DataCat focuses on training custom TTS models, like the Variational Inference for TTS (VITS), by leveraging larger models for high-quality, efficient voice generation tailored for specific applications.
Tiny Text-to-Speech (TTS) technology, representing a significant shift in the field of artificial voice generation, focuses on developing compact and efficient models capable of converting text into natural-sounding speech. Unlike traditional large-scale TTS models that require extensive computational resources, Tiny TTS aims to achieve high-quality speech synthesis with a minimalistic approach, making it highly suitable for applications in constrained environments such as games and smart NPCs.
Recent developments in the field have seen models like TorToiSe TTS, known for its multi-speaker framework and high-quality speech generation, albeit with slower processing times. Innovations in this area have largely been driven by advancements in machine learning and deep learning, enabling the creation of models that capture the nuances of human speech patterns more effectively.
However, challenges in Tiny TTS remain significant. Replicating the natural prosody, or the rhythm and intonation of speech, is a key hurdle. Many systems struggle with producing speech that doesn't sound monotonous or unnaturally paced. Emotional range and expressiveness in TTS systems also remain limited, with many models unable to convey complex emotional states convincingly.
At DataCat, we focus on training custom versions of the Variational Inference for TTS (VITS) model, designed to create synthetic voices that avoid copyright issues. We start by training larger and slower models like TorToiSe TTS and then transfer the synthesized voices to smaller models like VITS. This method allows us to maintain a library of hundreds of different voices served by a single model, perfectly tailored for games and smart NPCs.
One of the primary challenges in developing Tiny TTS models is balancing performance with computational efficiency. While larger models like TorToiSe TTS can generate highly realistic speech, they often require more processing time, which is not ideal for real-time applications. Our approach, leveraging the capabilities of larger models and transferring their knowledge to more compact models like VITS, aims to strike this balance effectively.
The application of Tiny TTS in gaming and smart NPC development is particularly promising. In gaming environments, where processing power and memory are often limited, having a TTS system that can deliver high-quality speech synthesis without overwhelming the system's resources is crucial. Our models can bring characters to life with realistic and varied voices, enhancing the immersive experience of games.
Training custom models is a client-per-client service, so it is not provided publicly like DataCats' text inference, labeling, and knowledge retrieval. If you are interested in partnering with us to train such models, you need to contact us via email.