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. Please refer to the supported models for further guidance.

1. 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 interface and implement the following class attributes and methods.

Declare supported languages and capabilities:

Code

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

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

Provide an ASR configuration via get_speech_to_text_config. This is for controlling general behavior of the API when serving your model:

Code

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 the “Audio preprocessing and chunking” section for what each field controls.

Implement the prompt construction via get_generation_prompt. The server passes you the resampled waveform and task parameters; you return a valid PromptType. There are two common patterns:

A. 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

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.

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

Return a dict with separate encoder_prompt and decoder_prompt entries:

Code

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)

(Optional) Language validation via validate_language

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

@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)

(Optional) Token accounting for streaming via get_num_audio_tokens

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

Code

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

2. 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

# 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

3. 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.

4. Examples in-tree¶

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

5. 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 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.