SPEECH & AUDIO

Audiobox: Unified Audio Generation with Natural Language Prompts

December 11, 2023

Abstract

Audio is an essential part of our life, but creating it often requires expertise and is time-consuming. Research communities have made great progress over the past year advancing the performance of large scale audio generative models for a single modality (speech, sound, or music) through adopting more powerful generative models and scaling data. However, these models lack controllability in several aspects: speech generation models cannot synthesize novel styles based on text description and are limited on domain coverage such as outdoor environments; sound generation models only provide coarse-grained control based on descriptions like “a person speaking” and would only generate mumbling human voices. This paper presents Audiobox, a unified model based on flow-matching that is capable of generating various audio modalities. We design description-based and example-based prompting to enhance controllability and unify speech and sound generation paradigms. We allow transcript, vocal, and other audio styles to be controlled independently when generating speech. To improve model generalization with limited labels, we adapt a self-supervised infilling objective to pre-train on large quantities of unlabeled audio. Audiobox sets new benchmarks on speech and sound generation (0.745 similarity on Librispeech for zero-shot TTS; 0.77 FAD on AudioCaps for text-to-sound) and unlocks new methods for generating audio with novel vocal and acoustic styles. We further integrate Bespoke Solvers, which speeds up generation by over 25 times compared to the default ODE solver for flow-matching, without loss of performance on several tasks.

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AUTHORS

Written by

Wei-Ning Hsu

Akinniyi Akinyemi

Alice Rakotoarison

Andros Tjandra

Apoorv Vyas

Baishan Guo

Bapi Akula

Bowen Shi

Brian Ellis

Ivan Cruz

Jeff Wang

Jiemin Zhang

Mary Williamson

Matt Le

Rashel Moritz

Robbie Adkins

William Ngan

Xinyue Zhang

Yael Yungster

Yi-Chiao Wu

Publisher

arXiv

Research Topics

Speech & Audio

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