vllm-omni/examples/online_serving/minicpmo

MiniCPM-o 4.5: Online serving

This directory contains MiniCPM-o 4.5 online serving demos for vLLM-Omni. Inputs can include text, image, audio, or video; outputs are text and optional 24 kHz speech.

For the native duplex runtime architecture, lifecycle invariants, capability boundary, and validation scope, see docs/design/fullduplex.md; for the DuplexClient API and the /v1/realtime?duplex=1 wire protocol, see docs/serving/realtime_duplex_api.md.

Installation

Install vLLM-Omni with the MiniCPM-o talker dependencies:

pip install stepaudio2-minicpmo

The minicpmo extra installs stepaudio2-minicpmo and its audio dependencies, including librosa.

Start the backend server

The deploy config auto-loads via --omni. The default vllm_omni/deploy/minicpmo_4_5.yaml keeps all three stages on logical device 0 with memory budgets of 55%, 15%, and 18%, leaving headroom for runtime kernels such as the HiFi-GAN vocoder's cuDNN workspace. The profile admits at most four sequences per stage and bounds Talker context to 4096 tokens. For throughput, minicpmo_4_5_2gpu.yaml gives the Thinker GPU 0 (90%) and colocates the Talker (55%) and Code2Wav (35%) on GPU 1. That profile admits at most four concurrent sequences per stage.

deploy config	GPUs	Notes
minicpmo_4_5.yaml (default)	1	Memory-constrained compatibility layout.
minicpmo_4_5_2gpu.yaml	2	Recommended continuous-batching layout; Talker and Code2Wav share GPU 1.
minicpmo_4_5_3gpu.yaml	3	One GPU per stage.
minicpmo_4_5_8x4090.yaml	8	Full 8x4090 layout.

Every profile sets session_mode: duplex, so native full-duplex is served from the same process as /v1/chat/completions.

The split pipeline preserves native-duplex epoch/turn identity, segment text, turn completion, reference voice, and terminal-audio metadata through Code2Wav. Focused CPU regressions cover this envelope; run the Realtime scenario below for live barge-in validation on the target GPU.

Default:

vllm serve openbmb/MiniCPM-o-4_5 \
    --omni \
    --deploy-config vllm_omni/deploy/minicpmo_4_5.yaml \
    --trust-remote-code \
    --host 0.0.0.0 --port 8099

For local ModelScope checkpoints, replace openbmb/MiniCPM-o-4_5 with the checkpoint path. For native full-duplex, connect /v1/realtime?duplex=1 on this same server.

Per-stage overrides

vllm serve openbmb/MiniCPM-o-4_5 --omni --trust-remote-code --port 8099 \
    --stage-overrides '{"0": {"gpu_memory_utilization": 0.55}}'

Send multimodal requests

cd examples/online_serving/minicpmo

curl

bash run_curl_multimodal_generation.sh text
bash run_curl_multimodal_generation.sh use_image
bash run_curl_multimodal_generation.sh use_audio '["text"]'

python openai_chat_completion_client_for_multimodal_generation.py \
    --query-type use_image \
    --port 8099 \
    --host localhost

# Text-only (faster; no <|tts_bos|>)
python openai_chat_completion_client_for_multimodal_generation.py \
    --query-type text \
    --modalities text \
    --prompt "Briefly introduce yourself."

Streaming text + audio (use --stream):

python openai_chat_completion_client_for_multimodal_generation.py \
    --query-type text \
    --prompt "Briefly introduce yourself." \
    --port 8099 \
    --stream

The client prints text deltas as they arrive and saves streamed audio chunks to WAV files.

Shared helpers also work if you pass MiniCPM defaults yourself:

python ../openai_chat_completion_client_for_multimodal_generation.py \
    --model openbmb/MiniCPM-o-4_5 \
    --query-type text \
    --port 8099

Speech output no longer depends on a MiniCPM-specific default in the generic serving layer. chat_template_kwargs.use_tts_template=true remains an explicitly supported model option.

Launch the Gradio demo

bash examples/online_serving/minicpmo/run_gradio_demo.sh

# Or run the Python entry point directly:
python examples/online_serving/minicpmo/gradio_demo.py \
    --minicpmo45-api-base http://localhost:8099/v1 \
    --minicpmo45-model openbmb/MiniCPM-o-4_5 \
    --port 7862

Open http://<host>:7862 in a browser.

Daily-Omni accuracy

Daily-Omni requires one A–D letter. Set chat_template_kwargs.enable_thinking=false explicitly in --extra-body; the generic benchmark CLI does not inject a MiniCPM-specific default. Leaving reasoning enabled can exhaust the 256-token answer budget inside <think> and make first-letter extraction score reasoning text.

For the established text benchmark, send --extra_body '{"modalities":["text"],"chat_template_kwargs":{"enable_thinking":false}}'. Requesting audio also benchmarks Talker and Code2Wav and changes the assistant template, so it is not an apples-to-apples accuracy run.

Run the Realtime duplex CLI demo

After the server is running, stream one WAV through the Realtime WebSocket endpoint:

python examples/online_serving/minicpmo/realtime_duplex_demo.py \
    --url ws://localhost:8099/v1/realtime?duplex=1 \
    --model openbmb/MiniCPM-o-4_5 \
    --input-wav /path/to/input_16k_mono_pcm16.wav \
    --ref-audio /path/to/MiniCPM-o-Demo/assets/ref_audio/ref_minicpm_signature.wav \
    --output-dir /tmp/minicpmo_realtime_duplex_demo

Video input uses the same session. PyAV demuxes JPEG frames at --video-fps (default 1.0) and vLLM load_audio extracts a 16 kHz mono WAV unless --input-wav overrides the soundtrack. Frames keep their capture size (--frame-max-side 0); the server process_image normalizes at 448.

--stack-frames N raises the visual refresh rate the way official duplex does: each 1 s unit also samples the N-1 sub-frames captured inside it and tiles them into a single composite image sent next to that unit's base frame. The audio cadence never changes — a unit is always one second — and the wire always carries 2 images per unit however large N is, because the sub-frames share one composite. Official uses 5 for high refresh rate mode:

python examples/online_serving/minicpmo/realtime_duplex_demo.py \
    --url ws://localhost:8099/v1/realtime?duplex=1 \
    --model openbmb/MiniCPM-o-4_5 \
    --input-video /path/to/clip.mp4 \
    --stack-frames 5 \
    --ref-audio /path/to/MiniCPM-o-Demo/assets/ref_audio/ref_minicpm_signature.wav \
    --output-dir /tmp/minicpmo_realtime_duplex_video_demo

Stage 0 processes a stacked unit the way official HD mode does (max_slice_nums=[2, 1]): the base frame keeps its own 66-token block plus the patches get_sliced_grid cuts for its size at scale_resolution=448, and the composite adds one block. The wire still rejects a caller-supplied max_slice_nums, so slicing is Stage 0's decision, not the client's.

That detail costs context. One 960x540 frame is 66 vision tokens per unit and a stacked pair is 264, on top of the unit's audio, so the Stage 0 prompt grows by 277 tokens per second stacked against 79 with a single frame. Against the 40960-token context this model ships with, a listen-heavy call reaches the limit after about 2.5 minutes stacked and about 8.5 minutes unstacked (measured on a 264 s 960x540 clip). Duplex sessions cannot yet evict old units, so keep multi-minute calls on the default --stack-frames 1; a stacked session that outgrows the context ends with a context-length error.

Open the experimental browser client

The browser UI serves the page and proxies the same-origin Realtime WebSocket to the backend:

python -m examples.online_serving.minicpmo.realtime_web \
    --port 7862 \
    --ws-backend ws://127.0.0.1:8099 \
    --ref-audio /path/to/MiniCPM-o-Demo/assets/ref_audio/ref_minicpm_signature.wav

Open http://<host>:7862/. When using a reverse proxy, open the URL mapped to port 7862; the browser derives its WebSocket endpoint relative to that URL.

If the page proxy serves HTTP but does not forward WebSocket upgrades, point the browser at a separately exposed Realtime endpoint:

python -m examples.online_serving.minicpmo.realtime_web \
    --port 7862 \
    --ws-backend ws://127.0.0.1:8099 \
    --public-realtime-url wss://public.example/v1/realtime

Validate soft-interrupt behavior

The soft-interrupt E2E driver defaults to --validation-mode model-policy, which checks lifecycle and streaming invariants for arbitrary input audio. The stronger response-required mode is diagnostic: it requires a purpose-built two-response WAV, its --input-sha256, and an --expect-second-response-substring value.

Run Omni-DuplexEval

Omni-DuplexEval generation uses the vLLM-Omni native MiniCPM-o duplex endpoint. Evaluation uses a separate OpenAI-compatible multimodal judge served from the same vLLM-Omni environment. This validates the vLLM-Omni duplex client and local judge integration; it does not reproduce the paper's original MiniCPM-o inference implementation.

Start the duplex generation server:

vllm serve openbmb/MiniCPM-o-4_5 --omni --trust-remote-code \
    --deploy-config vllm_omni/deploy/minicpmo_4_5.yaml \
    --served-model-name openbmb/MiniCPM-o-4_5 \
    --host 0.0.0.0 --port 8099

Start a separate multimodal judge. The allowed path must contain any local videos passed with --judge-video-mode video_url:

vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
    --served-model-name Qwen/Qwen2.5-VL-7B-Instruct \
    --allowed-local-media-path /data/omni-duplex-eval \
    --host 0.0.0.0 --port 8000

Generate, evaluate, and summarize:

vllm bench omni-duplex-eval --omni generate \
    --url ws://127.0.0.1:8099/v1/realtime?duplex=1 \
    --model openbmb/MiniCPM-o-4_5 \
    --ref-audio /data/ref.wav \
    --dataset Hothan/Omni-DuplexEval \
    --concurrency 2 \
    --response-root /data/omni-duplex-eval/responses

vllm bench omni-duplex-eval --omni evaluate \
    --dataset Hothan/Omni-DuplexEval \
    --response-root /data/omni-duplex-eval/responses \
    --score-root /data/omni-duplex-eval/scores \
    --judge-base-url http://127.0.0.1:8000 \
    --judge-model Qwen/Qwen2.5-VL-7B-Instruct \
    --judge-video-mode video_url \
    --eval-workers 4

vllm bench omni-duplex-eval --omni summarize \
    --score-root /data/omni-duplex-eval/scores

The dataset names such as RTD_OCR and PR_correction are Hugging Face splits, not dataset configurations. Pass one with --split, or omit it to run all nine splits. --limit 1 is useful for a smoke test. Realtime generation records clock=media; artifacts generated with --pace as-fast-as-possible record clock=invalid and evaluation rejects them unless --allow-invalid-clock is explicit.

Related examples

• Offline MiniCPM-o inference
• MiniCPM-o 4.5 recipe

Pipeline notes

• Stage 1 performs request-owned AR continuous batching. Stage 2 keeps request-owned Flow/HiFT caches and batches exact-shape-compatible chunks.
• Reference audio travels with the first codec chunk; Stage 2 owns its temporary prompt WAV and evicts prompt features when the request finishes.
• Codec sampling reads the checkpoint tts_config (default deterministic seed 42). Stage-1 YAML sampling parameters govern only the binary continue/stop token exposed to vLLM.
• StageRequestStats.batch_size is request-scoped and does not report the scheduler's execution batch.
• Stage 0 and Stage 1 use vLLM CUDA Graph capture. Stage 2 remains eager until a dedicated exact-shape graph wrapper owns static I/O buffers and copies request cache state outside capture.
• Co-locating all three stages minimizes hardware requirements but makes their CUDA contexts contend for one GPU. Use the 8x4090 layout or a custom multi-GPU deploy config when throughput is the primary goal.
• Output audio is base64 WAV in message.audio.data (24 kHz mono).
• Offline counterpart: examples/offline_inference/minicpmo/
• Recipe: recipes/OpenBMB/MiniCPM-o-4_5.md

Shared realtime UI implementation

The python -m examples.online_serving.minicpmo.realtime_web command remains available with the same MiniCPM defaults and required --ref-audio. Its assets now live in the shared realtime UI, with a dedicated minicpm-native profile preserving native duplex input, playback ACKs and camera frames. Qwen3 uses a separate profile in the same shell, supporting manual STT turns or Server VAD turns with speech interruption.