MioTTS-1.7B: Lightweight & Fast LLM-based TTS

MioTTS-1.7B is a lightweight, high-speed Text-to-Speech (TTS) model based on an LLM architecture. It is designed to generate high-quality speech in English and Japanese while maintaining low latency and minimal resource usage.

This model supports zero-shot voice cloning and is built on top of the efficient neural audio codec MioCodec-25Hz-24kHz.

📊 MioTTS Family

We offer a range of model sizes to suit different performance and resource requirements.

Model Name	Parameters	Base Model	License	RTF (Real-Time Factor)
MioTTS-0.1B	0.1B	tiiuae/Falcon-H1-Tiny-Multilingual-100M-Base	Falcon-LLM License	0.04 - 0.05
MioTTS-0.4B	0.4B	LiquidAI/LFM2-350M	LFM Open License v1.0	0.035 - 0.045
MioTTS-0.6B	0.6B	Qwen/Qwen3-0.6B-Base	Apache 2.0	0.055 - 0.065
MioTTS-1.2B	1.2B	LiquidAI/LFM2.5-1.2B-Base	LFM Open License v1.0	0.065 - 0.075
MioTTS-1.7B	1.7B	Qwen/Qwen3-1.7B-Base	Apache 2.0	0.10 - 0.11
MioTTS-2.6B	2.6B	LiquidAI/LFM2-2.6B	LFM Open License v1.0	0.135 - 0.145

RTF values represent the range observed when generating approximately 15 seconds of audio across multiple runs. Measured on an NVIDIA RTX 5090 using vLLM 0.15.1.

🌟 Key Features

Lightweight & Fast: Optimized for speed, making it suitable for consumer-grade GPUs and edge deployment.
Bilingual Support: Trained on approximately 100,000 hours of English and Japanese data.
Voice Cloning: Supports high-fidelity zero-shot voice cloning from a short reference audio clip.
Efficient Codec: Uses Aratako/MioCodec-25Hz-24kHz, which operates at a low framerate (25Hz) for faster generation without sacrificing quality.

🚀 Inference

We provide a dedicated repository for inference, including installation instructions and example WebUI.

👉 GitHub: Aratako/MioTTS-Inference

🎧 Audio Samples

Below are some samples generated by MioTTS-1.7B.

Note: The reference audio samples below were generated using Aratako/T5Gemma-TTS-2b-2b and gemini-2.5-pro-tts.

Case	Text	Reference Audio	Generated Audio
English 1	"The old library was silent, save for the gentle ticking of a clock somewhere in the shadows. As I ran my fingers along the dusty spines of the books, I felt a strange sense of nostalgia, as if I had lived a thousand lives within these walls."
English 2	"Hey! I haven't seen you in ages. Do you want to grab some coffee later? I've got so much to tell you!"
Japanese 1	"気象庁によりますと、大型の台風10号は、明日の明け方にかけて関東地方に接近する見込みです。沿岸部では高波に警戒が必要です。"
Japanese 2	"その森には、古い言い伝えがありました。月が最も高く昇る夜、静かに耳を澄ませば、風の歌声が聞こえるというのです。私は半信半疑でしたが、その夜、確かに誰かが私を呼ぶ声を聞いたのです。"

🏗️ Training Details

Data: ~100k hours of speech data (English & Japanese).
Codec: MioCodec-25Hz-24kHz
Base Model: Initialized from Qwen/Qwen3-1.7B-Base.

📜 License & Ethical Restrictions

License

This model is released under the Apache 2.0.

Ethical Considerations & Limitations

While this model is released under a permissive license, we aim to promote responsible AI development and urge users to respect the rights of others.

Voice Cloning: Please respect the privacy and rights of individuals. We strongly discourage using this model to clone the voices of real people (especially non-consenting individuals) for deceptive or harmful purposes.
No Misinformation: This model should not be used to generate deepfakes intended to mislead others or spread misinformation.
Disclaimer: The developers assume no liability for any misuse of this model. Users are solely responsible for ensuring their use of the generated content complies with applicable laws and regulations in their jurisdiction.

🙏 Acknowledgments

Compute Support: Part of the compute resources for this project were provided by Saldra, Witness and Lumina Logic Minds. We deeply appreciate their support.
Base Model: We thank the developers of the base LLM for their open-source contributions.
Community: Thanks to the open-source community for the datasets and tools that made this project possible.

🖊️ Citation

If you use MioTTS in your research or project, please cite it as follows:

@misc{miotts,
  author = {Chihiro Arata},
  title = {MioTTS: Lightweight and Fast LLM-based Text-to-Speech},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/collections/Aratako/miotts}}
}