Datasets:
audio audioduration (s) 0.5 10.5 | label class label 2
classes |
|---|---|
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
0duplex_session | |
1user_turns | |
1user_turns | |
1user_turns | |
1user_turns | |
1user_turns | |
1user_turns | |
1user_turns | |
1user_turns |
YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Trusted Full-Duplex Speech Agent — Training Data & Evidence
Companion dataset for model: jatshi/trusted-full-duplex-agent and GitHub: Jatshi/trusted-full-duplex-agent.
Contents
GRPO training data
| File | Rows | Task |
|---|---|---|
rl_streams/bargein_context.jsonl |
60 | barge-in context recall: each row = user request → truncated mid-sentence assistant turn (dangling, no end token) → user barge-in → probe instruction ("打断之前你说到哪了？"). 5 topics × 3 truncation points × 4 barge-in phrasings. interrupted_content field is the ground truth for the bigram-Jaccard context_recall metric. |
rl_streams/rl_streams.jsonl |
300 | turn-taking / guardrail alignment samples (execute/clarify/stop families) |
Turn-taking decision training
| File | Description |
|---|---|
turntaking/frames.npz |
200ms-frame features + hold/take/backchannel labels (synthesized pause patterns: filler vs. final pauses, energy decay, pre-pause drop). Trains the external turn-taking MLP (96.5% frame accuracy, 0% false speaking rate). |
User turn wavs (16kHz, Windows SAPI Huihui)
user_turns/*.wav — 5 guardrail scenarios + 3 barge-in turns
(open / stop / probe) used for the real-base duplex session and the barge-in
A/B attribution experiment (--bargein-delay-ms 500 → 5000).
Real-base evidence (MiniCPM-o 4.5 9B, AutoDL RTX 4080 SUPER 32GB)
duplex_session/ — five-scenario guardrail session audio, barge-in report
JSONs (immediate vs. delayed interruption) and turn wavs backing the A/B
attribution finding: probe bigram overlap 0.0 → 0.089 when the bot played 6s
instead of 1s before being interrupted; recall shows recency bias + hallucination
of un-played content.
Reproduce
git clone https://github.com/Jatshi/trusted-full-duplex-agent
cd trusted-full-duplex-agent
python scripts/33_prep_bargein_rl_data.py # regenerates bargein_context.jsonl
python scripts/31_prep_rl_data.py # regenerates rl_streams.jsonl
python scripts/60_turntaking_train.py # retrains the MLP from frames.npz
Seed = 42 everywhere (see configs/rl.yaml).
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