VOICES

Our model utilizes a custom-trained architecture optimized for on-device inference. By leveraging advanced quantization techniques and the ONNX runtime, we achieve state-of-the-art performance across a wide range of hardware configurations.

Training involved a diverse dataset of high-fidelity speech, processed to ensure robustness and natural prosody. The result is a lightweight, low-latency synthesis engine that operates entirely locally, preserving user privacy without sacrificing quality.

This hybrid approach combines the flexibility of deep learning with the efficiency of edge computing, enabling real-time voice generation even on consumer-grade CPUs and mobile accelerators.