Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations

no code implementations • 5 Feb 2024 • Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Iván Vallés-Pérez, Biel Tura-Vecino, Piotr Biliński, Mateusz Lajszczak, Grzegorz Beringer, Roberto Barra-Chicote, Jaime Lorenzo-Trueba

Using speaker-disentangled codes to train LLMs for text-to-speech (TTS) allows the LLM to generate the content and the style of the speech only from the text, similarly to humans, while the speaker identity is provided by the decoder of the VC model.

Decoder In-Context Learning +3

AE-Flow: AutoEncoder Normalizing Flow

no code implementations • 27 Dec 2023 • Jakub Mosiński, Piotr Biliński, Thomas Merritt, Abdelhamid Ezzerg, Daniel Korzekwa

The results show that the proposed training paradigm systematically improves speaker similarity and naturalness when compared to regular training methods of normalizing flows.

text-to-speech Text to Speech +1

Text-free non-parallel many-to-many voice conversion using normalising flows

no code implementations • 15 Mar 2022 • Thomas Merritt, Abdelhamid Ezzerg, Piotr Biliński, Magdalena Proszewska, Kamil Pokora, Roberto Barra-Chicote, Daniel Korzekwa

We investigate normalising flows for VC in both text-conditioned and text-free scenarios.

Normalising Flows Speech Synthesis +4

Machine Learning for Generalizable Prediction of Flood Susceptibility

no code implementations • 15 Oct 2019 • Chelsea Sidrane, Dylan J Fitzpatrick, Andrew Annex, Diane O'Donoghue, Yarin Gal, Piotr Biliński

In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network.

BIG-bench Machine Learning Earth Observation +1