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nineninesix/
kani-tts-400m-ar

Name: kani-tts-400m-ar
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KaniTTS Arabic

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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: 400M parameters
Sample Rate: 22kHz
Language: Arabic
License: Apache 2.0

Performance

On NovitaAI RTX 5090 using vLLM:

RTF: ~0.2 (5 times faster than realtime)
Memory: 16GB GPU VRAM used
Source Code: https://github.com/nineninesix-ai/kanitts-vllm

GPU Benchmark Results

GPU Model	VRAM	Cost ($/hr)	RTF
RTX 5090	32GB	$0.423	0.190
RTX 4080	16GB	$0.220	0.200
RTX 5060 Ti	16GB	$0.138	0.529
RTX 4060 Ti	16GB	$0.122	0.537
RTX 3060	12GB	$0.093	0.600

Lower RTF is better (< 1.0 means faster than real-time). Benchmarks conducted on Vast AI.

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

bash

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

Quick Start

python

from kani_tts import KaniTTS

model = KaniTTS('nineninesix/kani-tts-400m-ar')

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

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

Custom Configuration

python

from kani_tts import KaniTTS

model = KaniTTS(
    'nineninesix/kani-tts-400m-ar',
    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:

python

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

model = KaniTTS('nineninesix/kani-tts-400m-ar')
audio, text = model("Your text here")

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

Arabic Diacritics (Tashkeel) - Important!

For optimal pronunciation quality, we strongly recommend using Arabic text with diacritical marks (تشكيل/tashkeel).

Arabic text without diacritics can lead to ambiguous pronunciations. The model performs significantly better when diacritics are provided, as they explicitly indicate:

Short vowels (fatḥa, kasra, ḍamma)
Nunation (tanwin)
Sukun (absence of vowel)
Shadda (consonant doubling)

Example Comparison:

Without diacritics (less accurate):

أما أخوه غير الشقيق توماس، فكان يعيش حياةً طيبة في لندن، وكان مستعداً لمساعدته بما يلزم.

❌ May mispronounce words like "مستعداً" as "مستعدة"

With diacritics (much better):

أَمَّا أَخوهُ غَيْرُ الشَّقِيقِ تُوماسُ، فَكانَ يَعيشُ حَياةً طَيِّبَةً في لَنْدَنَ، وَكانَ مُسْتَعِدًّا لِمُساعَدَتِهِ بِما يَلْزَمُ.

✅ Produces accurate, natural pronunciation

Adding Diacritics to Your Text

You can use AI language models or specialized tools to automatically add diacritics to undiacritized Arabic text:

Ask Claude, ChatGPT, or other AI assistants to add diacritics
Use online tools like Mishkal (https://tahadz.com/mishkal)
Use Python libraries like pyarabic or camel-tools

Note: While the model can handle undiacritized text, pronunciation quality will be noticeably lower, especially for words with multiple possible vowelizations.

Voices Datasets

Audio Examples

Text	Audio
مرحباً، اسمي تارا، وأنا نموذج لتوليد الصوت يمكنه أن يتحدث كالبشر تماماً.
زقزقت عصافيرٌ مرِحة هذا الصباح على شجرة البلوط العتيقة خارج نافذتي.
يا عزيزي، ما زلت لا أشعر بتحسن... سأذهب للنوم.
أما أخوه غير الشقيق توماس، فكان يعيش حياةً طيبة في لندن، وكان مستعداً لمساعدته بما يلزم.

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 15 seconds (need to use sliding window chunking)
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: https://huggingface.co/nineninesix/kani-tts-500m-0.3-pt
Space: https://huggingface.co/spaces/nineninesix/KaniTTS-Arabic

Examples:

OpenAI compatible API Example: https://github.com/nineninesix-ai/kanitts-vllm
Finetuning code pipeline: https://github.com/nineninesix-ai/KaniTTS-Finetune-pipeline
Dataset preparation pipeline: https://github.com/nineninesix-ai/nano-codec-dataset-pipeline
Example Dataset: https://huggingface.co/datasets/nineninesix/expresso-conversational-en-nano-codec-dataset
GitHub Repository: https://github.com/nineninesix-ai/kani-tts
ComfyUI node: https://github.com/wildminder/ComfyUI-KaniTTS by WildAi
NextJS basic app: https://github.com/nineninesix-ai/open-audio. It uses the OpenAI npm package to connect to the OpenAI-compatible server API provided by kanitts-vllm.

Links:

Website: https://www.nineninesix.ai
Contact Form: https://airtable.com/appX2G2TpoRk4M5Bf/pagO2xbIOjiwulPcP/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

@inproceedings{emilialarge,
  author={He, Haorui and Shang, Zengqiang and Wang, Chaoren and Li, Xuyuan and Gu, Yicheng and Hua, Hua and Liu, Liwei and Yang, Chen and Li, Jiaqi and Shi, Peiyang and Wang, Yuancheng and Chen, Kai and Zhang, Pengyuan and Wu, Zhizheng},
  title={Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation},
  booktitle={arXiv:2501.15907},
  year={2025}
}

@article{emonet_voice_2025,
  author={Schuhmann, Christoph and Kaczmarczyk, Robert and Rabby, Gollam and Friedrich, Felix and Kraus, Maurice and Nadi, Kourosh and Nguyen, Huu and Kersting, Kristian and Auer, Sören},
  title={EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection},
  journal={arXiv preprint arXiv:2506.09827},
  year={2025}
}

@dataset{masrispeech_full,
  author       = {Yahya Muhammad Alnwsany},
  title        = {MasriSpeech-Full: Large-Scale Egyptian Arabic Speech Corpus},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/collections/NightPrince/masrispeech-dataset-68594e59e46fd12c723f1544}
}

@misc{linagora2024Linto-tn,
    title = {LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect},
    author = {Hedi Naouara and Jérôme Louradour and Jean-Pierre Lorré},
    year = {2025},
    month = {March},
    eprint={2504.02604},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    note={Good Data Workshop, AAAI 2025},
    url={arxiv.org/abs/2504.02604},
}

@misc{abdallah2023leveraging,
    title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, 
    author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem},
    year={2023},
    eprint={2309.11327},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

@data{e1qb-jv46-21,
    doi = {10.21227/e1qb-jv46},
    url = {https://dx.doi.org/10.21227/e1qb-jv46},
    author = {Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
    publisher = {IEEE Dataport},
    title = {MASC: Massive Arabic Speech Corpus},
    year = {2021}
}

@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}, 
}