Hugging Face trending modelspublished Aug 25, 16:39 UTC (21d ago)ingested Sep 8, 14:03 UTCBreezeBlue

BreezeBlue/Breeze-TTS-2 — new model trending #19 on Hugging Face

AI summary · glm-5.3-flash

BreezeBlue open-weights Breeze TTS 2, a bilingual text-to-speech model it ranks #1 among open-weight models on the Artificial Analysis TTS leaderboard.

BreezeBlue released open weights and Apache 2.0-licensed PyTorch inference code for Breeze TTS 2 on 2026-08-25. The text-to-speech model supports English and Chinese, voice cloning, reference-free voice design, voice direction, and inline vocal events like (laugh) and (sigh). Reported performance includes #1 open-weight ranking on the Artificial Analysis Elo leaderboard, under 40 ms time-to-first-audio, a 0.32 real-time factor on an NVIDIA H100, and about 7.7 GiB GPU memory for eager inference.

Open weights and inference code released; weights limited to research and non-commercial use
Claims #1 among open-weight TTS models on Artificial Analysis leaderboard, outperforming proprietary systems
Bilingual English-Chinese generation with voice clone, design, direction, and inline vocal events
Under 40 ms time-to-first-audio and 0.32 RTF streaming on an NVIDIA H100 fast path
Requires roughly 7.7 GiB GPU memory for eager inference; 12 GB GPU minimum recommended

Read the original

VendorsBreezeBlue

ProductsBreeze TTS 2

OrganizationsHugging Face Artificial Analysis

AI modelsBreeze-TTS-2

Tagstext-to-speech open-weights voice-cloning voice-design streaming breeze-tts-2 breezeblue hugging-face

Full article1,149 words · extracted from huggingface.co · click to collapse

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> [!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](#license-and-responsible-use).

## 📰 News

- **[2026.08.25]** 🎉 We open-source [Breeze TTS 2](https://huggingface.co/BreezeBlue/breeze-tts-2) model weights and the [PyTorch inference code](https://github.com/breezeblue-ai/breeze-tts).

- **[2026.08.07]** 🔥 We release the TTS benchmark suite for [voice design](https://github.com/breezeblue-ai/tts-voice-design-benchmark), [voice direction](https://github.com/breezeblue-ai/TTS-Voice-Direction-Benchmark), and [latency evaluation](https://github.com/breezeblue-ai/TTS-Latency-Benchmark).

## 📖 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.

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## ✨ 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:

```bash

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

cd breeze-tts

```

Install the dependencies:

```bash

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

bash docker/build.sh

```

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

```bash

FLASH_ATTN_CUDA_ARCHS=80 bash docker/build.sh

```

### 🎙️ Voice Clone

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

#### English

```bash

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

```bash

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

```bash

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

```bash

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.

```bash

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:

```bash

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:

```bash

curl -X POST http://127.0.0.1:7860/v1/audio/speech \

-F "cfg_scale=4" \

-F "[email protected]" \

-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 be controlled independently:

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/BreezeBlue/Breeze-TTS-2