Text-to-Speech
Transformers
Safetensors
English
vibevoice_streaming
Realtime TTS
Streaming text input
Long-form speech generation
Instructions to use microsoft/VibeVoice-Realtime-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/VibeVoice-Realtime-0.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="microsoft/VibeVoice-Realtime-0.5B")# Load model directly from transformers import VibeVoiceStreamingForConditionalGenerationInference model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained("microsoft/VibeVoice-Realtime-0.5B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## VibeVoice: A Frontier Open-Source Text-to-Speech Model
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VibeVoice-Realtime is a **lightweight real‑time** text-to-speech model supporting **streaming text input**. It can be used to build realtime TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in **~300 ms** (hardware dependent).
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The model uses an interleaved, windowed design: it incrementally encodes incoming text chunks while, in parallel, continuing diffusion-based acoustic latent generation from prior context. Unlike the full multi-speaker long-form variants, this streaming model removes the semantic tokenizer and relies solely on an efficient acoustic tokenizer operating at an ultra-low frame rate (7.5 Hz).
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## VibeVoice: A Frontier Open-Source Text-to-Speech Model
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VibeVoice-Realtime is a **lightweight real‑time** text-to-speech model supporting **streaming text input** and **robust long-form speech generation**. It can be used to build realtime TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in **~300 ms** (hardware dependent).
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The model uses an interleaved, windowed design: it incrementally encodes incoming text chunks while, in parallel, continuing diffusion-based acoustic latent generation from prior context. Unlike the full multi-speaker long-form variants, this streaming model removes the semantic tokenizer and relies solely on an efficient acoustic tokenizer operating at an ultra-low frame rate (7.5 Hz).
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