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docs/contributing/model/transcription.md
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@ -37,7 +37,7 @@ It is assumed you have already implemented your model in vLLM according to the b
- The `supported_languages` mapping is validated at init time. - The `supported_languages` mapping is validated at init time.
- Set `supports_transcription_only=True` if the model should not serve text generation (eg Whisper). - 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]. - 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: This is for controlling general behavior of the API when serving your model:
??? code ??? code
@ -65,7 +65,7 @@ It is assumed you have already implemented your model in vLLM according to the b
- 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: - 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) ### 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`: Return a dict containing `multi_modal_data` with the audio, and either a `prompt` string or `prompt_token_ids`:
@ -102,7 +102,7 @@ It is assumed you have already implemented your model in vLLM according to the b
For further clarification on multi modal inputs, please refer to [Multi-Modal Inputs](../../features/multimodal_inputs.md). 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) ### B. Encoder–decoder audio-only (e.g., Whisper)
Return a dict with separate `encoder_prompt` and `decoder_prompt` entries: Return a dict with separate `encoder_prompt` and `decoder_prompt` entries:
@ -142,7 +142,6 @@ It is assumed you have already implemented your model in vLLM according to the b
return cast(PromptType, prompt) return cast(PromptType, prompt)
``` ```
- (Optional) Language validation via [validate_language][vllm.model_executor.models.interfaces.SupportsTranscription.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): If your model requires a language and you want a default, override this method (see Whisper):
@ -177,7 +176,6 @@ It is assumed you have already implemented your model in vLLM according to the b
return int(audio_duration_s * stt_config.sample_rate // 320) # example return int(audio_duration_s * stt_config.sample_rate // 320) # example
``` ```
## 2. Audio preprocessing and chunking ## 2. Audio preprocessing and chunking
The API server takes care of basic audio I/O and optional chunking before building prompts: The API server takes care of basic audio I/O and optional chunking before building prompts:
@ -264,10 +262,11 @@ Once your model implements `SupportsTranscription`, you can test the endpoints (
-F "model=$MODEL_ID" \ -F "model=$MODEL_ID" \
http://localhost:8000/v1/audio/translations http://localhost:8000/v1/audio/translations
``` ```
Or check out more examples in <gh-file:examples/online_serving>. Or check out more examples in <gh-file:examples/online_serving>.
!!! note !!! 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. - 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. - 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. - For multilingual behavior, keep `supported_languages` aligned with actual model capabilities.