Audio Translations#

This guide uses Whisper ASR as an example model with the OpenAI-compatible POST /v1/audio/translations endpoint, which translates source speech to English. The endpoint is served only by pipelines that declare audio-translation capability; other ASR models return an explicit HTTP 400 instead of silently transcribing.

Prerequisites#

Install sglang-omni by following Installation, then download a multilingual Whisper checkpoint:

hf download openai/whisper-large-v3

Translation requires a multilingual, non-turbo checkpoint: *.en checkpoints have no translate task, and whisper-large-v3-turbo was distilled without it, so serving those checkpoints returns transcriptions regardless of endpoint.

Launch the Server#

sgl-omni serve \
  --model-path openai/whisper-large-v3 \
  --port 8000

Translate Audio#

curl -X POST http://localhost:8000/v1/audio/translations \
  -F model=openai/whisper-large-v3 \
  -F file=@tests/data/query_to_cars.wav \
  -F language=fr \
  -F response_format=json

language is an optional source-language hint and a SGLang-Omni extension; OpenAI’s official audio translations schema does not define it, and the translation target is English in both APIs.

Model Support#

Model	`/v1/audio/translations`
Whisper ASR	Supported
Qwen3-ASR	HTTP 400
Fun-ASR	HTTP 400
ARK-ASR	HTTP 400
MOSS-Transcribe-Diarize	HTTP 400

See each model’s cookbook for its transcription workflow.

Response Formats#

`response_format`	Behavior
`json`	JSON object containing `text`
`verbose_json`	JSON with `task="translate"`, text, duration, and segments
`text`	Raw translated text with `text/plain` content type
`srt`, `vtt`	Not supported; HTTP 400

verbose_json returns a single segment spanning the audio duration, matching /v1/audio/transcriptions.

Streaming is available with stream=true for json and text, using the same SSE lifecycle as transcriptions: transcript.text.delta events, one transcript.text.done event, then data: [DONE].

Audio Translations

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