81 lines
3.3 KiB
Markdown
81 lines
3.3 KiB
Markdown
---
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base_model: enochlev/MiniCPM-duplex
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tags:
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- full-duplex
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- speech
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- turn-taking
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- reinforcement-learning
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---
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# MiniCPM-duplex-rl
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A full-duplex turn-taking RL fine-tune of
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[`enochlev/MiniCPM-duplex`](https://huggingface.co/enochlev/MiniCPM-duplex)
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(MiniCPM-duplex, from `xinrongzhang2022/MiniCPM-duplex`). The model decides every
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~1.7 s block whether to speak or stay silent while the user may also be speaking;
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this checkpoint was trained with REINFORCE over block-level turn-taking rewards
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(interruption penalties, timely-response rewards, silence penalties) for 180 steps.
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**Effect:** compared to the base model it interrupts the user less, yields to
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barge-ins, and resumes after overlapping speech — trading away some take-turn
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responsiveness on direct interruptions.
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Training + serving code: [enochlev/text-only-duplex-model](https://github.com/enochlev/text-only-duplex-model)
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## Serving
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Serve **bf16** (fp8 + greedy sampling breaks the idle/speak decision):
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```bash
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vllm serve enochlev/MiniCPM-duplex-rl \
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--served-model-name cpm-text-duplex --max-model-len 3000 \
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--gpu_memory_utilization 0.30 --trust-remote-code
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```
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then point the repo's `server.py --cpm` at it for the real-time audio stack
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(Kokoro TTS + Parakeet ASR + WebSocket client protocol).
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## FullDuplexBench results (base vs this model)
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Evaluated with [Full-Duplex-Bench](https://github.com/DanielLin94144/Full-Duplex-Bench)
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(GPT-4o behavior classification).
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### v1.5 — behavior distribution + stop/response latency (pooled, seconds)
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| Task (desired) | Model | n | RESPOND | RESUME | Stop (s) | Resp (s) |
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|---|---|---|---|---|---|---|
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| user_interruption (RESPOND ↑) | base | 200 | **0.65** | 0.20 | 2.17 | 1.93 |
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| | rl | 175 | 0.49 | 0.36 | 2.21 | 2.60 |
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| user_backchannel (RESUME ↑) | base | 98 | 0.00 | 0.52 | 0.73 | 1.93 |
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| | rl | 98 | 0.00 | **0.63** | 0.67 | 2.03 |
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| talking_to_other (RESUME ↑) | base | 100 | 0.47 | 0.24 | 1.43 | 1.90 |
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| | rl | 100 | 0.28 | **0.43** | 1.53 | 2.18 |
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| background_speech (RESUME ↑) | base | 100 | 0.63 | 0.25 | 1.21 | 2.27 |
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| | rl | 98 | 0.45 | **0.31** | 1.19 | 2.32 |
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The RL model wins the three tasks whose desired behavior is *staying quiet /
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resuming* (backchannels, third-party speech, background speech) and is less eager
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on direct user interruptions.
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### v1.0 — turn-taking dimensions
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| Metric | base | rl |
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|---|---|---|
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| Candor Pause Handling · take-turn | 0.916 | 0.635 |
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| Candor Turn Taking · take-turn / latency | 0.992 / 0.31s | 0.861 / 0.85s |
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| ICC Backchannel · JSD / TOR / Freq | 0.44 / 0.71 / 0.44 | 0.69 / 0.73 / 0.15 |
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| Synthetic Pause Handling · take-turn | 0.934 | 0.653 |
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| Synthetic User Interruption · rating / take-turn / latency | 4.15 / 1.0 / 0.71s | 4.04 / 0.98 / 1.76s |
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v1.0's take-turn/latency conventions favor the eager base model; the consistent
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direction across both versions reflects the RL objective — restraint over
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eagerness.
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## Training summary
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- 180 REINFORCE steps, lr 5e-6, 32 episodes/step, γ=0.90, per-batch z-scored advantages
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- Block-level rewards: interruption penalty, timely-response reward, silence
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penalty, missed-turn penalty, backchannel-loop penalty
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- Seed-reproducible (two independent seeds: best avg reward +1.36 / +1.35);
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replay eval cut stale-overlap speech ~45% vs base without going over-silent
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