IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Abstract
Flow-based generative models have greatly improved text-to-speech (TTS) synthesis quality, but inference speed remains limited by the iterative sampling process and multiple function evaluations (NFE). The recent MeanFlow model accelerates generation by modeling average velocity instead of instantaneous velocity. However, its direct application to TTS encounters challenges, including GPU memory overhead from Jacobian-vector products (JVP) and training instability due to self-bootstrap processes. To address these issues, we introduce IntMeanFlow, a framework for few-step speech generation with integral velocity distillation. By approximating average velocity with the teacher's instantaneous velocity over a temporal interval, IntMeanFlow eliminates the need for JVPs and self-bootstrap, improving stability and reducing GPU memory usage. We also propose the Optimal Step Sampling Search (O3S) algorithm, which identifies the model-specific optimal sampling steps, improving speech synthesis without additional inference overhead. Experiments show that IntMeanFlow achieves 1-NFE inference for token-to-spectrogram and 3-NFE for text-to-spectrogram tasks while maintaining high-quality synthesis. Demo samples are available at https://vvwangvv.github.io/intmeanflow.
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 paper is the introduction of IntMeanFlow, a framework that improves few-step speech generation through integral velocity distillation, achieving significant efficiency gains in TTS synthesis. This work represents a meaningful advancement in the field, addressing critical challenges in generative modeling while maintaining high-quality output.
Methodology
The paper introduces IntMeanFlow, a novel framework that leverages integral velocity distillation to improve the efficiency of text-to-speech (TTS) generation. The methodology effectively addresses the limitations of the MeanFlow model, particularly in terms of GPU memory usage and training stability. By approximating average velocity over a temporal interval rather than relying on instantaneous velocity, the authors enhance the training process and model performance. The introduction of the Optimal Step Sampling Search (O3S) algorithm is a significant methodological advancement, allowing for model-specific optimization of sampling steps, which is a crucial aspect of generative modeling. Overall, the methodology is well-structured, innovative, and provides a clear improvement over existing approaches.
Results & evaluation
The experiments conducted are thorough and demonstrate the effectiveness of the proposed IntMeanFlow framework across two widely used TTS models (F5-TTS and CosyVoice2). The results show a significant reduction in the number of function evaluations (NFE) while maintaining high-quality synthesis, which is a critical metric in TTS systems. The use of multiple evaluation metrics, including Word Error Rate (WER) and speaker similarity, adds robustness to the findings. However, the paper lacks comparative results against the original MeanFlow model for the text2mel task, which could have provided additional context regarding the improvements made.
Reproducibility
The paper provides a clear description of the experimental setup, including datasets and evaluation metrics, which aids in reproducibility. However, the absence of specific implementation details or a code repository limits the ability for others to fully replicate the results. The authors mention demo samples available online, which is a positive aspect, but a more comprehensive project URL would enhance reproducibility.
Limitations
One limitation of the work is the reliance on a teacher model for the distillation process, which may not always be available or feasible in practical applications. Additionally, while the paper addresses memory overhead and training instability, it does not explore the trade-offs between model complexity and performance in depth. The lack of comparisons with other state-of-the-art methods in the TTS domain also limits the contextual understanding of the contributions.
Broader impact
The advancements presented in this paper have the potential to significantly enhance the efficiency of TTS systems, making them more accessible for real-time applications. The reduction in inference time while maintaining synthesis quality could lead to broader adoption of TTS technologies in various fields, including virtual assistants, audiobooks, and accessibility tools. The methodologies developed could also inspire further research in generative modeling and distillation techniques across other domains. The main contribution of this paper is the introduction of IntMeanFlow, a framework that improves few-step speech generation through integral velocity distillation, achieving significant efficiency gains in TTS synthesis. This work represents a meaningful advancement in the field, addressing critical challenges in generative modeling while maintaining high-quality output.
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