Music is an art that is an integral part of our lives, with harmony, melody, and rhythm shaping our experiences. With the advancements in deep generative models, there has been a growing interest in music generation. One popular type of generative model is the language model (LM), which has shown exceptional capabilities in capturing complex relationships and contexts. Another competitive type is the diffusion probabilistic model (DPM), known for its ability to synthesize speech, sounds, and music.
However, generating music from free-form text is still a challenge, as there are diverse descriptions related to genres, instruments, tempo, scenarios, and subjective feelings. Traditional text-to-music generation models often prioritize specific properties such as audio continuation or fast sampling, while some models focus on robust testing conducted by music producers. Although these models are trained on large-scale music datasets and show state-of-the-art generative performances, they come with high computational costs.
In comparison, DPM-based approaches have made efficient samplings of high-quality music possible, although their demonstrated cases are limited. To create a practical music generation tool, a high-efficiency generative model is necessary for interactive creation with human feedback.
To leverage the advantages of both LMs and DPMs, a new approach called MeLoDy has been developed. This approach combines the semantic structure modeling of LMs with the efficient acoustics modeling of DPMs. It uses a dual-path diffusion (DPD) model to reduce computational expenses by reducing the raw data to a low-dimensional latent representation. The raw data can then be reconstructed using a pre-trained autoencoder.
MeLoDy is an efficient LM-guided diffusion model that generates high-quality music audios. Although the code is not yet available for testing, you can check out some output samples produced by the model at the provided link.
If you’re interested in learning more about MeLoDy, you can read the full paper linked below. Don’t forget to join our ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and projects.
[Check Out The Paper](https://arxiv.org/abs/2305.15719)
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