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

Khái niệm cốt lõi

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

Tóm tắt

• Introduces VOICECRAFT, a Transformer-based neural codec language model for speech editing and zero-shot TTS.
• Utilizes a two-step token rearrangement procedure for autoregressive generation with bidirectional context.
• Evaluates on challenging datasets like REALEDIT, showcasing consistent performance across diverse accents and recording conditions.
• Outperforms prior models like FluentSpeech and VALL-E in both speech editing and zero-shot TTS tasks.
• Provides insights into the model's architecture, training process, ablation studies, and ethical implications.

Thống kê

We introduce VOICECRAFT, a token infilling neural codec language model, that 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 speech editing evaluation, we introduce a high quality, challenging, and realistic dataset named REALEDIT.
Our contributions include introducing VOICECRAFT for speech editing that generates synthesized speech nearly indistinguishable from in-the-wild recordings according to human listeners.
VOICECRAFT generalizes well to zero-shot TTS without finetuning.
We release a high quality, challenging, and realistic speech editing evaluation dataset REALEDIT.

Trích dẫn

"I found this um incredible model"
"I found the amazing VoiceCraft model"

Thông tin chi tiết chính được chắt lọc từ

VoiceCraft

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

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

Yêu cầu sâu hơn

How can VOICECRAFT address concerns related to biases in ethnicity during synthesis?

VOICECRAFT can address concerns related to biases in ethnicity during synthesis by implementing measures to mitigate bias at various stages of the model development and deployment. One approach is to ensure diverse representation in the training data used for the model. By including a wide range of voices from different ethnicities, accents, and backgrounds, the model can learn more robust and inclusive patterns without favoring any particular group.
Additionally, post-training evaluation techniques such as bias detection algorithms can be employed to identify and rectify any biases that may have inadvertently crept into the model. These algorithms analyze the outputs of the model for disparities or inconsistencies across different demographic groups and provide insights on how to adjust the training process accordingly.
Furthermore, transparency and accountability are crucial aspects in addressing biases. Providing detailed documentation on data sources, preprocessing steps, and model architecture allows researchers and users to understand how decisions were made throughout the development process. This transparency enables external audits and scrutiny that help uncover potential biases early on.
Lastly, continuous monitoring and feedback loops are essential for detecting bias drift over time. Regularly evaluating performance metrics across diverse groups ensures that any emerging biases are promptly identified and corrected before they lead to harmful outcomes.

How can measures be implemented to prevent misuse of voice cloning technologies like VOICECRAFT?

To prevent misuse of voice cloning technologies like VOICECRAFT, several proactive measures can be implemented:

Ethical Guidelines: Establish clear ethical guidelines outlining acceptable use cases for voice cloning technology. These guidelines should highlight prohibited activities such as impersonation for fraudulent purposes or spreading misinformation.

User Authentication: Implement robust user authentication mechanisms that verify identity before granting access to voice cloning tools. Multi-factor authentication processes can add an extra layer of security against unauthorized usage.

Watermarking: Incorporate digital watermarking techniques into synthesized speech output generated by VOICECRAFT. Watermarks serve as unique identifiers embedded within audio files that help track their origin back to specific users or applications.

Regulatory Compliance: Adhere strictly to regulatory frameworks governing privacy rights, data protection laws, intellectual property rights (IPR), etc., ensuring compliance with legal standards set forth by relevant authorities.

5 .Education & Awareness: Conduct awareness campaigns among users about responsible use practices when leveraging voice cloning technologies like VOICECRAFT.
6 .Monitoring & Reporting Mechanisms: Implement real-time monitoring systems capable of flagging suspicious activities involving cloned voices while enabling reporting channels for users who encounter misuse instances.

How can open collaboration help advance research into safeguarding mechanisms against misuse of synthetic speech?

Open collaboration plays a pivotal role in advancing research into safeguarding mechanisms against misuse of synthetic speech generated by models like VOICECRAFT:
1 .Knowledge Sharing: Open collaboration fosters knowledge sharing among researchers working on similar challenges related
to AI safety issues associated with synthetic speech generation.
2 .Peer Review: Collaborative efforts enable peer review processes where experts from diverse backgrounds scrutinize proposed safeguarding mechanisms,
providing valuable feedback leading
to refinement
3 .Benchmark Creation: Collective efforts facilitate benchmark creation comprising standardized datasets,
metrics,and evaluation protocols necessary
for assessing effectivenessof safeguardsagainstmisuse
4 .Innovation Acceleration:
Collaborative environments stimulate innovation through collective brainstormingsessions,hackathons,andjointresearchprojects aimedat developing novel solutionsand strategies
5 .Cross-Disciplinary Insights:
Interdisciplinary collaborations bring together expertsfrom various fields,suchasAI ethics,laws,cybersecurity,andpsychology,tooffercomprehensiveperspectivesonaddressingsafetyconcernsassociatedwithsyntheticspeech
By embracing open collaboration principles,the research communitycan leverage collective intelligenceanddiverseexpertiseinthe questtodeveloprobustandsustainablemechanismsthatmitigatetherisksassociatedwiththemisuseofsyntheticspeechtechnologieslikeVOICECRFT