MioTTS-0.1B 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.
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.
"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!"
We evaluated MioTTS-0.1B using J-HARD-TTS-Eval, a challenging benchmark for Japanese zero-shot TTS. The evaluation involves synthesizing each test case 5 times and measuring the best, average, and worst performance.
Key Findings
Despite its ultra-lightweight size (0.1B parameters), MioTTS shows interesting characteristics:
Exceptional Peak Performance: In the Rhyme test (closest to standard reading), the model achieves the best scores in both 'best' and 'average' metrics, outperforming significantly larger models. It also attains the top 'best' scores in Repetition and Continuation tasks.
Stability Trade-offs: While peak performance is high, the 'average' and 'worst' scores for Repetition and Continuation degrade compared to the best runs. This indicates some instability in generation consistency across attempts.
Limitations: Performance on Short text inputs is currently lower and remains a topic for future improvement.
Speaker Similarity: The lower similarity scores are an expected trade-off of the architecture, as voice cloning is handled by the highly compressed, lightweight codec rather than the LLM itself.
Character Error Rate (CER)
Lower is better.
Model
Size
short best
short avg
short worst
rep best
rep avg
rep worst
rhyme best
rhyme avg
rhyme worst
cont best
cont avg
cont worst
MioTTS-0.1B
AR: 114.5M (74.4M) + NAR: 81.3M
13.39
50.55
107.9
4.963
22.29
50.92
0.1419
1.022
3.654
0.2884
2.664
9.285
XTTS-v2
441.0M (424.2M)
5.512
14.33
31.50
7.792
12.12
18.61
0.1419
1.064
3.122
0.3460
1.396
3.287
CosyVoice2-0.5B
AR: 505.8M (357.9M) + NAR: 112.5M
22.83
71.50
123.6
8.139
15.25
28.68
0.1774
1.398
4.576
0.4614
5.456
16.03
FishAudio-S1-mini
AR: 801.4M (440.5M) + NAR: 58.73M
0.7874
15.59
48.82
11.81
35.19
79.90
0.4966
1.313
3.015
0.4037
1.257
2.364
Qwen3-TTS-0.6B
AR: 764.2M (437.3M) + NAR: 141.6M
7.087
22.36
45.67
6.799
13.01
21.49
2.128
4.292
7.627
0.7497
2.076
4.037
Qwen3-TTS-1.7B
AR: 1.703B (1.403B) + NAR: 175.1M
1.575
4.724
11.02
5.261
10.57
17.67
0.6031
2.469
4.753
0.5767
1.488
2.884
Speaker Similarity (Sim)
Higher is better. Computed using varying CER thresholds.
Model
Size
SS (CER=0)
SS (CER<=10)
SS (CER<=30)
SS (CER<=50)
SS (CER<=100)
SS (Unfiltered)
MioTTS-0.1B
AR: 114.5M (74.4M) + NAR: 81.3M
0.5696
0.5651
0.5576
0.5523
0.5430
0.5387
XTTS-v2
441.0M (424.2M)
0.6267
0.6273
0.6218
0.6178
0.6155
0.6145
CosyVoice2-0.5B
AR: 505.8M (357.9M) + NAR: 112.5M
0.7325
0.7251
0.7152
0.7087
0.6858
0.6848
FishAudio-S1-mini
AR: 801.4M (440.5M) + NAR: 58.73M
0.6864
0.6833
0.6722
0.6646
0.6531
0.6440
Qwen3-TTS-0.6B
AR: 764.2M (437.3M) + NAR: 141.6M
0.7419
0.7496
0.7451
0.7418
0.7354
0.7298
Qwen3-TTS-1.7B
AR: 1.703B (1.403B) + NAR: 175.1M
0.7623
0.7614
0.7549
0.7539
0.7537
0.7530
📝 Benchmark Notes
Data Source: Baseline results (rows other than MioTTS) are transcribed from the Zero-shot results in the J-HARD-TTS-Eval README.
Model Size: Values in parentheses within the Size column indicate AR model parameters excluding the embedding and output head layers.
Generation Settings: MioTTS inference was performed using vLLM 0.15.1 with temperature=0.8, top_p=1.0, and repetition_penalty=1.0.
Preprocessing: MioTTS inference applies the same text preprocessing used during its training. This process removes trailing punctuation (e.g., final commas). As a result, the synthesized target text may differ slightly from the original benchmark text, which can influence CER scores.
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:
bibtex
1@misc{miotts,
2 author = {Chihiro Arata},
3 title = {MioTTS: Lightweight and Fast LLM-based Text-to-Speech},
4 year = {2026},
5 publisher = {Hugging Face},
6 journal = {Hugging Face repository},
7 howpublished = {\url{https://huggingface.co/collections/Aratako/miotts}}
8}
