Voice Cloning

17 papers with code • 0 benchmarks • 2 datasets

Voice cloning is a highly desired feature for personalized speech interfaces. Neural voice cloning system learns to synthesize a person’s voice from only a few audio samples.

Libraries

Use these libraries to find Voice Cloning models and implementations

Most implemented papers

Discovery of Single Independent Latent Variable

shaham-lab/disilv 12 Oct 2021

Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science.

SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines

HaydenCaffrey/SIG-VC 6 Nov 2021

Moreover, speaker information control is added to our system to maintain the voice cloning performance.

Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis

ndkgit339/fastspeech2-filled_pause_speech_synthesis 14 Oct 2022

We present a comprehensive empirical study for personalized spontaneous speech synthesis on the basis of linguistic knowledge.

Low-Resource Multilingual and Zero-Shot Multispeaker TTS

digitalphonetics/ims-toucan 21 Oct 2022

While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6, 000 spoken languages.

Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features

audio-df-ucb/clonedvoicedetection 15 Jul 2023

Synthetic-voice cloning technologies have seen significant advances in recent years, giving rise to a range of potential harms.

OpenVoice: Versatile Instant Voice Cloning

myshell-ai/openvoice 3 Dec 2023

The voice styles are not directly copied from and constrained by the style of the reference speaker.

Proactive Detection of Voice Cloning with Localized Watermarking

facebookresearch/audioseal 30 Jan 2024

In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning.