Qwen3-TTS-TBLive-Base

A Chinese live-streaming domain TTS checkpoint fine-tuned from Qwen3-TTS-12Hz-1.7B-Base via continual pre-training (CPT) and GRPO post-training on live-streaming speech data.

Recommended starting point for downstream fine-tuning. If you need production-ready built-in live-streaming voices, use the sibling checkpoint TaoLiveAIGC/Qwen3-TTS-TBLive-CustomVoice.

Benchmark

Zero-shot voice-cloning results on the public seed-tts-eval test sets, compared against open-source baselines. / marks numbers not reported.

Model	test-en SIM-o ↑	test-en WER ↓	test-en UTMOS ↑	test-zh SIM-o ↑	test-zh WER ↓	test-zh UTMOS ↑
Ground-truth	0.734	2.14	3.52	0.755	1.25	2.78
IndexTTS2	0.706	2.33	3.65	0.764	1.05	3.00
CosyVoice3	0.696	2.17	3.96	0.778	1.14	3.32
VoxCPM	0.731	1.92	3.77	0.772	0.99	2.94
MossTTS Local	0.732	1.93	/	0.796	1.44	/
Qwen3-TTS	0.708	1.54	4.16	0.766	1.15	3.46
Qwen3-TTS-TBLive-Base	0.732	1.63	4.11	0.782	1.28	3.38

Quickstart

pip install -U qwen-tts

import torch
from qwen_tts import Qwen3TTSModel

model = Qwen3TTSModel.from_pretrained(
    "TaoLiveAIGC/Qwen3-TTS-TBLive-Base",
    device_map="cuda:0",
    dtype=torch.bfloat16,
)

# Zero-shot voice cloning with a short reference audio
wavs, sr = model.generate_voice_clone(
    text="家人们，今天直播间的福利真的拉满了！",
    language="Chinese",
    ref_audio="path/to/reference.wav",
    ref_text="参考音频对应的文本",
)

For training pipelines (SFT / GRPO), inference post-selection, and advanced usage, see the companion training repository. For upstream features (vLLM serving, DashScope API, deployment), refer to the upstream Qwen3-TTS repository.

Checkpoint contents

├── model.safetensors            # main checkpoint (~3.6 GB)
├── config.json / configuration.json / generation_config.json
├── preprocessor_config.json
├── tokenizer_config.json / vocab.json / merges.txt
└── speech_tokenizer/            # 12 Hz speech tokenizer (~651 MB, same as upstream)

License

Released under the Apache License 2.0, consistent with upstream Qwen3-TTS.

Acknowledgements

Our sincere thanks to the Qwen team for open-sourcing Qwen3-TTS — the base model, tokenizer, and reference training / inference code that make this work possible.