Qwen3-TTS Technical Report
Paper • 2601.15621 • Published • 77
license: apache-2.0 tag: demodokos
Model
This model is hosted for Demodokos Foundry but it can be used for other purposes, enjoy a stable download location and custom quantizations not available elsewhere.
Qwen3-TTS
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We release Qwen3-TTS, a series of powerful speech generation models developed by Qwen, offering comprehensive support for voice cloning, voice design, ultra-high-quality human-like speech generation, and natural language-based voice control.
Overview
Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles. Key features:
- Powerful Speech Representation: Powered by the self-developed Qwen3-TTS-Tokenizer-12Hz, it achieves efficient acoustic compression and high-dimensional semantic modeling.
- Universal End-to-End Architecture: Utilizing a discrete multi-codebook LM architecture to bypass traditional information bottlenecks.
- Extreme Low-Latency Streaming Generation: Supports streaming generation with end-to-end synthesis latency as low as 97ms.
- Intelligent Voice Control: Supports speech generation driven by natural language instructions for flexible control over timbre, emotion, and prosody.
Quickstart
Environment Setup
Install the qwen-tts Python package from PyPI:
pip install -U qwen-tts
Python Package Usage
import torch
import soundfile as sf
from qwen_tts import Qwen3TTSModel
# Load the model
model = Qwen3TTSModel.from_pretrained(
"Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
device_map="cuda:0",
dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
# Custom Voice Generation
wavs, sr = model.generate_custom_voice(
text="其实我真的有发现，我是一个特别善于观察别人情绪的人。",
language="Chinese",
speaker="Vivian",
instruct="用特别愤怒的语气说",
)
sf.write("output.wav", wavs[0], sr)
Evaluation
Zero-shot speech generation on the Seed-TTS test set (Word Error Rate (WER, ↓)):
| Model | test-zh | test-en |
|---|---|---|
| Qwen3-TTS-12Hz-1.7B-Base | 0.77 | 1.24 |
Citation
If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝:
@article{Qwen3-TTS,
title={Qwen3-TTS Technical Report},
author={Hangrui Hu and Xinfa Zhu and Ting He and Dake Guo and Bin Zhang and Xiong Wang and Zhifang Guo and Ziyue Jiang and Hongkun Hao and Zishan Guo and Xinyu Zhang and Pei Zhang and Baosong Yang and Jin Xu and Jingren Zhou and Junyang Lin},
journal={arXiv preprint arXiv:2601.15621},
year={2026}
}
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