Breeze-TTS-2

[!IMPORTANT] Source code is licensed under Apache 2.0. Breeze TTS 2 model weights, derivative models, and self-hosted outputs are for research and non-commercial use only. See License.

📰 News

• [2026.08.25] 🎉 We open-source Breeze TTS 2 model weights and the PyTorch inference code.
• [2026.08.07] 🔥 We release the TTS benchmark suite for voice design, voice direction, and latency evaluation.

📖 Introduction

Breeze TTS 2 is an open-weight text-to-speech model built for real-time interaction. It ranks #1 among open-weight models on the Artificial Analysis TTS leaderboard, while outperforming frontier proprietary systems. Its open-ended natural-language instruction-following capability supports reference-free voice design and reference-guided voice direction, while ultra-low-latency streaming enables responsive, expressive interaction.

✨ Highlights

• 🎙️ Voice Clone — Uses reference audio with its exact transcript to preserve timbre, rhythm, emotion, and style.
• 🎨 Voice Design — Creates a distinctive voice from a natural-language description, without reference audio.
• 🎛️ Voice Direction — Clones a voice from reference audio while steering tone, emotion, pace, and delivery.
• 🎭 Vocal Events — Adds expressive inline events directly in the text: use parentheses in English, such as (laugh), (cough), (clears throat), and (sigh); use square brackets in Chinese, such as [笑], [咳嗽], [清嗓子], and [叹气].
• ⚡ Ultra-Low Latency — Achieves under 40 ms time to first audio (TTFA) with the warmed-up fast path on an NVIDIA H100.
• 🌊 Real-Time Streaming — Reaches a 0.32 real-time factor (RTF), generating audio at approximately 3.1× real time with the warmed-up fast path on an NVIDIA H100.
• 💾 GPU-Efficient — Eager inference uses approximately 7.7 GiB of GPU memory; a 12 GB GPU is the minimum recommended configuration.
• 🌏 Bilingual Support — Generates natural English and Chinese speech with a single model.

🚀 Quick Start

Requirements

• Linux and Python 3.10 or newer
• A CUDA-capable NVIDIA GPU
• GPU memory: approximately 7.7 GiB for eager inference or 14.4 GiB with --fast-all; use a 12 GB GPU for eager or a 24 GB GPU for the fast path
• The Breeze TTS 2 checkpoint

Installation

Download the inference code:

git clone https://github.com/breezeblue-ai/breeze-tts.git
cd breeze-tts

Install the dependencies:

python -m pip install -r requirements.txt

All required model components are included in the Breeze TTS 2 checkpoint.

For the tested CUDA environment, build the included Docker image:

bash docker/build.sh

The default image targets H100/Hopper (sm90). For A100:

FLASH_ATTN_CUDA_ARCHS=80 bash docker/build.sh

🎙️ Voice Clone

Clone a speaker from clean reference audio and its exact transcript.

English

python infer.py ../breeze-tts-2 \
  --ref-audio reference_en.wav \
  --ref-text "This is the exact transcript of the English reference audio." \
  --text "(sigh) It is good to hear your voice again after all this time." \
  --output outputs/voice_clone_en.wav

Chinese

python infer.py ../breeze-tts-2 \
  --ref-audio reference_zh.wav \
  --ref-text "这是中文参考音频的准确文字稿。" \
  --text "[叹气] 没想到过了这么久，你还记得我的声音。" \
  --output outputs/voice_clone_zh.wav

Reference audio should contain clean speech with minimal background noise.

🎨 Voice Design

Create a voice from a natural-language description without reference audio. Match the instruction language to the target text. Use --cfg-scale 4 to strengthen instruction-following.

English

python infer.py ../breeze-tts-2 \
  --text "(sigh) Welcome aboard. Your journey begins now." \
  --instruction "A warm, thoughtful young woman with a clear voice and a calm, reflective delivery." \
  --cfg-scale 4 \
  --output outputs/voice_design_en.wav

Chinese

python infer.py ../breeze-tts-2 \
  --text "[笑] 欢迎来到今晚的故事时间，让我们一起开始吧。" \
  --instruction "一位温柔自信的年轻女性，声音清晰，语气亲切，表达轻快而富有感染力。" \
  --cfg-scale 4 \
  --output outputs/voice_design_zh.wav

🎛️ Voice Direction

Keep the identity of a reference speaker while directing tone, emotion, pace, and delivery. Use --cfg-scale 4 to strengthen instruction-following.

python infer.py ../breeze-tts-2 \
  --ref-audio reference.wav \
  --ref-text "This is the exact transcript of the reference audio." \
  --text "(clears throat) We need to discuss what happened last night." \
  --instruction "Speak slowly with a restrained, serious tone." \
  --cfg-scale 4 \
  --output outputs/voice_direction.wav

🌐 Streaming API

Start the single-concurrency streaming API. It uses the same PyTorch runtime and eager execution by default:

python -m breeze_infer.api ../breeze-tts-2 --host 0.0.0.0 --port 7860

Send a Voice Direction request with reference audio and CFG 4:

curl -X POST http://127.0.0.1:7860/v1/audio/speech \
  -F "cfg_scale=4" \
  -F "ref_audio=@reference.wav" \
  -F "ref_text=This is the exact transcript of the reference audio." \
  -F "text=(clears throat) We need to discuss what happened last night." \
  -F "instruction=Speak slowly with a restrained, serious tone." \
  -F "seed=42" \
  --output voice_direction.pcm

The response is streaming mono 24 kHz signed 16-bit little-endian PCM. Start the API with --fast-all to enable the fast path.

⚡ Fast Inference Options

Both the CLI and API use eager streaming by default and skip graph warmup. Pass --fast-all to enable the best configuration for every inference stage when the additional cold-start time is acceptable. Each stage can also