NaturalSpeech 2: Latent Diffusion Models are Natural and Zer | Data Science by ODS.ai 🦜
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
In the rapidly evolving domain of text-to-speech (TTS) technology, an exciting breakthrough has been made with the development of NaturalSpeech 2. This innovative system brings new levels of diversity to the table, by uniquely capturing a wide range of human speech characteristics such as speaker identities, prosodies, and even styles like singing. By employing a neural audio codec and residual vector quantizers, it transcends the limitations of existing TTS systems, which often suffer from unstable prosody, word skipping/repeating issues, and subpar voice quality.
More impressively, NaturalSpeech 2 enhances the "zero-shot" capability, a crucial factor for diverse speech synthesis. By designing a unique speech prompting mechanism, it facilitates in-context learning in both the diffusion model and the duration/pitch predictor. Its expansive training on 44K hours of speech and singing data has yielded unprecedented results. NaturalSpeech 2 significantly outperforms previous TTS systems in prosody/timbre similarity, robustness, and voice quality, even demonstrating novel zero-shot singing synthesis.
Project link: https://speechresearch.github.io/naturalspeech2/
Paper link: https://arxiv.org/pdf/2304.09116.pdf
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-naturalspeech2
#deeplearning #nlp #tts #texttospeech
