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VibeVoice

Build open-source frontier voice AI together with VibeVoice.

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VibeVoice is an open-source frontier voice AI framework for long-form speech recognition and realtime text-to-speech, with multilingual support and structured transcription. It integrates with Transformers and vLLM, offering model weights, finetuning pipelines, and demos for researchers and developers building advanced speech experiences.
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More about VibeVoice

VibeVoice is an open-source frontier voice AI framework from Microsoft that unifies long-form speech recognition and high-fidelity text-to-speech into a single research-grade ecosystem. Built around continuous acoustic and semantic tokenizers running at an ultra-low 7.5 Hz frame rate, it delivers efficient processing for extended audio while preserving rich vocal detail and conversational nuance.

The VibeVoice-ASR model supports up to 60-minute recordings in a single pass, producing structured transcripts with speaker attribution (Who), precise timestamps (When), and content segmentation (What), plus user-customized context to improve accuracy in domain-specific scenarios. It is natively multilingual, covering 50+ languages, and now integrates directly with Hugging Face Transformers and vLLM for streamlined deployment and accelerated inference.

On the generative side, VibeVoice-Realtime-0.5B offers streaming text-to-speech and robust long-form speech generation, including experimental multilingual speakers and multiple English speaking styles. A next-token diffusion framework combines a Large Language Model for dialogue understanding with a diffusion head for high-fidelity acoustics, enabling natural, expressive output.

Developers and researchers can leverage:

  • Open-source model weights for ASR and realtime TTS
  • Finetuning pipelines for domain adaptation in speech recognition
  • Colab and playground demos for rapid experimentation
  • Technique reports and documentation for reproducible research

VibeVoice is designed to advance collaborative innovation in speech AI while emphasizing responsible use and transparent research practices.

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