Spaces:
Running on Zero
Running on Zero
File size: 2,420 Bytes
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title: GibbsTTS Demo
emoji: 🎙️
colorFrom: indigo
colorTo: gray
sdk: gradio
sdk_version: "5.49.1"
python_version: "3.10"
app_file: app.py
pinned: false
license: mit
short_description: Zero-shot voice cloning TTS (EN/ZH) — GibbsTTS demo
models:
- ydqmkkx/GibbsTTS
tags:
- tts
- text-to-speech
- voice-cloning
- zero-shot
- english
- chinese
- flow-matching
---
# GibbsTTS — Zero-Shot Voice Cloning TTS
A Hugging Face Space for **GibbsTTS**, a zero-shot text-to-speech model
based on metric-induced discrete flow matching with the proposed
kinetic-optimal scheduler and finite-step CTMC moment correction.
- 📄 Paper: <https://arxiv.org/abs/2605.09386>
- 💻 Code: <https://github.com/ydqmkkx/GibbsTTS>
- 🎛️ Weights: <https://huggingface.co/ydqmkkx/GibbsTTS>
## How to use
1. **Reference audio** — upload (or record) a short clip of the voice you want
to clone. A few seconds is enough.
2. **Reference transcript** — type exactly what the reference clip says.
3. **Target text** — the sentence you want the model to speak in that voice.
4. **Language** — choose `English`, `Chinese (Mandarin)`, or `Mixed EN/ZH`.
5. Click **Synthesize**.
The model was trained on
[Emilia-en/zh](https://huggingface.co/datasets/amphion/Emilia-Dataset), so it
supports English and Mandarin. The mixed mode is experimental and provided
for fun.
## Hardware
Inference is fast on a single GPU (a couple of seconds per sentence on an
H100). The model is ~1.6 GB plus the MaskGCT codec — choose at least a small
GPU runtime. Weights are downloaded automatically from
[`ydqmkkx/GibbsTTS`](https://huggingface.co/ydqmkkx/GibbsTTS) on the first run.
## Citation
```bibtex
@article{GibbsTTS,
author = {Dong Yang and Yiyi Cai and Haoyu Zhang and Yuki Saito and Hiroshi Saruwatari},
title = {Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech},
year = {2026},
journal = {arXiv preprint arXiv:2605.09386},
}
@inproceedings{MaskGCT,
author = {Yuancheng Wang and Haoyue Zhan and Liwei Liu and Ruihong Zeng and Haotian Guo and Jiachen Zheng and Qiang Zhang and Xueyao Zhang and Shunsi Zhang and Zhizheng Wu},
title = {{MaskGCT}: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer},
year = {2025},
booktitle = {International Conference on Learning Representations (ICLR)},
}
```
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