---
license: apache-2.0
language:
- th
- en
base_model:
- SWivid/F5-TTS
pipeline_tag: text-to-speech
---
# JaiTTS-F5TTS: Thai Voice Cloning Model Research Prototype 

[![Paper](https://img.shields.io/badge/Paper-link-green.svg)](https://arxiv.org/pdf/2604.27607)
[![Website](https://img.shields.io/badge/Website-JAI-orange.svg)](https://jts.co.th/jai/)
[![GitHub](https://img.shields.io/badge/GitHub-repository-black.svg)](https://github.com/JTS-AI-Team/JaiTTS)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

<img src="JaiTTS-logo.jpg" width="313"/>

**JaiTTS-F5TTS** is a non-autoregressive JaiTTS voice cloning model based on [F5-TTS](https://huggingface.co/SWivid/F5-TTS). It targets Thai zero-shot voice cloning.

> **Research prototype:** JaiTTS-F5TTS is one of our experimental variants within the JaiTTS project. It is released for research and benchmarking only.

## Highlights

- F5-TTS-based non-autoregressive voice cloning for Thai
- Duration predictor for improved pacing and intelligibility
- Fast synthesis with Real-Time Factor (RTF) below `0.2`

## Duration Modeling

The original F5-TTS duration estimate uses a UTF-8 byte-ratio formula. This is brittle for Thai and mixed-script input because Thai characters, English words, Arabic numerals, and punctuation do not have a consistent byte-to-pronunciation relationship. In practice, the mismatch can produce rushed, compressed, or unstable speech.

We address this with an XLM-R-based neural duration predictor that estimates target duration from text more robustly than the UTF-8 byte-ratio baseline.

The data used to train and evaluate the duration predictor is sampled from the JaiTTS-v1.0 training set.

### Duration Predictor Architecture

The duration predictor uses [XLM-R base](https://huggingface.co/FacebookAI/xlm-roberta-base) as the text encoder. Text representations are aggregated with masked mean pooling, then passed to a regression head composed of linear layers with GELU activation and dropout. The predictor also uses log-transformed syllable counts as an auxiliary feature, which provides a more pronunciation-aware signal than byte length for Thai and mixed-script text.

### Duration Prediction Metrics

Errors are reported in seconds. Lower is better.

- `MAE`: Mean absolute error across all samples.
- `p50 Error`: The 50th-percentile absolute error.
- `p90 Error`: The 90th-percentile absolute error.
- `p95 Error`: The 95th-percentile absolute error.

| Model | MAE ↓ | p50 Error ↓ | p90 Error ↓ | p95 Error ↓ |
| :-- | --: | --: | --: | --: |
| F5-TTS UTF-8 baseline | 1.7064 | 1.0987 | 4.0461 | 5.3914 |
| **XLM-R predictor** | **1.0924** | **0.7118** | **2.6319** | **3.4425** |

## Benchmark Results

### Objective Evaluation

Objective evaluation is measured on the same benchmark used in the paper: [JaiTTS: A Thai Voice Cloning Model](https://arxiv.org/pdf/2604.27607). Results can be reproduced using the benchmark instructions in the [GitHub repository](https://github.com/JTS-AI-Team/JaiTTS).

| Model | Short CER (%) ↓ | Short SIM ↑ | Long CER (%) ↓ | Long SIM ↑ |
| :-- | --: | --: | --: | --: |
| ThonburianTTS | 6.26 | 0.48 | -- | -- |
| JaiTTS-F5TTS | 4.78 | 0.60 | 12.63 | **0.80** |
| JaiTTS-F5TTS + Duration Predictor | 4.26 | 0.58 | 11.57 | **0.80** |
| [JaiTTS-v1.0](https://arxiv.org/pdf/2604.27607) | **1.94** | **0.62** | **2.55** | 0.76 |

### Inference Speed

| Model | RTF ↓ |
| :-- | --: |
| ThonburianTTS | 0.1150 |
| JaiTTS-F5TTS | 0.1138 |
| JaiTTS-F5TTS + Duration Predictor | 0.1652 |
| [JaiTTS-v1.0](https://arxiv.org/pdf/2604.27607) | **0.1136** |

## Installation

This inference code and pipeline structure are adapted from the [ThonburianTTS](https://huggingface.co/biodatlab/ThonburianTTS) project by biodatlab.

### 1. Install Dependencies

```bash
pip install torch cached-path librosa transformers f5-tts
sudo apt install ffmpeg
```

### 2. Clone the Inference Codebase

This model uses the `flowtts` pipeline adapted from ThonburianTTS:

```bash
git clone https://github.com/biodatlab/thonburian-tts.git
cd thonburian-tts
```

## Quick Usage

Use the following snippet to run inference with the JaiTTS-F5TTS checkpoint. Ensure you are inside the `thonburian-tts` directory or have the `flowtts` module in your Python path.

```python
import torch
import soundfile as sf
from flowtts.inference import FlowTTSPipeline, ModelConfig, AudioConfig

model_config = ModelConfig(
    language="th",
    model_type="F5",
    checkpoint="hf://JTS-AI/JaiTTS-F5TTS/model.pt",
    vocab_file="hf://JTS-AI/JaiTTS-F5TTS/vocab.txt",
    vocoder="vocos",
    device="cuda" if torch.cuda.is_available() else "cpu"
)

audio_config = AudioConfig(
    silence_threshold=-45,
    cfg_strength=2.5,
    nfe_step=32,
    speed=1.0
)

pipeline = FlowTTSPipeline(model_config, audio_config)

audio, sr = pipeline.generate(
    reference_audio="path/to/reference.wav",
    reference_text="Transcription of the reference audio.",
    gen_text="สวัสดีครับ ยินดีที่ได้รู้จัก ผมคือ AI ที่สร้างโดย JTS"
)

sf.write("output.wav", audio, sr)
```

## Citation

If you find this work useful, please cite our paper:

```bibtex
@misc{karnjanaekarin2026jaittsthaivoicecloning,
      title={JaiTTS: A Thai Voice Cloning Model}, 
      author={Jullajak Karnjanaekarin and Pontakorn Trakuekul and Narongkorn Panitsrisit and Sumana Sumanakul and Vichayuth Nitayasomboon and Nithid Guntasin and Thanavin Denkavin and Attapol T. Rutherford},
      year={2026},
      eprint={2604.27607},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.27607}, 
}
```

## Acknowledgements

- Codebase adapted from [ThonburianTTS](https://github.com/biodatlab/thonburian-tts).