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adventists-ai
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Model Details
Model Provider
adventists-ai
Model Tree
FunAudioLLM/SenseVoiceSmall
Input Modalities
Output Modalities
Supported Functionality
bash
pip install "duplexjev[speech]>=0.2.1" torchaudio # the SenseVoice encoder needs torchaudiopython
from duplexjev import Decider, Question d = Decider.from_pretrained("adventists-ai/DuplexJev-B-SenseVoice-Small-Qwen3-0.6B") # fits on one small GPU or CPU d.decide("utterance_en.wav", [ Question("turn", "Has the user finished speaking?", ["finished", "not finished"], lang="en"), Question("intent", "What does the user want?", ["climate", "media", "navigation", "phone", "chit-chat"], lang="en"),], lang="en")d.decide("utterance_zh.wav", [ # Chinese speech: Chinese question and options Question("turn", "用户说完了吗？", ["说完了", "还没说完"], lang="zh"), Question("intent", "用户想做什么？", ["空调", "媒体", "导航", "电话", "闲聊"], lang="zh"),], lang="zh")| Evaluation (%) | paper protocol | duplexjev 0.2.1 defaults |
|---|---|---|
| qa100 (spoken QA) | 33 | 37 |
| qa100, Qwen3-0.6B reading the transcript | 46 | – |
| ZJU-ML (spoken QA, real + TTS) | 24 | – |
| Gender, 800 real utterances | 54.9 | 51.9 |
| Emotion, 4-way, 800 utterances | 24.8 | 27.1 |
Paper protocol: the evaluation prompts of the paper (single option order, one H200). duplexjev defaults: the package's
own prompts with lang matched to the clip. Spoken QA with a small LLM is sensitive to the prompt wording, and it is
bounded by the LLM itself (see the transcript-reading row); use these connectors for short decisions and speaker cues
rather than open knowledge questions. For comparison, the Qwen3-32B connectors reach 90 (qa100), 89.9 (gender) and
90.0 (emotion); see adventists-ai.
Latency of one decision event (10 questions, one 4.5 s clip, one forward pass, duplexjev 0.2.1, plain PyTorch):
128 ms on one H200 (bf16); 1036 ms on 8 CPU threads (Xeon Platinum 8558, fp32, not quantized).
On-device NPUs and quantization have not been tested yet.
Frozen encoder and LLM; only the connector is trained, with the paper's recipe: R1 and R2 transcript distillation on two disjoint 0.5 M-utterance packs of the Ultravox v0.6 mixture (32k steps each, global batch 16). Code and scripts: https://github.com/adventists-ai/duplexjev.
Apache-2.0 for the connector weights. The frozen base models keep their own licences. Some training corpora (e.g. WenetSpeech, CoVoST 2) are licensed for non-commercial use only; check them before commercial use.
The SenseVoice-Small encoder (Alibaba Group, FunAudioLLM) is redistributed under the FunASR Model Open Source License; keep its attribution and model name.
bibtex
@inproceedings{jin2027duplexjev, title = {Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen {LLM} Hear Beyond the Transcript}, author = {Jin, Jie and Ma, Ziyin and Yin, Min and Chen, Jinyu and Song, Haigang and Pang, Zhikun and Zhang, Xiaowen}, booktitle = {Submitted to IEEE ICASSP}, year = {2027}}Built by Adventists.ai. Claude (Anthropic) assisted with code.
pip install "duplexjev[speech]>=0.2.1" torchaudio # the SenseVoice encoder needs torchaudiofrom duplexjev import Decider, Question d = Decider.from_pretrained("adventists-ai/DuplexJev-B-SenseVoice-Small-Qwen3-0.6B") # fits on one small GPU or CPU d.decide("utterance_en.wav", [ Question("turn", "Has the user finished speaking?", ["finished", "not finished"], lang="en"), Question("intent", "What does the user want?", ["climate", "media", "navigation", "phone", "chit-chat"], lang="en"),], lang="en")d.decide("utterance_zh.wav", [ # Chinese speech: Chinese question and options Question("turn", "用户说完了吗？", ["说完了", "还没说完"], lang="zh"), Question("intent", "用户想做什么？", ["空调", "媒体", "导航", "电话", "闲聊"], lang="zh"),], lang="zh")@inproceedings{jin2027duplexjev, title = {Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen {LLM} Hear Beyond the Transcript}, author = {Jin, Jie and Ma, Ziyin and Yin, Min and Chen, Jinyu and Song, Haigang and Pang, Zhikun and Zhang, Xiaowen}, booktitle = {Submitted to IEEE ICASSP}, year = {2027}}