arXiv:2305.07243
공식 코드 · Papers with Code
Better speech synthesis through scaling
원문 초록
In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts.
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RapFlow-TTS: Rapid and High-Fidelity Text-to-Speech with Improved Consistency Flow Matching
InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems
ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching
Ming-Omni: A Unified Multimodal Model for Perception and Generation
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
토론
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