AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Abstract
Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the continuous audio representations from contrastive language-audio pretraining (CLAP) latents. The pretrained CLAP models enable us to train LDMs with audio embedding while providing text embedding as a condition during sampling. By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves state-of-the-art TTA performance measured by both objective and subjective metrics (e.g., frechet distance). Moreover, AudioLDM is the first TTA system that enables various text-guided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at https://audioldm.github.io.
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 AudioLDM, a text-to-audio generation system that leverages latent diffusion models and CLAP embeddings to achieve state-of-the-art performance while enabling zero-shot audio manipulations. This work significantly advances the field of audio generation by improving both the quality and efficiency of synthesized audio, while also addressing the limitations of previous models that relied heavily on paired data.
Methodology
The paper presents a novel approach to text-to-audio generation using latent diffusion models (LDMs) conditioned on contrastive language-audio pretraining (CLAP) embeddings. This method effectively circumvents the need for paired audio-text data during training, enhancing both generation quality and computational efficiency. The use of continuous latent representations rather than discrete ones is a significant methodological advancement, allowing for improved audio synthesis and manipulation capabilities. The paper also explores zero-shot audio manipulations, which is a notable contribution to the field.
Results & evaluation
The experimental section is robust, utilizing both objective metrics (Freshet distance, inception score, KL divergence) and subjective evaluations from audio professionals. The results demonstrate that AudioLDM outperforms existing models like DiffSound and AudioGen significantly, both in terms of generation quality and computational efficiency, which strengthens the validity of the proposed method. The evaluation on multiple datasets (AudioCaps, AudioSet, FreeSound, BBC SFX) adds to the reliability of the findings.
Reproducibility
The authors have provided a demo link and mentioned that their implementation is available, which is a positive aspect for reproducibility. However, detailed implementation specifics, such as hyperparameters and training configurations, are somewhat scattered throughout the paper, which could hinder full reproducibility for other researchers.
Limitations
The paper acknowledges limitations such as the potential for misalignment between different modules due to separate training and the insufficient sampling rate for music generation. Additionally, the reliance on subjective evaluations may introduce variability in results. The authors also note the ethical implications of their technology, particularly concerning the generation of misleading audio content.
Broader impact
The implications of this research are significant, particularly for applications in augmented and virtual reality, game development, and content creation. The ability to generate high-quality audio from textual descriptions opens up new avenues for creativity and automation in multimedia production. Furthermore, the potential for zero-shot audio manipulation could enhance user experiences in interactive applications. The main contribution of this paper is the introduction of AudioLDM, a text-to-audio generation system that leverages latent diffusion models and CLAP embeddings to achieve state-of-the-art performance while enabling zero-shot audio manipulations. This work significantly advances the field of audio generation by improving both the quality and efficiency of synthesized audio, while also addressing the limitations of previous models that relied heavily on paired data.
Curator note
Liu et al.; latent diffusion for text-to-audio; CLAP-conditioned; first practical text-to-sound system
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