NVIDIA has released NemotronLabs VoiceChat 11B：https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B, an open 11B end-to-end speech-to-speech model for real-time, full-duplex conversation. Instead of chaining ASR, an LLM, and TTS, it performs streaming speech understanding and speech generation in one unified network. That removes the multi-model orchestration and API handoffs a cascaded stack requires, and cuts end-to-end latency: measured smooth turn-taking latency is 448 ms on Full-Duplex-Bench 1.0：https://arxiv.org/abs/2503.04721. The model listens while it speaks, so a user can barge in mid-turn and the agent yields, with a take-over rate of 1.00 at 480 ms. It is also first open full-duplex model to support tool calling while conversation keeps flowing, using a separate output channel for scripts along with operator-defined “on-hold” lines that fill the gap while an API runs.
PARTIAL — deployable today for pilots, not for production. Weights and container are both public, and the license is permissive. But NVIDIA team states the checkpoint is ‘ready for research purposes only,’ and the repo documents real failure modes: a two-minute audio context ceiling, degradation into non-recoverable gibberish after several turns, runaway self-talk after a turn ends, and dropped words in user transcription.
The model is a hybrid Mamba/Transformer, assembled from three existing NVIDIA components along with one new output path:
Outputs include agent audio, agent text, and a running user transcription. Training used roughly 550k hours of audio across real and synthetic corpora, building on SALM-Duplex：https://arxiv.org/abs/2505.15670 and Audio Flamingo 3：https://arxiv.org/abs/2507.08128.
Tool calls are emitted on the side channel as a block; your code returns results in a block. The notable piece is the on-hold message : per tool, an operator defines a line the agent speaks the moment the model generates the text triggering the call, so the conversation does not fall silent while an API runs.
Constraints are explicit. NVIDIA recommends a maximum of five tools per session, the model cannot reliably call multiple tools simultaneously, and the user cannot interrupt the agent during tool execution. System prompts and tool responses must be ASCII-only and TTS-friendly.
On Full-Duplex-Bench 1.0：https://arxiv.org/abs/2503.04721: smooth turn-taking TOR 0.82 at 448 ms, user-interruption TOR 1.00 at 480 ms, and pause-handling TOR of 0.153 (synthetic) and 0.255 (Candor), where lower is better.
On AU Harness：https://github.com/ServiceNow/AU-Harness BFCL-v3 spoken tool calling: 58.5% simple, 62.5% multiple, 42.5% parallel, 27.5% parallel-multiple, 89.6% irrelevance, 56.1% average . On Full-Duplex-Bench v3：https://arxiv.org/abs/2604.04847: 82.5% tool selection, 44.2% argument accuracy, 33% pass@1.
NVIDIA reports the model ranks #2 among open full-duplex models on VoiceBench：https://arxiv.org/abs/2410.17196 and #2 among open models on Full-Duplex-Bench 1.0.
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