Three new Kitten TTS models
smallest less than 25MB
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Kitten TTS (https://github.com/KittenML/KittenTTS) is an open-source series of tiny and expressive text-to-speech models for on-device applications. We had a thread last year here: https://news.ycombinator.com/item?id=44807868.Today we're releasing three new models with 80M, 40M and 14M parameters.The largest model (80M) has the highest quality. The 14M variant reaches new SOTA in expressivity among similar sized models, despite being <25MB in size. This release is a major upgrade from the previous one and supports English text-to-speech applications in eight voices: four male and four female.Here's a short demo: https://www.youtube.com/watch?v=ge3u5qblqZA.Most models are quantized to int8 + fp16, and they use ONNX for runtime. Our models are designed to run anywhere eg. raspberry pi, low-end smartphones, wearables, browsers etc. No GPU required! This release aims to bridge the gap between on-device and cloud models for tts applications. Multi-lingual model release is coming soon.On-device AI is bottlenecked by one thing: a lack of tiny models that actually perform. Our goal is to open-source more models to run production-ready voice agents and apps entirely on-device.We would love your feedback!
HN · launched Mar 19, 2026 · 561 upvotes (top 1%) · 1 view
Niche: speech and voice ai models · 143 launches, 7 in the last 7 days ▼ -30% · Explore this niche →
Category and tags
- Horizontal
- Model & infra
- Developer
- AI-native
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 143 launches in speech and voice ai models →
Could you build this?
No Developing tiny, high-quality, expressive TTS neural network architectures from scratch requires specialized deep learning research and high-performance audio synthesis expertise.
What it would actually take: Building novel sub-25MB TTS models requires deep expertise in audio DSP, neural vocoders (like HiFi-GAN or StyleTTS architectures), acoustic modeling, and extensive compute clusters. The team needs curated multi-speaker speech datasets, phonetic aligners, and rigorous loss functions balancing latency and perceptual quality. Quantization and pruning techniques are necessary to achieve fast on-device inference on mobile and edge chips.
Discussion
20 comments analyzed.
Competitors mentioned: Qwen3-TTS (1.7B model), Gemini TTS, Google Translate TTS, CopySpeak
Concerns raised: Inference latency on consumer GPUs for <25MB models unclear, Japanese TTS quality issues with pronunciation consistency and dataset mismatches, Language support limited to English only currently, Latency at 150ms per word becomes problematic for multi-sentence content
Feature requests: Support for tiny model size (beyond nano), Mini model tier, Multilingual support beyond English, Determinism information and stochastic generation options
Competitors
Other products that read as similar to this one : 244 launches clear the similarity bar, closest 8 shown.
Attention rank: #6 of 245 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 132 days after the earliest competitor.
- Audio AI had a wild day · hn · 2026-01-23 · 5 upvotes · similarity 0.57
- Nari Qwen3-TTS and Qwen3-ASR · hn · 2026-09-14 · 90 upvotes · similarity 0.57
- Voxtral TTS by Mistral AI · ph · 2026-03-27 · 156 upvotes · similarity 0.51
- TTSLab · hn · 2026-02-23 · 5 upvotes · similarity 0.51
- Inflect TTS v2+ONNX, 9M/4M text-to-speech models running in the browser · hn · 2026-07-26 · 7 upvotes · similarity 0.50
- Lightning V3 · ph · 2026-04-02 · 321 upvotes · similarity 0.50
- Teen V1 · ph · 2026-09-22 · 3 upvotes · similarity 0.49
- Z80-μLM, a 'Conversational AI' That Fits in 40KB · hn · 2025-12-29 · 514 upvotes · similarity 0.49
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