MM-Sonate: Multimodal Controllable Audio-Video Generation with Zero-Shot Voice Cloning
Abstract
Joint audio-video generation aims to synthesize synchronized multisensory content, yet current unified models struggle with fine-grained acoustic control, particularly for identity-preserving speech. Existing approaches either suffer from temporal misalignment due to cascaded generation or lack the capability to perform zero-shot voice cloning within a joint synthesis framework. In this work, we present MM-Sonate, a multimodal flow-matching framework that unifies controllable audio-video joint generation with zero-shot voice cloning capabilities. Unlike prior works that rely on coarse semantic descriptions, MM-Sonate utilizes a unified instruction-phoneme input to enforce strict linguistic and temporal alignment. To enable zero-shot voice cloning, we introduce a timbre injection mechanism that effectively decouples speaker identity from linguistic content. Furthermore, addressing the limitations of standard classifier-free guidance in multimodal settings, we propose a noise-based negative conditioning strategy that utilizes natural noise priors to significantly enhance acoustic fidelity. Empirical evaluations demonstrate that MM-Sonate establishes new state-of-the-art performance in joint generation benchmarks, significantly outperforming baselines in lip synchronization and speech intelligibility, while achieving voice cloning fidelity comparable to specialized Text-to-Speech systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this work is the introduction of MM-Sonate, a unified framework for multimodal audio-video generation that achieves state-of-the-art performance and introduces innovative techniques for zero-shot voice cloning. This paper significantly advances the field by addressing critical challenges in audio-video synchronization and speaker identity preservation, paving the way for more sophisticated generative models.
Methodology
The proposed methodology, MM-Sonate, introduces a unified multimodal flow-matching framework that integrates audio-video generation with zero-shot voice cloning capabilities. The use of a unified instruction-phoneme input format is innovative, allowing for precise synchronization and control over the generated outputs. The timbre injection mechanism effectively decouples speaker identity from linguistic content, which is a significant advancement over existing models that struggle with this aspect. The introduction of a noise-based negative conditioning strategy enhances acoustic fidelity, addressing limitations in traditional classifier-free guidance approaches. Overall, the methodology is well-structured and addresses key challenges in the field.
Results & evaluation
The empirical evaluations are robust, demonstrating that MM-Sonate achieves state-of-the-art performance across various benchmarks, particularly in lip synchronization and speech intelligibility. The paper provides comprehensive comparisons against existing models, showcasing significant improvements in both objective metrics and human preference evaluations. The extensive dataset preparation, including a high-fidelity synthetic dataset and a large-scale multimodal pre-training corpus, supports the model's generalization capabilities across diverse generation tasks.
Reproducibility
The paper includes detailed implementation details, including architecture specifications, training strategies, and evaluation metrics. However, the lack of a publicly available code repository or demo limits reproducibility. The methodology is described in sufficient detail for other researchers to replicate the experiments, but access to the actual model and datasets would enhance reproducibility further.
Limitations
The paper acknowledges several limitations, including challenges in generating long-form content and potential loss of high-frequency details due to compression. Additionally, the model may struggle with extreme head poses or occlusions, leading to synchronization issues. Ethical concerns regarding the misuse of voice cloning technology are also highlighted, emphasizing the need for responsible deployment.
Broader impact
MM-Sonate has significant potential applications in various domains, including entertainment, education, and virtual reality, where personalized audio-visual content is valuable. However, the ethical implications of voice cloning and the potential for misuse in creating deepfakes and misinformation must be carefully managed. The authors propose mitigation strategies, such as embedding watermarks in generated content, which is a proactive approach to addressing these concerns. The main contribution of this work is the introduction of MM-Sonate, a unified framework for multimodal audio-video generation that achieves state-of-the-art performance and introduces innovative techniques for zero-shot voice cloning. This paper significantly advances the field by addressing critical challenges in audio-video synchronization and speaker identity preservation, paving the way for more sophisticated generative models.
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