AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
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.
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 paper presents a unified framework for audio generation that effectively integrates self-supervised learning and latent diffusion models, showcasing significant advancements in generating intelligible speech and diverse audio types. The comprehensive evaluation and innovative methodology position this work as a valuable contribution to the field of machine learning and audio generation.
Methodology
The paper introduces a novel framework for audio generation that leverages a unified representation termed "language of audio" (LOA) to facilitate the generation of various audio types (speech, music, sound effects) using a self-supervised pretraining approach. The integration of AudioMAE with a GPT-2 model for conditioning and a latent diffusion model for audio synthesis is a significant methodological advancement. The approach emphasizes the reusability of pretrained models and the ability to generate intelligible speech, which is a notable improvement over existing models. The methodology is well-structured, with clear delineation of the processes involved in audio representation learning and generation.
Results & evaluation
The experiments are comprehensive, covering major benchmarks for text-to-audio, text-to-music, and text-to-speech tasks. The results demonstrate state-of-the-art performance across various metrics, including FAD, KL divergence, and CLAP scores, indicating the effectiveness of the proposed framework. The use of both objective and subjective evaluation metrics strengthens the credibility of the results. However, the paper could benefit from more detailed comparisons with a wider range of existing models to fully contextualize its contributions.
Reproducibility
The paper provides sufficient details regarding the architecture, training procedures, and datasets used, which aids in reproducibility. The availability of code and pretrained models further enhances the potential for replication of the results. However, some hyperparameter settings and specific training configurations could be elaborated for better clarity.
Limitations
One limitation is the potential overfitting observed during training, particularly with smaller datasets, which may affect generalization. Additionally, while the framework shows promise in generating intelligible speech, the paper does not extensively address the challenges in achieving high fidelity across all audio types. The reliance on large-scale datasets for training may also limit accessibility for researchers with fewer resources.
Broader impact
The proposed framework has significant implications for various applications, including digital assistants, content creation, and entertainment. By providing a unified approach to audio generation, it opens avenues for more versatile and efficient audio synthesis technologies. The ability to generate intelligible speech alongside music and sound effects could enhance user experiences in interactive media and AI-driven applications. The paper presents a unified framework for audio generation that effectively integrates self-supervised learning and latent diffusion models, showcasing significant advancements in generating intelligible speech and diverse audio types. The comprehensive evaluation and innovative methodology position this work as a valuable contribution to the field of machine learning and audio generation.
Curator note
Liu et al.; unified audio/speech/music generation via GPT-2 + diffusion pipeline
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