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Achieving 80% throughput with Classifier-Free Guidance challenges the assumption that CFG drastically reduces efficiency in multimodal audio processing.
Bagpiper-TTS can seamlessly transform natural language requests into high-quality speech across diverse applications, outperforming traditional TTS systems.
LLMs can outperform humans in predicting the next speaker in meetings, even without audio or visual data.
Forget optimizing for just one thing: multi-reward RLAIF dramatically improves both semantic quality and audio naturalness in spoken dialogue systems, where single-reward methods fall flat.
