KaniTTS EXPO2025 Osaka japanese

A high-speed, high-fidelity Text-to-Speech model optimized for real-time conversational AI applications.

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『いのち輝く未来社会のデザイン』という大阪・関西万博2025のテーマを祝し、キルギスの人々から日本の皆さまへ --心と心 をつなぐ贈り物として、どうぞお受け取り ください。

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In honor of Expo Osaka 2025 and its motto 'Designing Future Society for Our Lives,' we humbly present this gift from the people of the Kyrgyz Republic to the people of Japan - heart to heart.

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Overview

KaniTTS uses a two-stage pipeline combining a large language model with an efficient audio codec for exceptional speed and audio quality. The architecture generates compressed token representations through a backbone LLM, then rapidly synthesizes waveforms via neural audio codec, achieving extremely low latency.

Key Specifications:

• Model Size: 370M parameters
• Sample Rate: 22kHz
• Languages: Japanese
• License: Apache 2.0

Quickstart: Install from PyPI & Run Inference

It’s a lightweight so you can install, load a model, and speak in minutes. Designed for quick starts and simple workflows—no heavy setup, just pip install and run. More detailes...

Install

pip install kani-tts
pip install -U "transformers==4.57.1" # for LFM2 !!!

Quick Start

from kani_tts import KaniTTS

model = KaniTTS('nineninesix/kani-tts-370m-expo2025-osaka-ja')

# Generate audio from text
audio, text = model("Your text here")

# Save to file (requires soundfile)
model.save_audio(audio, "output.wav")

Custom Configuration

from kani_tts import KaniTTS

model = KaniTTS(
    'nineninesix/kani-tts-370m-expo2025-osaka-ja',
    temperature=0.7,           # Control randomness (default: 1.0)
    top_p=0.9,                 # Nucleus sampling (default: 0.95)
    max_new_tokens=2000,       # Max audio length (default: 1200)
    repetition_penalty=1.2,    # Prevent repetition (default: 1.1)
    suppress_logs=True,        # Suppress library logs (default: True)
    show_info=True,            # Show model info on init (default: True)
)

audio, text = model("Your text here")

Playing Audio in Jupyter Notebooks

You can listen to generated audio directly in Jupyter notebooks or IPython:

from kani_tts import KaniTTS
from IPython.display import Audio as aplay

model = KaniTTS('nineninesix/kani-tts-370m-expo2025-osaka-ja')
audio, text = model("Your text here")

# Play audio in notebook
aplay(audio, rate=model.sample_rate)

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Performance

Nvidia RTX 5090 Benchmarks:

• Latency: ~1 second to generate 15 seconds of audio
• Memory: 2GB GPU VRAM
• Quality Metrics: MOS 4.3/5 (naturalness), WER <5% (accuracy)

Pretraining:

• Dataset: ~80k hours from LibriTTS, Common Voice, and Emilia
• Hardware: 8x H100 GPUs, 45 hours training time on Lambda AI

Voices Datasets

• https://huggingface.co/datasets/MomoyamaSawa/Voice-KusanagiNene

Audio Examples

Use Cases

• Conversational AI: Real-time speech for chatbots and virtual assistants
• Edge/Server Deployment: Resource-efficient inference on affordable hardware
• Accessibility: Screen readers and language learning applications
• Research: Fine-tuning for specific voices, accents, or emotions

Limitations

• Performance degrades with inputs exceeding 2000 tokens
• Limited expressivity without fine-tuning for specific emotions
• May inherit biases from training data in prosody or pronunciation

Optimization Tips

• Multilingual Performance: Continually pretrain on target language datasets and fine-tune NanoCodec
• Batch Processing: Use batches of 8-16 for high-throughput scenarios
• Hardware: Optimized for NVIDIA Blackwell architecture GPUs

Resources

Models:

• Pretrained Model
• Fine-tuned Model
• HuggingFace Space

Examples:

• Inference Example
• Fine-tuning-code
• Example Dataset
• GitHub Repository

Links:

• Website
• Contact Form

Acknowledgments

Built on top of LiquidAI LFM2 350M as the backbone and Nvidia NanoCodec for audio processing.

Responsible Use

Prohibited activities include:

• Illegal content or harmful, threatening, defamatory, or obscene material
• Hate speech, harassment, or incitement of violence
• Generating false or misleading information
• Impersonating individuals without consent
• Malicious activities such as spamming, phishing, or fraud

By using this model, you agree to comply with these restrictions and all applicable laws.

Contact

Have a question, feedback, or need support? Please fill out our contact form and we'll get back to you as soon as possible.