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<a href="https://aka.ms/GeneralAI" target="_blank">MSRA GeneralAI Group</a>
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<p class="links" style="text-align:center; margin:0 0 14px;">
<a href="https://github.com/microsoft/VibeVoice/report/TechnicalReport.pdf" target="_blank">📄 Report</a>
<a href="https://github.com/microsoft/VibeVoice/blob/main/report/TechnicalReport.pdf" target="_blank">📄 Report</a>
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<p class="muted" style="margin:0;">
VibeVoice is a novel framework designed for generating <b>expressive, long-form, multi-speaker conversational audio</b>, 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.
VibeVoice is a novel framework designed for generating <b>expressive, long-form, multi-speaker </b>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 framework, leveraging a Large Language Model (LLM) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details.
The model can synthesize speech up to 90 minutes long with up to 4 distinct speakers, surpassing the typical 1-2 speaker limits of many prior models.
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