mindlogicinc/context-spanning-7b

Context Spanning · DuetaSpan-7B

This repository holds the released DuetaSpan-7B weights described in Context Spanning: A Communication Framework for Full-Duplex Speech Models and External LLM Backends (Go, Kim, Cha, Shin — Mindlogic, 2026, arXiv:2609.33443). The inference and training code lives at mindlogic-ai/Context-Spanning; the weights load only through that package.

A full-duplex speech model that keeps listening and talking while it calls an external backend. When the model emits <ret>, the recent user audio is transcribed, a tool router (with an LLM for knowledge questions) returns one reference, and that reference is written into the model's own context stream as-is as a Context Span block. The frame clock never waits, and the model reads exact values (time, weather, prices, distances) instead of a latent summary.

Files

file	content
context_spanning_7b.pt	{"model": state_dict} in bf16, loadable with main.py infer --checkpoint or fetched automatically
voices/{main,f0,f1,f2,f3,m0,m1,m2,m3}.pt	voice prompts ({"codes": LongTensor[8, P]}, agent-voice Mimi codes; 8 s each)
assets/figures/	the paper's figures used by this card

Mimi and the text tokenizer are taken from the PersonaPlex repository at load time; the moshi package is vendored in the code repository (contextspan/moshi/).

Model Overview

• Type: full-duplex speech-to-speech model (Moshi architecture, PersonaPlex fork)
• Base model: nvidia/personaplex-7b-v1, all trainable parameters fine-tuned
• Parameters: 7B
• Audio codec: Mimi, 12.5 Hz, 8 codebooks per channel (user and agent)
• Frame clock: 80 ms per step
• Sequence convention: <ret> = 4, span open = 12, span close = 13, text pad = 3
• Released checkpoint: 2026-09-23
• Backends used for the results: router google/gemma-4-26B-A4B-it on vLLM, ASR Qwen3-ASR-1.7B
• Language: English

Benchmark Results

Full runs of the released checkpoint with the released router (Gemma-4-26B-A4B on vLLM) and ASR (Qwen3-ASR-1.7B); protocols and scorers are in benchmark/README.md. Rows marked † are reprinted from the original benchmark papers (Full-Duplex-Bench, PersonaPlex) or from MoshiRAG. Arrows give the direction of better.

Table 1. Full-Duplex-Bench v1 (727 clips).

Task	Metric	Ours	PersonaPlex†	Moshi†	Gemini Live 2.5†
Pause Handling	TOR, Candor ↓	0.824	0.431	0.980	0.310
	TOR, synthetic ↓	0.861	0.358	0.985	0.255
Backchannel	TOR ↓	0.782	0.273	1.000	0.091
	Frequency (per s) ↑	0.150	0.042	0.001	0.012
	JSD ↓	0.716	0.662	0.957	0.896
Turn-Taking	TOR ↑	0.899	0.908	0.941	0.655
	Latency (s) ↓	0.064	0.170	0.265	1.301
User Interruption	TOR ↑	0.925	0.950	1.000	0.891
	GPT-4o rating (0-5) ↑	3.924	4.290	0.765	3.376
	Latency (s) ↓	0.598	0.240	0.257	1.183

TOR is the take-over rate, the fraction of clips in which the model takes the turn; JSD is the Jensen-Shannon divergence from the human backchannel timing distribution.

Table 2. Full-Duplex-Bench v3 (100 scenarios, 12 mock tools). MoshiRAG is the released kyutai/moshika-rag checkpoint run with its own inference code, its reference LLM replaced by the same tool router and prompt as Ours (router LLM Gemma-3-27B-it).

Task	Metric	Ours	MoshiRAG	GPT-Realtime†	Gemini Live 3.1†
Tool Use	Tool selection ↑	0.855	0.738	0.876	0.817
	Argument accuracy ↑	0.567	0.440	0.680	0.588
	Response quality ↑	0.411	0.255	0.792	0.718
	Pass rate ↑	0.470	0.280	0.600	0.540
Turn-Taking Dynamics	Take-turn rate (%) ↑	95.0	94.0	96.0	78.0
	Latency, task completion (s) ↓	5.83	7.69	6.89	4.25
	Interruption rate (%) ↓	73.7	58.5	13.5	19.2
	Filler rate (%) ↓	96.0	92.3	16.9	31.7

Tool-use metrics are fractions of the 100 scenarios; rates are percentages; latency is the task-completion latency in seconds.

Table 3. Spoken QA and math reasoning (accuracy, %). ref. = the router's reference answer is judged; resp. = the model's spoken response is judged. LlamaQ 300, WebQ 1000, TriviaQA 1000, HaluEval 1000; math 3,822 (AddSub 395, MultiArith 600, SinglEq 508, SVAMP 1000, GSM8K 1319).

Model	LlamaQ ref.	LlamaQ resp.	WebQ ref.	WebQ resp.	TriviaQA ref.	TriviaQA resp.	HaluEval ref.	HaluEval resp.
GLM-4-Voice†		64.7		32.2		39.1		21.2
STITCH-S†		73.3		50.2		50.0		–
MoshiRAG (Gemma 3)†	83.0	80.3	71.5	67.2	73.7	69.6	42.0	36.3
MoshiRAG (GPT-4.1)†	87.8	80.6	77.7	68.9	86.8	78.2	61.2	51.3
MoshiRAG (Tavily)†	84.6	78.2	73.5	66.1	84.9	77.5	54.3	47.0
Vanilla Moshi†		62.3		26.6		22.8		10.5
Ours (Gemma 4)	85.3	81.7	67.7	59.1	71.5	68.9	39.6	33.7

Model	AddSub ref.	AddSub resp.	MultiArith ref.	MultiArith resp.	SinglEq ref.	SinglEq resp.	SVAMP ref.	SVAMP resp.	GSM8K ref.	GSM8K resp.
GLM-4-Voice†		59.4		62.0		71.0		4.0		29.0
STITCH-S†		81.7		87.9		91.7		72.2		56.7
MoshiRAG (Gemma 3)†	76.6	61.7	87.1	69.0	83.2	68.2	74.1	55.0	66.2	33.9
MoshiRAG (GPT-4.1)†	87.9	64.8	87.1	76.0	89.6	72.9	80.5	61.1	70.8	43.2
Vanilla Moshi†		8.3		9.8		18.4		9.7		2.1
Ours (Gemma 4)	78.5	76.2	94.5	89.7	82.1	77.6	86.0	81.1	70.4	62.5

Quickstart

The weights load through the contextspan package; there is no transformers loader.

git clone https://github.com/mindlogic-ai/Context-Spanning && cd Context-Spanning
pip install torch --index-url https://download.pytorch.org/whl/cu128
pip install -e .
export HF_TOKEN=<token with access to nvidia/personaplex-7b-v1 and this repo>

Start the two backends (router LLM and ASR; two GPUs by default, one 96 GB GPU with the flags in the repo README), then:

bash scripts/backends.sh start && source scripts/env.sh

# a wav through the model; out.wav is stereo, L = user, R = agent
python main.py infer --input-wav assets/test/question.wav --output-wav out.wav  \
    --output-text out.json --user-name Priya --user-city Sydney

# live conversation in the browser (microphone needs https or localhost)
python main.py serve --host 0.0.0.0 --port 8080 --token <value>

Any OpenAI-compatible server replaces the router and any POST /transcribe -> {"text"} endpoint replaces the ASR. Servers, GPU layout, vLLM flags and environment variables: docs/BACKENDS.md.

License

• Weights (this repository): NVIDIA Open Model License.
• Voice prompts: main.pt MIT; f*/m* CC0 (Kyutai Unmute Voice Donation).
• Code (mindlogic-ai/Context-Spanning): MIT; contextspan/moshi/ keeps Kyutai's own license files.

Acknowledgements

Fine-tuned from PersonaPlex-7B (NVIDIA), which builds on Moshi and the Mimi codec (Kyutai). The benchmark protocols follow MoshiRAG and Full-Duplex-Bench.