Jukebox - ai tOOler
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Jukebox
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Music creation (94)

Jukebox

A neural network that creates music in various styles.

Tool Information

Jukebox is a cutting-edge AI tool from OpenAI that creates unique music, including basic singing, using advanced neural network technology.

With Jukebox, you can generate music that spans various genres and styles, tapping into the vibe of different artists. What makes it stand out is how it starts from scratch, using details like genre, artist influences, and even lyrics to craft music that sounds original and fresh.

Traditional music creation tools often have their limits, especially when it comes to capturing the nuances of human voice and complex musical elements. Jukebox breaks through these barriers by using an autoencoder model. This clever approach compresses raw audio into a simpler format while still keeping the richness and depth of the piece intact, even over longer sequences.

It takes things a step further with its quantization-based technique called VQ-VAE for audio compression, along with Sparse Transformers for autoregressive modeling. This means the music generated isn't just random sounds; it's structured in a way that reflects the high-level semantics of music. So when you listen to the output, you can really appreciate the nuances of singing and melodies along with a balanced sound quality and local musical elements.

By effectively mimicking the intricacies of musical sounds, Jukebox opens up endless possibilities for creating and exploring new musical experiences with generative models.

Pros and Cons

Pros

  • Has lyrics conditioning feature
  • Matches audio parts to the right lyrics
  • Produces original music samples
  • Adapts to different music and singing styles
  • Uses autoencoder for audio compression
  • Can create original music samples from nothing
  • Has multi-genre capabilities
  • Simulates music and melody
  • Includes exploration tool
  • Can produce long
  • Replicates genre and artist styles
  • Can compress raw audio
  • Creates music and singing
  • melodies
  • Handles raw audio sequence issues
  • Output in various genres and artist styles
  • Open-source tool
  • Can be changed based on user input about genre
  • Can condition on short audio clips
  • Creates long
  • coherent songs
  • Expands possibilities for generative models
  • Creates basic singing
  • coherent songs
  • Produces a wide variety of music
  • and lyrics
  • Sound quality better with improved VQ-VAE
  • Based on artist and genre
  • Balances local musical patterns
  • artist
  • Uses VQ-VAE for audio compression
  • Model weights and code made available
  • Aligns lyrics with song duration
  • Captures the deeper meanings of music
  • Produces high-quality raw audio
  • Uses Sparse Transformers for modeling music
  • More expressive and flexible than symbolic music tools
  • LyricsMusic Alignment learned by an EncoderDecoder attention layer
  • Supports diversity and long sequences
  • Can make music not related to training data
  • Models music directly as raw audio
  • Learned to group similar artists and genres
  • Artist and Genre Conditioning
  • High musical quality compared to similar tools
  • Model learns to include more conditioning information.
  • Autoencoder compresses raw audio streams
  • and dynamics
  • Can grasp elements like timbre
  • Models raw audio directly

Cons

  • Only in English lyrics
  • Loses audio details
  • Doesn't have repeated chorus structure
  • Needs a lot of computer power
  • Only works for Western music
  • Less useful for musicians
  • Slow at making songs
  • Makes noticeable noise

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