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.

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][vllm.model_executor.models.interfaces.SupportsTranscription] interface and implement the following class attributes and methods.

• Declare supported languages and capabilities:

  ??? code

    ```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
    ```
  

  • 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][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

    ```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 the “Audio preprocessing and chunking” section 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:

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

    ```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).

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

Return a dict with separate `encoder_prompt` and `decoder_prompt` entries:

??? code

    ```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)
    ```

• (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):

  @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][vllm.model_executor.models.interfaces.SupportsTranscription.get_num_audio_tokens]

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

  ??? code ```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
    ```
  

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 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

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): 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

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 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.