How to Install Meta AI Audiocraft for Text-to-Music Generation

Audiocraft provides the code and models for MusicGen, a simple and adaptable approach for music generation. MusicGen employs a single-stage auto-regressive Transformer model that was trained with a 32kHz EnCodec tokenizer and 4 codebooks sampled at 50 Hz. Unlike prior methods such as MusicLM, MusicGen does not rely on a self-supervised semantic representation and can produce all four codebooks in a single pass.

We demonstrated the capacity to anticipate them in parallel by adding a tiny delay between them, resulting in only 50 auto-regressive steps per second of audio. In this article, I’ll show you how to Install Meta AI Audiocraft for Text-to-Music Generation step by step procedures.

What is MusicGen and How does it Work

  1. Meta AI created MusicGen, a text-to-music generating model. It is a single-stage transformer language paradigm that acts on several streams of compressed, discrete music representations, known as tokens. This enables MusicGen to generate high-quality music samples while being conditioned on verbal description or melodic elements, providing greater control over the generated output.
  2. MusicGen is still in the works, but it has already been used to generate a wide range of musical styles such as pop, rock, classical, and jazz. It has also been used to create music for video games, films, and television shows.
  3. MusicGen is a powerful tool with the potential to change the way music is made. It can be used to generate fresh and inventive music by artists, composers, and producers.
See also  Install WordPress with Nginx Reverse Proxy to Apache on Ubuntu 22.04

Features of MusicGen

  • It possesses the capability to create music across various genres including pop, rock, classical, and jazz. It can generate music based on specific descriptions or musical components.
  • While still undergoing development, it has already been utilized to compose music for a range of media such as video games, movies, and TV shows.
  • This remarkable tool holds immense potential to revolutionize the process of music generation.

MusicGen Usage

MusicGen, created by Meta, is more than just a theoretical concept; it is also a working tool for music composition. Here are three ways to make the most of its strong capabilities:

  • DEMO: Try out MusicGen’s demo version to see for yourself how powerful it can be. This interactive demo allows you to play around with basic features and make music based on simple instructions. Engaging with the demo will provide you with an idea of the creative possibilities that MusicGen provides. If you are interested in word with MusicGen READ MusicGen Text-to-Music Using Meta AI Audiocraft.
  • COLLABORATION: Use MusicGen as a collaborative tool to collaborate with others. Whether you’re working on a musical project or simply want to enjoy the process of making music with others, MusicGen can help. It promotes teamwork and encourages team members to collaborate creatively.
  • CODE: For those with technical knowledge, MusicGen offers open-source code that may be viewed and updated to suit your needs. You can explore the code, make changes, and compose your own symphonies. This amount of customization enables you to tailor MusicGen to your specific musical tastes and needs.
See also  Instagram Audio Tab: How to Find and Use Music and Audio for Your Reels

MusicGen is a wonderful tool for generating music, whether you are a beginner or a seasoned musician. It is simple to use, adaptable, and powerful. You may bring your musical thoughts to life using MusicGen. In this article we can seehow to install and run musicGen.

Prerequisites

  • Audiocraft requires Python 3.9, PyTorch 2.0.0
  • GPU with at least 16 GB of memory

Installation

To install Audiocraft, make sure you have Python 3.9 and PyTorch version 1.9.0 or above installed. Also, make sure you have access to a GPU with at least 16 GB of memory, especially if you plan to use the medium-sized model.

It’s best to have torch installed first, especially before installing xformers. # If you already have PyTorch installed, do not execute this.

pip install ‘torch>=2.0’

Then proceed to one of the following,

pip install -U audiocraft # stable releas

Following command used for bleeding edge,

pip install -U git+https://[email protected]/facebookresearch/audiocraft#egg=audiocraft

If you cloned the repo locally,

pip install -e

API for Generating Music

MusicGen provides a simple API for generating music from text. There are four pre-trained models to choose from:

  1. Small: This model has 300M parameters and can only generate music from text. It is the quickest model, however it may not yield the best results.
  2. Medium: This model has 1.5B parameters and can generate music from text as well. It is slower than the little model, but it produces better results.
  3. Melody: This 1.5B parameter model can generate music from both text and melody. It is the slowest model, but it produces the best results.
  4. Large: This model has 3.3B parameters and can only generate music from text. It is the slowest model, but it produces the best results.
See also  10 Best AI Tools to Boost Your Productivity in 2023

Local use of MusicGen requires a GPU with at least 16GB of memory. With the tiny model, smaller GPUs may be able to generate short sequences or longer sequences.

Note: Please ensure that you have ffmpeg installed when using a newer version of torchaudio. To install ffmpeg, you can follow the instructions below based on your operating system:

apt-get install ffmpeg

Example for using API

import torchaudio from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained(‘melody’) model.set_generation_params(duration=8) # generate 8 seconds. wav = model.generate_unconditional(4) # generates 4 unconditional audio samples descriptions = [‘happy rock’, ‘energetic EDM’, ‘sad jazz’] wav = model.generate(descriptions) # generates 3 samples. melody, sr = torchaudio.load(‘./assets/bach.mp3′) # generates using the melody from the given audio and the provided descriptions. wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr) for idx, one_wav in enumerate(wav): audio_write(f'{idx}’, one_wav.cpu(), model.sample_rate, strategy=”loudness”, loudness_compressor=True) # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.

Conclusion

Installing Meta AI Audiocraft for Text-to-Music Generation is a rather simple process. You may be up and running in no time if you follow the steps provided in this article. After installing Audiocraft, you can use it to create your own unique music or to explore with other types and genres. You may use Audiocraft to produce incredibly unique and emotive music with a little imagination. Please feel free to share your thoughts and feedback in the comment section below.