Views
No views yet
GGUF quantizations for use with qwen3-tts.cpp (fork of predict-woo/qwen3-tts.cpp). Converted from Qwen/Qwen3-TTS-Tokenizer-12Hz usingscripts/convert_tts_to_gguf.py. Available quants: F16, Q8_0.
qwen-tts Python package from PyPI:pip install -U qwen-tts1import soundfile as sf
2from qwen_tts import Qwen3TTSTokenizer
3
4tokenizer = Qwen3TTSTokenizer.from_pretrained(
5 "Qwen/Qwen3-TTS-Tokenizer-12Hz",
6 device_map="cuda:0",
7)
8
9# Encode audio from a URL (or local path)
10enc = tokenizer.encode("https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/tokenizer_demo_1.wav")
11
12# Decode codes back into waveforms
13wavs, sr = tokenizer.decode(enc)
14sf.write("decode_output.wav", wavs[0], sr)

| Tokenizer Name | Description |
|---|---|
| Qwen3-TTS-Tokenizer-12Hz | The Qwen3-TTS-Tokenizer-12Hz model which can encode the input speech into codes and decode them back into speech. |
1@article{Qwen3-TTS,
2 title={Qwen3-TTS Technical Report},
3 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},
4 journal={arXiv preprint arXiv:2601.15621},
5 year={2026}
6}