vibe-voice

---
name: vibe-voice
description: >-
  Build voice AI applications using Microsoft's VibeVoice — open-source frontier voice
  synthesis, recognition, and real-time conversation. Use when: building voice assistants,
  adding TTS/STT to applications, creating real-time voice chat, voice cloning.
license: MIT
compatibility: "Python 3.10+"
metadata:
  author: terminal-skills
  version: "1.0.0"
  category: data-ai
  tags:
    - voice
    - tts
    - stt
    - speech
    - real-time
---

# VibeVoice

Open-source frontier voice AI from Microsoft. Family of models for text-to-speech (TTS), automatic speech recognition (ASR), and real-time streaming synthesis.

GitHub: [microsoft/VibeVoice](https://github.com/microsoft/VibeVoice)

## Overview

VibeVoice is Microsoft's open-source voice AI platform offering three model sizes: ASR (7B) for transcription with speaker diarization, TTS (1.5B) for multi-speaker long-form synthesis, and Realtime (0.5B) for low-latency streaming. It supports 50+ languages and runs on consumer GPUs.

## Instructions

### Installation

```bash
pip install vibevoice
# Or clone for development
git clone https://github.com/microsoft/VibeVoice.git
cd VibeVoice
pip install -e .
```

### Hardware Requirements

- **ASR (7B)**: ~16GB VRAM (GPU recommended)
- **TTS (1.5B)**: ~6GB VRAM
- **Realtime (0.5B)**: ~2GB VRAM (runs on consumer GPUs)

### Text-to-Speech (TTS)

```python
from vibevoice import VibeVoiceTTS

model = VibeVoiceTTS.from_pretrained("microsoft/VibeVoice-1.5B")

# Single speaker
audio = model.synthesize(
    text="Hello, welcome to the future of voice AI.",
    speaker="default"
)
audio.save("output.wav")
```

### Multi-Speaker Conversation

Generate podcast-style audio with up to 4 distinct speakers:

```python
conversation = [
    {"speaker": "host", "text": "Welcome to the show! Today we're discussing AI."},
    {"speaker": "guest1", "text": "Thanks for having me. I'm excited to dive in."},
    {"speaker": "host", "text": "Let's start with the biggest trends you're seeing."},
    {"speaker": "guest2", "text": "I think voice AI is the most underrated development."},
]

audio = model.synthesize_conversation(conversation)
audio.save("podcast.wav")  # Up to 90 minutes in a single pass
```

### Real-Time Streaming TTS

```python
from vibevoice import VibeVoiceRealtime

model = VibeVoiceRealtime.from_pretrained("microsoft/VibeVoice-Realtime-0.5B")

for audio_chunk in model.stream("This is being generated in real time."):
    play_audio(audio_chunk)
```

### Automatic Speech Recognition (ASR)

```python
from vibevoice import VibeVoiceASR

model = VibeVoiceASR.from_pretrained("microsoft/VibeVoice-ASR")

# Basic transcription
result = model.transcribe("meeting_recording.wav")
print(result.text)

# Rich transcription with diarization
result = model.transcribe("meeting.wav", diarize=True, timestamps=True)
for segment in result.segments:
    print(f"[{segment.start:.1f}s - {segment.end:.1f}s] "
          f"Speaker {segment.speaker}: {segment.text}")
```

### Custom Hotwords

```python
result = model.transcribe(
    "medical_consultation.wav",
    hotwords=["Lisinopril", "Metformin", "HbA1c", "systolic"]
)
```

## Examples

### Example 1: Build a Voice Assistant

```python
from vibevoice import VibeVoiceASR, VibeVoiceRealtime

asr = VibeVoiceASR.from_pretrained("microsoft/VibeVoice-ASR")
tts = VibeVoiceRealtime.from_pretrained("microsoft/VibeVoice-Realtime-0.5B")

# Listen → Transcribe → Respond → Speak
user_text = asr.transcribe("user_input.wav").text
response = generate_response(user_text)  # Your LLM call
for chunk in tts.stream(response):
    play_audio(chunk)
```

### Example 2: Meeting Transcription with Speaker Labels

```python
result = asr.transcribe("team_standup.wav", diarize=True, timestamps=True)
for segment in result.segments:
    print(f"[{segment.start:.1f}s] Speaker {segment.speaker}: {segment.text}")

# Output:
# [0.0s] Speaker 1: Let's review the Q3 numbers.
# [3.5s] Speaker 2: Revenue is up 15% from last quarter.
# [8.4s] Speaker 1: That's great. What about customer acquisition?
```

## Guidelines

- Use the Realtime (0.5B) model for voice assistants where latency matters
- The ASR model handles up to 60 minutes of audio in a single pass
- TTS supports up to 90 minutes of multi-speaker audio generation
- Use custom hotwords for domain-specific terms (medical, legal, technical)
- For production, consider vLLM for faster ASR inference
- Multilingual voices are experimental — test quality before deploying
- 7.5 Hz frame rate enables efficient long-sequence processing

## Resources

- [Project Page](https://microsoft.github.io/VibeVoice)
- [HuggingFace Collection](https://huggingface.co/collections/microsoft/vibevoice-68a2ef24a875c44be47b034f)
- [TTS Paper](https://arxiv.org/pdf/2508.19205)
- [ASR Paper](https://arxiv.org/pdf/2601.18184)
- [Colab: Streaming TTS](https://colab.research.google.com/github/microsoft/VibeVoice/blob/main/demo/VibeVoice_colab.ipynb)