VibeVoice Technical Report

Papers with Code Trending Papers

Summary

VibeVoice is a new model from Microsoft that synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer. It achieves superior fidelity and compression, supporting up to 90 minutes of audio with multiple speakers.

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.
Original Article
View Cached Full Text

Cached at: 05/08/26, 08:34 AM

Paper page - VibeVoice Technical Report

Source: https://huggingface.co/papers/2508.19205 Published on Aug 26, 2025

·

Submitted byhttps://huggingface.co/unilm

Li Dongon Aug 27, 2025

#1 Paper of the day Authors:

,

,

,

,

,

,

,

,

,

Abstract

VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.

This report presents VibeVoice, a novel model designed to synthesizelong-form speechwith multiple speakers by employingnext-token diffusion, which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion. To enable this, we introduce a novelcontinuous speech tokenizerthat, when compared to the popularEncodecmodel, improves data compression by 80 times while maintaining comparable performance. The tokenizer effectively preservesaudio fidelitywhile significantly boostingcomputational efficiencyfor processing long sequences. Thus, VibeVoice can synthesizelong-form speechfor 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 proprietarydialogue models.

View arXiv pageView PDFProject pageGitHub46.8kAdd to collection

Get this paper in your agent:

hf papers read 2508\.19205

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper66

#### microsoft/VibeVoice-1.5B Text-to-Speech• 3B• UpdatedJan 22 • 259k • 2.36k #### microsoft/VibeVoice-Realtime-0.5B Text-to-Speech• 1B• UpdatedDec 12, 2025 • 949k • 1.22k #### aoi-ot/VibeVoice-Large Text-to-Speech• 9B• UpdatedSep 25, 2025 • 8.38k • 230 #### vibevoice/VibeVoice-7B Text-to-Speech• 9B• UpdatedSep 5, 2025 • 8.42k • 178 Browse 66 models citing this paper## Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2508.19205 in a dataset README.md to link it from this page.

Spaces citing this paper123

Collections including this paper22

Browse 22 collections that include this paper

Similar Articles

microsoft/VibeVoice

GitHub Trending (daily)

Microsoft open-sourced VibeVoice, a unified speech-to-text model handling 60-minute audio in a single pass with speaker/timestamp/content transcription, and a real-time text-to-speech model supporting streaming input and multilingual voices. The project includes an edge CPU inference engine for ASR via quantization.

microsoft/VibeVoice-ASR-BitNet

Reddit r/LocalLLaMA

Microsoft releases VibeVoice-ASR-BitNet, a compressed multilingual ASR model for real-time CPU inference. It achieves 1.6-2.3x faster inference than Whisper.cpp with real-time capability on as few as 3 CPU threads.

@uniswap12: Microsoft open-sourced a voice AI that can transcribe 60 minutes of long audio in one go, handling 4 people speaking simultaneously. VibeVoice, open-sourced by Microsoft, 24.8k stars, I only found out about it today. For converting recordings to text, I've been using Whisper, but it often times out on long meeting recordings and struggles with multi-speaker recognition...

X AI KOLs Timeline

Microsoft open-sourced the VibeVoice speech AI framework, which supports one-shot transcription of 60-minute long audio, multi-speaker diarization and timestamp labeling, and also provides multi-role TTS synthesis capabilities. It is based on Qwen2.5 and comes with a 0.5B lightweight real-time version. It has received 24.8k stars on GitHub.