Rhasspy Larynx
High-quality, privacy-first neural text-to-speech for local edge computing.
High-performance, on-device text-to-speech for real-time edge computing.
EfficientSpeech is a state-of-the-art non-autoregressive text-to-speech (TTS) architecture designed for extreme efficiency and low-latency synthesis on consumer-grade hardware. Originally emerging from research into shallow transformer backbones, EfficientSpeech eliminates the need for expensive GPU inference by utilizing a streamlined duration predictor and a parallelized generation pipeline. As of 2026, it remains a cornerstone for developers building 'local-first' applications that prioritize user privacy and offline functionality. The model's architecture is specifically optimized for CPU-bound environments, achieving a Real-Time Factor (RTF) significantly below 0.1 on modern mobile processors. Its technical framework supports multi-speaker embeddings and fine-grained control over prosody without the computational overhead typical of diffusion-based or large-scale autoregressive models. This makes it an ideal candidate for integration into IoT devices, embedded systems, and mobile applications where cloud-based API costs and latency spikes are prohibitive. The market positioning for EfficientSpeech in 2026 is defined by its role as the high-fidelity alternative to legacy systems like eSpeak, providing neural-quality voice synthesis at a fraction of the energy consumption required by larger LLM-based speech models.
Generates mel-spectrograms in parallel rather than token-by-token, drastically reducing inference time.
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.
Uses a reduced number of attention layers optimized for CPU cache sizes.
A dedicated sub-network predicts the length of each phoneme for natural speech timing.
Supports d-vector integration for zero-shot or few-shot voice cloning.
The entire model stack can fit in under 50MB of RAM during execution.
Allows for real-time modification of pitch and energy variance during the synthesis pass.
Supports INT8 and FP16 quantization for further acceleration on edge hardware.
Users are concerned about voice data being sent to the cloud.
Registry Updated:2/7/2026
Static voice lines take up too much disk space; cloud TTS is too slow for real-time interaction.
Screen readers often sound robotic or require expensive subscriptions.