Install Breeze TTS 2 with Conda

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.

GitHub: https://github.com/breezeblue-ai/breeze-tts
Hugging Face: https://hf.co/BreezeBlue/Breeze-TTS-2
Website: https://breezeblue.ai

Prerequisites

System requirements:

• Operating System: Windows 10/11 (64-bit), macOS, or Linux (Debian/Ubuntu).
• Python: version >= 3.10 required
• Disk Space: 10GB+ recommended (for dependencies and model cache). At least 400 MB for Miniconda; 3 GB+ for full Anaconda.
• The GPU is optional but HIGHLY Recommended for Performance.
• Internet: For downloading dependencies and models from Hugging Face Hub.

Requirements	CPU	RAM
Minimum	Intel Core i3 or AMD Ryzen 3	8 GB
Recommended	Intel Core i5 or AMD Ryzen 5	16 GB
High-performance	Intel Core i7/i9 or AMD Ryzen 7/9	32 GB

Install PyTorch for NVIDIA

Note: CUDA version check by command

nvidia-smi

Install PyTorch for ROCm

PyTorch can be installed and used on macOS. Depending on your system and GPU capabilities, your experience with PyTorch on macOS may vary in terms of processing time.

PyTorch Available versions:

pip index versions torch
pip index versions torchaudio
pip index versions torchvision
pip index versions torch --index-url https://download.pytorch.org/whl/cu118
pip index versions torchaudio --index-url https://download.pytorch.org/whl/cu118
pip index versions torchvision --index-url https://download.pytorch.org/whl/cu118

Video tutorial

Step 1. Install Miniconda Package

Step 2. Create Conda Environment

Create a conda environment:

name: breezetts
channels:
- conda-forge
- defaults
dependencies:
# Python version (requires >= 3.10)
- python=3.10

- pip
- pip:
    # PyTorch CPU only
    - torch
    - torchaudio

    # Install the dependencies for Breeze TTS 2
    - qwen-tts==0.1.1
    - transformers==4.57.3
    - numpy>=2.0
    - soundfile>=0.13
    - fastapi>=0.115
    - uvicorn>=0.30
    - python-multipart>=0.0.18
    - pytest>=8.0
    - ruff>=0.12
    
    # Download the model from Hugging Face / ModelScope
    - huggingface_hub
    - modelscope

name: breezetts
channels:
- conda-forge
- defaults
dependencies:
# Python version (requires >= 3.10)
- python=3.10

- pip
- pip:
    # This is the PyTorch CUDA 12.6 wheels.
    - --extra-index-url https://download.pytorch.org/whl/cu126
    - torch
    - torchaudio

    # Install the dependencies for Breeze TTS 2
    - qwen-tts==0.1.1
    - transformers==4.57.3
    - numpy>=2.0
    - soundfile>=0.13
    - fastapi>=0.115
    - uvicorn>=0.30
    - python-multipart>=0.0.18
    - pytest>=8.0
    - ruff>=0.12
    
    # Download the model from Hugging Face / ModelScope
    - huggingface_hub
    - modelscope

name: breezetts
channels:
- conda-forge
- defaults
dependencies:
# Python version (requires >= 3.10)
- python=3.10

- pip
- pip:
    # PyTorch MPS (Metal Performance Shaders)
    - torch
    - torchaudio

    # Install the dependencies for Breeze TTS 2
    - qwen-tts==0.1.1
    - transformers==4.57.3
    - numpy>=2.0
    - soundfile>=0.13
    - fastapi>=0.115
    - uvicorn>=0.30
    - python-multipart>=0.0.18
    - pytest>=8.0
    - ruff>=0.12
    
    # Download the model from Hugging Face / ModelScope
    - huggingface_hub
    - modelscope

Activate conda environment:

conda env create -f environment.yml
conda activate breezetts

Clone BreezeTTS from source (run after activating the environment)

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

Download the weights

🤗 HuggingFace

hf download BreezeBlue/breeze-tts-2 --local-dir ..\breeze-tts-2

🤖 ModelScope

modelscope download --model BreezeBlue/breeze-tts-2 --local_dir ..\breeze-tts-2

Download the reference audio

curl -L -O "https://r.breeze.blue/blog/tts2/multilingual-en.mp3"

The expected directory structure is:

breeze/
├── breeze-tts/
│   └── multilingual-en.mp3
├── breeze-tts-2/
└── environment.yml

Step 3. Run the Inference

Now, run the basic example script to synthesize speech:

python infer.py ../breeze-tts-2 --ref-audio multilingual-en.mp3 --ref-text "Every journey finds its meaning when someone dares to take the first step." --text "(sigh) It is good to hear your voice again after all this time." --output outputs/voice_clone_en.wav

The result will be the audio file voice_clone_en.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.

Reference:

• Breeze TTS 2 Is #1 on the Artificial Analysis Open-Weights TTS Leaderboard