KaniTTS

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

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: English, German, Chinese, Korean, Arabic, Spanish
• License: Apache 2.0

Performance

Nvidia RTX 5080 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/nytopop/expresso-conversational
• https://huggingface.co/datasets/shb777/gemini-flash-2.0-speech
• https://huggingface.co/datasets/jazza234234/david-dataset
• https://huggingface.co/datasets/reach-vb/jenny_tts_dataset
• https://huggingface.co/datasets/MBZUAI/ArVoice
• https://huggingface.co/datasets/Thorsten-Voice/TV-44kHz-Full
• https://huggingface.co/datasets/SinclairSchneider/german_voice_cb
• https://huggingface.co/datasets/Bingsu/KSS_Dataset
• https://huggingface.co/datasets/ciempiess/ciempiess_fem
• https://huggingface.co/datasets/TingChen-ppmc/Shanghai_Dialect_TTS_openai
• https://huggingface.co/datasets/boniromou/zh-yue-tts-dataset
• https://huggingface.co/datasets/zeeshanparvez/andrew-v3

Voices:

• david — David, English (British)
• puck — Puck, English (Gemini)
• kore — Kore, English (Gemini)
• andrew — Andrew, English
• jenny — Jenny, English (Irish)
• simon — Simon, English
• katie — Katie, English
• seulgi — Seulgi, Korean
• bert — Bert, German
• thorsten — Thorsten, German (Hessisch)
• maria — Maria, Spanish
• mei — Mei, Chinese (Cantonese)
• ming — Ming, Chinese (Shanghai OpenAI)
• karim — Karim, Arabic
• nur — Nur, Arabic

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')

# Generate audio from text
audio, text = model("Hello, world!")

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

Working with Multi-Speaker Models

This model support multiple speakers. You can check if your model supports speakers and select a specific voice:

from kani_tts import KaniTTS

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

# Check if model supports multiple speakers
print(f"Model type: {model.status}")  # 'singlspeaker' or 'multispeaker'

# Display available speakers (pretty formatted)
model.show_speakers()

# Or access the speaker list directly
print(model.speaker_list)  # ['andrew', 'katie', ...]

# Generate audio with a specific speaker
audio, text = model("Hello, world!", speaker_id="andrew")

Custom Configuration

from kani_tts import KaniTTS

model = KaniTTS(
    'nineninesix/kani-tts-370m',
    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')
audio, text = model("Hello, world!")

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

---

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
• Optimized primarily for English; other languages may require additional training

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.

Citation

@misc {sb_2025,
	author       = { SB },
	title        = { gemini-flash-2.0-speech },
	year         = 2025,
	url          = { https://huggingface.co/datasets/shb777/gemini-flash-2.0-speech },
	doi          = { 10.57967/hf/4237 },
	publisher    = { Hugging Face }
}

@misc{toyin2025arvoicemultispeakerdatasetarabic,
      title={ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis}, 
      author={Hawau Olamide Toyin and Rufael Marew and Humaid Alblooshi and Samar M. Magdy and Hanan Aldarmaki},
      year={2025},
      eprint={2505.20506},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20506}, 
}

@misc {thorsten_müller_2024,
    author       = { {Thorsten Müller} },
    title        = { TV-44kHz-Full (Revision ff427ec) },
    year         = 2024,
    url          = { https://huggingface.co/datasets/Thorsten-Voice/TV-44kHz-Full },
    doi          = { 10.57967/hf/3290 },
    publisher    = { Hugging Face }
}

@misc{carlosmenaciempiessfem2019,
      title={CIEMPIESS FEM CORPUS: Audio and Transcripts of Female Speakers in Spanish.}, 
      ldc_catalog_no={LDC2019S07},
      DOI={https://doi.org/10.35111/xdx5-n815},
      author={Hernandez Mena, Carlos Daniel},
      journal={Linguistic Data Consortium, Philadelphia},
      year={2019},
      url={https://catalog.ldc.upenn.edu/LDC2019S07},
}