AI Tutorials
Text-to-Speech
Qwen3-TTS

Install Qwen3-TTS on Windows with Conda

Introduction

Qwen3-TTS is an open-source series of TTS models developed by the Qwen team at Alibaba Cloud, supporting stable, expressive, and streaming speech generation, free-form voice design, and vivid voice cloning.

GitHub: https://github.com/QwenLM/Qwen3-TTS
Hugging Face: https://hf.co/collections/Qwen/qwen3-tts
Demo: https://hf.co/spaces?q=Qwen3-TTS

Prerequisites

System requirements:

  • Operating System: Windows 10/11 (64-bit), macOS, or Linux (Debian/Ubuntu).
  • Python: Version 3.12 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.
Environment Run this Command
CPU only pip3 install torch torchvision
CUDA 11.8 pip3 install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cu118
CUDA 12.1 pip3 install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cu121
CUDA 12.6 pip3 install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cu126
CUDA 12.8 pip3 install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cu128
CUDA 13.0 pip3 install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cu130

PyTorch

Note: CUDA version check by command

nvidia-smi

PyTorch

Video tutorial

Step 1. Install Miniconda Package

Download Miniconda: https://www.anaconda.com/download/success?reg=skipped

Direct link: https://anaconda.com/api/installers/Miniconda3-latest-Windows-x86_64.exe

Step 2. Create Conda Environment

Create a conda environment:

name: qwen3-tts
channels:
  - pytorch
  - nvidia
  - defaults
dependencies:
  - python=3.12
  - pip
  - pip:
      # PyTorch CUDA 12.6 wheels
      - --extra-index-url https://download.pytorch.org/whl/cu126
      - torch
      - torchvision
      - torchaudio

      # Qwen3-TTS
      - qwen-tts

      # Read/write audio files
      - soundfile

      # Download the model from Hugging Face / ModelScope
      - huggingface_hub[cli]
      - modelscope

Activate conda environment:

conda env create -f environment.yml
conda activate qwen3-tts

Step 3. Create a Python Script

Create a file named qwen3_tts_test.py:

import torch
import soundfile as sf
from qwen_tts import Qwen3TTSModel

# Use float16 instead of bfloat16 if your older GPU doesn't support bfloat16.
model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    # Do not use attn_implementation="flash_attention_2" on Windows.
    #attn_implementation="flash_attention_2",
)

# single inference
wavs, sr = model.generate_custom_voice(
    text="Hello, this is a test.",
    language="English",
    speaker="Ryan",
)

sf.write("output.wav", wavs[0], sr)
print("output.wav has been saved!")

Run it:

python qwen3_tts_test.py

The result will be the audio file output.wav

Audio samples

Speaker Voice Description Native language
Vivian Bright, slightly edgy young female voice. Chinese
Serena Warm, gentle young female voice. Chinese
Uncle_Fu Seasoned male voice with a low, mellow timbre. Chinese
Dylan Youthful Beijing male voice with a clear, natural timbre. Chinese (Beijing Dialect)
Eric Lively Chengdu male voice with a slightly husky brightness. Chinese (Sichuan Dialect)
Ryan Dynamic male voice with strong rhythmic drive. English
Aiden Sunny American male voice with a clear midrange. English
Ono_Anna Playful Japanese female voice with a light, nimble timbre. Japanese
Sohee Warm Korean female voice with rich emotion. Korean
OmniVoiceSupertonic-3