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Long-form speech generation can now achieve remarkable coherence and naturalness without the need for extensive retraining on long-form datasets.
By decoupling patch details from semantics, Cheers achieves state-of-the-art multimodal performance at 20% of the training cost of comparable models.
Time-shifted anechoic speech beats early reflections as a training target for universal speech enhancement, leading to better perceptual quality and ASR performance.
