magenta-js

Pros of Jukebox

• More advanced and capable of generating high-quality, multi-instrumental music
• Produces complete songs with vocals and lyrics
• Utilizes state-of-the-art deep learning techniques for music generation

Cons of Jukebox

• Requires significant computational resources and training time
• Less accessible for beginners or those with limited hardware
• Fewer pre-trained models and examples available for immediate use

Code Comparison

Magenta-js (MusicVAE model):

const model = new mm.MusicVAE('https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_4bar_small_q2');
model.initialize().then(() => {
  const sample = model.sample(1);
  mm.Player.tone.Transport.start();
  mm.Player.tone.Transport.scheduleOnce(() => {
    mm.Player.tone.Transport.stop();
  }, '+4m');
});

Jukebox (Python):

import jukebox
import torch as t
import librosa

vqvae, prior = jukebox.load_model('5b')
sample_rate, duration = 44100, 20
artist = 'Alan Jackson'
genre = 'Country'
lyrics = 'I\'m walking on sunshine'
tokens = jukebox.make_tokens(artist, genre, lyrics, sample_rate, duration)

Note: The code snippets demonstrate basic usage and are not directly comparable due to different languages and functionalities.

Pros of Basic-pitch

• Focused specifically on pitch detection and transcription
• Lightweight and easy to integrate into web applications
• Provides pre-trained models for immediate use

Cons of Basic-pitch

• Limited scope compared to Magenta-js's broader music generation capabilities
• Less extensive documentation and community support
• Fewer pre-built models and tools for music creation

Code Comparison

Basic-pitch example:

import { BasicPitch } from '@spotify/basic-pitch';

const basicPitch = new BasicPitch();
const audioBuffer = // ... load audio buffer
const result = await basicPitch.predict(audioBuffer);

Magenta-js example:

import * as mm from '@magenta/music';

const model = new mm.OnsetsAndFrames('https://storage.googleapis.com/magentadata/js/checkpoints/transcription/onsets_frames_uni');
const ns = await model.transcribeFromAudioFile(audioFile);

Both libraries offer straightforward APIs for their respective tasks. Basic-pitch focuses on pitch detection, while Magenta-js provides a wider range of music-related functionalities, including transcription, generation, and more.

Basic-pitch is ideal for projects specifically requiring pitch detection, while Magenta-js is better suited for more comprehensive music AI applications. The choice between them depends on the specific requirements of your project and the depth of music-related features needed.

Pros of Spleeter

• Specialized in audio source separation, particularly for isolating vocals and instruments
• Offers pre-trained models for quick and easy use
• Supports both CPU and GPU processing for flexibility

Cons of Spleeter

• Limited to audio source separation tasks
• Requires more computational resources for processing large audio files
• Less versatile compared to Magenta.js's broader range of music and audio capabilities

Code Comparison

Spleeter (Python):

from spleeter.separator import Separator

separator = Separator('spleeter:2stems')
separator.separate_to_file('audio_example.mp3', 'output/')

Magenta.js (JavaScript):

const mm = require('@magenta/music');

const model = new mm.OnsetsAndFrames('https://storage.googleapis.com/magentadata/js/checkpoints/onsets_frames_uni/acoustic_guitar');
model.initialize().then(() => {
  // Perform transcription or other tasks
});

Key Differences

• Spleeter focuses on audio source separation, while Magenta.js offers a wider range of music generation and analysis tools
• Spleeter is primarily Python-based, whereas Magenta.js is JavaScript-oriented, making it more suitable for web applications
• Magenta.js provides more extensive options for music creation and manipulation, including melody generation and style transfer

Both projects serve different purposes within the audio processing domain, with Spleeter excelling in source separation tasks and Magenta.js offering a broader toolkit for music-related applications.

Pros of Audiocraft

• More focused on audio generation and manipulation
• Includes advanced models like MusicGen and AudioGen
• Offers better support for long-form audio generation

Cons of Audiocraft

• Less extensive documentation compared to Magenta.js
• Narrower scope, primarily focused on audio generation
• Steeper learning curve for beginners

Code Comparison

Audiocraft example (PyTorch):

import torchaudio
from audiocraft.models import MusicGen

model = MusicGen.get_pretrained('medium')
wav = model.generate_unconditional(4, progress=True)
torchaudio.save('generated_music.wav', wav[0], model.sample_rate)

Magenta.js example (JavaScript):

import * as mm from '@magenta/music';

const player = new mm.SoundFontPlayer('https://storage.googleapis.com/magentadata/js/soundfonts/sgm_plus');
const melody = {
  notes: [
    {pitch: 60, startTime: 0.0, endTime: 0.5},
    {pitch: 62, startTime: 0.5, endTime: 1.0},
    {pitch: 64, startTime: 1.0, endTime: 1.5},
    {pitch: 65, startTime: 1.5, endTime: 2.0},
  ],
};

player.start(melody);

Both repositories offer powerful tools for audio and music generation, but they cater to different needs and skill levels. Audiocraft is more specialized in audio generation with advanced models, while Magenta.js provides a broader range of music-related functionalities with easier integration for web applications.