NVIDIA/personaplexPublic

PersonaPlex code.

AI summary: A real-time, full-duplex speech-to-speech model enabling consistent persona control via text and audio conditioning.

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PythonMITCreated Jan 5, 2026Last push 7mo ago+13 stars this week+190 this month

Quick answers

What is personaplex?
A real-time, full-duplex speech-to-speech model enabling consistent persona control via text and audio conditioning.
What does personaplex do?
PersonaPlex is a real-time, full-duplex speech-to-speech conversational AI model built on the Moshi architecture and Helium LLM. It enables advanced persona control by utilizing text-based role prompts alongside audio-based voice conditioning. Trained on a diverse dataset of synthetic and real conversations from the Fisher English Corpus, the model generates natural, low-latency spoken interactions. It maintains a highly consistent persona throughout the conversation, supporting both assistant roles and casual conversational styles. It is designed to run locally, providing built-in tools for launching interactive web servers and offline evaluation scripts.
Who is personaplex for?
AI researchers, game developers, and voice application engineers building advanced, real-time conversational agents with specific personality traits.
How do I get started with personaplex?
pip install moshi/.
How popular is personaplex on GitHub?
NVIDIA/personaplex has 10,590 stars and 1,471 forks on GitHub, and gained 13 stars in the last 7 days.
What license does personaplex use?
NVIDIA/personaplex is released under the MIT license.

Star history

since Jul 28, 2026
05K10KJul 2026Aug 2026Sep 2026Oct 2026
10.6K stars as of Oct 4, 2026. Measured daily since Jul 28, 2026; GitHub no longer exposes earlier star timestamps.

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Signals and awards

derived from tracked data
  • Widely adopted

    10,590 stars

  • Permissive license

    MIT

  • Repeat trending

    4 trending appearances

What personaplex does

PersonaPlex is a real-time, full-duplex speech-to-speech conversational AI model built on the Moshi architecture and Helium LLM. It enables advanced persona control by utilizing text-based role prompts alongside audio-based voice conditioning. Trained on a diverse dataset of synthetic and real conversations from the Fisher English Corpus, the model generates natural, low-latency spoken interactions. It maintains a highly consistent persona throughout the conversation, supporting both assistant roles and casual conversational styles. It is designed to run locally, providing built-in tools for launching interactive web servers and offline evaluation scripts.

AI researchers, game developers, and voice application engineers building advanced, real-time conversational agents with specific personality traits.

  • Full-Duplex Speech: Supports real-time, two-way conversational speech without requiring explicit turn-taking mechanisms.
  • Persona Conditioning: Allows precise control over the model's persona using both text-based role prompts and specific audio voice samples.
  • Low-Latency Interaction: Engineered to produce natural spoken interactions with minimal delay, built upon the established Moshi architecture.
  • Hybrid Training Dataset: Trained on a combination of synthetic and real conversational data to ensure natural speech patterns and realistic backchanneling.
  • Local Server Deployment: Provides integrated scripts to launch a local server for live web UI interaction, with support for CPU offloading.

Where teams use it

Interactive Character Simulation

Developers build real-time AI characters for games that maintain a consistent voice and personality during natural, flowing conversation.

Virtual Customer Service

Enterprises deploy low-latency, full-duplex voice agents that adopt specific brand personas and knowledge contexts for customer interactions.

Language Practice Tools

Educational platforms use the model to create conversational partners that speak naturally and adopt different customized tutoring roles.

Accessibility Interfaces

Engineers integrate the model into applications to provide fluid, two-way conversational interfaces for visually impaired users.

Getting started: pip install moshi/.

README

main branch

PersonaPlex: Voice and Role Control for Full Duplex Conversational Speech Models

Weights Paper Demo Discord

PersonaPlex is a real-time, full-duplex speech-to-speech conversational model that enables persona control through text-based role prompts and audio-based voice conditioning. Trained on a combination of synthetic and real conversations, it produces natural, low-latency spoken interactions with a consistent persona. PersonaPlex is based on the Moshi architecture and weights.

PersonaPlex Model Architecture
PersonaPlex Architecture

Usage

Prerequisites

Install the Opus audio codec development library:

# Ubuntu/Debian
sudo apt install libopus-dev

# Fedora/RHEL
sudo dnf install opus-devel

Installation

Download this repository and install with:

pip install moshi/.

Extra step for Blackwell based GPUs as suggested in (See #2):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130

Accept Model License

Log in to your Huggingface account and accept the PersonaPlex model license here.
Then set up your Huggingface authentication:

export HF_TOKEN=<YOUR_HUGGINGFACE_TOKEN>

Launch Server

Launch server for live interaction (temporary SSL certs for https):

SSL_DIR=$(mktemp -d); python -m moshi.server --ssl "$SSL_DIR"

CPU Offload: If your GPU has insufficient memory, use the --cpu-offload flag to offload model layers to CPU. This requires the accelerate package (pip install accelerate):

SSL_DIR=$(mktemp -d); python -m moshi.server --ssl "$SSL_DIR" --cpu-offload

Access the Web UI from a browser at localhost:8998 if running locally, otherwise look for the access link printed by the script:

Access the Web UI directly at https://11.54.401.33:8998

Offline Evaluation

For offline evaluation use the offline script that streams in an input wav file and produces an output wav file from the captured output stream. The output file will be the same duration as the input file.

Add --cpu-offload to any command below if your GPU has insufficient memory (requires accelerate package). Or install cpu-only PyTorch for offline evaluation on pure CPU.

Assistant example:

HF_TOKEN=<TOKEN> \
python -m moshi.offline \
  --voice-prompt "NATF2.pt" \
  --input-wav "assets/test/input_assistant.wav" \
  --seed 42424242 \
  --output-wav "output.wav" \
  --output-text "output.json"

Service example:

HF_TOKEN=<TOKEN> \
python -m moshi.offline \
  --voice-prompt "NATM1.pt" \
  --text-prompt "$(cat assets/test/prompt_service.txt)" \
  --input-wav "assets/test/input_service.wav" \
  --seed 42424242 \
  --output-wav "output.wav" \
  --output-text "output.json"

Voices

PersonaPlex supports a wide range of voices; we pre-package embeddings for voices that sound more natural and conversational (NAT) and others that are more varied (VAR). The fixed set of voices are labeled:

Natural(female): NATF0, NATF1, NATF2, NATF3
Natural(male):   NATM0, NATM1, NATM2, NATM3
Variety(female): VARF0, VARF1, VARF2, VARF3, VARF4
Variety(male):   VARM0, VARM1, VARM2, VARM3, VARM4

Prompting Guide

The model is trained on synthetic conversations for a fixed assistant role and varying customer service roles.

Assistant Role

The assistant role has the prompt:

You are a wise and friendly teacher. Answer questions or provide advice in a clear and engaging way.

Use this prompt for the QA assistant focused "User Interruption" evaluation category in FullDuplexBench.

Customer Service Roles

The customer service roles support a variety of prompts. Here are some examples for prompting style reference:

You work for CitySan Services which is a waste management and your name is Ayelen Lucero. Information: Verify customer name Omar Torres. Current schedule: every other week. Upcoming pickup: April 12th. Compost bin service available for $8/month add-on.
You work for Jerusalem Shakshuka which is a restaurant and your name is Owen Foster. Information: There are two shakshuka options: Classic (poached eggs, $9.50) and Spicy (scrambled eggs with jalapenos, $10.25). Sides include warm pita ($2.50) and Israeli salad ($3). No combo offers. Available for drive-through until 9 PM.
You work for AeroRentals Pro which is a drone rental company and your name is Tomaz Novak. Information: AeroRentals Pro has the following availability: PhoenixDrone X ($65/4 hours, $110/8 hours), and the premium SpectraDrone 9 ($95/4 hours, $160/8 hours). Deposit required: $150 for standard models, $300 for premium.

Casual Conversations

The model is also trained on real conversations from the Fisher English Corpus with LLM-labeled prompts for open-ended conversations. Here are some example prompts for casual conversations:

You enjoy having a good conversation.
You enjoy having a good conversation. Have a casual discussion about eating at home versus dining out.
You enjoy having a good conversation. Have an empathetic discussion about the meaning of family amid uncertainty.
You enjoy having a good conversation. Have a reflective conversation about career changes and feeling of home. You have lived in California for 21 years and consider San Francisco your home. You work as a teacher and have traveled a lot. You dislike meetings.
You enjoy having a good conversation. Have a casual conversation about favorite foods and cooking experiences. You are David Green, a former baker now living in Boston. You enjoy cooking diverse international dishes and appreciate many ethnic restaurants.

Use the prompt You enjoy having a good conversation. for the "Pause Handling", "Backchannel" and "Smooth Turn Taking" evaluation categories of FullDuplexBench.

Generalization

Personaplex finetunes Moshi and benefits from the generalization capabilities of the underlying Helium LLM. Thanks to the broad training corpus of the backbone, we find that the model will respond plausibly to out-of-distribution prompts and lead to unexpected or fun conversations. We encourage experimentation with different prompts to test the model's emergent ability to handle scenarios outside its training distribution. As an inspiration we feature the following astronaut prompt in the WebUI:

You enjoy having a good conversation. Have a technical discussion about fixing a reactor core on a spaceship to Mars. You are an astronaut on a Mars mission. Your name is Alex. You are already dealing with a reactor core meltdown on a Mars mission. Several ship systems are failing, and continued instability will lead to catastrophic failure. You explain what is happening and you urgently ask for help thinking through how to stabilize the reactor.

License

The present code is provided under the MIT license. The weights for the models are released under the NVIDIA Open Model license.

Citation

If you use PersonaPlex in your research, please cite our paper:

@misc{roy2026personaplexvoicerolecontrol,
      title={PersonaPlex: Voice and Role Control for Full Duplex Conversational Speech Models}, 
      author={Rajarshi Roy and Jonathan Raiman and Sang-gil Lee and Teodor-Dumitru Ene and Robert Kirby and Sungwon Kim and Jaehyeon Kim and Bryan Catanzaro},
      year={2026},
      eprint={2602.06053},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.06053}, 
}
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When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 0 commitsSun 2:00 — 1 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 0 commitsSun 12:00 — 0 commitsSun 13:00 — 0 commitsSun 14:00 — 0 commitsSun 15:00 — 0 commitsSun 16:00 — 0 commitsSun 17:00 — 0 commitsSun 18:00 — 0 commitsSun 19:00 — 0 commitsSun 20:00 — 0 commitsSun 21:00 — 0 commitsSun 22:00 — 1 commitsSun 23:00 — 0 commitsMon 0:00 — 0 commitsMon 1:00 — 0 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 1 commitsMon 9:00 — 0 commitsMon 10:00 — 0 commitsMon 11:00 — 0 commitsMon 12:00 — 0 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 0 commitsMon 16:00 — 0 commitsMon 17:00 — 0 commitsMon 18:00 — 0 commitsMon 19:00 — 0 commitsMon 20:00 — 0 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 0 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 0 commitsTue 10:00 — 0 commitsTue 11:00 — 0 commitsTue 12:00 — 0 commitsTue 13:00 — 0 commitsTue 14:00 — 0 commitsTue 15:00 — 1 commitsTue 16:00 — 0 commitsTue 17:00 — 0 commitsTue 18:00 — 0 commitsTue 19:00 — 0 commitsTue 20:00 — 0 commitsTue 21:00 — 0 commitsTue 22:00 — 0 commitsTue 23:00 — 0 commitsWed 0:00 — 0 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 0 commitsWed 9:00 — 0 commitsWed 10:00 — 0 commitsWed 11:00 — 0 commitsWed 12:00 — 0 commitsWed 13:00 — 0 commitsWed 14:00 — 1 commitsWed 15:00 — 0 commitsWed 16:00 — 0 commitsWed 17:00 — 0 commitsWed 18:00 — 0 commitsWed 19:00 — 0 commitsWed 20:00 — 0 commitsWed 21:00 — 0 commitsWed 22:00 — 0 commitsWed 23:00 — 0 commitsThu 0:00 — 2 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 0 commitsThu 10:00 — 1 commitsThu 11:00 — 0 commitsThu 12:00 — 0 commitsThu 13:00 — 0 commitsThu 14:00 — 0 commitsThu 15:00 — 0 commitsThu 16:00 — 1 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 0 commitsThu 20:00 — 1 commitsThu 21:00 — 0 commitsThu 22:00 — 0 commitsThu 23:00 — 0 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 2 commitsFri 8:00 — 0 commitsFri 9:00 — 1 commitsFri 10:00 — 0 commitsFri 11:00 — 1 commitsFri 12:00 — 0 commitsFri 13:00 — 1 commitsFri 14:00 — 1 commitsFri 15:00 — 0 commitsFri 16:00 — 0 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 0 commitsFri 20:00 — 0 commitsFri 21:00 — 0 commitsFri 22:00 — 1 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 0 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 0 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 0 commitsSat 16:00 — 0 commitsSat 17:00 — 0 commitsSat 18:00 — 0 commitsSat 19:00 — 0 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 0 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Jan 25, 2026daily#24+141
Jan 24, 2026daily#20+159
Jan 23, 2026daily#11+297
Jan 22, 2026daily#16+190