VibeVoice is a novel framework designed to generate expressive, emotional, and lifelike long-form, multi-actor audio, such as podcasts, from text. The model aims to solve the significant challenges of traditional text-to-speech (TTS) systems in terms of scalability, speaker consistency, and natural conversational turns.
The capabilities and special features of the VibeVoice model are as follows:
- Capable of synthesizing conversations with up to four different speakers and generating up to 90 minutes of speech, which exceeds the typical limitations of many previous models.
- Excellent for creating podcasts and similar long-form audio content.
- Allows voice cloning from voice samples. This requires clean, minimal background noise voice samples, at least 3-10 seconds long, but 30 seconds is recommended for better quality.
- Text File Loading: Suitable for loading text scripts from .txt files.
- Flexible configuration: Adjustable with parameters such as temperature, sampling, and guidance scale (cfg_scale).
Two model options:
- VibeVoice-1.5B: Provides faster inference and has a download size of approximately 5 GB, ideal for single speakers and rapid prototyping.
- VibeVoice-7B-Preview: Provides higher quality output, especially for multi-actor conversations, has slower inference and has a download size of approximately 17 GB.
- Technological innovation: One of its fundamental innovations is the use of continuous speech tokenizers (acoustic and semantic) that operate at an extremely low frame rate of 7.5 Hz. These tokenizers achieve a compression ratio of 3200x while maintaining audio fidelity, drastically increasing computational efficiency when processing long sequences.
- LLM-based next-token diffusion framework: The model uses a large-scale language model (LLM, e.g. Qwen2.5) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details.
Results and performance: The VIBEVOICE-7B model outperforms most state-of-the-art models in long-discussion speech generation, both subjectively and objectively, showing better realism, richness, and overall preference.
It is important to note that the model works best primarily with English and Chinese text. The VibeVoice model itself is for research purposes and is subject to Microsoft’s license terms.
Links
Microsoft VibeVoice: https://microsoft.github.io/VibeVoice/Technical Report: https://arxiv.org/pdf/2508.19205GitHub: https://github.com/microsoft/VibeVoiceGoogle Colab: https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/VibeVoice_colab.ipynbHugging Face VibeVoice-1.5B: https://huggingface.co/microsoft/VibeVoice-1.5BHugging Face VibeVoice-7B-Large: https://huggingface.co/WestZhang/VibeVoice-Large-ptComfyUI: https://github.com/Enemyx-net/VibeVoice-ComfyUIAudacity: https://www.audacityteam.org/