vllm/docs/contributing/model/transcription.md

Speech-to-Text (Transcription/Translation) Support

This document walks you through the steps to add support for speech-to-text (ASR) models to vLLM’s transcription and translation APIs by implementing [SupportsTranscription][vllm.model_executor.models.interfaces.SupportsTranscription]. Please refer to the supported models for further guidance.

Update the base vLLM model

It is assumed you have already implemented your model in vLLM according to the basic model guide. Extend your model with the [SupportsTranscription][vllm.model_executor.models.interfaces.SupportsTranscription] interface and implement the following class attributes and methods.

supported_languages and supports_transcription_only

Declare supported languages and capabilities:

• The supported_languages mapping is validated at init time.
• Set supports_transcription_only=True if the model should not serve text generation (eg Whisper).

??? code "supported_languages and supports_transcription_only" ```python from typing import ClassVar, Mapping, Optional, Literal import numpy as np import torch from torch import nn

from vllm.config import ModelConfig, SpeechToTextConfig
from vllm.inputs.data import PromptType
from vllm.model_executor.models.interfaces import SupportsTranscription

class YourASRModel(nn.Module, SupportsTranscription):
    # Map of ISO 639-1 language codes to language names
    supported_languages: ClassVar[Mapping[str, str]] = {
        "en": "English",
        "it": "Italian",
        # ... add more as needed
    }
    
    # If your model only supports audio-conditioned generation
    # (no text-only generation), enable this flag.
    supports_transcription_only: ClassVar[bool] = True
```

Provide an ASR configuration via [get_speech_to_text_config][vllm.model_executor.models.interfaces.SupportsTranscription.get_speech_to_text_config].

This is for controlling general behavior of the API when serving your model:

??? code "get_speech_to_text_config()" ```python class YourASRModel(nn.Module, SupportsTranscription): ...

    @classmethod
    def get_speech_to_text_config(
        cls,
        model_config: ModelConfig,
        task_type: Literal["transcribe", "translate"],
    ) -> SpeechToTextConfig:
        return SpeechToTextConfig(
            sample_rate=16_000,
            max_audio_clip_s=30,
            # Set to None to disable server-side chunking if your
            # model/processor handles it already
            min_energy_split_window_size=None,
        )
```

See 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.data.PromptType]. There are two common patterns:

Multimodal LLM with audio embeddings (e.g., Voxtral, Gemma3n)

Return a dict containing multi_modal_data with the audio, and either a prompt string or prompt_token_ids:

??? code "get_generation_prompt()" ```python class YourASRModel(nn.Module, SupportsTranscription): ...

    @classmethod
    def get_generation_prompt(
        cls,
        audio: np.ndarray,
        stt_config: SpeechToTextConfig,
        model_config: ModelConfig,
        language: Optional[str],
        task_type: Literal["transcribe", "translate"],
        request_prompt: str,
        to_language: Optional[str],
    ) -> PromptType:
        # Example with a free-form instruction prompt
        task_word = "Transcribe" if task_type == "transcribe" else "Translate"
        prompt = (
            "<start_of_turn>user\n"
            f"{task_word} this audio: <audio_soft_token>"
            "<end_of_turn>\n<start_of_turn>model\n"
        )

        return {
            "multi_modal_data": {"audio": (audio, stt_config.sample_rate)},
            "prompt": prompt,
        }
```

For further clarification on multi modal inputs, please refer to [Multi-Modal Inputs](../../features/multimodal_inputs.md).

Encoder–decoder audio-only (e.g., Whisper)

Return a dict with separate encoder_prompt and decoder_prompt entries:

??? code "get_generation_prompt()" ```python class YourASRModel(nn.Module, SupportsTranscription): ...

    @classmethod
    def get_generation_prompt(
        cls,
        audio: np.ndarray,
        stt_config: SpeechToTextConfig,
        model_config: ModelConfig,
        language: Optional[str],
        task_type: Literal["transcribe", "translate"],
        request_prompt: str,
        to_language: Optional[str],
    ) -> PromptType:
        if language is None:
            raise ValueError("Language must be specified")

        prompt = {
            "encoder_prompt": {
                "prompt": "",
                "multi_modal_data": {
                    "audio": (audio, stt_config.sample_rate),
                },
            },
            "decoder_prompt": (
                (f"<|prev|>{request_prompt}" if request_prompt else "")
                + f"<|startoftranscript|><|{language}|>"
                + f"<|{task_type}|><|notimestamps|>"
            ),
        }
        return cast(PromptType, prompt)
```

validate_language (optional)

Language validation via [validate_language][vllm.model_executor.models.interfaces.SupportsTranscription.validate_language]

If your model requires a language and you want a default, override this method (see Whisper):

??? code "validate_language()" python @classmethod def validate_language(cls, language: Optional[str]) -> Optional[str]: if language is None: logger.warning( "Defaulting to language='en'. If you wish to transcribe audio in a different language, pass the `language` field.") language = "en" return super().validate_language(language)

get_num_audio_tokens (optional)

Token accounting for streaming via [get_num_audio_tokens][vllm.model_executor.models.interfaces.SupportsTranscription.get_num_audio_tokens]

Provide a fast duration→token estimate to improve streaming usage statistics:

??? code "get_num_audio_tokens()" ```python class YourASRModel(nn.Module, SupportsTranscription): ...

    @classmethod
    def get_num_audio_tokens(
        cls,
        audio_duration_s: float,
        stt_config: SpeechToTextConfig,
        model_config: ModelConfig,
    ) -> Optional[int]:
        # Return None if unknown; otherwise return an estimate.
        return int(audio_duration_s * stt_config.sample_rate // 320)  # example
```

Audio preprocessing and chunking

The API server takes care of basic audio I/O and optional chunking before building prompts:

• Resampling: Input audio is resampled to SpeechToTextConfig.sample_rate using librosa.
• Chunking: If SpeechToTextConfig.allow_audio_chunking is True and the duration exceeds max_audio_clip_s, the server splits the audio into overlapping chunks and generates a prompt per chunk. Overlap is controlled by overlap_chunk_second.
• Energy-aware splitting: When min_energy_split_window_size is set, the server finds low-energy regions to minimize cutting within words.

Relevant server logic:

??? code "_preprocess_speech_to_text()" python # vllm/entrypoints/openai/speech_to_text.py async def _preprocess_speech_to_text(...): language = self.model_cls.validate_language(request.language) ... y, sr = librosa.load(bytes_, sr=self.asr_config.sample_rate) duration = librosa.get_duration(y=y, sr=sr) do_split_audio = (self.asr_config.allow_audio_chunking and duration > self.asr_config.max_audio_clip_s) 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( 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, ) prompts.append(prompt) return prompts, duration

Exposing tasks automatically

vLLM automatically advertises transcription support if your model implements the interface:

if supports_transcription(model):
    if model.supports_transcription_only:
        return ["transcription"]
    supported_tasks.append("transcription")

When enabled, the server initializes the transcription and translation handlers:

state.openai_serving_transcription = OpenAIServingTranscription(...) if "transcription" in supported_tasks else None
state.openai_serving_translation = OpenAIServingTranslation(...) if "transcription" in supported_tasks else None

No extra registration is required beyond having your model class available via the model registry and implementing SupportsTranscription.

Examples in-tree

• Whisper encoder–decoder (audio-only): gh-file:vllm/model_executor/models/whisper.py
• Voxtral decoder-only (audio embeddings + LLM): gh-file:vllm/model_executor/models/voxtral.py
• Gemma3n decoder-only with fixed instruction prompt: gh-file:vllm/model_executor/models/gemma3n_mm.py

Test with the API

Once your model implements SupportsTranscription, you can test the endpoints (API mimics OpenAI):

• Transcription (ASR):

  curl -s -X POST \
    -H "Authorization: Bearer $VLLM_API_KEY" \
    -H "Content-Type: multipart/form-data" \
    -F "file=@/path/to/audio.wav" \
    -F "model=$MODEL_ID" \
    http://localhost:8000/v1/audio/transcriptions
  

• Translation (source → English unless otherwise supported):

  curl -s -X POST \
    -H "Authorization: Bearer $VLLM_API_KEY" \
    -H "Content-Type: multipart/form-data" \
    -F "file=@/path/to/audio.wav" \
    -F "model=$MODEL_ID" \
    http://localhost:8000/v1/audio/translations
  

Or check out more examples in gh-file:examples/online_serving.

!!! note - If your model handles chunking internally (e.g., via its processor or encoder), set min_energy_split_window_size=None in the returned SpeechToTextConfig to disable server-side chunking. - Implementing get_num_audio_tokens improves accuracy of streaming usage metrics (prompt_tokens) without an extra forward pass. - For multilingual behavior, keep supported_languages aligned with actual model capabilities.