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Breeze TTS 2

Breeze TTS 2

A bilingual (English/Chinese) text-to-speech model built for ultra-low-latency streaming that enables text-driven voice creation, voice cloning, and fine-grained emotional control

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Features

Open SourceTTS

Screenshots

Breeze TTS 2 screenshot 1

System Requirements

Minimum 16GB RAM. 21GB+ storage recommended.
macOS 15+: M-series chips required.
Windows 10/11: NVIDIA GPU with 8GB+ VRAM required.
Note: For NVIDIA GPUs, install a newer driver.

Introduction

License Notice: The code is licensed under Apache-2.0, while the model weights are governed by the BreezeBlue Non-Commercial License. Strictly for personal learning and academic research; any commercial use is prohibited.

1. Key Features & Capabilities

breeze-tts is the official PyTorch inference repository for Breeze TTS 2, an open-weight text-to-speech model. Put simply, it transforms written text into highly expressive, human-like speech with full bilingual support (English and Chinese).

It offers three primary ways for users to control voices:

  • Voice Design: Generate a unique, brand-new voice from scratch using a natural language text description (e.g., "a warm young woman with a clear voice and a calm delivery"), with no audio sample required.
  • Voice Clone: Replicate a specific person's timbre, emotion, and speaking style by providing a short reference audio snippet and its exact transcript.
  • Voice Direction: Steer the tone, emotion, and pace of a cloned voice using text instructions (e.g., "deliver this in a fast, excited tone").
  • Inline Vocal Events: Insert natural human expressions directly into text, such as (laugh), (sigh), (cough), or (clears throat) in English, or [笑] / [叹气] in Chinese.

2. Developer & Target Use Cases

  • Developer: Developed and open-sourced by the BreezeBlue team (RESONIA, INC.).
  • Use Cases:
  • Real-time Conversational AI: Voice assistants, real-time translation, customer service agents, and interactive NPC dialogues in gaming.
  • Content Creation: Audiobooks, podcast generation, and short-video voiceovers.

3. Underlying Technology & Performance

  • Underlying Tech: Built on PyTorch, with its core audio tokenizer component developed based on Alibaba Qwen Team's Qwen3-TTS.
  • Low-Latency Streaming: Supports real-time streaming generation with a Real-Time Factor (RTF) of 0.32 (generating audio ~3.1x faster than real-time playback). With CUDA Graph acceleration enabled, it achieves a Time-To-First-Audio (TTFA) latency of under 40 milliseconds on an NVIDIA H100.
GitHubhttps://github.com/breezeblue-ai/breeze-tts
LicenseApache-2.0, breezeblue-research-and-non-commercial-license