Score: 2

VibeVoice Technical Report

Published: August 26, 2025 | arXiv ID: 2508.19205v1

By: Zhiliang Peng , Jianwei Yu , Wenhui Wang and more

BigTech Affiliations: Microsoft

Potential Business Impact:

Creates long, natural-sounding conversations with many voices.

Business Areas:
Speech Recognition Data and Analytics, Software

This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion. To enable this, we introduce a novel continuous speech tokenizer that, when compared to the popular Encodec model, improves data compression by 80 times while maintaining comparable performance. The tokenizer effectively preserves audio fidelity while significantly boosting computational efficiency for processing long sequences. Thus, VibeVoice can synthesize long-form speech for up to 90 minutes (in a 64K context window length) with a maximum of 4 speakers, capturing the authentic conversational ``vibe'' and surpassing open-source and proprietary dialogue models.

Country of Origin
🇺🇸 United States

Repos / Data Links

Page Count
8 pages

Category
Computer Science:
Computation and Language