Musicgen AI: Music Generator by facebook

MusicGen is an AI tool developed by Meta that generates music from text descriptions or audio inputs. It’s an open-source model that allows users to create short music clips by simply providing a prompt, such as "a light and cheerful EDM track."
Price: FREE
Operating System: Web Application
Application Category: AI Music Generator
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What is MusicGen AI?
MusicGen is an AI tool developed by Meta that generates music from text descriptions or audio inputs. It’s an open-source model that allows users to create short music clips by simply providing a prompt, such as “a light and cheerful EDM track.”

MusicGen has been trained on 20,000 hours of licensed music, which includes a mix of high-quality tracks and instrumentals. This training enables the model to produce diverse musical compositions based on user input.
AI Tool | MusicGen AI App |
---|---|
Category | Music Generator |
Feature | Text to Music, Audio to Music |
Official App | musicgenai.org |
Launch Date | 20 October 2023 |
App Version | v1 |
Cost | Free |
HuggingFace Link | Click Here |
Features of Musicgen AI:
1. Text-to-Music Generation:
Users can input a text description to generate music. For example, providing a prompt like “a cheerful country song with acoustic guitars” will produce a short music clip that aligns with the description.
2. Audio-to-Music Generation:
MusicGen can also take an existing audio sample as input and create new music based on that reference. This feature is particularly useful for remixing or extending existing tracks.
3. Melody Conditioning:
The AI allows for melody conditioning, meaning users can guide the music generation process using a melody (humming, singing, or instrumental input) that the AI will follow to produce a composition.
4. Multiple Model Sizes:
MusicGen comes in several model sizes, from smaller versions for basic tasks to a large model with 3.3 billion parameters that can generate more complex and intricate music.
5. Open-Source Availability:
The tool is open-source, making it accessible for developers and musicians who want to explore its capabilities or integrate it into their projects.
6. High-Quality Music Generation:
Trained on 20,000 hours of licensed music, Musicgen is capable of producing high-quality compositions, making it a valuable tool for musicians and content creators alike.
MusicGen AI
MusicGen AI transforms simple text into symphonies, making musical creativity accessible to everyone.
Who Should Use Musicgen AI?
1. Musicians and Composers:
Those looking to experiment with new sounds or generate music based on text descriptions or existing audio samples can use MusicGen to explore creative possibilities and enhance their compositions.
2. Content Creators:
Video creators, podcasters, and streamers who need custom background music or soundtracks can quickly generate unique tracks tailored to their specific needs without the need for deep musical expertise.
3. AI Enthusiasts and Developers:
Those interested in AI and machine learning can explore the open-source nature of MusicGen, customize its capabilities, or integrate it into other projects.
4. Educators and Students:
Music educators and students can use MusicGen as a tool for learning and teaching music theory, composition, and the intersection of music with technology.
MusicGen AI Review
Summary
MusicGen AI is an open-source tool by Meta that generates music from text or audio inputs, making music creation accessible and customizable for everyone.

Key Features:
- Music Generation
- AI Techniques
- Text to Music
- Melody Conditioning
How to Access Musicgen App?
Step 1. Access Musicgen space:
Visit the official huggingface website and search for “Musicgen”.
Step 2: Input a Text Prompt:
Enter a description of the type of music you want to generate. For example, you might type “a calm acoustic guitar melody with soft drums.”

Step 3. Provide Audio Input:
If you want Musicgen to generate music based on an existing melody, you can upload an audio file as a reference.
Step 4. Generate Music:
Click the button to generate music. Musicgen will create a short music clip based on your input.
Step 5. Download or Share:
Once the music is generated, you can listen to it, download the file, or share it directly from the platform.
Musicgen AI Pros and Cons:
- Text to Music
- AI Music Generation
- Long Generation
- Melody-guided music generation
- Quick Results
- Huggingface Access
- Discord Dependency
- Instruction Sensitivity
FAQs:
1. What is Musicgen AI?
Musicgen AI is an open-source tool by Meta that generates music from text descriptions or audio inputs.
2. How does Musicgen AI work?
It uses a transformer-based model trained on 20,000 hours of licensed music to create music based on text prompts or reference audio.
3. Is Musicgen AI free to use?
Yes, Musicgen AI is free and you can use it on huggingface.
4. Who can use Musicgen AI?
Musicians, content creators, AI enthusiasts, educators, and hobbyists can all use Musicgen AI for music creation and experimentation.
5. Where can I access Musicgen AI?
You can access it on the https://huggingface.co/spaces/facebook/MusicGen
Share Musicgen AI
Introduction to MusicGen
All right, let’s go over MusicGen. MusicGen is a simple and controllable music generation model made by Meta. It’s one of the music generation models that you’ve been seeing lately. This is just one of them, and I think Google made one as well. There were a few that came out recently, but yeah, this is one of them that I want to go over to see the current state of music generation. It’s pretty good, and you should check out the demo. They have a GitHub for it, which is pretty nice, and they open-sourced the models. It’s really awesome since you don’t often see people open-sourcing their models lately. I’m a big fan of that.
Overview of the MusicGen Paper
Let’s go over the paper. As I mentioned, they tackle the goal of music generation, and they do so in a language model task where they are modeling discrete tokens like a language model. But first, what are the difficulties with audio? Whenever you see audio, you usually see a waveform represented by a vector. This vector represents the value in time, like the amplitude at various points, and it’s obviously in a continuous space. Audio, speech, and music—all of them are continuous and require a very long vector for representation.
Challenges of Audio Modeling
For instance, if you listen to music, the sampling rate is something like 32,000 Hz, which means to get one second of audio, you need a vector of size 32,000, which is massive. Doing anything with a vector that big is very challenging. If you want more than a second, you multiply the sampling rate by the duration of the audio, and you end up with a massive vector. This is known as F sub S, which is equal to 32,000 Hz, and that’s what they’re trying to model.
One of the things they bring up is that for speech, you usually sample at 16k Hz, but for music, humans are very good at detecting even minor inaccuracies. Therefore, the sampling rate needs to be even higher, which they bring up right here. So, you need a really high sampling rate, which is very difficult to model.
Introduction to Encodec
What they do is use a model called Encodec, which I covered in my last video. You should check that out for a more detailed explanation. Briefly, Encodec is an autoencoder model that compresses the audio by encoding it into a smaller space, making it easier to deal with. Instead of working with the raw audio signal, which is not a good representation of the data, they encode it into a smaller latent space, which they call F sub R, compressing the audio from 32,000 Hz to 50 Hz—a massive compression.
The Process of Residual Vector Quantization
After encoding, they apply residual vector quantization (RVQ) to transform this vector from a continuous space to a discrete space, which is essential for MusicGen as it models using discrete tokens. Transformers are made for modeling discrete tokens, so they first take the audio and convert it into a discrete format.
How RVQ Works
RVQ works by breaking the vector into multiple vectors using lookup tables or code books. These code books contain vectors, and the RVQ process looks for the closest match between the input vector and the vectors in the code book. It then calculates the residual, which is the difference between the two, and stores it. This process is repeated for multiple code books, each refining the residual vector further.
Encoding and Decoding in Encodec
Once the vector is discretized, they reverse the process through a deconvolutional neural network, returning it to its original space, F sub S. Encodec, in essence, is an autoencoder with RVQ in the latent space, designed to simplify the modeling of audio by converting it into a discrete, easier-to-handle format.
Modeling with Transformers
With the encoded and discretized audio, they use a Transformer model to predict the next token in the sequence, following an auto-regressive training strategy. Since the encoded representation is inherently time-based, predicting tokens in sequence corresponds to predicting the next part of the music sequence.
Challenges with Token Generation
One problem with this approach is that errors can compound when generating tokens sequentially. The first code book is crucial because it models the initial input vector, while subsequent code books model the residuals. To address this, they use a staggered approach where they model tokens across code books in a way that mitigates compounding errors.
Staggered Token Modeling
Instead of stacking tokens, they stagger them like a staircase, where each token in a sequence corrects the errors of the previous tokens. This method ensures that each token in the sequence is modeled more accurately, although it takes longer to generate.
Efficient Generation with Transformers
To balance accuracy and efficiency, they adopt a strategy where the first code book is modeled independently, and the subsequent code books are generated together. This method allows for faster generation without sacrificing much accuracy.
Results and Comparisons
The MusicGen model performs well compared to other models, such as MusicLM. They found that while increasing the model size improved adherence to prompts, it didn’t necessarily improve the quality of the music. The model performs significantly better at following prompts as the model size increases, which is an interesting observation.
Getting Started with MusicGen
MusicGen is an open-source language model that can generate high-quality music from either a line of text or a base melody.
Created by Facebook, that’s right—a team working for Facebook is actually behind this MusicGen model. Since they released everything to the public, we can now use it for free on our own computers, and I’m going to show you how.
Requirements for Installation
Before we begin the installation, what exactly do we need? In this video, I’m going to show you how to install this on Windows, so if you have Linux or Mac, I’m not quite sure this installation will work for you. The second piece of hardware you need is a powerful GPU.
Now, on the GitHub page, they say you need a GPU with at least 16 gigabytes of memory, but in reality, in practice, you don’t actually need this much VRAM. You could use this locally with around four to five gigabytes of VRAM.
However, don’t worry—even if you don’t have this much VRAM, I’m going to show you later how you can use it for absolutely free without any GPU. So basically, no worries—even if you don’t have a powerful computer or GPU, you will not miss anything, and that’s pretty cool.
Installing Git, Python, and PyTorch
Before we begin the installation, you need a few things. The first is Git, so if you haven’t installed it already, it’s very easy. Just click on the download button for Windows and then follow the installation process to install it on your computer.
Next, you need Python. Again, if you haven’t already, come here, scroll down, click on the Windows installer, and then follow the installation process. Just do not forget to check the box to add Python to PATH, because otherwise, it’s not going to work.
You also need to install PyTorch 2.0.1. I personally had a lot of errors and issues with the Torch version, but I managed to make it work by using this command to uninstall everything and then reinstalling PyTorch 2.0.1 with CUDA using the following command.
If you don’t know how to do this, you can click here to copy the entire line of code, open the command prompt, paste the command, and press Enter.
You’re going to wait for everything to uninstall and then do the same thing with the second line of code. If you don’t have PyTorch 2.0.1 installed, you absolutely need to do this first; otherwise, it will not work.
Cloning the Repository and Running the Tool
Once everything is installed on your computer, you’re going to create a new folder—I’m going to call mine “MusicGen.” Once inside the folder, click on the folder path, type CMD, press Enter to bring up the command prompt window, and then copy and paste the command line that you will find in the description down below.
Press Enter, and this will clone the repository inside your computer, creating a new AudioCraft folder. Inside, you’ll see a bunch of files. From here, click on the folder path, type CMD, press Enter, and then copy and paste the line that you will find on the GitHub page or in the description down below into the command prompt window, and then press Enter. This will download all the requirements needed to run the tool.
Now, we’re done. That’s right—it was that easy. If you want to run the web UI, all you have to do is type python app.py
, or you can just copy and paste the command from the description down below so you don’t have to type anything, and then press Enter.
Using MusicGen to Create Music
In the web UI, you’ll see a bunch of different zones. For example, here is where you’ll input your text. Let’s say I want a peaceful meditative Zen track infused with jazz elements and a smooth saxophone solo. You can choose different music models—there are four models to choose from: Melody, Medium, Small, and Large.
The larger the model, the more VRAM it will use. For example, if you only have 5 gigabytes of VRAM, you can use the small model, and it will work perfectly fine. You can also choose the duration of your music, which is basically how long the generated music will be.
Currently, the limitation is around 30 seconds. It might be possible in the future to generate more than 30 seconds of audio, but for now, 30 seconds is the maximum limit. For this example, I’ll choose around 10 seconds, which will be more than enough.
There are also parameters to control the quality of the generation. Top K means that the higher the number, the more varied the generation will be. P is similar to Top K, except it’s more of a percentage and probability-based.
Temperature controls how random and unpredictable the generation is—the higher the number, the more random it is. Classifier-free guidance controls everything and guides the generation in a certain direction. All of this can be complex to explain, so I’d suggest leaving everything by default or playing around a bit.
Once you’re ready, just click on “Submit” and wait for your music to be generated. The first time you generate music, it will start downloading the model you chose, so it might take a few minutes. After that, it will be much faster.
In around 40 seconds, we generated a brand-new piece of music from scratch. If we listen to it, it sounds pretty good—exactly what I asked for: a peaceful, meditative Zen track infused with jazz elements and a smooth saxophone solo. What do you want me to say?
Seeking Feedback and Exploring More Features
Now, I’m not a music specialist—I know absolutely nothing about music and don’t even listen to a lot of music myself. That’s why I need your opinion in the comments down below to tell me if the music is good or not. Since we’re going to play around with the tool and generate a few more pieces of music, I’ll definitely need your input because, as I said, I’m really bad at this.
One other thing I haven’t shown you is the “Melody Condition” option, where you can drop an audio file and use that melody as a base to create your music.
You can use a melody you downloaded or created yourself and then transform it into a completely different style. For example, if I click on it, it takes this base music and, using the following prompt—an ’80s driving pop song with heavy drums and a synth pad in the background—we get something entirely different after around 40 seconds.
Benefits for Content Creators
This tool is absolutely incredible for small creators on YouTube and Twitch. If you know how YouTube works or have a YouTube channel, you’re aware of the big issue with content ID and copyright claims. If you want to use copyrighted music in your video on YouTube, you can’t because you’ll get a copyright strike and lose the monetization of your video.
That’s why so many YouTubers use different services and pay to use a bunch of copyright-free music in their videos. However, using a tool like MusicGen allows you to generate any music you want in any style you want for absolutely free. If you’re a small YouTube creator, this tool is a must-have.
It’s a game changer because finding music to fit a certain style or convey a certain emotion can be difficult and expensive, but if you can generate your own music in any style and convey any emotion you want in a few seconds for free, it’s incredible.
Every piece of music you generate with MusicGen is 100% copyright-free, so bye-bye copyright claims and welcome monetization.
Running MusicGen for Free
I know some of you will ask, “Okay, that’s great, but what if I don’t have a powerful GPU to run this? How can I run this tool for free?” The answer is simple: we’re going to use a free Google Colab doc. That’s right—simple as that.
We’ll be using the free Google Colab doc provided by Kamenduru, who is kind of like a king when it comes to Google Colab docs. If you want to use the Google Colab doc, click the link in the description below. You’ll arrive on this page, click on the icon, which will open a very simple and easy-to-use Google Colab doc that you can run by clicking on the cell and then clicking “Run anyway.”
Wait for everything to be installed—it might take a few minutes— and in the end, you’ll get a public URL. If you click on it, it will launch the MusicGen UI exactly as if it was running locally on your computer. Simple as that. You can use it the same way: input text, select a model, and click “Submit.”
Testing the Tool with Examples
Now, let’s have some fun and generate a few AI music tracks to see how good they really are. For the first example, I’ll start with a spaghetti western theme turned into a chilled Lo-Fi hip-hop tune for study and relaxation. I’ll choose the large model and a 30-second limit.
After around two minutes, we get something like this. It’s not bad—let me know what you think in the comments, but I think it sounds pretty good.
How about we try something different, maybe an 8-bit video game music? This time, I’ll set it to 10 seconds and click “Submit.” We get something like this. Again, not bad—it reminds me of my childhood with all those old games I used to play that had similar-sounding music. This sounds really good.
The last test I want to do is to input a melody as a base. I’ll use the beginning of Mozart’s Piano Sonata No. 11, which sounds like this. I want to transform it into a vibrant retro ’80s synthwave.
Don’t forget to choose the melody model, set it to 30 seconds, and click “Submit.” We go from this to this. I don’t know about you, but this sounds really good. I wish we could generate more than 30 seconds because I’d love to hear the rest of the song. I think I’m going to keep this one.
Final Thoughts
Facebook Research has had an insane influence on the open-source community lately. First, with the release of LLaMA, the NN model that everybody used as a base for months, then they released Segment Anything, which can analyze an image and create different masks for every element in that image very precisely.
Now, we have MusicGen, which is simply the best tool right now for generating music using AI. I cannot believe you can use this tool for free—it’s absolutely insane.
This is so cool, and as I said previously, not only is it a cool tool to generate any music you want just for fun, but if you’re a content creator on YouTube, you’ll never have to worry about copyright claims ever again. That is fantastic.