ArchiSound: Audio Generation with Diffusion (2301.13267v1)

Abstract: The recent surge in popularity of diffusion models for image generation has brought new attention to the potential of these models in other areas of media generation. One area that has yet to be fully explored is the application of diffusion models to audio generation. Audio generation requires an understanding of multiple aspects, such as the temporal dimension, long term structure, multiple layers of overlapping sounds, and the nuances that only trained listeners can detect. In this work, we investigate the potential of diffusion models for audio generation. We propose a set of models to tackle multiple aspects, including a new method for text-conditional latent audio diffusion with stacked 1D U-Nets, that can generate multiple minutes of music from a textual description. For each model, we make an effort to maintain reasonable inference speed, targeting real-time on a single consumer GPU. In addition to trained models, we provide a collection of open source libraries with the hope of simplifying future work in the field. Samples can be found at https://bit.ly/audio-diffusion. Codes are at https://github.com/archinetai/audio-diffusion-pytorch.

Insights into "ARCHISOUND: Audio Generation with Diffusion"

The paper "ARCHISOUND: Audio Generation with Diffusion" explores the application of diffusion models, traditionally used in image synthesis, to the domain of audio generation. The research investigates the potential of these models in synthesizing complex audio structures, focusing especially on music generation. This exploration pushes the boundaries of audio synthesis by introducing innovative computational models tailored for not only producing high-fidelity audio but also sustaining real-time performance on consumer-grade hardware.

Core Contributions

The paper introduces several models under the ARCHISOUND umbrella. Notably, the Long, Crisp, Upsampler, and Vocoder models demonstrate varying approaches to handling audio generation and reconstruction tasks. These models differ primarily in their focus—ranging from extended contextual audio generation to high-quality waveform restoration.

• Long Model: This is a latent diffusion model specifically for text-conditional music generation. With a substantial parameter set (~857M), it is adept at generating long sequences of audio while maintaining structural integrity across minutes of sound.
• Crisp Model: Characterized by a focus on simplicity and quality, this model generates high-quality audio for shorter contexts.
• Upsampler and Vocoder: These models focus on the enhancement and reconstruction of audio signals, primarily transforming low-resolution signals to full-fidelity audio.

Additionally, the research introduces open-source libraries such as ARCHISOUND and audio-diffusion-pytorch, supporting further advancements in the field through community collaboration.

Methodologies and Evaluation

The paper explores the use of various diffusion models, including DDPM, DDIM, and V-Diffusion, adapting them for audio tasks with 1D U-Net architectures. The unique approach involves employing these mechanisms to maintain sampling efficiency and model scalability.

• Efficiency: The models are designed to achieve a balance between computational demand and output quality. Specifically, the text-conditional models can generate multi-minute 48kHz stereo audio in real time on a consumer GPU.
• Transform Techniques: Innovative transformations such as patching and STFT are utilized to improve generation speed and maintain quality. These methods preprocess audio signals to make them more manageable for model training and inference.
• Evaluation: The research demonstrates that attention mechanisms are crucial for maintaining the temporal consistency of generated audio. Such details ensure the generated audio retains coherence across extended periods.

Implications and Future Directions

The implications of successfully applying diffusion models to audio are significant. This research opens pathways for more nuanced audio synthesis, potentially impacting music production, sound design, and interactive media.

The paper outlines several future directions, emphasizing the need for models that adeptly balance long-context awareness with quality. Suggested advancements include leveraging perceptual losses during training, exploring non-textual conditioning methods, and enhancing the compression capabilities of audio representations.

Conclusion

"ARCHISOUND: Audio Generation with Diffusion" presents a meticulous examination of how diffusion models can be repurposed for audio synthesis. By integrating advanced modeling techniques and providing robust open-source tools, this work contributes significantly to the evolving landscape of AI-driven audio generation. The research paves the way for further exploration in creating complex audio experiences with high fidelity, pushing the boundaries of what is achievable with machine learning in media content creation.