AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining (2308.05734v3)

Abstract: Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called "language of audio" (LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate any modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on LOA. The proposed framework naturally brings advantages such as in-context learning abilities and reusable self-supervised pretrained AudioMAE and latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art or competitive performance against previous approaches. Our code, pretrained model, and demo are available at https://audioldm.github.io/audioldm2.

A Formal Overview of "AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining"

The field of audio generation stands at an intriguing intersection of artificial intelligence and digital content creation, forming a fundamental part of the innovation in AIGC (Artificial Intelligence Generated Content). The paper "AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining," introduces a robust framework for audio synthesis that unifies the generative process across various types of audio including speech, music, and sound effects.

At its core, the proposed framework for audio generation hinges on the introduction of a universal audio representation termed "language of audio" (LOA). This versatile representation underpins the model's ability to integrate self-supervised learning pretraining with the task-specific models, offering a broad capacity for in-context learning. The model strategically employs AudioMAE, a self-supervised pre-trained model, to learn the LOA effectively, enabling the translation of diverse modalities into a coherent audio generation framework.

Architectural Overview

The architecture of AudioLDM 2 is largely structured around two primary components: a GPT-2 model for modality translation into LOA and a latent diffusion model for audio generation. Initially, the framework employs the AudioMAE model to convert any input audio into LOA, capturing both semantic and acoustic characteristics. This LOA, then processed by a fine-tuned GPT-2 model, forms the foundation for generating audio, conditioned across a variety of input modalities (e.g., text, image, phoneme).

In transitioning from LOA to concrete audio generation, the model leverages a latent diffusion approach, which is known for its high fidelity in generative tasks. This methodology optimizes the LOA-driven generation of VAE-sourced features, hence, not only enhancing the model's versatility across different domains but also enabling high-quality audio synthesis that maintains contextual relevance with the input conditions.

Empirical Evaluation

The empirical evaluation in the paper demonstrates AudioLDM 2's state-of-the-art performance across significant benchmarks, including text-to-audio (AudioCaps), text-to-music (MusicCaps), and text-to-speech synthesis tasks. The model achieves competitive results on metrics such as Frechet Audio Distance (FAD), Kullback-Leibler Divergence (KL), and CLAP scores, illustrating its proficient text-conditioned audio generation capability.

Additionally, the paper explores the model's scalability concerning data scales and architecture sizes, indicating the benefits of expansive datasets and increased model parameters in enhancing generative quality. Moreover, even with reduced or focused datasets, AudioLDM 2 maintains competitive performance, underscoring its efficiency and flexibility across varied conditions.

Implications and Future Trajectories

AudioLDM 2's design implications suggest an evident breakthrough in unified audio generation frameworks, promoting a shift from domain-specific biases to more generalized model architectures. This transition offers promising applications in diverse content creation domains, ranging from film sound design to interactive media and assistive audio technologies.

Theoretically, the model's foundation in self-supervised learning can inspire further research into more generalized forms of audio generation irrespective of the modality-specific data constraints. Future iterations could potentially explore real-time audio-generative applications, expand on multi-modal learning capabilities, and refine the pre-training architectures to encapsulate more complex audio dynamics.

The paper positions AudioLDM 2 not merely as a solution for holistic audio generation but as a blueprint for future AI applications striving towards unified, modality-agnostic frameworks in content generation. This evolution holds significant promise in driving forward the scalability, adaptability, and richness of AI-generated audio content.