or None."""
         server = self.ctx.server
@@ -51,7 +87,7 @@ class CosyVoice3Adapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt = await self.ctx.server._build_cosyvoice3_prompt(request, has_inline_ref_audio=has_inline_ref_audio)
+        prompt = await self._build_prompt(request, has_inline_ref_audio=has_inline_ref_audio)
         return PreparedRequest(prompt=prompt, tts_params={}, model_type="cosyvoice3")
 
     def apply_sampling_overrides(
@@ -76,7 +112,7 @@ class CosyVoice3Adapter(ARTTSAdapter):
         hf_cfg = server.model_config.hf_config
         # Build the Qwen tokenizer once per process (resolving the model dir via
         # snapshot_download at most once) and reuse it across requests.
-        tokenizer = server._cosyvoice3_tokenizer
+        tokenizer = self._tokenizer
         if tokenizer is None:
             model_path = server.engine_client.model_config.model
             if not os.path.isdir(model_path):
@@ -88,7 +124,7 @@ class CosyVoice3Adapter(ARTTSAdapter):
                 skip_special_tokens=hf_cfg.skip_special_tokens,
                 version=hf_cfg.version,
             )
-            server._cosyvoice3_tokenizer = tokenizer
+            self._tokenizer = tokenizer
         _, text_token_len = extract_text_token(
             request.input,
             tokenizer,
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/covo_audio.py b/vllm_omni/entrypoints/openai/tts_adapters/covo_audio.py
index 28e64bc2a..a9840b4a0 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/covo_audio.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/covo_audio.py
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """CoVo-Audio serving adapter."""
 
 from typing import TYPE_CHECKING
@@ -15,6 +16,10 @@ class CovoAudioAdapter(ARTTSAdapter):
     stage_keys = frozenset({"fused_thinker_talker"})
     name = "covo_audio"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._tokenizer = None
+
     @classmethod
     def matches(cls, model_stage: str | None, model_arch: str | None) -> bool:
         """``fused_thinker_talker`` is a generic stage key that non-CoVo fused
@@ -30,5 +35,21 @@ class CovoAudioAdapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt = self.ctx.server._build_covo_audio_prompt(request)
-        return PreparedRequest(prompt=prompt, tts_params={}, model_type="covo_audio")
+        """Build the tokenized chat prompt expected by Covo-Audio-Chat.
+
+        The model requires a specific system prompt instructing it to
+        interleave text and audio tokens. Passing token IDs avoids a second
+        engine-side tokenization step.
+        """
+        from transformers import AutoTokenizer
+
+        from vllm_omni.model_executor.models.covo_audio.prompt_utils import build_covo_audio_prompt_token_ids
+
+        if self._tokenizer is None:
+            model_name = self.ctx.engine_client.model_config.model
+            try:
+                self._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._tokenizer, request.input)
+        return PreparedRequest(prompt={"prompt_token_ids": prompt_ids}, tts_params={}, model_type="covo_audio")
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/fish_speech.py b/vllm_omni/entrypoints/openai/tts_adapters/fish_speech.py
index a6426a13d..f6dc84d52 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/fish_speech.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/fish_speech.py
@@ -1,8 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Fish Speech serving adapter (retires the legacy ``_is_fish_speech`` flag)."""
 
+import math
 from typing import TYPE_CHECKING, Any
 
+import numpy as np
+import torch
+from vllm.inputs import tokens_input
+from vllm.utils.async_utils import make_async
+
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
 from vllm_omni.entrypoints.openai.tts_adapters.base import (
     ARTTSAdapter,
@@ -20,6 +27,101 @@ class FishSpeechAdapter(ARTTSAdapter):
     stage_keys = frozenset({"fish_speech_slow_ar"})
     name = "fish_tts"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._tokenizer = None
+        self._build_prompt_async = make_async(self._build_prompt, executor=getattr(ctx.server, "_tts_executor", None))
+
+    @staticmethod
+    def _estimate_ref_code_len(ref_audio: object) -> int | None:
+        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)
+        if sr <= 0 or len(wav) <= 0:
+            return None
+        return max(1, math.ceil(max(1, math.ceil(len(wav) * DAC_SAMPLE_RATE / sr)) / DAC_HOP_LENGTH))
+
+    def _get_tokenizer(self):
+        if self._tokenizer is None:
+            from transformers import AutoTokenizer
+
+            self._tokenizer = AutoTokenizer.from_pretrained(
+                self.ctx.engine_client.model_config.model, trust_remote_code=True
+            )
+        return self._tokenizer
+
+    def _estimate_prompt_len(self, text: str, ref_text: str, ref_audio: object) -> int:
+        from vllm_omni.model_executor.models.fish_speech.prompt_utils import (
+            estimate_fish_voice_clone_prompt_len_from_normalized,
+        )
+
+        try:
+            semantic_len = self._estimate_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(
+                self._get_tokenizer(), text, ref_text, semantic_len
+            )
+        except Exception as exc:
+            import logging
+
+            logging.getLogger(__name__).warning(
+                "Failed to estimate Fish Speech prompt length, using fallback 2048: %s", exc
+            )
+            return 2048
+
+    def _build_prompt(
+        self,
+        request: "OpenAICreateSpeechRequest",
+        ref_audio_data: tuple[list[float], int] | None,
+        has_inline_ref_audio: bool,
+    ) -> dict[str, Any]:
+        """Build either the text-only or structured Fish Speech clone prompt.
+
+        Structured clone metadata uses concrete scalar fields because model-side
+        preprocess consumes one request directly; text-only metadata retains the
+        legacy single-item list representation used across EngineCore IPC.
+        """
+        from vllm_omni.model_executor.models.fish_speech.prompt_utils import (
+            build_fish_text_only_prompt_ids,
+            normalize_fish_voice_clone_texts,
+        )
+
+        tokenizer = self._get_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)
+            info: dict[str, Any] = {"text": [normalized_text]}
+            if request.max_new_tokens is not None:
+                info["max_new_tokens"] = [request.max_new_tokens]
+            prompt = tokens_input(prompt_token_ids=prompt_ids)
+            prompt["additional_information"] = info
+            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_prompt_len(normalized_text, normalized_ref_text, ref_audio_data)
+        info = {
+            "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,
+        }
+        server = self.ctx.server
+        if request.voice is not None:
+            voice_lower = request.voice.lower()
+            if voice_lower in server.uploaded_speakers and not has_inline_ref_audio:
+                info["voice_name"] = voice_lower
+                info["voice_created_at"] = server._voice_created_at(voice_lower)
+        if request.max_new_tokens is not None:
+            info["max_new_tokens"] = request.max_new_tokens
+        prompt = tokens_input(prompt_token_ids=[1] * ph_len)
+        prompt["additional_information"] = info
+        return prompt
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         """Validate Fish Speech request parameters. Returns error message or None."""
         server = self.ctx.server
@@ -54,9 +156,7 @@ class FishSpeechAdapter(ARTTSAdapter):
             wav_list, sr, cache_key = await server._resolve_ref_audio(request.ref_audio)
             ref_audio_data = (wav_list, sr)
             tts_params["ref_audio_cache_key"] = cache_key
-        prompt = await server._build_fish_speech_prompt_async(
-            request, ref_audio_data=ref_audio_data, has_inline_ref_audio=has_inline_ref_audio
-        )
+        prompt = await self._build_prompt_async(request, ref_audio_data, has_inline_ref_audio)
         prompt["cache_salt"] = conditioning_cache_salt(request, tts_params)
         return PreparedRequest(prompt=prompt, tts_params=tts_params, model_type="fish_speech")
 
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/glm_tts.py b/vllm_omni/entrypoints/openai/tts_adapters/glm_tts.py
index e65edb01b..22dbfc6b6 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/glm_tts.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/glm_tts.py
@@ -1,8 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """GLM-TTS serving adapter."""
 
 from typing import TYPE_CHECKING, Any
 
+import numpy as np
 from vllm.logger import init_logger
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
@@ -19,6 +21,72 @@ class GlmTTSAdapter(ARTTSAdapter):
     stage_keys = frozenset({"glm_tts"})
     name = "glm_tts"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._text_tokenizer = None
+        self._text_frontend = None
+
+    def _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
+
+        if self._text_tokenizer is None:
+            config = self.ctx.engine_client.model_config
+            tokenizer_path = getattr(config, "tokenizer", None) or resolve_glm_tts_tokenizer_path(config.model)
+            self._text_tokenizer = load_glm_tts_tokenizer(
+                tokenizer_path,
+                model_name_or_path=config.model,
+                trust_remote_code=bool(getattr(config, "trust_remote_code", False)),
+            )
+        if self._text_frontend is None:
+            self._text_frontend = GLMTTSTextFrontend()
+        return self._text_tokenizer, self._text_frontend
+
+    def _estimate_text_token_len(self, text: str | None, *, add_trailing_space: bool = False) -> int:
+        tokenizer, frontend = self._text_tokenizer_and_frontend()
+        normalized = (frontend.text_normalize(text or "") or text or "").strip()
+        if add_trailing_space and normalized:
+            normalized = f"{normalized} "
+        return max(1, len(tokenizer.encode(normalized)))
+
+    def _build_prefill_metadata(self, text: str, prompt_text: str | None) -> dict[str, Any]:
+        text_len = self._estimate_text_token_len(text)
+        prompt_len = self._estimate_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_len],
+            "input_len": [prompt_len + text_len + 1],
+        }
+
+    async def _build_prompt(
+        self, request: "OpenAICreateSpeechRequest", *, has_inline_ref_audio: bool = False
+    ) -> dict[str, Any]:
+        """Build GLM-TTS multimodal voice-cloning input and prefill metadata.
+
+        AR preprocess constructs PromptText, Text, BOA and prompt speech tokens;
+        the DiT consumes the resulting prompt tokens, features and embedding as
+        conditioning.
+        """
+        server = self.ctx.server
+        if request.ref_audio is None or not request.ref_text:
+            raise ValueError("GLM-TTS requires ref_audio and ref_text for voice cloning.")
+        wav_samples, sr, _ = await server._resolve_ref_audio(request.ref_audio)
+        mm_kwargs: dict[str, Any] = {"prompt_text": request.ref_text}
+        if request.voice:
+            voice_lower = request.voice.lower()
+            if voice_lower in server.uploaded_speakers and not has_inline_ref_audio:
+                mm_kwargs["voice_name"] = voice_lower
+                mm_kwargs["voice_created_at"] = server._voice_created_at(voice_lower)
+        return {
+            "prompt": request.input,
+            "multi_modal_data": {"audio": (np.asarray(wav_samples, dtype=np.float32), int(sr))},
+            "mm_processor_kwargs": mm_kwargs,
+            "additional_information": self._build_prefill_metadata(request.input, request.ref_text),
+        }
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         """Validate GLM-TTS request — requires ref_audio for voice cloning."""
         server = self.ctx.server
@@ -46,7 +114,7 @@ class GlmTTSAdapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt = await self.ctx.server._build_glm_tts_prompt(request, has_inline_ref_audio=has_inline_ref_audio)
+        prompt = await self._build_prompt(request, has_inline_ref_audio=has_inline_ref_audio)
         return PreparedRequest(prompt=prompt, tts_params={}, model_type="glm_tts")
 
     def _load_supported_speakers(self) -> set[str]:
@@ -71,9 +139,7 @@ class GlmTTSAdapter(ARTTSAdapter):
             if isinstance(text_len_value, list) and text_len_value:
                 text_len_value = text_len_value[0]
         text_token_len = (
-            int(text_len_value)
-            if text_len_value is not None
-            else server._estimate_glm_tts_text_token_len(request.input)
+            int(text_len_value) if text_len_value is not None else self._estimate_text_token_len(request.input)
         )
         hf_cfg = server.model_config.hf_config
         min_ratio = getattr(hf_cfg, "min_token_text_ratio", 2)
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v2.py b/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v2.py
index 74ec591cc..207d8b47b 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v2.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v2.py
@@ -1,8 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Higgs-Audio v2 serving adapter."""
 
+import asyncio
 from typing import TYPE_CHECKING, Any
 
+import numpy as np
+from vllm.inputs import tokens_input
+
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
 from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest, apply_max_new_tokens
 
@@ -12,6 +17,89 @@ if TYPE_CHECKING:
 
 @register_tts_adapter
 class HiggsAudioV2Adapter(ARTTSAdapter):
+    @property
+    def engine_client(self):
+        return self.ctx.engine_client
+
+    async def _resolve_ref_audio(self, ref_audio: str):
+        return await self.ctx.server._resolve_ref_audio(ref_audio)
+
+    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 ----
+
     stage_keys = frozenset({"higgs_audio_v2"})
     name = "higgs_audio_v2"
 
@@ -91,7 +179,7 @@ class HiggsAudioV2Adapter(ARTTSAdapter):
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
         server = self.ctx.server
-        prompt = await server._build_higgs_audio_v2_params(request)
+        prompt = await self._build_higgs_audio_v2_params(request)
         if request.voice:
             voice_lower = request.voice.lower()
             if voice_lower in server.uploaded_speakers and not has_inline_ref_audio:
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v3.py b/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v3.py
index 411d97f31..eb00fe784 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v3.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/higgs_audio_v3.py
@@ -1,10 +1,25 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Higgs-Audio v3 serving adapter."""
 
+import asyncio
+from collections import OrderedDict
 from typing import TYPE_CHECKING, Any
 
+import numpy as np
+import torch
+from vllm.inputs import tokens_input
+
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
-from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest, apply_max_new_tokens
+from vllm_omni.entrypoints.openai.tts_adapters.base import (
+    ARTTSAdapter,
+    PreparedRequest,
+    apply_max_new_tokens,
+    conditioning_cache_salt,
+)
+
+_REF_CODE_CACHE_MAX_ENTRIES = 256
+_REF_CODE_CACHE_MAX_BYTES = 64 * 1024 * 1024
 
 if TYPE_CHECKING:
     from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
@@ -12,6 +27,159 @@ if TYPE_CHECKING:
 
 @register_tts_adapter
 class HiggsAudioV3Adapter(ARTTSAdapter):
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        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]] = {}
+
+    @property
+    def engine_client(self):
+        return self.ctx.engine_client
+
+    async def _resolve_ref_audio(self, ref_audio: str):
+        return await self.ctx.server._resolve_ref_audio(ref_audio)
+
+    def _get_resolved_ref_audio_artifact_key(self, cache_key: str):
+        return self.ctx.server._get_resolved_ref_audio_artifact_key(cache_key)
+
+    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, and
+        records reference-audio prompt positions while submitting valid token IDs.
+        """
+        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, cache_key = await self._resolve_ref_audio(request.ref_audio)
+        artifact_key = self._get_resolved_ref_audio_artifact_key(cache_key)
+        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_ids, audio_placeholder_positions = adapter.prepare_prompt_for_engine(prompt_ids)
+        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),
+            "audio_placeholder_positions": audio_placeholder_positions,
+            "ref_audio_cache_key": cache_key,
+        }
+        prompt["cache_salt"] = conditioning_cache_salt(request, prompt["additional_information"])
+        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 _REF_CODE_CACHE_MAX_ENTRIES <= 0 or _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 > _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) > _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 > _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
+
     stage_keys = frozenset({"higgs_audio_v3"})
     name = "higgs_audio_v3"
 
@@ -34,7 +202,7 @@ class HiggsAudioV3Adapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt = await self.ctx.server._build_higgs_audio_v3_params(request)
+        prompt = await self._build_higgs_audio_v3_params(request)
         return PreparedRequest(prompt=prompt, tts_params={}, model_type="higgs_audio_v3")
 
     def apply_sampling_overrides(
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/ming_flash_omni_tts.py b/vllm_omni/entrypoints/openai/tts_adapters/ming_flash_omni_tts.py
index bc084d6b4..05cd3a64e 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/ming_flash_omni_tts.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/ming_flash_omni_tts.py
@@ -1,12 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """MingFlashOmniTTS serving adapter."""
 
 from typing import TYPE_CHECKING
 
+from vllm.inputs import tokens_input
 from vllm.logger import init_logger
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
 from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest
+from vllm_omni.entrypoints.openai.tts_adapters.ming_tts import parse_ming_instruction_fields
 
 if TYPE_CHECKING:
     from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
@@ -61,8 +64,36 @@ class MingFlashOmniTTSAdapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
+        """Build the standalone Ming Flash TTS prompt."""
+        from vllm_omni.model_executor.models.ming_flash_omni.prompt_utils import (
+            DEFAULT_PROMPT,
+            create_instruction,
+        )
+
         server = self.ctx.server
-        prompt = server._build_ming_flash_omni_prompt(request)
+        # ``instructions`` accepts either plain style text or a JSON object.
+        # Unknown caption keys are filtered by ``create_instruction``.
+        caption_fields = parse_ming_instruction_fields(server, request) or {}
+        has_spk_emb = request.speaker_embedding is not None
+        # Standalone TTS uses the Ming ``instruct`` task. ``voice_name`` lets
+        # the talker resolve registered presets such as DB30.
+        info = {
+            "ming_task": "instruct",
+            "prompt": DEFAULT_PROMPT,
+            "text": request.input,
+            "instruction": 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 self.max_new_tokens_max,
+            "cfg": 2.0,
+            "sigma": 0.25,
+            "temperature": 0.0,
+        }
+        if has_spk_emb:
+            # Keep embeddings as plain floats for EngineCore serialization.
+            info["spk_emb"] = list(request.speaker_embedding)
+        prompt = tokens_input(prompt_token_ids=[0])
+        prompt["additional_information"] = info
         return PreparedRequest(prompt=prompt, tts_params={}, model_type="ming_flash_omni_tts")
 
     def _load_supported_speakers(self) -> set[str]:
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/ming_tts.py b/vllm_omni/entrypoints/openai/tts_adapters/ming_tts.py
index 613968b8c..8dc5731a0 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/ming_tts.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/ming_tts.py
@@ -1,12 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Ming-TTS (dense) serving adapter."""
 
+import json
 from typing import TYPE_CHECKING, Any
 
+import torch
+from vllm.inputs import tokens_input
 from vllm.logger import init_logger
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
 from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest
+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_tts.constants import SPEAKER_EMBEDDING_DIM
 
 if TYPE_CHECKING:
@@ -15,8 +20,140 @@ if TYPE_CHECKING:
 logger = init_logger(__name__)
 
 
+def parse_ming_instruction_fields(server, 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 server.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
+
+
 @register_tts_adapter
 class MingTTSAdapter(ARTTSAdapter):
+    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(self, request: "OpenAICreateSpeechRequest") -> Any:
+        """Build a Ming instruction payload from OpenAI speech fields."""
+        return parse_ming_instruction_fields(
+            self.ctx.server,
+            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.ctx.server._tts_tokenizer is None:
+            model_name = self.ctx.engine_client.model_config.model
+            trust_remote_code = bool(getattr(self.ctx.engine_client.model_config, "trust_remote_code", False))
+            self.ctx.server._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.ctx.server._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
+
     # Ming dense has no dedicated model_stage value, so both stage discovery and
     # model-type detection go through the architecture. ``ming_flash_omni_tts``
     # is the model that owns the ``ming_tts`` *stage key*, and is matched first.
@@ -66,7 +203,7 @@ class MingTTSAdapter(ARTTSAdapter):
         elif ref_audio_source is not None and isinstance(ref_audio_source, str):
             wav_list, sr, _ = await server._resolve_ref_audio(ref_audio_source)
             ref_audio_data = (wav_list, sr)
-        prompt = server._build_ming_dense_prompt(
+        prompt = self._build_ming_dense_prompt(
             request,
             ref_audio_data=ref_audio_data,
             voice_name=uploaded_audio_voice,
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/moss_tts.py b/vllm_omni/entrypoints/openai/tts_adapters/moss_tts.py
index 3d6e99552..e264c7af5 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/moss_tts.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/moss_tts.py
@@ -6,7 +6,7 @@ Both variants share the same build/validate flow (``_build_moss_tts_params``
 handles each); they are registered under distinct model-type names.
 """
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from vllm.inputs import tokens_input
 
@@ -23,6 +23,255 @@ if TYPE_CHECKING:
 
 
 class _MossTTSAdapterBase(ARTTSAdapter):
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._moss_variant = None if self.name == "moss_tts_nano" else self._detect_moss_variant()
+        self._moss_processor_cache = None
+
+    @property
+    def engine_client(self):
+        return self.ctx.engine_client
+
+    @property
+    def uploaded_speakers(self):
+        return self.ctx.server.uploaded_speakers
+
+    @property
+    def _speaker_cache(self):
+        return self.ctx.server._speaker_cache
+
+    async def _resolve_ref_audio(self, ref_audio: str):
+        return await self.ctx.server._resolve_ref_audio(ref_audio)
+
+    def _voice_created_at(self, voice: str) -> int:
+        return self.ctx.server._voice_created_at(voice)
+
+    def _get_resolved_ref_audio_artifact_key(self, cache_key: str):
+        return self.ctx.server._get_resolved_ref_audio_artifact_key(cache_key)
+
+    def _get_moss_ref_encoder(self):
+        """Lazily build the per-server MOSS reference-audio encoder once."""
+        cached = getattr(self, "_moss_ref_encoder", None)
+        if cached is not None:
+            return cached
+        from vllm_omni.model_executor.models.moss_tts.reference_encoder import build_reference_encoder
+
+        # The variant's encode geometry (n_vq, working sample rate) is derived
+        # inside the model package; this layer only supplies the processor and
+        # the process-wide speaker cache.
+        encoder = build_reference_encoder(
+            self._get_moss_processor(),
+            variant=self._moss_variant,
+            speaker_cache=self._speaker_cache,
+        )
+        self._moss_ref_encoder = encoder
+        return encoder
+
+    async def _encode_moss_references(
+        self,
+        request: "OpenAICreateSpeechRequest",
+        *,
+        has_inline_ref_audio: bool,
+        two_speaker: bool,
+    ) -> tuple[list, dict[int, str]]:
+        """Encode the request's reference clip(s) into MOSS RVQ code tensors.
+
+        Reference 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. This method only resolves the serving-side
+        inputs the model helper needs: the generic audio resolver/artifact-key
+        lookups and whether the request may use the named-voice cache.
+
+        Returns ``(codes_per_speaker, resolve_keys)`` where ``resolve_keys``
+        maps the reference slot (0 = ref_audio, 1 = ref_audio_2) to its
+        content-aware resolve key, for salting the KV prefix cache.
+        """
+        from vllm_omni.model_executor.models.moss_tts.reference_encoder import encode_request_references
+
+        # Named-voice caching is only valid for uploaded speakers without an
+        # inline ref_audio: ``request.voice`` plus a file/URL would otherwise
+        # key on (name, created_at=0) and skip the content-aware resolve.
+        raw_voice = getattr(request, "voice", None)
+        raw_voice = raw_voice.strip() if isinstance(raw_voice, str) else ""
+        voice_lower = raw_voice.lower()
+        use_named_voice = bool(voice_lower) and voice_lower in self.uploaded_speakers and not has_inline_ref_audio
+        voice = voice_lower if use_named_voice else ""
+        voice_created = self._voice_created_at(voice) if voice else 0
+
+        return await encode_request_references(
+            self._get_moss_ref_encoder(),
+            request.ref_audio,
+            request.ref_audio_2 if two_speaker else None,
+            resolve_ref_audio=self._resolve_ref_audio,
+            get_artifact_key=self._get_resolved_ref_audio_artifact_key,
+            voice_name=voice or None,
+            voice_created_at=voice_created,
+        )
+
+    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",
+        *,
+        has_inline_ref_audio: bool = False,
+    ) -> 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, cache_key = await self._resolve_ref_audio(request.ref_audio)
+            params["prompt_audio_array"] = [[wav_list, sr]]
+            params["ref_audio_cache_key"] = cache_key
+            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, cache_key = await self._resolve_ref_audio(request.ref_audio)
+            params["prompt_audio_array"] = [[wav_list, sr]]
+            params["ref_audio_cache_key"] = cache_key
+            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()
+
+        user_kwargs: dict[str, Any] = {"text": request.input or ""}
+        resolve_keys: dict[int, str] = {}
+        if v in ("tts", "local", "ttsd"):
+            user_kwargs["reference"], resolve_keys = await self._encode_moss_references(
+                request,
+                has_inline_ref_audio=has_inline_ref_audio,
+                two_speaker=(v == "ttsd"),
+            )
+        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]
+        if 0 in resolve_keys:
+            params["ref_audio_cache_key"] = resolve_keys[0]
+        if 1 in resolve_keys:
+            params["ref_audio_2_cache_key"] = resolve_keys[1]
+        return params
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         """Validate any MOSS-TTS-family request (nano + 5 full variants).
 
@@ -42,10 +291,10 @@ class _MossTTSAdapterBase(ARTTSAdapter):
 
         if not request.input or not request.input.strip():
             # SoundEffect can legitimately have empty input (just ambient_sound).
-            if server._moss_variant != "sound_effect":
+            if self._moss_variant != "sound_effect":
                 return "Input text cannot be empty"
 
-        v = server._moss_variant
+        v = self._moss_variant
         if v in (None, "tts", "realtime", "local"):
             if request.ref_audio is None:
                 label = (
@@ -92,7 +341,7 @@ class _MossTTSAdapterBase(ARTTSAdapter):
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
         server = self.ctx.server
-        tts_params = await server._build_moss_tts_params(request, has_inline_ref_audio=has_inline_ref_audio)
+        tts_params = await self._build_moss_tts_params(request, has_inline_ref_audio=has_inline_ref_audio)
         if request.voice:
             voice_lower = request.voice.lower()
             if voice_lower in server.uploaded_speakers and not has_inline_ref_audio:
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/qwen3_tts.py b/vllm_omni/entrypoints/openai/tts_adapters/qwen3_tts.py
index 48d019eeb..3a2bb9860 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/qwen3_tts.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/qwen3_tts.py
@@ -2,12 +2,15 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Qwen3-TTS serving adapter."""
 
+import math
 from collections.abc import Mapping
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 import regex as re
+from vllm.inputs import tokens_input
 from vllm.logger import init_logger
+from vllm.utils.async_utils import make_async
 
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
 from vllm_omni.entrypoints.openai.tts_adapters.base import (
@@ -15,6 +18,7 @@ from vllm_omni.entrypoints.openai.tts_adapters.base import (
     ARTTSAdapter,
     PreparedRequest,
     TTSGenerationError,
+    conditioning_cache_salt,
 )
 from vllm_omni.entrypoints.openai.tts_adapters.capabilities import load_precomputed_speakers
 from vllm_omni.utils.speaker_cache import validate_qwen3_tts_profile
@@ -23,6 +27,7 @@ if TYPE_CHECKING:
     from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
 
 logger = init_logger(__name__)
+_REF_AUDIO_CACHE_KEY = "_qwen3_tts_ref_audio_cache_key"
 
 QWEN3_TTS_EFFECTIVE_MAX_TOKENS_KEY = "_qwen3_tts_effective_max_tokens"
 _MIN_CODEC_FRAMES = 192
@@ -45,6 +50,33 @@ class Qwen3TTSAdapter(ARTTSAdapter):
     name = "qwen3_tts"
     supported_output_sample_rates = frozenset({8000, 24000})
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._estimate_prompt_len_async = make_async(
+            self._estimate_prompt_len, executor=getattr(ctx.server, "_tts_executor", None)
+        )
+
+    def _estimate_ref_code_len(self, ref_audio: object) -> int | None:
+        codec_frame_rate = self.capabilities.codec_frame_rate
+        if codec_frame_rate is None:
+            return None
+        try:
+            item = ref_audio
+            while isinstance(item, list) and item:
+                if len(item) == 2 and isinstance(item[1], (int, float)):
+                    break
+                item = item[0]
+            if not isinstance(item, (list, tuple)) or len(item) != 2:
+                return None
+            wav, sr = item
+            sr = int(sr)
+            n_samples = len(wav) if hasattr(wav, "__len__") else wav.shape[-1]
+            if sr <= 0 or n_samples <= 0:
+                return None
+            return math.ceil(n_samples / sr * codec_frame_rate)
+        except Exception:
+            return None
+
     def _get_model_variant(self) -> str | None:
         """Return the task supported by the loaded Qwen3-TTS checkpoint.
 
@@ -223,15 +255,208 @@ class Qwen3TTSAdapter(ARTTSAdapter):
 
         return None
 
+    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.
+        """
+        server = self.ctx.server
+
+        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()
+            precomputed_speakers = self.capabilities.precomputed_speakers
+            params["speaker"] = [request.voice]
+            params["voice_created_at"] = [server._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 server.uploaded_speakers and request.ref_audio is None:
+                speaker_info = server.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 = server._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 = server._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 precomputed_speakers and request.ref_audio is None:
+                profile = 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
+
+    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,
+            )
+
+            server = self.ctx.server
+            if server._tts_tokenizer is None:
+                from transformers import AutoTokenizer
+
+                model_name = self.ctx.engine_client.model_config.model
+                server._tts_tokenizer = AutoTokenizer.from_pretrained(
+                    model_name,
+                    trust_remote_code=True,
+                    padding_side="left",
+                )
+            hf_config = self.ctx.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: server._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 _qwen3_tts_can_use_ref_audio_artifact_only(self, tts_params: dict[str, Any], artifact_key: str | None) -> bool:
+        server = self.ctx.server
+        x_vector_only = server._tts_x_vector_only(tts_params)
+        if not artifact_key or (artifact_key, x_vector_only) not in server._ref_audio_model_artifact_ready:
+            return False
+        return (tts_params.get("task_type") or ["CustomVoice"])[0] == "Base"
+
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt, tts_params, warmup_key = await self.ctx.server._build_qwen3_tts_request(request)
+        """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()``.
+        """
+        server = self.ctx.server
+        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, cache_key = await server._resolve_ref_audio(ref_audio_source)
+            tts_params["ref_audio_cache_key"] = cache_key
+            artifact_key = server._get_resolved_ref_audio_artifact_key(cache_key)
+            if artifact_key:
+                tts_params[_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 PreparedRequest(
             prompt=prompt,
             tts_params=tts_params,
             model_type=tts_params.get("task_type", ["unknown"])[0],
-            warmup_artifact_key=warmup_key,
+            warmup_artifact_key=qwen3_ref_audio_warmup_artifact_key,
         )
 
     def _get_expected_speaker_embedding_dim(self) -> int:
@@ -340,6 +565,16 @@ class Qwen3TTSAdapter(ARTTSAdapter):
             if isinstance(additional_information, dict) and effective_cap is not None:
                 additional_information[QWEN3_TTS_EFFECTIVE_MAX_TOKENS_KEY] = [effective_cap]
 
+        # Propagate the deploy/YAML stage seed to residual MTP sampling. The
+        # generic serving layer applies an explicit request.seed afterwards,
+        # so the request value still has higher precedence.
+        stage0_params = sampling_params_list[0]
+        default_seed = getattr(stage0_params, "seed", None)
+        if default_seed is not None:
+            if stage0_params.extra_args is None:
+                stage0_params.extra_args = {}
+            stage0_params.extra_args.setdefault("tts_local_seed", int(default_seed))
+
         logger.debug(
             "Qwen3-TTS codec budget: task_type=%s text_tokens=%s dynamic_cap=%s "
             "configured_cap=%s request_cap=%s effective_cap=%s",
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/step_audio2.py b/vllm_omni/entrypoints/openai/tts_adapters/step_audio2.py
index 2c720d8e1..7ca5a44ff 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/step_audio2.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/step_audio2.py
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Step-Audio2 serving adapter."""
 
 from typing import TYPE_CHECKING
@@ -30,5 +31,17 @@ class StepAudio2Adapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt = self.ctx.server._build_step_audio2_prompt(request)
+        """Build a chat prompt ending at ``<tts_start>``.
+
+        The assistant turn deliberately omits ``<|im_end|>`` so the thinker
+        continues by generating audio tokens.
+        """
+        system_prompt = request.instructions or "You are a voice assistant. Read the text aloud."
+        prompt = {
+            "prompt": (
+                f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
+                f"<|im_start|>user\n{request.input}<|im_end|>\n"
+                "<|im_start|>assistant\n<tts_start>"
+            )
+        }
         return PreparedRequest(prompt=prompt, tts_params={}, model_type="step_audio2")
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/voxcpm2.py b/vllm_omni/entrypoints/openai/tts_adapters/voxcpm2.py
index 760bb3339..f4574004e 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/voxcpm2.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/voxcpm2.py
@@ -1,9 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """VoxCPM2 serving adapter (AR base-LM + diffusion side-computation)."""
 
 import time
 from typing import Any
 
+import numpy as np
 from vllm.logger import init_logger
 
 from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
@@ -28,6 +30,63 @@ class VoxCPM2Adapter(ARTTSAdapter):
     # claims still resolves to VoxCPM2.
     detect_priority = 10
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._tokenizer = None
+        self._split_map: dict[int, list[int]] = {}
+
+    def _encode(self, text: str) -> list[int]:
+        from transformers import AutoTokenizer
+
+        from vllm_omni.model_executor.models.voxcpm2.voxcpm2_talker import (
+            build_cjk_split_map,
+            split_multichar_chinese,
+        )
+
+        if self._tokenizer is None:
+            self._tokenizer = AutoTokenizer.from_pretrained(
+                self.ctx.engine_client.model_config.model, trust_remote_code=True
+            )
+            self._split_map = build_cjk_split_map(self._tokenizer)
+            logger.info("VoxCPM2 serving: built multichar split map (%d entries)", len(self._split_map))
+        ids = self._tokenizer.encode(text, add_special_tokens=True)
+        return split_multichar_chinese(ids, self._split_map)
+
+    async def _build_prompt(
+        self,
+        request: "OpenAICreateSpeechRequest",
+        uploaded_ref: tuple[np.ndarray, int] | None = None,
+    ) -> dict[str, Any]:
+        """Build the full-length VoxCPM2 prefill prompt.
+
+        ``uploaded_ref`` supplies an uploaded voice waveform when the request
+        has no explicit ``ref_audio`` so prefill-length accounting includes it.
+        """
+        from vllm_omni.model_executor.models.voxcpm2.voxcpm2_talker import build_voxcpm2_prompt
+
+        server = self.ctx.server
+        self._encode("")
+        ref_audio = None
+        ref_sr = None
+        voice_profile = None
+        if request.ref_audio is not None:
+            ref_audio, ref_sr, _ = await server._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.capabilities.precomputed_speakers.get(request.voice.lower())
+        return build_voxcpm2_prompt(
+            hf_config=self.ctx.engine_client.model_config.hf_config,
+            tokenizer=self._tokenizer,
+            split_map=self._split_map,
+            text=request.input,
+            ref_audio=ref_audio,
+            ref_sr=ref_sr,
+            ref_text=request.ref_text,
+            voice_profile=voice_profile,
+        )
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         """Validate VoxCPM2 request parameters. Returns error message or None."""
         server = self.ctx.server
@@ -66,7 +125,7 @@ class VoxCPM2Adapter(ARTTSAdapter):
                     )
                 if request.ref_audio is None:
                     uploaded_ref = server._load_uploaded_audio(voice_lower)
-        prompt = await server._build_voxcpm2_prompt(request, uploaded_ref=uploaded_ref)
+        prompt = await self._build_prompt(request, uploaded_ref=uploaded_ref)
         tts_params = {}
         if request.voice:
             voice_lower = request.voice.lower()
diff --git a/vllm_omni/entrypoints/openai/tts_adapters/voxtral.py b/vllm_omni/entrypoints/openai/tts_adapters/voxtral.py
index c9bea7bb0..f9d7639ca 100644
--- a/vllm_omni/entrypoints/openai/tts_adapters/voxtral.py
+++ b/vllm_omni/entrypoints/openai/tts_adapters/voxtral.py
@@ -1,8 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Voxtral TTS serving adapter."""
 
 from typing import TYPE_CHECKING, Any
 
+from vllm.utils.async_utils import make_async
+
 from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
 from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest, apply_max_new_tokens
 from vllm_omni.entrypoints.openai.tts_adapters.capabilities import load_supported_speakers
@@ -16,6 +19,39 @@ class VoxtralTTSAdapter(ARTTSAdapter):
     stage_keys = frozenset({"audio_generation"})
     name = "voxtral_tts"
 
+    def __init__(self, ctx) -> None:
+        super().__init__(ctx)
+        self._build_prompt_async = make_async(self._build_prompt, executor=getattr(ctx.server, "_tts_executor", None))
+
+    def _build_prompt(self, request: "OpenAICreateSpeechRequest") -> dict[str, Any]:
+        """Build a Voxtral prompt for either a preset voice or inline reference audio."""
+        from mistral_common.protocol.speech.request import SpeechRequest
+        from vllm.inputs import tokens_input
+
+        ref_audio = request.ref_audio
+        if not request.voice and not ref_audio:
+            raise ValueError("Voxtral requires either a voice name or ref_audio.")
+        if isinstance(ref_audio, str) and ref_audio.startswith("data:"):
+            _, _, ref_audio = ref_audio.partition(",")
+        server = self.ctx.server
+        if server._tts_tokenizer is None:
+            from vllm.tokenizers import cached_tokenizer_from_config
+
+            server._tts_tokenizer = cached_tokenizer_from_config(self.ctx.engine_client.model_config).instruct
+        if request.voice is not None:
+            tokenized = server._tts_tokenizer.encode_speech_request(
+                SpeechRequest(input=request.input, voice=request.voice)
+            )
+            prompt = tokens_input(prompt_token_ids=tokenized.tokens)
+            prompt["additional_information"] = {"voice": [request.voice]}
+            return prompt
+        tokenized = server._tts_tokenizer.encode_speech_request(SpeechRequest(input=request.input, ref_audio=ref_audio))
+        audio = tokenized.audios[0]
+        return {
+            "prompt_token_ids": tokenized.tokens,
+            "multi_modal_data": {"audio": [(audio.audio_array, audio.sampling_rate)]},
+        }
+
     def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
         """Validate Voxtral TTS request parameters. Returns error message or None."""
         server = self.ctx.server
@@ -48,7 +84,7 @@ class VoxtralTTSAdapter(ARTTSAdapter):
     async def build(
         self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
     ) -> PreparedRequest:
-        prompt = await self.ctx.server._build_voxtral_prompt_async(request)
+        prompt = await self._build_prompt_async(request)
         return PreparedRequest(prompt=prompt, tts_params={}, model_type="voxtral_tts")
 
     def _load_supported_speakers(self) -> set[str]:
diff --git a/vllm_omni/model_executor/models/voxcpm2/voxcpm2_talker.py b/vllm_omni/model_executor/models/voxcpm2/voxcpm2_talker.py
index f8dc962f3..5febb8912 100644
--- a/vllm_omni/model_executor/models/voxcpm2/voxcpm2_talker.py
+++ b/vllm_omni/model_executor/models/voxcpm2/voxcpm2_talker.py
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """VoxCPM2 AR talker — PagedAttention pipeline with per-request state.
 
 Architecture:
@@ -158,7 +158,7 @@ def build_voxcpm2_prompt(
     """Build a VoxCPM2 prefill prompt whose ``prompt_token_ids`` length matches
     the talker-side prefill length.
 
-    Used by both online serving (``serving_speech._build_voxcpm2_prompt``) and
+    Used by both online serving (``VoxCPM2Adapter._build_prompt``) and
     the offline example, so the talker-side length assertion never fires.
     """
     ids = split_multichar_chinese(tokenizer.encode(text, add_special_tokens=True), split_map)
@@ -2208,7 +2208,7 @@ class VoxCPM2TalkerForConditionalGeneration(nn.Module):
         assert scaffold_len == tts_len, (
             f"voxcpm2 prefill length mismatch: scaffold_len={scaffold_len} tts_len={tts_len}; "
             "caller must pad prompt_token_ids to the full prefill length "
-            "(see serving_speech._build_voxcpm2_prompt or the offline example)."
+            "(see VoxCPM2Adapter._build_prompt or the offline example)."
         )
         enc_out = base_lm_out.unsqueeze(0)