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[![Technical Report](https://img.shields.io/badge/Technical-Report-red)](https://arxiv.org/pdf/2508.19205)
[![Colab](https://img.shields.io/badge/Colab-Demo-orange?logo=googlecolab)](https://colab.research.google.com/github/vibevoice-community/VibeVoice/blob/main/demo/VibeVoice_colab.ipynb)
https://discord.gg/ZDEYTTRxWG
VibeVoice is a novel framework designed for generating **expressive**, **long-form**, **multi-speaker** conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking.
A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a [next-token diffusion](https://arxiv.org/abs/2412.08635) framework, leveraging a Large Language Model (LLM) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details.