#Generative Model
StyleTTS
Explore an innovative solution addressing text-to-speech synthesis challenges, emphasizing natural prosodic variations and diverse speaking styles. The style-based generative model incorporates the novel Transferable Monotonic Aligner (TMA) and duration-invariant data augmentation to surpass state-of-the-art performances. It facilitates self-supervised learning of speaking styles, enabling the generation of varied speech with precise prosody and emotional tones without explicit categorization. This advanced TTS model enhances naturalness and similarity across single and multi-speaker datasets, promoting efficient speech synthesis.
esm
ESM3 is a generative model that effectively analyzes protein sequences, structures, and functions using a scalable transformer architecture, trained with data from 2.78 billion proteins. The compact ESM3-open-small variant, with 1.4 billion parameters, provides efficient performance under a non-commercial license. Accessible through HuggingFace Hub, ESM3 facilitates protein research with easy-to-use Python interfaces. Explore ESM3's capabilities in advancing biological research.
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