Simba is a proprietary family of generative audio models developed by the Speechify AI Research Lab. Designed for production-scale voice applications, the model powers high-fidelity text-to-speech (TTS), automatic speech recognition (ASR), and speech-to-speech (STS) capabilities. It is engineered to maintain stability and clarity across long-form content, such as documents and articles, while supporting low-latency real-time conversational agents.
Technical Architecture
The architecture of Simba is based on transformer-based acoustic modeling integrated with diffusion vocoders. This combination enables the model to capture complex vocal patterns, including spectral qualities and human-like prosody. By compressing linguistic and speaker features into efficient embeddings, Simba produces emotionally resonant speech with realistic breath control and micro-pauses. The model supports zero-shot voice cloning, requiring only seconds of reference material to replicate a target speaker's voice.
Performance and Variants
Simba is optimized for high-performance environments, typically delivering a time-to-first-audio latency of under 250 milliseconds. The model family includes specialized variants such as simba-english, simba-multilingual (supporting over 50 languages), and simba-turbo, which prioritizes throughput for real-time synthesis. The latest generation, Simba 3.0, introduced in early 2026, focuses on further refining naturalness and maintaining intelligibility at high playback speeds.