Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
Abstract
Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, we propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps by 1) introducing pseudo prompt enhancement with a distill-then-reprogram approach, it alleviates data scarcity with orders of magnitude concept compositions by using language-free audios; 2) leveraging spectrogram autoencoder to predict the self-supervised audio representation instead of waveforms. Together with robust contrastive language-audio pretraining (CLAP) representations, Make-An-Audio achieves state-of-the-art results in both objective and subjective benchmark evaluation. Moreover, we present its controllability and generalization for X-to-Audio with "No Modality Left Behind", for the first time unlocking the ability to generate high-definition, high-fidelity audios given a user-defined modality input. Audio samples are available at https://Text-to-Audio.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 development of Make-An-Audio, an advanced text-to-audio generation model that effectively utilizes prompt-enhanced diffusion techniques to generate high-quality audio from various input modalities. This work represents a significant step forward in the field of multimodal generative modeling, addressing critical challenges and paving the way for future research in audio synthesis.
Methodology
The proposed methodology in Make-An-Audio is innovative, leveraging a prompt-enhanced diffusion model that addresses the challenges of data scarcity and the complexity of continuous audio modeling. The introduction of pseudo prompt enhancement via a distill-then-reprogram approach is particularly noteworthy, as it allows for the effective use of language-free audio data, significantly expanding the training dataset. The use of a spectrogram autoencoder for audio representation is a clever adaptation that enhances both efficiency and semantic understanding. The integration of contrastive language-audio pretraining (CLAP) further strengthens the model's ability to align text and audio effectively. Overall, the methodology is well-structured and addresses critical gaps in the current state of text-to-audio generation.
Results & evaluation
The experimental evaluation is robust, utilizing both objective and subjective metrics to assess the performance of Make-An-Audio. The paper reports state-of-the-art results on benchmark datasets, including AudioCaption and Clotho, demonstrating significant improvements in audio quality and text-audio alignment. The use of human evaluations (MOS scores) alongside automated metrics (FID, KL, CLAP) provides a comprehensive view of the model's performance. The experiments are well-documented, and the results are convincingly presented, showcasing the model's effectiveness across various modalities.
Reproducibility
The paper provides sufficient implementation details, including model configurations, training procedures, and dataset descriptions, which enhance reproducibility. However, the absence of a direct link to the code repository limits the ease of replication for other researchers. The detailed experimental setup and evaluation metrics are beneficial for those looking to replicate or build upon this work.
Limitations
The paper acknowledges several limitations, including the computational resources required for training and the potential degradation of performance with decreased training data. Additionally, the model's reliance on generative diffusion processes may introduce latency in audio generation, which could be a concern for real-time applications. The authors also highlight societal implications, such as the risk of misinformation and potential job displacement in audio-related fields.
Broader impact
Make-An-Audio has the potential to significantly impact various applications, including content creation, audio editing, and personalized audio experiences. Its ability to generate high-fidelity audio from diverse input modalities opens up new avenues for creativity and innovation in multimedia production. However, the risks associated with misuse, such as non-consensual voice cloning and misinformation, necessitate careful consideration and ethical guidelines in deployment. The main contribution of this paper is the development of Make-An-Audio, an advanced text-to-audio generation model that effectively utilizes prompt-enhanced diffusion techniques to generate high-quality audio from various input modalities. This work represents a significant step forward in the field of multimodal generative modeling, addressing critical challenges and paving the way for future research in audio synthesis.
Curator note
Huang et al., Tencent; prompt-enhanced audio generation; pseudo-prompts for data augmentation
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