Kitten TTS is an open-source series of tiny and expressive text-to-speech models for on-device applications. We are excited to launch a preview of our smallest model, which is less than 25 MB. This model has 15M parameters.This release supports English text-to-speech applications in eight voices: four male and four female. The model is quantized to int8 + fp16, and it uses onnx for runtime. The model is designed to run literally anywhere eg. raspberry pi, low-end smartphones, wearables, browsers etc. No GPU required!We&#x27;re releasing this to give early users a sense of the latency and voices that will be available in our next release (hopefully next week). We&#x27;d love your feedback! Just FYI, this model is an early checkpoint trained on less than 10% of our total data.We started working on this because existing expressive OSS models require big GPUs to run them on-device and the cloud alternatives are too expensive for high frequency use. We think there&#x27;s a need for frontier open-source models that are tiny enough to run on edge devices!

Show HN: Kitten TTS – 25MB CPU-Only, Open-Source TTS Model

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Hi HN! I&#x27;ve been working on BookWith, an open-source e-book reader that integrates AI as your reading companion.The problem: Traditional e-readers are passive. When you encounter something unclear, you have to context-switch to search for it. Your highlights and notes remain isolated, and you can&#x27;t easily connect ideas across different books.My solution: BookWith embeds an AI that maintains full context of what you&#x27;re reading. It features:- Context-aware AI chat: Ask questions about the current page&#x2F;chapter and get instant answers- AI podcast generation: Automatically converts book content into conversational podcasts using Google Cloud TTS- Multi-layer memory system: Short-term (last 5 conversations), mid-term (summarized every 20), and long-term (vector search) memory that maintains continuity across reading sessions- Smart annotations: 5-color highlighting system that AI can reference and analyzeTechnical stack: Built as a fork of Flow (epub reader), with added LLM integration and vector database for semantic search. Supports multiple LLMs and languages (EN&#x2F;JA&#x2F;ZH).

Show HN: An Open-Source E-Book Reader for Conversational Reading with an LLM

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