VibeVoice-Realtime

VibeVoice: A Frontier Open-Source Text-to-Speech Model

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).

Although the model is primarily built for English, we found that it still exhibits a certain level of multilingual capability—and even performs reasonably well in some languages. We provide nine additional languages (German, French, Italian, Japanese, Korean, Dutch, Polish, Portuguese, and Spanish) for users to explore and share feedback.

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).

Key features:

• Parameter size: 0.5B (deployment-friendly)
• Realtime TTS (~300 ms first audible latency)
• Streaming text input
• Robust long-form speech generation

This realtime variant supports only a single speaker. For multi-speaker conversational speech generation, please use other VibeVoice models. The model is currently intended for English speech only; other languages may produce unpredictable results.

➡️ Technical Report: VibeVoice Technical Report

➡️ Project Page: microsoft/VibeVoice

➡️ Code: microsoft/VibeVoice-Code

➡️ App: anycoderapps/VibeVoice-Realtime-0.5B

Training Details

Transformer-based Large Language Model (LLM) integrated with specialized acoustic tokenizer and a diffusion-based decoding head.

• LLM: Qwen2.5-0.5B for this release.
• Tokenizers:
  • Acoustic Tokenizer: Based on a σ-VAE variant (proposed in LatentLM), with a mirror-symmetric encoder-decoder structure featuring 7 stages of modified Transformer blocks. Achieves 3200x downsampling from 24kHz input. Decoder component is ~340M parameters.
• Diffusion Head: Lightweight module (4 layers, ~40M parameters) conditioned on LLM hidden states. Predicts acoustic VAE features using a Denoising Diffusion Probabilistic Models (DDPM) process. Uses Classifier-Free Guidance (CFG) and DPM-Solver (and variants) during inference.
• Context Length: Trained with a curriculum increasing up to 8,192 tokens.
• Training Stages:
  • Tokenizer Pre-training: Acoustic tokenizer is pre-trained.
  • VibeVoice Training: Pre-trained tokenizer is frozen; only the LLM and diffusion head parameters are trained. A curriculum learning strategy is used for input sequence length (4k -> 8K). Text tokenizer not explicitly specified, but the LLM (Qwen2.5) typically uses its own. Audio is "tokenized" via the acoustic tokenizer.

Models

Results

The model achieves satisfactory performance on short-sentence benchmarks, while the model is more focused on long‑form speech generation.

Zero-shot TTS performance on LibriSpeech test-clean set

Zero-shot TTS performance on SEED test-en set

Installation and Usage

Please refer to GitHub README

Responsible Usage

Direct intended uses

The VibeVoice-Realtime model is limited to research purposes exploring real-time highly realistic audio generation detailed in the tech report.

Out-of-scope uses

Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by MIT License. Use to generate any text transcript. Furthermore, this release is not intended or licensed for any of the following scenarios:

• Voice impersonation without explicit, recorded consent, including but not limited to, cloning a real individual’s voice for satire, advertising, ransom, social‑engineering, or authentication bypass.
• Disinformation or impersonation, includin