🔥 microsoft/VibeVoice
Hey there, little superstar! ✨
Imagine you have a magic friend named VibeVoice! This friend is super smart with voices.
Sometimes, you talk, and VibeVoice writes down everything you say, like a super-fast helper. It even knows who is talking and when! That's like a special listening ear. 👂📝
Other times, you type words, and VibeVoice can speak them out loud for you, in different fun voices! Like a talking book! 📖🗣️
Lots of smart grown-ups are helping VibeVoice get even better and share its magic with everyone. It's like building a super cool toy car together so all your friends can play! Isn't that neat? 🎉
Open-Source Frontier Voice AI — Trending on GitHub today with 538 new stars.
📰 News
2026-03-29: 🎉 VibeVoice-ASR is being adopted by the open-source community! Vibing, a voice-powered input method, is now built on top of VibeVoice-ASR. Download: macOS | Windows
1.mov
2026-03-06: 🚀 VibeVoice ASR is now part of a Transformers release! You can now use our speech recognition model directly through the Hugging Face Transformers library for seamless integration into your projects.
2026-01-21: 📣 We open-sourced VibeVoice-ASR, a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for User-Customized Context. Try it in Playground.
-
⭐️ VibeVoice-ASR is natively multilingual, supporting over 50 languages — check the supported languages for details.
-
🔥 The VibeVoice-ASR finetuning code is now available!
-
⚡️ vLLM inference is now supported for faster inference; see vllm-asr for more details.
-
📑 VibeVoice-ASR Technique Report is available.
2025-12-16: 📣 We added experimental speakers to VibeVoice‑Realtime‑0.5B for exploration, including multilingual voices in nine languages (DE, FR, IT, JP, KR, NL, PL, PT, ES) and 11 distinct English style voices. Try it. More speaker types will be added over time.
2025-12-03: 📣 We open-sourced VibeVoice‑Realtime‑0.5B, a real‑time text‑to‑speech model that supports streaming text input and robust long-form speech generation. Try it on Colab.
2025-09-05: VibeVoice is an open-source research framework intended to advance collaboration in the speech synthesis community. After release, we discovered instances where the tool was used in ways inconsistent with the stated intent. Since responsible use of AI is one of Microsoft’s guiding principles, we have removed the VibeVoice-TTS code from this repository.
2025-08-25: 📣 We open-sourced VibeVoice-TTS, a long-form multi-speaker text-to-speech model that can synthesize speech up to 90 minutes long with up to 4 distinct speakers. — accepted as an Oral at ICLR 2026! 🔥
Overview
VibeVoice is a family of open-source frontier voice AI models that includes both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) models.
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.
For more information, demos, and examples, please visit our Project Page.
Model Weight Quick Try
VibeVoice-ASR-7B HF Link Playground
VibeVoice-TTS-1.5B HF Link Disabled
VibeVoice-Realtime-0.5B HF Link Colab
Models
1. 📖 VibeVoice-ASR - Long-form Speech Recognition
VibeVoice-ASR is a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for Customized Hotwords.
-
🕒 60-minute Single-Pass Processing: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to 60 minutes of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
-
👤 Customized Hotwords: Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content.
-
📝 Rich Transcription (Who, When, What): The model jointly performs ASR, diarization, and timestamping, producing a structured output that indicates who said what and when.
📖 Documentation | 🤗 Hugging Face | 🎮 Playground | 🛠️ Finetuning | 📊 Paper
small.mp4
2. 🎙️ VibeVoice-TTS - Long-form Multi-speaker TTS
Best for: Long-form conversational audio, podcasts, multi-speaker dialogues
-
⏱️ 90-minute Long-form Generation: Synthesizes conversational/single-speaker speech up to 90 minutes in a single pass, maintaining speaker consistency and semantic coherence throughout.
-
👥 Multi-speaker Support: Supports up to 4 distinct speakers in a single conversation, with natural turn-taking and speaker consistency across long dialogues.
-
🎭 Expressive Speech: Generates expressive, natural-sounding speech that captures conversational dynamics and emotional nuances.
-
🌐 Multi-lingual Support: Supports English, Chinese and other languages.
📖 Documentation | 🤗 Hugging Face | 📊 Paper
English
ES_._3.mp4
Chinese
default.mp4
Cross-Lingual
1p_EN2CH.mp4
Spontaneous Singing
2p_see_u_again.mp4
Long Conversation with 4 people
4p_climate_45min.mp4
3. ⚡ VibeVoice-Streaming - Real-time Streaming TTS
VibeVoice-Realtime is a lightweight real‑time text-to-speech model supporting streaming text input and robust long-form speech generation.
-
Parameter size: 0.5B (deployment-friendly)
-
Real-time TTS (~300 milliseconds first audible latency)
-
Streaming text input
-
Robust long-form speech generation (~10 minutes)
📖 Documentation | 🤗 Hugging Face | 🚀 Colab
VibeVoice_Realtime.mp4
Contributing
Please see CONTRIBUTING.md for detailed contribution guidelines.
⚠️ Risks and Limitations
While efforts have been made to optimize it through various techniques, it may still produce outputs that are unexpected, biased, or inaccurate. VibeVoice inherits any biases, errors, or omissions produced by its base model (specifically, Qwen2.5 1.5b in this release). Potential for Deepfakes and Disinformation: High-quality synthetic speech can be misused to create convincing fake audio content for impersonation, fraud, or spreading disinformation. Users must ensure transcripts are reliable, check content accuracy, and avoid using generated content in misleading ways. Users are expected to use the generated content and to deploy the models in a lawful manner, in full compliance with all applicable laws and regulations in the relevant jurisdictions. It is best practice to disclose the use of AI when sharing AI-generated content.
We do not recommend using VibeVoice in commercial or real-world applications without further testing and development. This model is intended for research and development purposes only. Please use responsibly.
Star History
Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
githubtrendingopen-source
"I Built a Web Browser from Scratch in 42 Days — No Libraries, Just Node.js"
I Built a Web Browser from Scratch in 42 Days 42 days ago I made a decision. I wanted to understand how the internet actually works. Not just use it. Not just build on top of it. Actually understand it — at the wire level. So I started building a web browser from scratch. In Node.js. No external libraries. Every line written by hand. I called it Courage. What Courage can do today ... Parse URLs into protocol, host, port, path Open raw TCP and TLS connections Build and send HTTP GET requests Parse HTTP responses including chunked encoding Tokenize raw HTML character by character Build a DOM tree using a stack Match CSS rules to DOM nodes Calculate layout (x, y, width, height) for every element Paint rectangles and text on a Canvas using Electron Execute JavaScript via eval() Navigate with b
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Open Source AI

Help running Qwen3-Coder-Next TurboQuant (TQ3) model
I found a TQ3-quantized version of Qwen3-Coder-Next here: https://huggingface.co/edwardyoon79/Qwen3-Coder-Next-TQ3_0 According to the page, this model requires a compatible inference engine that supports TurboQuant. It also provides a command, but it doesn’t clearly specify which version or fork of llama.cpp should be used (or maybe I missed it). llama-server I’ve tried the following llama.cpp forks that claim to support TQ3, but none of them worked for me: https://github.com/TheTom/llama-cpp-turboquant https://github.com/turbo-tan/llama.cpp-tq3 https://github.com/drdotdot/llama.cpp-turbo3-tq3 If anyone has successfully run this model, I’d really appreciate it if you could share how you did it. submitted by /u/UnluckyTeam3478 [link] [comments]



Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!