VOICECRAFT: Zero-Shot Speech Editing and Text-to-Speech in the Wild

核心概念

VOICECRAFT achieves state-of-the-art performance on speech editing and zero-shot TTS with innovative token rearrangement.

要約

Introduces VOICECRAFT, a Transformer-based neural codec language model achieving SotA in speech editing and zero-shot TTS.
Utilizes a two-step token rearrangement procedure for autoregressive generation with bidirectional context.
Evaluation on challenging datasets shows superior performance compared to prior models.
Includes a high-quality dataset named REALEDIT for realistic speech editing evaluation.
Demonstrates human preference for VOICECRAFT edited speech over original recordings in terms of naturalness.

統計

VOICECRAFT achieves state-of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on audiobooks, internet videos, and podcasts1.
Crucially, the models are evaluated on challenging and realistic datasets, that consist of diverse accents, speaking styles, recording conditions, and background noise and music.
For each utterance, we determine the type of modification using predefined probability distributions of editing type, number of disjoint spans to be edited, and editing span length.
The training loss is calculated as the negative log likelihood L(θ) = −log Pθ(Z|W) = −PK k=1 αkLk(θ).
During inference for speech editing tasks, we select masked spans by comparing the original transcript and the target transcript to identify the words that should be masked out.
For zero-shot TTS evaluation, we randomly sampled 80 utterances from LibriTTS and YouTube datasets for human rating evaluations.

引用

"I found this um incredible model"
"We introduce VOICECRAFT"
"Our contributions are..."

抽出されたキーインサイト

VoiceCraft

by Puyuan Peng,... 場所 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16973.pdf

深掘り質問

How can VOICECRAFT's innovative token rearrangement procedure impact future developments in speech technology

VOICECRAFT's innovative token rearrangement procedure can have a significant impact on future developments in speech technology. By enabling efficient and effective autoregressive codec generation with bidirectional context, this approach opens up new possibilities for improving the quality and naturalness of synthesized speech. The ability to rearrange tokens in a causal manner allows for more accurate modeling of co-articulation effects and ensures that the generated speech remains coherent and realistic.
This innovation could lead to advancements in various areas of speech technology, such as improved speech editing capabilities, enhanced zero-shot text-to-speech performance, and more robust models for handling diverse accents and speaking styles. Additionally, the token rearrangement procedure may inspire further research into optimizing autoregressive generation processes in other domains beyond speech synthesis.

What ethical considerations should be taken into account when deploying advanced speech synthesis models like VOICECRAFT

When deploying advanced speech synthesis models like VOICECRAFT, several ethical considerations must be taken into account to ensure responsible use of the technology. One primary concern is the potential exacerbation of biases related to ethnicity or gender present in training data. It is essential to monitor model outputs carefully to detect any bias amplification and implement measures to mitigate these disparities.
Another critical ethical consideration is the risk of misuse associated with voice cloning technologies like VOICECRAFT. The ease with which voices can be replicated raises concerns about impersonation, fraud, misinformation spread through deepfake audio content. Safeguards should be put in place to prevent malicious actors from exploiting these capabilities for harmful purposes.
Furthermore, ensuring transparency about how synthetic voices are created using models like VOICECRAFT is crucial for maintaining trust with users and stakeholders. Clear guidelines on data privacy protection, consent requirements for voice recordings used as references, and mechanisms for detecting synthesized content will help address ethical concerns surrounding advanced speech synthesis technologies.

How can open collaboration help address potential risks associated with voice cloning technologies like VOICECRAFT

Open collaboration plays a vital role in addressing potential risks associated with voice cloning technologies like VOICECRAFT by fostering collective efforts towards developing safeguards against misuse. By making methods open source, researchers can collaborate on identifying vulnerabilities within these systems and exploring innovative strategies to counteract them effectively.
Through open collaboration initiatives focused on AI safety research specific to synthetic speech technologies like VOICECRAFT, experts can work together towards developing robust watermarking techniques or deepfake detection algorithms tailored specifically for combating voice cloning threats. This collaborative approach enables rapid identification of vulnerabilities while promoting knowledge sharing across different disciplines involved in AI ethics and security.
Moreover, open collaboration facilitates greater transparency around potential risks associated with advanced speech synthesis models by encouraging dialogue among researchers from diverse backgrounds who bring unique perspectives on mitigating ethical challenges posed by these technologies.