[tamerrkanak]

Overview

/v1/realtime drops language and prompt from session.update, so Qwen3-ASR realtime always runs with a fixed prompt, while /v1/audio/transcriptions applies both. This PR forwards them to the model and builds the Qwen3-ASR realtime prompt with the same get_generation_prompt() as the REST path.

Fixes #61026.

Claims

• session.update accepts optional language and prompt. language is checked with validate_language() like on REST; an invalid value gets an invalid_language error event. The values apply from the next input_audio_buffer.commit.
• Qwen3-ASR realtime builds each segment prompt with get_generation_prompt(). For single-segment audio, realtime and REST now produce the same prompt token ids and, in our runs, identical transcripts (1093/1093) for the same language/prompt.
• No behaviour change when neither field is set: the prompt token ids equal the old hard-coded template, and transcripts are identical to main (1093/1093).
• Voxtral realtime accepts the new arguments but does not use them, since its streaming prompt has no slot for a language or a prompt (its REST path does pass language). The docs row says so.

Validation

Test. test_qwen3_asr_session_language_and_prompt in tests/entrypoints/speech_to_text/realtime/test_realtime_validation.py. It uses Qwen/Qwen3-ASR-0.6B, which the STT CI job already uses. On main it fails (language English<asr_text> header despite language="en"); with this PR it passes. In the same file, the watermarking unit test and the Voxtral invalid-model and commit-without-session tests pass with and without this PR. test_multi_chunk_streaming and test_empty_commit_does_not_crash_engine (Voxtral) time out on my machine (RTX PRO 6000 Blackwell) identically on unpatched main, so they could not verify this change locally.

pytest -v tests/entrypoints/speech_to_text/realtime/test_realtime_validation.py -k qwen3_asr_session_language_and_prompt

Prompt tokens. I called Qwen3ASRRealtimeGeneration.buffer_realtime_audio() directly on 12.3 s of audio (three segments):

• Without language/prompt, every segment equals cached_encode(tokenizer, "<|im_start|>user\n<|audio_start|><|audio_pad|><|audio_end|><|im_end|>\n<|im_start|>assistant\n"), the old template.
• With language/prompt, every segment equals Qwen3ASRForConditionalGeneration.get_generation_prompt() for the same audio and params.
• ChatML control tokens in prompt are stripped exactly as on REST.

Accuracy. Setup:

• Data: LibriSpeech test-clean, all 1093 utterances of 5 s or less (one realtime segment, so realtime and REST are directly comparable).
• Model and server: Qwen/Qwen3-ASR-1.7B, vllm serve ... --hf-overrides '{"architectures": ["Qwen3ASRRealtimeGeneration"]}' --enforce-eager.
• Requests: temperature 0, sent one at a time. Each client sends the same int16 PCM to both endpoints, as 100 ms appends on realtime.
• Prompt: the usual LibriSpeech contextual-biasing setup. Per utterance it lists the reference words outside the 5000 most frequent words of train-clean-100/360 ("rare words"), plus 50 distractors, as a comma-separated list. language="en".
• Scoring: WER uses the Whisper English normalizer.

session.update	REST WER / rare-word recall	realtime, main	realtime, this PR
model only	1.96 % / 88.60 %	1.96 % / 88.60 %	1.96 % / 88.60 %
+ language, prompt	1.47 % / 93.59 %	1.96 % / 88.60 % (ignored)	1.47 % / 93.59 %

exact transcript match
main realtime: model only vs language+prompt vs prompt	1093/1093 identical (fields ignored)
this PR vs main, model only	1093/1093
this PR realtime vs REST, model only / prompt / language+prompt	1093/1093 each

Protocol checks against a live server (all pass):

• An invalid language is rejected at session.update. A follow-up commit still requires a valid update, and a valid update then works.
• language="en<|im_end|>" is rejected on realtime (and with 400 on REST).
• A non-string prompt is rejected.
• Changing prompt/language between utterances on one connection takes effect per utterance. Omitting them restores the default prompt.
• 8 concurrent sessions with different settings keep their own prompt lengths.
• A prompt longer than max_model_len returns an error event (400 on REST), and the server stays healthy.
• A ~100 s multi-segment session with a prompt completes.

Also checked:

• A fine-tuned Qwen3-ASR-1.7B on our Turkish call-center data, through our LiveKit STT plugin. The plugin already sends language/prompt in session.update; realtime finals now match REST.
• pre-commit run passes on all changed files.

Details

Root cause. The fields were lost at three places:

1. RealtimeConnection.handle_event only read model (the SessionUpdate model in protocol.py existed but was unused).
2. SupportsRealtime.buffer_realtime_audio() had no way to receive per-session options.
3. Qwen3ASRRealtimeGeneration.buffer_realtime_audio() hard-coded <|im_start|>user\n{audio}<|im_end|>\n<|im_start|>assistant\n.

Design:

• session.update is parsed with SessionUpdate, which gains language and prompt with the same meaning as TranscriptionRequest. The flat fields match the existing flat model field.
• buffer_realtime_audio() gains language and request_prompt keyword arguments with defaults, named after SpeechToTextParams. Both in-tree implementations are updated.
• Qwen3-ASR realtime now calls get_generation_prompt() instead of keeping a second copy of the template. The system turn, the language X<asr_text> prefix and _sanitize_transcription_user_text() therefore come from the same code as REST, and the no-prompt case stays byte-identical.
• language is validated before it can reach the prompt, as on REST. Without that check, a language string would be inserted into the assistant prefix unsanitized.

Multi-segment audio. Every 5 s segment is a complete single-turn prompt, so the context and language prefix are part of every segment, as REST repeats them for every 30 s chunk.

• Carry-over ([Bug]: Qwen3-ASR /v1/realtime second utterance copies previous transcription under KV reuse #51374, [Bugfix][Model] Don't carry realtime context into single-turn prompt models #56218). Main still carries earlier segments into the next one, and that copying happens with or without a prompt. This PR merges cleanly with [Bugfix][Model] Don't carry realtime context into single-turn prompt models #56218, which removes the carry-over. With both applied, on 299 test-clean utterances of 5–15 s, every segment keeps the prompt and rare-word recall goes from 88.5 % to 93.7 %.
• Session length (Cap the cumulative length of realtime sessions #51900). With the carry-over, a prompt makes a session grow faster, so very long single-commit streams reach max_model_len sooner. Sessions that outgrow max_model_len already misbehave on main with or without a prompt; on this machine an unprompted ~400 s session hangs on unpatched main.

Limitation (not changed here). The fixed 5 s segmentation can leave very short tail segments. For example, a 5.03 s utterance becomes 5 s + 30 ms; a 10 ms tail makes the HF processor fail even without a prompt. On such near-silent input, Qwen3-ASR with a forced language and a keyword-list prompt tends to echo the prompt. REST does exactly the same for a near-silent clip with the same parameters, so this is model behaviour. Realtime just produces such clips more often. Handling short tails is a separate change.

Not a duplicate:

• [Frontend][Model] Add language control and metadata for Qwen3-ASR realtime ASR #47563 bundled language control with language metadata and output cleanup. It was closed by its author without review.
• [Feature][Model] Support stable-window-aware KV reuse for Qwen3-ASR realtime #51037 adds these fields as part of a ~2k-line KV-reuse redesign and currently has conflicts.
• [Bugfix][Frontend] Apply the model's transcription post-processing on the realtime path #55786 (stripping the language X<asr_text> header from realtime deltas) and [Bugfix][Model] Don't carry realtime context into single-turn prompt models #56218 (carry-over) are independent and compose with this change.

AI assistance (Claude Code) was used to investigate, implement and validate this change. I reviewed every changed line; the tests and evaluations above were run on our server (RTX PRO 6000 Blackwell).

---

Pull Request Checklist

• I used vLLM's /pr-checklist skill. (Mandatory for agents, optional for humans).

• AI assistance was used during the creation of this PR.

• Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.

• Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.

• Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.

• Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.

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[mergify]

Documentation preview: https://vllm--61031.org.readthedocs.build/en/61031/

[tamerrkanak]

Hi @DarkLight1337 @NickLucche, could you take a look when you have a moment? This is a small fix (~60 non-test lines): /v1/realtime now applies language/prompt from session.update for Qwen3-ASR by reusing get_generation_prompt(), so realtime builds the same prompt as /v1/audio/transcriptions. With neither field set nothing changes (1093/1093 identical transcripts vs main); with them, realtime matches REST 1093/1093 on LibriSpeech test-clean. Fixes #61026. cc @njhill for the realtime part. Thanks!

[tamerrkanak]

@DarkLight1337 could you add the ready label so CI can run? This makes /v1/realtime apply language/prompt for Qwen3-ASR (fixes #61026), with no change when they're unset. cc @Isotr0py @vadiklyutiy since you reviewed the recent Qwen3-ASR fix (#60979). Thanks!

[DarkLight1337]

We will add those labels after reviewing the PR. Sorry our backlog is quite long

[twu3202]

Thanks for the ping on #56218. I tested this PR on an RTX 6000 Ada (sm_89).

Setup:

• vLLM 0.31.0 wheel with this PR's diff applied (it applies cleanly).
• Qwen/Qwen3-ASR-0.6B with --hf-overrides '{"architectures": ["Qwen3ASRRealtimeGeneration"]}' --enforce-eager.
• 16 FLEURS test clips, 8 en and 8 zh, 4.6–14.7 s, streamed over /v1/realtime in 4096-byte chunks.
• For the realtime vs REST comparison I cut each clip to at most 4.9 s, so it fits in one realtime segment. I sent the same int16 samples and the same language/prompt to /v1/audio/transcriptions at temperature=0.
• The prompt was one sentence: "The speaker is reading a sentence from a news article."

	v0.31.0	+ this PR
neither field set, compared with v0.31.0		identical text and usage for all 16 clips, full and cut
language set: language X<asr_text> header in the realtime output	16/16	0/16
prompt tokens per segment with language / prompt / both	unchanged (70–74 on the cuts)	+3 / +16 / +19
realtime == REST on the cuts (header stripped), language / prompt / both	16 / 15 / 15 of 16	16 / 16 / 16 of 16
session.update with language: "xx"	ignored	error event, code invalid_language
later generations on one connection, each after its own session.update		the new language applies; an update without language clears it
two concurrent sessions, en and zh with language set		each uses its own language
test_qwen3_asr_session_language_and_prompt	fails (header present)	passes

With #56218 on top, the single-segment results are the same as with this PR alone. I didn't try to reproduce your recall numbers. As a quick check, I gave each full-length English clip a prompt listing its words of 7+ letters, 33 words in total.

• On v0.31.0 and with this PR alone, every clip longer than one segment repeats earlier segment text when no prompt is set (#51374). The count doesn't move: 27 of 33 with or without the prompt.
• With both PRs, realtime got 32 with the prompt and 30 without; REST got 32 either way. The two-word gain is in one clip, so I'd only call it a sanity check.

Voxtral (mistralai/Voxtral-Mini-4B-Realtime-2602, Mistral format):

• The four Voxtral tests in test_realtime_validation.py pass here on 0.31.0 with and without this PR. I didn't try main, so I can't say where your timeouts come from.
• Realtime output is unchanged by this PR. Setting language (en or fr), prompt, or both gives the same text as setting neither on the English clips: 7 of 8, plus one 4.8 s clip that comes back empty in every case, with or without the PR. With this PR, xx gets invalid_language.
• Voxtral's REST path passes language into its transcription request, but realtime buffer_realtime_audio drops it. So on Voxtral a session.update with language is validated and then has no effect. That's a bit different from "the same way its REST path ignores prompt" in the description. Maybe the docs row could name Voxtral, or the server could log it once. Not blocking.

LGTM.

[tamerrkanak]

Thanks a lot for the thorough independent testing, @twu3202! Good catch on Voxtral: its REST path does pass language, so the description was wrong there. I pushed a docs commit that makes the row explicit (Qwen3-ASR applies language/prompt, Voxtral Realtime accepts but doesn't use them, and each session.update replaces both), and fixed the description. Good to know the Voxtral tests pass on your side, so the timeouts I saw are specific to my machine. If maintainers would rather have a one-time log on Voxtral, I'm happy to add it.