Best model for music generation

Models
1

Hi there! :wave:
I am going to develop music generation app using Python or React.
But I don’t know which model I should use for my project.
I need your help!
Note: Case 1: description text as input.
Case 2: genre and instrument as input

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2

Hmm, there are people on HF Discord who are knowledgeable about voice and music models…

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3

Could you suggest exact name of model I should use?
And how can I use that model for my project?

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4

Hi there! :wave: Developing a music generation app sounds like an exciting project! Here’s some guidance on selecting models for your app based on your two cases:

Case 1: Description Text as Input

When users provide a text description (e.g., “calm piano music for relaxation”), you’ll need a model capable of converting natural language into music.

  • Recommended Model:
    OpenAI’s MuseNet or MusicLM (Google Research).
    These models can generate music based on text descriptions and understand high-level prompts, making them ideal for this case. However, their access may be restricted, so consider pre-trained open-source alternatives.

  • Open-Source Options:

    • Jukebox by OpenAI: Generates music in various styles but is resource-intensive.
    • Riffusion: Uses Stable Diffusion adapted for audio generation, taking descriptions as input.

  • Pipeline:

    1. Use a text-to-music model to process descriptions.
    2. Convert output into a user-friendly format like MIDI or audio (e.g., WAV).

Case 2: Genre and Instrument as Input

If the input specifies genre and instrument (e.g., “jazz guitar”), the app needs to synthesize music based on structured tags.

  • Recommended Model:

  • Pipeline:

    1. Map genre and instrument inputs to embeddings or structured prompts.
    2. Use models like MusicVAE to generate MIDI sequences.
    3. Optionally, use tools like FluidSynth or MuseScore to render MIDI files into audio.

Development Stack

  • Frontend: Use React for a user-friendly UI to collect inputs and display/download outputs.
  • Backend: Python with frameworks like Flask or FastAPI to integrate models and handle generation tasks.

Tips for Your App

  1. Start Simple: Focus on one case first (e.g., Case 2 with predefined genres/instruments).
  2. Optimize Performance: Use GPU acceleration (e.g., PyTorch or TensorFlow) for model inference.
  3. Iterate with Feedback: Allow users to provide feedback on generated music to improve your model’s performance.

Good luck with your music generation app! :musical_note: Feel free to ask if you need more help. :rocket:

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