[Audio] Bundle get_generation_prompt() params into SpeechToTextParams (#36268)

Signed-off-by: Ekagra Ranjan <3116519+ekagra-ranjan@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

docs/contributing/model/transcription.md

@@ -66,7 +66,7 @@ This is for controlling general behavior of the API when serving your model:
 
 See [Audio preprocessing and chunking](#audio-preprocessing-and-chunking) for what each field controls.
 
-Implement the prompt construction via [get_generation_prompt][vllm.model_executor.models.interfaces.SupportsTranscription.get_generation_prompt]. The server passes you the resampled waveform and task parameters; you return a valid [PromptType][vllm.inputs.llm.PromptType]. There are two common patterns:
+Implement the prompt construction via [get_generation_prompt][vllm.model_executor.models.interfaces.SupportsTranscription.get_generation_prompt]. The server builds a [SpeechToTextParams][vllm.config.speech_to_text.SpeechToTextParams] object that bundles the resampled waveform, task parameters, and request-specific options. Your model receives this single object and returns a valid [PromptType][vllm.inputs.llm.PromptType]. There are two common patterns:
 
 #### Multimodal LLM with audio embeddings (e.g., Voxtral, Gemma3n)
 
@@ -75,21 +75,20 @@ Return a dict containing `multi_modal_data` with the audio, and either a `prompt
 ??? code "get_generation_prompt()"
 
     ```python
+    from vllm.config.speech_to_text import SpeechToTextParams
+
     class YourASRModel(nn.Module, SupportsTranscription):
         ...
 
         @classmethod
         def get_generation_prompt(
             cls,
-            audio: np.ndarray,
-            stt_config: SpeechToTextConfig,
-            model_config: ModelConfig,
-            language: str | None,
-            task_type: Literal["transcribe", "translate"],
-            request_prompt: str,
-            to_language: str | None,
+            stt_params: SpeechToTextParams,
         ) -> PromptType:
-            # Example with a free-form instruction prompt
+            audio = stt_params.audio
+            stt_config = stt_params.stt_config
+            task_type = stt_params.task_type
+
             task_word = "Transcribe" if task_type == "transcribe" else "Translate"
             prompt = (
                 "<start_of_turn>user\n"
@@ -112,20 +111,22 @@ Return a dict with separate `encoder_prompt` and `decoder_prompt` entries:
 ??? code "get_generation_prompt()"
 
     ```python
+    from vllm.config.speech_to_text import SpeechToTextParams
+
     class YourASRModel(nn.Module, SupportsTranscription):
         ...
 
         @classmethod
         def get_generation_prompt(
             cls,
-            audio: np.ndarray,
-            stt_config: SpeechToTextConfig,
-            model_config: ModelConfig,
-            language: str | None,
-            task_type: Literal["transcribe", "translate"],
-            request_prompt: str,
-            to_language: str | None,
+            stt_params: SpeechToTextParams,
         ) -> PromptType:
+            audio = stt_params.audio
+            stt_config = stt_params.stt_config
+            language = stt_params.language
+            task_type = stt_params.task_type
+            request_prompt = stt_params.request_prompt
+
             if language is None:
                 raise ValueError("Language must be specified")
 
@@ -213,15 +214,13 @@ Relevant server logic:
         chunks = [y] if not do_split_audio else self._split_audio(y, int(sr))
         prompts = []
         for chunk in chunks:
-            prompt = self.model_cls.get_generation_prompt(
+            stt_params = request.build_stt_params(
                 audio=chunk,
                 stt_config=self.asr_config,
                 model_config=self.model_config,
-                language=language,
                 task_type=self.task_type,
-                request_prompt=request.prompt,
-                to_language=to_language,
             )
+            prompt = self.model_cls.get_generation_prompt(stt_params)
             prompts.append(prompt)
         return prompts, duration
     ```

vllm/config/__init__.py

@@ -37,7 +37,7 @@ from vllm.config.profiler import ProfilerConfig
 from vllm.config.reasoning import ReasoningConfig
 from vllm.config.scheduler import SchedulerConfig
 from vllm.config.speculative import SpeculativeConfig
-from vllm.config.speech_to_text import SpeechToTextConfig
+from vllm.config.speech_to_text import SpeechToTextConfig, SpeechToTextParams
 from vllm.config.structured_outputs import StructuredOutputsConfig
 from vllm.config.utils import (
     ConfigType,
@@ -113,6 +113,7 @@ __all__ = [
     "SpeculativeConfig",
     # From vllm.config.speech_to_text
     "SpeechToTextConfig",
+    "SpeechToTextParams",
     # From vllm.config.structured_outputs
     "StructuredOutputsConfig",
     # From vllm.config.profiler

vllm/config/speech_to_text.py

@@ -1,9 +1,49 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
 
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
 from vllm.config.utils import config
 
+if TYPE_CHECKING:
+    import numpy as np
+
+    from vllm.config.model import ModelConfig
+
+
+@dataclass
+class SpeechToTextParams:
+    """All parameters consumed by ``get_generation_prompt()``.
+
+    ``TranscriptionRequest.build_stt_params()`` constructs this object,
+    mapping API-level fields into typed attributes.  Models only receive
+    this object, so new parameters can be added here without changing the
+    ``get_generation_prompt`` signature.
+    """
+
+    audio: np.ndarray
+    """Resampled audio waveform for a single chunk."""
+
+    stt_config: SpeechToTextConfig
+    """Server-level speech-to-text configuration."""
+
+    model_config: ModelConfig
+    """Model configuration."""
+
+    language: str | None = None
+    """ISO 639-1 language code (validated / auto-detected)."""
+
+    task_type: str = "transcribe"
+    """``"transcribe"`` or ``"translate"``."""
+
+    request_prompt: str = ""
+    """Optional text prompt to guide the model."""
+
+    to_language: str | None = None
+    """Target language for translation (model-dependent)."""
+
 
 @config
 class SpeechToTextConfig:

vllm/entrypoints/openai/speech_to_text/protocol.py

@@ -1,9 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import json
 import time
 from http import HTTPStatus
-from typing import Literal, TypeAlias
+from typing import TYPE_CHECKING, Literal, TypeAlias
 
 import torch
 from fastapi import HTTPException, UploadFile
@@ -12,6 +13,7 @@ from pydantic import (
     model_validator,
 )
 
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.entrypoints.openai.engine.protocol import (
     DeltaMessage,
     OpenAIBaseModel,
@@ -26,6 +28,11 @@ from vllm.sampling_params import (
 )
 from vllm.utils import random_uuid
 
+if TYPE_CHECKING:
+    import numpy as np
+
+    from vllm.config import ModelConfig, SpeechToTextConfig
+
 logger = init_logger(__name__)
 _LONG_INFO = torch.iinfo(torch.long)
 
@@ -183,6 +190,23 @@ class TranscriptionRequest(OpenAIBaseModel):
         "min_p": 0.0,
     }
 
+    def build_stt_params(
+        self,
+        audio: "np.ndarray",
+        stt_config: "SpeechToTextConfig",
+        model_config: "ModelConfig",
+        task_type: str,
+    ) -> SpeechToTextParams:
+        return SpeechToTextParams(
+            audio=audio,
+            stt_config=stt_config,
+            model_config=model_config,
+            language=self.language,
+            task_type=task_type,
+            request_prompt=self.prompt,
+            to_language=self.to_language,
+        )
+
     def to_beam_search_params(
         self,
         default_max_tokens: int,
@@ -277,6 +301,17 @@ class TranscriptionRequest(OpenAIBaseModel):
                 parameter=invalid_param,
             )
 
+        # Parse vllm_xargs from JSON string (form data sends it as a string)
+        xargs = data.get("vllm_xargs")
+        if isinstance(xargs, str):
+            try:
+                data["vllm_xargs"] = json.loads(xargs)
+            except json.JSONDecodeError as e:
+                raise VLLMValidationError(
+                    f"Failed to parse vllm_xargs. Must be valid JSON: {e}",
+                    parameter="vllm_xargs",
+                ) from e
+
         return data
 
 
@@ -472,6 +507,23 @@ class TranslationRequest(OpenAIBaseModel):
         "temperature": 0,
     }
 
+    def build_stt_params(
+        self,
+        audio: "np.ndarray",
+        stt_config: "SpeechToTextConfig",
+        model_config: "ModelConfig",
+        task_type: str,
+    ) -> SpeechToTextParams:
+        return SpeechToTextParams(
+            audio=audio,
+            stt_config=stt_config,
+            model_config=model_config,
+            language=self.language,
+            task_type=task_type,
+            request_prompt=self.prompt,
+            to_language=self.to_language,
+        )
+
     def to_beam_search_params(
         self,
         default_max_tokens: int,

vllm/entrypoints/openai/speech_to_text/speech_to_text.py

@@ -184,9 +184,8 @@ class OpenAISpeechToText(OpenAIServing):
         request_id: str,
     ) -> tuple[list[EngineInput], float]:
         # Validate request
-        language = self.model_cls.validate_language(request.language)
-        # Skip to_language validation to avoid extra logging for Whisper.
-        to_language = (
+        request.language = self.model_cls.validate_language(request.language)
+        request.to_language = (
             self.model_cls.validate_language(request.to_language)
             if request.to_language
             else None
@@ -229,28 +228,23 @@ class OpenAISpeechToText(OpenAIServing):
                 min_energy_window_size=self.asr_config.min_energy_split_window_size,
             )
 
-        if language is None and getattr(
+        if request.language is None and getattr(
             self.model_cls, "supports_explicit_language_detection", False
         ):
             # Auto-detect language from the first chunk.
-            language = await self._detect_language(
+            request.language = await self._detect_language(
                 chunks[0], f"{request_id}-lang_detect"
             )
-            request.language = language
 
         parsed_prompts: list[DictPrompt] = []
         for chunk in chunks:
-            # The model has control over the construction, as long as it
-            # returns a valid PromptType.
-            prompt = self.model_cls.get_generation_prompt(
+            stt_params = request.build_stt_params(
                 audio=chunk,
                 stt_config=self.asr_config,
                 model_config=self.model_config,
-                language=language,
                 task_type=self.task_type,
-                request_prompt=request.prompt,
-                to_language=to_language,
             )
+            prompt = self.model_cls.get_generation_prompt(stt_params)
 
             parsed_prompt: DictPrompt
             if request.response_format == "verbose_json":

vllm/model_executor/models/cohere_asr.py

@@ -3,9 +3,7 @@
 
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Literal
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -13,6 +11,8 @@ from transformers import PretrainedConfig
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import MultiModalDataDict, PromptType, TextPrompt
 from vllm.logger import init_logger
@@ -1900,7 +1900,7 @@ class CohereASRDummyInputsBuilder(BaseDummyInputsBuilder[CohereASRProcessingInfo
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-        mm_options=None,
+        mm_options: Mapping[str, BaseDummyOptions],
         mm_processor_kwargs=None,
     ) -> MultiModalDataDict:
         feature_extractor = self.info.get_feature_extractor()
@@ -2021,16 +2021,12 @@ class CohereAsrForConditionalGeneration(
         return super().validate_language(language)
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        request_prompt = stt_params.request_prompt
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the CohereASR prompt"

vllm/model_executor/models/fireredasr2.py

@@ -2,9 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, cast
+from typing import Annotated, cast
 
-import numpy as np
 import torch
 from torch import nn
 from transformers import (
@@ -14,6 +13,7 @@ from transformers import (
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
@@ -356,14 +356,12 @@ class FireRedASR2ForConditionalGeneration(
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the fireredasr2 prompt"

vllm/model_executor/models/funasr.py

@@ -3,9 +3,8 @@
 
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, cast
+from typing import Annotated, cast
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -16,6 +15,7 @@ from transformers import (
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import MultiModalDataDict, PromptType
 from vllm.logger import init_logger
@@ -876,14 +876,12 @@ class FunASRForConditionalGeneration(
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the funasr prompt"

vllm/model_executor/models/gemma3n_mm.py

@@ -3,7 +3,6 @@
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Any, Literal
 
-import numpy as np
 import torch
 from torch import nn
 from transformers import AutoModel, BatchFeature
@@ -19,6 +18,7 @@ from transformers.models.siglip import SiglipImageProcessorFast
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType, TextPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -769,21 +769,17 @@ class Gemma3nForConditionalGeneration(
             raise ValueError(f"Unsupported modality: {modality}")
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        stt_config: SpeechToTextConfig,
-        model_config: ModelConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """
         Gemma3n supports "free-form" transcription.
         We fix its prompt here to standardize transcriptions/translations
         requests.
         """
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
         # Transcribe this audio [into <>] | for transcription
         # Translate this audio [from <> into <>] | for translation
         prompt = "<start_of_turn>user\n"

vllm/model_executor/models/glmasr.py

@@ -4,7 +4,6 @@
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Any, Literal, TypeAlias
 
-import numpy as np
 import torch
 import torch.nn as nn
 from transformers import BatchFeature
@@ -13,6 +12,7 @@ from transformers.models.whisper import WhisperFeatureExtractor
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
 from vllm.inputs import ModalityData, MultiModalDataDict, PromptType, TokensPrompt
 from vllm.model_executor.layers.activation import get_act_fn
@@ -1131,17 +1131,12 @@ class GlmAsrForConditionalGeneration(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """Get the generation prompt to be used for transcription requests."""
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
         tokenizer = cached_tokenizer_from_config(model_config)
         audio_token = cls._get_audio_token(model_config)
 

vllm/model_executor/models/granite_speech.py

@@ -26,9 +26,8 @@
 
 import math
 from collections.abc import Iterable, Mapping
-from typing import Annotated, Literal
+from typing import Annotated
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -36,6 +35,7 @@ from transformers import BatchFeature, PretrainedConfig
 
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType, TokensPrompt
 from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -852,15 +852,14 @@ class GraniteSpeechForConditionalGeneration(
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
         """Get the generation prompt to be used for transcription requests."""
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
+
         # Audio placeholders don't use an index, so value doesn't matter
         audio_tok = cls.get_placeholder_str("audio", 0)
 

vllm/model_executor/models/interfaces.py

@@ -29,7 +29,7 @@ from torch import Tensor
 from transformers.models.whisper.tokenization_whisper import LANGUAGES
 from typing_extensions import Self, TypeIs
 
-from vllm.config import ModelConfig, SpeechToTextConfig
+from vllm.config import ModelConfig, SpeechToTextConfig, SpeechToTextParams
 from vllm.inputs import PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.mamba.mamba_utils import MambaStateCopyFunc
@@ -1119,13 +1119,7 @@ class SupportsTranscription(Protocol):
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        stt_config: SpeechToTextConfig,
-        model_config: ModelConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
         """Get the prompt for the ASR model.
         The model has control over the construction, as long as it

vllm/model_executor/models/kimi_audio.py

@@ -14,6 +14,7 @@ from transformers import WhisperConfig as HFWhisperConfig
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import PromptType, TokensPrompt
 from vllm.model_executor.model_loader import DefaultModelLoader
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -626,16 +627,12 @@ class KimiAudioForConditionalGeneration(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        request_prompt = stt_params.request_prompt
+
         tokenizer = cached_get_tokenizer(
             model_config.tokenizer,
             tokenizer_cls=KimiAudioTokenizer,

vllm/model_executor/models/qwen3_asr.py

@@ -23,9 +23,8 @@
 """Inference-only Qwen3-ASR model."""
 
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Any, Literal
+from typing import Any
 
-import numpy as np
 import torch
 import torch.nn as nn
 from transformers.feature_extraction_utils import BatchFeature
@@ -33,6 +32,7 @@ from transformers.models.whisper import WhisperFeatureExtractor
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import ModalityData, MultiModalDataDict, PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.models.interfaces import (
@@ -549,17 +549,12 @@ class Qwen3ASRForConditionalGeneration(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """Get the generation prompt to be used for transcription requests."""
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
         tokenizer = cached_tokenizer_from_config(model_config)
         audio_placeholder = cls.get_placeholder_str("audio", 0)
 

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -24,7 +24,7 @@
 
 from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
 from functools import partial
-from typing import Any, Literal, cast
+from typing import Any, cast
 
 import numpy as np
 import torch
@@ -46,6 +46,7 @@ from transformers.models.whisper import WhisperFeatureExtractor
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.inputs import PromptType
 from vllm.logger import init_logger
@@ -2201,19 +2202,17 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
         )
 
     @classmethod
-    def get_generation_prompt(
-        cls,
-        audio: np.ndarray,
-        stt_config: SpeechToTextConfig,
-        model_config: ModelConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
-    ) -> PromptType:
+    def get_generation_prompt(cls, stt_params: SpeechToTextParams) -> PromptType:
         """
         Construct a transcription/translation prompt for Qwen3-Omni.
         """
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        model_config = stt_params.model_config
+        language = stt_params.language
+        task_type = stt_params.task_type
+        to_language = stt_params.to_language
+        request_prompt = stt_params.request_prompt
         # Transcribe this audio [into <language>] | for transcription
         # Translate this audio [from <language> into <to_language>] | for translation
         instruction = "Transcribe" if task_type == "transcribe" else "Translate"

vllm/model_executor/models/voxtral.py

@@ -4,7 +4,7 @@
 import math
 from collections.abc import Iterable, Mapping, Sequence
 from functools import partial
-from typing import Literal, cast
+from typing import cast
 
 import numpy as np
 import regex as re
@@ -19,6 +19,7 @@ from transformers import BatchFeature, WhisperConfig
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.inputs import MultiModalDataDict, PromptType, TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -446,14 +447,13 @@ class VoxtralForConditionalGeneration(
     # for speech-to-text transcription
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         tokenizer = cached_tokenizer_from_config(model_config)
         audio = Audio(audio, int(stt_config.sample_rate), format="wav")  # lossless
         req = TranscriptionRequest(

vllm/model_executor/models/voxtral_realtime.py

@@ -4,7 +4,6 @@
 import asyncio
 import math
 from collections.abc import AsyncGenerator, Iterable, Iterator, Mapping
-from typing import Literal
 
 import numpy as np
 import torch
@@ -18,6 +17,7 @@ from mistral_common.tokens.tokenizers.audio import AudioConfig
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.engine.protocol import StreamingInput
 from vllm.envs import VLLM_ENGINE_ITERATION_TIMEOUT_S
 from vllm.inputs import PromptType, TokensPrompt
@@ -465,14 +465,13 @@ class VoxtralRealtimeGeneration(VoxtralForConditionalGeneration, SupportsRealtim
     # for speech-to-text transcription
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        model_config = stt_params.model_config
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+
         tokenizer = cached_tokenizer_from_config(model_config)
         audio = Audio(audio, int(stt_config.sample_rate), format="wav")  # lossless
 

vllm/model_executor/models/whisper.py

@@ -5,7 +5,7 @@ import enum
 import math
 from collections.abc import Iterable, Mapping, Sequence
 from contextlib import nullcontext
-from typing import Annotated, Literal
+from typing import Annotated
 
 import numpy as np
 import torch
@@ -20,6 +20,7 @@ from transformers.models.whisper.modeling_whisper import sinusoids
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.config.speech_to_text import SpeechToTextParams
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import (
     ExplicitEncoderDecoderPrompt,
@@ -830,14 +831,14 @@ class WhisperForConditionalGeneration(
     @classmethod
     def get_generation_prompt(
         cls,
-        audio: np.ndarray,
-        model_config: ModelConfig,  # not needed here
-        stt_config: SpeechToTextConfig,
-        language: str | None,
-        task_type: Literal["transcribe", "translate"],
-        request_prompt: str,
-        to_language: str | None,
+        stt_params: SpeechToTextParams,
     ) -> PromptType:
+        audio = stt_params.audio
+        stt_config = stt_params.stt_config
+        language = stt_params.language
+        task_type = stt_params.task_type
+        request_prompt = stt_params.request_prompt
+
         if language is None:
             raise ValueError(
                 "Language must be specified when creating the Whisper prompt"