Rhasspy Larynx
High-quality, privacy-first neural text-to-speech for local edge computing.
Ultra-fast, multilingual text-to-speech optimized for real-time CPU inference.
MeloTTS, developed by MyShell.ai, is a high-performance, open-weights text-to-speech library designed to overcome the latency and hardware bottlenecks of traditional transformer-based TTS models. Built on an optimized end-to-end VITS (Variational Inference with adversarial learning) architecture, MeloTTS is uniquely engineered for high-speed CPU inference without sacrificing phonetic accuracy or prosody. As of 2026, it is recognized for its exceptional real-time factor (RTF), making it the preferred choice for developers building interactive applications that require localized, low-latency speech synthesis. The library natively supports a wide array of languages including English, Spanish, French, Chinese, Japanese, and Korean, each with high-quality, natural-sounding base voices. Its architectural efficiency allows it to run on consumer-grade hardware and edge devices, providing a privacy-focused alternative to cloud-based TTS providers like ElevenLabs or Google Cloud TTS. By open-sourcing the model under the MIT license, MyShell has enabled a robust ecosystem of self-hosted integrations, ranging from real-time translation tools to accessibility-focused screen readers, positioning MeloTTS as a critical component in the 2026 decentralized AI stack.
Uses a highly optimized VITS architecture that allows for faster-than-real-time audio generation on standard CPUs without a discrete GPU.
High-quality, privacy-first neural text-to-speech for local edge computing.
A high-speed, fully convolutional neural architecture for multi-speaker text-to-speech synthesis.
Real-time neural text-to-speech architecture for massive-scale multi-speaker synthesis.
A Multilingual Single-Speaker Speech Corpus for High-Fidelity Text-to-Speech Synthesis.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Custom front-end tokenizers for English, Spanish, French, Chinese, Japanese, and Korean ensuring accurate pronunciation of complex characters.
Dynamic adjustment of the duration predictor within the VITS framework to change speaking rate without pitch distortion.
Pre-configured Docker images for quick deployment as a REST API endpoint using FastAPI.
Optimized memory management allows the model to reside in less than 2GB of VRAM or system RAM.
The model can apply various accents (British, American, Australian) to English text by switching speaker IDs.
Integrates text analysis, acoustic modeling, and vocoding into a single inference pass.
Cloud-based TTS adds 500ms-2s of latency, ruining the conversational flow of AI agents.
Registry Updated:2/7/2026
Screen readers for sensitive documents cannot send data to third-party cloud providers for synthesis.
High costs and complexity of managing multiple language voice licenses for automated phone systems.