Browse free open source AI Music Generators and projects below. Use the toggles on the left to filter open source AI Music Generators by OS, license, language, programming language, and project status.

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  • 1
    MusicLM - Pytorch

    MusicLM - Pytorch

    Implementation of MusicLM music generation model in Pytorch

    Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch. They are basically using text-conditioned AudioLM, but surprisingly with the embeddings from a text-audio contrastive learned model named MuLan. MuLan is what will be built out in this repository, with AudioLM modified from the other repository to support the music generation needs here.
    Downloads: 4 This Week
    Last Update:
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  • 2
    DiffRhythm

    DiffRhythm

    Di♪♪Rhythm: Blazingly Fast & Simple End-to-End Song Generation

    DiffRhythm is an open-source, diffusion-based model designed to generate full-length songs. Focused on music creation, it combines advanced AI techniques to produce coherent and creative audio compositions. The model utilizes a latent diffusion architecture, making it capable of producing high-quality, long-form music. It can be accessed on Huggingface, where users can interact with a demo or download the model for further use. DiffRhythm offers tools for both training and inference, and its flexibility makes it ideal for AI-based music production and research in music generation.
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    Downloads: 59 This Week
    Last Update:
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  • 3
    DeepMozart

    DeepMozart

    Audio generation using diffusion models

    Audio generation using diffusion models in PyTorch. The code is based on the audio-diffusion-pytorch repository.
    Downloads: 3 This Week
    Last Update:
    See Project
  • 4
    AudioLM - Pytorch

    AudioLM - Pytorch

    Implementation of AudioLM audio generation model in Pytorch

    Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper. Yes, this means VALL-E can be trained from this repository. It is essentially the same. This repository now also contains a MIT licensed version of SoundStream. It is also compatible with EnCodec, however, be aware that it has a more restrictive non-commercial license, if you choose to use it.
    Downloads: 2 This Week
    Last Update:
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    AudioGenerator

    AudioGenerator

    Generates a sound given: volume, frequency, duration

    Generates a sound given: volume, frequency, duration! Download build.zip, unpack zip, and run the executable.
    Downloads: 1 This Week
    Last Update:
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  • 6
    audio-diffusion-pytorch

    audio-diffusion-pytorch

    Audio generation using diffusion models, in PyTorch

    A fully featured audio diffusion library, for PyTorch. Includes models for unconditional audio generation, text-conditional audio generation, diffusion autoencoding, upsampling, and vocoding. The provided models are waveform-based, however, the U-Net (built using a-unet), DiffusionModel, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats. Note: no pre-trained models are provided here, this library is meant for research purposes.
    Downloads: 1 This Week
    Last Update:
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  • 7
    Melodfy

    Melodfy

    ✨:AI-Powered Piano Audio to MIDI Converter 🎶

    Melodfy is an application that utilizes the power of artificial intelligence (developed by ByteDance) to seamlessly convert audio recordings of piano playing into playable MIDI files.
    Downloads: 6 This Week
    Last Update:
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  • 8
    Audiogen Codec

    Audiogen Codec

    48khz stereo neural audio codec for general audio

    AGC (Audiogen Codec) is a convolutional autoencoder based on the DAC architecture, which holds SOTA. We found that training with EMA and adding a perceptual loss term with CLAP features improved performance. These codecs, being low compression, outperform Meta's EnCodec and DAC on general audio as validated from internal blind ELO games. We trained (relatively) very low compression codecs in the pursuit of solving a core issue regarding general music and audio generation, low acoustic quality, and audible artifacts, which hinder industry use for these models. Our hope is to encourage researchers to build hierarchical generative audio models that can efficiently use high sequence length representations without sacrificing semantic abilities.
    Downloads: 0 This Week
    Last Update:
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  • 9
    My MP3 Player

    My MP3 Player

    Put your music in it then play it forever. Free MP3 player for Windows

    Put your .mp3 files in a folder then click +Add Music button then click play button and listen it forever. (You have to put .mp3 files in a folder then choose the folder unless the application won't work) This application is free forever no ad. Made by HorizonPublish ⚠️Windows will show unknown application error, click "More info" then "Run anyway".
    Downloads: 0 This Week
    Last Update:
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Open Source AI Music Generators Guide

Open source AI music generators are computer algorithms that can generate musical sequences and compositions that resemble human-written music. The aim of these algorithms is to automate the process of creating music, giving users the ability to create pieces that might otherwise be too difficult or time consuming to produce. The algorithms use artificial intelligence (AI), such as machine learning, natural language processing, and deep learning, to generate different kinds of musical output. In some cases, they can even imitate styles or individuals.

The main advantage of open source AI music generators is their flexibility and adaptability. Users are free to customize the sounds produced by the algorithm according to their own tastes and preferences. This gives them a lot more control over the outcome than if they were working with traditional software tools. For example, they can adjust tempo, instruments used in a piece, or even create entirely new melodies from scratch. Additionally, open source AI music generators often provide access to an ever-growing library of samples and loops which users can combine into unique creations without any extra effort on their part – thus saving both time and money in producing a professional-sounding piece of music from scratch.

Additionally, open source AI music generators typically have a low barrier to entry when it comes to using them effectively & efficiently for creating professional-level tunes. Many come with built-in tutorials & demos which makes it easy for newcomers who may not have much prior experience producing digital audio learn what they need quickly & easily before setting out on their musical journey within minutes after downloading the software’s core files.

Finally, these types of programs also support collaborative efforts between producers who use different systems & platforms since many are compatible with existing tools like Pro Tools or Ableton Live Suite allowing everyone involved in the production collaborate much easier thanks to consistent sounds regardless if one is using OSX while others Windows computers all working on one project together sharing ideas full real time.

Features of Open Source AI Music Generators

  • Online Editing: Open source AI music generators typically allow users to create, edit, and customize their music online. This often includes the ability to add or delete instruments, chord progressions, and effects like reverb and delay.
  • Instrument Samples: Many open-source AI music generators provide an extensive library of instrument samples that can be used in your song production. This allows you to create realistic sounding tracks without having to buy expensive hardware synthesizers or sample libraries.
  • Automation Features: Certain open-source AI music generators feature automation tools that allow you to set certain parameters (such as tempo or track volume) so they are automatically adjusted when you switch between sections of a song. This is especially useful when producing longer pieces of music with multiple sections.
  • Audio Effects: Many open-source AI music generators will come with a selection of audio effects such as delay, chorus, flanger, phaser and more. These can be used to enhance your soundtrack in various ways from adding extra depth and texture to completely transforming the soundscape of your track.
  • Visualization Tools: Certain open-source AI music generator software packages include helpful visualization features which help visualize what’s happening sonically within each track at any given time. This can help identify areas where adjustments could be made for a better overall mix.
  • Mixing/Mastering Tools: Most open-source AI music generator software packages also offer basic mixing tools such as equalizers, compressors, limiters etc., so users can tailor their soundtracks before mastering them for commercial release if desired.

Types of Open Source AI Music Generators

  • Neural Network Generators: These open-source AI music generators generate music by using artificial neural networks. The AI algorithm is designed to learn from a set of input data and create its own outputs, often in the form of melodies.
  • Markov Chain Generators: Open-source AI music generators based on Markov chains use probabilistic models that generate new sequences based on the patterns observed in their training data. They can be used to create more complex pieces of music than those created by neural networks.
  • Evolutionary Algorithms Generators: Open source AI music generators based on evolutionary algorithms (EAs) are similar to Markov chain generators but they involve a process of selection and variation during which solutions become better adapted to specific tasks. EAs often generate unique informational structures, such as melodies or rhythms, while trying to optimize a given objective function over successive generations of solutions.
  • Grammar-Based Generators: An open source AI music generator based on grammar uses rulesets composed of musical elements that can be combined in various ways, thus providing a controlled space for creativity and novelty. Examples include traditional musical styles like jazz or classical, although these rulesets could potentially be used with any genre.

Open Source AI Music Generators Advantages

  1. Cost-Effective: Open source AI music generators provide a cost-effective way to create or modify existing music. Since the software is free and open source, there are no expensive licensing fees or royalties.
  2. Community Support: With open source AI music generators, users benefit from an active community of developers and contributors who offer support for various aspects of the software. This helps reduce development time and ensures that features stay up-to-date.
  3. Customizable: Open source AI music generators are highly customizable because they can be modified by users as needed. This gives more control over the application and allows users to tailor it to their specific needs.
  4. Versatile: Open source AI music generators are versatile tools that can be used for a variety of purposes, ranging from creating basic melodies to complex compositions. They also allow users to experiment with different sounds quickly, enabling them to explore new musical styles.
  5. Accessible: Another advantage of open source AI music generators is that they are accessible to everyone regardless of expertise level or technical knowledge since they do not require any special coding skills or programming knowledge. This makes them perfect for aspiring musicians who want to start creating their own tracks without needing any special training or tutorials first.

What Types of Users Use Open Source AI Music Generators?

  • Creative Artists: Creative artists use open source AI music generators to generate musical ideas and explore the unique sounds that come from artificial intelligence. They can tweak parameters to create one-of-a-kind compositions or play around with generated notes and rhythms to discover new possibilities in music.
  • Music Producers: Music producers use open source AI music generators to expand their production capabilities, allowing them to craft a wide array of tracks with ease. From soundscapes and ambient textures to full songs, producers can get creative when it comes to music without having to spend hours tinkering with complex tools by hand.
  • DJs: DJs are using open source AI music generators more and more as they search for ways to switch up their sets in real time. By selecting different sounds on the fly, these cutting edge performers can fuse sampled sounds with those generated by AI for an irresistible combination of futuristic beats and retro vibes.
  • Educators: Educators are beginning to utilize open source AI music generators in the classroom as a way of engaging students in learning about technology while developing their musical skills at the same time. With exercises such as creating soundtracks or remixing existing pieces, students can learn how powerful software applications like these really are while experiencing the wonder of machine-generated sounds first hand.
  • Hobbyists: Hobbyists enjoy tinkering around with open source AI music generators simply for fun because it is so easy yet provides limitless possibilities when it comes creativity. Whether you’re looking for a cool beat for your next video project or just want something fresh for your next dancefloor move, hobbyists have ample access without leaving home—all thanks to convenient online apps.

How Much Do Open Source AI Music Generators Cost?

Open source AI music generators are available at no cost from various websites on the internet. Many developers, musicians and artists have taken advantage of open source platforms such as Magenta and Tensorflow to create unique pieces of music using artificial intelligence. With access to a wide range of libraries, tools, algorithms and environments that can be used for music production, open source AI music generators offer an incredible way for non-programmers to explore creativity in digital soundscapes without spending money on costly hardware or software. Open source programs are often supported by generous donations from the community, allowing anyone with basic computer skills to access powerful technology which they would otherwise not be able to afford. By leveraging existing intellectual property rights and adhering to copyleft licenses, many developers can make sure their code is free for others to use while still protecting their own innovation. In addition, these programs tend to come with extensive documentation and tutorials so users can jump right in and start creating beautiful musical works quickly and easily.

What Software Do Open Source AI Music Generators Integrate With?

Open source AI music generators can integrate with many different types of software, including digital audio workstations (DAWs), music sequencers, and signal processor plugins. DAWs allow users to compose, record, edit and mix music tracks in a digital format. Music sequencing refers to the arrangement of sounds into a complete song by combining multiple elements like chords and loops from different sound sources. Lastly, signal processor plugins enable musicians to enhance the audio content of their compositions with effects such as reverb or echo. Using these tools together helps artists transform their projects into creative pieces of art.

Trends Related to Open Source AI Music Generators

  1. Increased Availability: As open source AI music generators become more widely available, it is easier for users to access and use them. This means that more people are able to experiment with and explore the potential of using AI-generated music in their own projects.
  2. Improved Quality: Open source AI music generators have improved in terms of quality over time. As developers continue to update these tools and refine their algorithms, they are able to produce higher quality sounds and tracks. Additionally, user feedback can be used to further refine the results generated by open source AI music generators.
  3. Lower Cost: The cost of using an open source AI music generator is significantly less than hiring a professional composer or musician to create original music. This makes it easier for users on a budget to generate original music with AI.
  4. Creative Uses: Open source AI music generators can be used in a variety of creative ways. For example, they can be used as a starting point for creating new compositions or as a way to quickly generate backing tracks for video games or podcasts. Additionally, users can easily customize the results generated by open source AI music generators according to their own preferences.

How Users Can Get Started With Open Source AI Music Generators

Getting started with using open source AI music generators is easy and can provide an exciting new way to experience creating music. The first step is to find the right software for your desired tasks. There are a wide range of open source AI music generators available, so it's important to research which one best fits your needs.

Once you have selected the software, download it onto your computer or other device. Make sure you check out any associated documentation that comes with the download; this will help get you comfortable with how the software works before you dive into creating your own compositions.

The next step is to use samples - either from existing audio or ones provided by the program - to train the generator on how to create different types of sounds and styles depending on what types of input samples you feed it. This might require some trial and error as each type of generated sound may require different sample inputs to achieve different effects. Don't worry if it takes a few attempts before achieving desired results; this part can take time but understanding sampling techniques will help optimize your output from the generator faster over time and provide more consistent results in many cases.

Once comfortable navigating around the generator interface, explore ways for taking advantage of all its features as well as integrating outside instruments and effects into generate even more unique kind of soundscapes, melodies and beats. Most generators come equipped with preset variations that allow users to change up parameters such as tempo, tonality, etc., giving them full control over their composition process while getting instant feedback from their composed musical ideas during every step along the way; plus, many also feature integrated support for sharing tracks online or exporting them in digital formats like MP3s or WAV files which makes collaboration much easier too. Finally, consider joining online communities where other musicians are discussing their experiences using similar programs for creative inspiration on new projects.

Overall getting started with open source AI music generators can seem daunting at first but following these basic steps will make things smoother when learning how they work and providing great opportunities for creativity when composing.

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