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Music Consistency Models (2404.13358v1)

Published 20 Apr 2024 in cs.SD, cs.AI, and eess.AS

Abstract: Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps. It has proven to be advantageous in mitigating the computational burdens associated with diffusion models. Nevertheless, the application of consistency models in music generation remains largely unexplored. To address this gap, we present Music Consistency Models (\texttt{MusicCM}), which leverages the concept of consistency models to efficiently synthesize mel-spectrogram for music clips, maintaining high quality while minimizing the number of sampling steps. Building upon existing text-to-music diffusion models, the \texttt{MusicCM} model incorporates consistency distillation and adversarial discriminator training. Moreover, we find it beneficial to generate extended coherent music by incorporating multiple diffusion processes with shared constraints. Experimental results reveal the effectiveness of our model in terms of computational efficiency, fidelity, and naturalness. Notable, \texttt{MusicCM} achieves seamless music synthesis with a mere four sampling steps, e.g., only one second per minute of the music clip, showcasing the potential for real-time application.

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Efficient Synthesis of High-Quality Music Clips Using Music Consistency Models (MusicCM)

Introduction

MusicCM is a novel application of consistency models, primarily used in image/video generation, applied to the domain of music synthesis. In stark contrast to traditional diffusion models that are sampling-intensive and computationally demanding, MusicCM efficiently generates high-quality music clips from text prompts with significantly fewer sampling steps. Using adversarial discriminator training and consistency distillation, MusicCM reduces the steps required for music synthesis from typical 50-step procedures to about 4 to 6 steps, demonstrating its potential for real-time applications.

Methods and Technical Innovations

MusicCM builds upon the theoretical and practical foundations laid by existing diffusion models in text-to-music synthesis, such as Noise2Music and MusicLDM, by incorporating the principles of consistency models. The primary innovations and methodological advancements of MusicCM include:

  • Consistency Distillation: This process involves training a student model (MusicCM) to mimic a teacher diffusion model, allowing the system to generate music in fewer steps while retaining the quality and characteristics achieved by the original model.
  • Adversarial Discriminator Training: In conjunction with consistency distillation, an adversarial training component compels the model to produce outputs indistinguishable from real music compositions, enhancing the naturalness and fidelity of the generated music.

Key Advantages and Performance

  1. Computational Efficiency: By reducing the sampling steps required, MusicCM presents a significant enhancement in computational efficiency, enabling faster music generation without compromising quality. It achieves seamless music synthesis with an impressive reduction in required computation, demonstrating only one second per minute of generated music clip.
  2. High Fidelity and Naturalness: The integration of adversarial training ensures that the music clips generated maintain high fidelity and exhibit natural musical qualities, as demonstrated by competitive scores on metrics like Frechet Distance and Inception Score against other state-of-the-art models.
  3. Long Music Coherence: Through the introduction of a shared restricted process for long music generation, MusicCM addresses challenges related to maintaining coherence and quality in longer music sequences. This is achieved by blending multiple diffusion processes, each constrained by shared restrictions, enhancing the final output's cohesiveness.

Future Directions and Speculations

Given its performance and efficiency, MusicCM has the potential to revolutionize real-time music synthesis applications. However, there are several avenues for future research:

  • Further exploration into optimizing the balance between the number of sampling steps and the quality of generated music.
  • Expansion of the adversarial training methods to incorporate newer and more robust discriminator models for improved fidelity in generated music.
  • Exploration of MusicCM’s application scope beyond text-to-music generation, potentially applying its principles to other areas of audio synthesis or even cross-modal generative tasks.

Conclusion

MusicCM represents a significant advancement in the domain of text-to-music generation, primarily through its innovative use of consistency models adapted from image synthesis. By significantly reducing the need for extensive sampling while ensuring high-quality output, MusicCM not only improves computational efficiency but also opens new possibilities for real-time music generation applications. As this field continues to evolve, MusicCM provides a strong foundation for future research and development in efficient and high-fidelity music generation technologies.

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Authors (3)
  1. Zhengcong Fei (27 papers)
  2. Mingyuan Fan (35 papers)
  3. Junshi Huang (24 papers)
Citations (5)
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