musicgen

Maintainer: meta

Total Score

1.8K

Last updated 6/20/2024
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Model overview

musicgen is a simple and controllable model for music generation developed by Meta. Unlike existing methods like MusicLM, musicgen doesn't require a self-supervised semantic representation and generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, the authors show they can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. musicgen was trained on 20K hours of licensed music, including an internal dataset of 10K high-quality music tracks and music data from ShutterStock and Pond5.

Model inputs and outputs

musicgen takes in a text prompt or melody and generates corresponding music. The model's inputs include a description of the desired music, an optional input audio file to influence the generated output, and various parameters to control the generation process like temperature, top-k, and top-p sampling. The output is a generated audio file in WAV format.

Inputs

  • Prompt: A description of the music you want to generate.
  • Input Audio: An optional audio file that will influence the generated music. If "continuation" is set to true, the generated music will be a continuation of the input audio. Otherwise, it will mimic the input audio's melody.
  • Duration: The duration of the generated audio in seconds.
  • Continuation Start/End: The start and end times of the input audio to use for continuation.
  • Various generation parameters: Settings like temperature, top-k, top-p, etc. to control the diversity and quality of the generated output.

Outputs

  • Generated Audio: A WAV file containing the generated music.

Capabilities

musicgen can generate a wide variety of music styles and genres based on the provided text prompt. For example, you could ask it to generate "tense, staccato strings with plucked dissonant strings, like a scary movie soundtrack" and it would produce corresponding music. The model can also continue or mimic the melody of an input audio file, allowing for more coherent and controlled music generation.

What can I use it for?

musicgen could be used for a variety of applications, such as:

  • Background music generation: Automatically generating custom music for videos, games, or other multimedia projects.
  • Music composition assistance: Helping musicians and composers come up with new musical ideas or sketches to build upon.
  • Audio creation for content creators: Allowing YouTubers, podcasters, and other content creators to easily add custom music to their projects.

Things to try

One interesting aspect of musicgen is its ability to generate music in parallel by predicting the different codebook components separately. This allows for faster generation compared to previous autoregressive music models. You could try experimenting with different generation parameters to find the right balance between generation speed, diversity, and quality for your use case.

Additionally, the model's ability to continue or mimic input audio opens up possibilities for interactive music creation workflows, where users could iterate on an initial seed melody or prompt to refine the generated output.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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