VibeVoice 1.5B is a cutting-edge AI voice synthesis model engineered to deliver high-quality, natural-sounding speech with expressive tone modulation across diverse languages and contexts. Designed for scalability and versatility, VibeVoice supports content creators, developers, and enterprises requiring advanced voice generation for applications like virtual assistants, audiobooks, gaming, and multimedia production.
VibeVoice 1.5B processes various input types including plain text, SSML (Speech Synthesis Markup Language), and emotional/style tags to generate lifelike speech with nuanced prosody. The model effectively handles conversational dialogue, narration, and character voices with dynamic intonation.
Built upon a transformer-based neural TTS backbone augmented with advanced prosody modeling modules, VibeVoice 1.5B leverages multi-layer self-attention mechanisms and convolutional layers tailored for temporal acoustic feature extraction. The model has been trained on a vast corpus of multi-lingual speech recordings and annotated emotional speech datasets, ensuring robust generalization across speakers and styles.
vs Eleven Music: While Eleven Music specializes in AI-driven music generation with complex composition capabilities, VibeVoice excels in natural and expressive voice synthesis, focusing on spoken audio rather than music.
vs Suno AI: Compared to Suno AI’s music generation features, VibeVoice’s strength lies in superior speech quality, more in-depth prosody control, and multilingual voice delivery designed for conversational contexts rather than musical content.
vs Udio: Udio targets simple audio production with limited voice synthesis. VibeVoice offers significantly higher fidelity, detailed emotional variation, and broader application support for professional voice generation needs.
vs MusicAI Sandbox: MusicAI Sandbox focuses on creative music experimentation. In contrast, VibeVoice prioritizes realistic spoken voice output with advanced fine-tuning options for diverse vocal characteristics and styles.
vs AIMusic.fm: AIMusic.fm largely automates music creation with limited customization. VibeVoice provides granular control over speech parameters and extensive style adaptability tailored for speech-centric projects.
import requests r = requests.post( "https://api.aimlapi.com/v1/tts", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "vibevoice", "text": "Hello from AI/ML API" }, ) print(r.json()["audio"]) # URL to the generated audio
const r = await fetch("https://api.aimlapi.com/v1/tts", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "vibevoice", "text": "Hello from AI/ML API" }), }); const { audio } = await r.json(); console.log(audio); // URL to the generated audio
curl -X POST https://api.aimlapi.com/v1/tts \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"vibevoice","text":"Hello from AI/ML API"}' # the JSON response contains "audio" — a URL to the generated speech
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Output | |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| TTS Arena | 954 | Human preference Elo from blind pairwise listening comparisons of speech samples, measured independently by Artificial Analysis | Source | September 28, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
VibeVoice 1.5B This page | Speech synthesis | |||
| Speech synthesis | ||||
| Speech synthesis | ||||
| Speech synthesis | ||||
| Speech synthesis |
VibeVoice 1.5B takes text as input and returns audio.
VibeVoice 1.5B is priced at $0.0008667 / sec.
VibeVoice 1.5B is billed per generation — a fixed charge per output rather than by prompt length.
VibeVoice 1.5B was built by Microsoft.
Send a request to https://api.aimlapi.com/v1/tts with vibevoice as the model id.
Yes. VibeVoice 1.5B is served through AI/ML API, so the same key and endpoint format used for other models applies.
It is designed for generating multi-speaker dialogue with natural-sounding voices in an efficient text-to-speech pipeline.
