Parameter	Value
Base model	`Qwen/Qwen3-TTS-12Hz-0.6B-Base`
Speaker	`duarte`
Training samples	200
Test samples	10
Learning rate	1e-06
Epochs	10
Batch size	4
Gradient accumulation	4
Seed	42
Subcodec input	false

Evaluation Results

Metric	Value
WER (Word Error Rate)	0.0229
CER (Character Error Rate)	0.0019
Avg Duration Diff	+0.12 s

Qwen3-TTS-12Hz-0.6B-Base (Base Model)

Qwen3-TTS Technical Report | GitHub Repository | Hugging Face Demo

Qwen3-TTS is a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Trained on over 5 million hours of speech data spanning 10 languages, Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control.

This specific checkpoint is the 0.6B Base model, which is capable of rapid voice cloning from a user-provided audio input.

Quickstart

Installation

bash

pip install -U qwen-tts
# Optional: for optimized performance
pip install -U flash-attn --no-build-isolation

Sample Usage (Voice Clone)

To clone a voice and synthesize new content using the Base model, you can use the following code snippet:

python

import torch
import soundfile as sf
from qwen_tts import Qwen3TTSModel

# Load the model
model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
    device_map="cuda:0",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

# Reference audio for cloning
ref_audio = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/clone.wav"
ref_text  = "Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you."

# Generate speech
wavs, sr = model.generate_voice_clone(
    text="I am solving the equation: x = [-b ± √(b²-4ac)] / 2a? Nobody can — it's a disaster (◍•͈⌔•͈◍), very sad!",
    language="English",
    ref_audio=ref_audio,
    ref_text=ref_text,
)

# Save the resulting audio
sf.write("output_voice_clone.wav", wavs[0], sr)

Overview

Introduction

Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles to meet global application needs. 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, it realizes full-information end-to-end speech modeling.
Extreme Low-Latency Streaming Generation: End-to-end synthesis latency as low as 97ms, meeting the rigorous demands of real-time interactive scenarios.
Intelligent Text Understanding and Voice Control: Supports speech generation driven by natural language instructions, allowing for flexible control over multi-dimensional acoustic attributes.

Model Architecture

Citation

If you find this work useful, please consider citing the technical report:

BibTeX

@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}
}

duarteocarmo/qwen_tts_finetune_0.6B_e10_l1e6

qwen_tts_finetune_0.6B_e10_l1e6

Fine-Tuning Parameters