pipecat-duplexjev

Audio end-of-turn detection for Pipecat, powered by DuplexJev. It listens to how the user speaks, not only what they say, and tells your bot whether the user is finished, still talking, just backchanneling ("mm-hmm") or asking you to wait. The same request can also return emotion, laughter / breathing / coughs / sighs and barge-in at no extra cost.

• Four turn states, not a yes/no. A backchannel or a "hold on" is not a reason to start talking.
• Hears beyond the transcript. Every answer is read from the audio as one constrained token of a speech LLM. Nothing is decoded and no STT is needed for the decision.
• More than turns, same forward pass. Ask for emotion, non-verbal sounds or your own multiple-choice questions. They come back in last_answers with the turn decision.
• ~55 ms on the server for a turn decision with DuplexJev-4B-Para on one GPU.
• Chinese and English.
• On the CoDeTT turn-taking benchmark (English, zero-shot), DuplexJev-32B-Turn scores 70.0, against 51.4 for Smart Turn v3. See the paper and the project page.

Tested with pipecat-ai 1.12.0. Maintained by Adventists.ai (AI降临派).

Install

pip install pipecat-duplexjev

Use

Drop it in wherever Pipecat takes a turn analyzer (Pipecat >= 1.0):

from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair, LLMUserAggregatorParams
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies
from pipecat_duplexjev import DuplexJevTurnAnalyzer

turn = DuplexJevTurnAnalyzer(
    url="http://localhost:8420",          # your own gateway; omit to use the free hosted trial API
    extra_questions=["emotion", "sound"],
    on_answers=lambda a: print({k: v["answer"] for k, v in a.items()}),
)

aggregators = LLMContextAggregatorPair(context, user_params=LLMUserAggregatorParams(
    vad_analyzer=SileroVADAnalyzer(),
    user_turn_strategies=UserTurnStrategies(stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=turn)]),
))

A complete snippet is in examples/wiring.py. examples/live_check.py feeds real clips through the analyzer, frame by frame, the way a transport does:

ex4.wav: COMPLETE   p(finished)=1.00  turn='finished, the assistant can reply'   emotion=neutral  server 59.6 ms
ex3.wav: INCOMPLETE p(finished)=0.11  turn='not finished, still talking'          emotion=sad      server 53.2 ms
ex5.wav: INCOMPLETE p(finished)=0.28  turn='hesitating or asking to wait'         emotion=angry    server 56.0 ms

Run the model yourself (recommended for real-time)

The default URL is our free trial API. It is rate limited (30 requests per minute) and served from mainland China, so the network round trip from elsewhere can be over a second. For a live bot, serve the model next to your agent. DuplexJev-4B-Para needs about 10 GB of GPU memory:

pip install "vllm[audio]>=0.29" duplexjev-vllm "duplexjev[server]>=0.4.1"
vllm serve adventists-ai/DuplexJev-4B-Para --max-model-len 4096 --port 8000 &
duplexjev gateway --vllm http://localhost:8000/v1 --port 8420

Then pass url="http://localhost:8420".

Options

If the gateway can't be reached, the analyzer logs the error and reports "not finished", so Pipecat falls back to its silence timeout and the conversation never blocks.

License

The plugin code is BSD-2-Clause. DuplexJev model weights are CC BY-NC 4.0 (non-commercial); see the model cards. For commercial use, contact jiejin@adventists.ai.