top of page

Group

Public·55 members

Download and Explore the Incredible Music Samples from OpenAI Jukebox


How to Download and Use OpenAI Jukebox




Have you ever wondered how to create music with artificial intelligence? If so, you might be interested in OpenAI Jukebox, a neural network that can generate music in various genres and styles, including singing. In this article, we will show you how to download and use OpenAI Jukebox, as well as its limitations and alternatives.




openai jukebox download



What is OpenAI Jukebox and what can it do?




OpenAI Jukebox is a project by OpenAI, a research organization that aims to create artificial intelligence that can benefit humanity. OpenAI Jukebox is a deep neural network that can generate music as raw audio, based on genre, artist, and lyrics as input. It can also imitate the voice of a particular singer, or blend different styles together. It can produce songs from scratch, or continue or re-render existing songs.


OpenAI Jukebox is powered by a large-scale transformer model that was trained on over a million songs in various genres and languages. It uses an autoencoder to compress raw audio into a lower-dimensional space, and then upsamples it back to the original space. It also uses a sparse transformer to model long-range dependencies in music.


OpenAI Jukebox is an impressive example of how artificial intelligence can learn to create music by discovering patterns of harmony, rhythm, and style. It can generate music that sounds realistic and diverse, as well as novel and creative. It can also capture human voices and emotions in singing.


How to download OpenAI Jukebox




If you want to try out OpenAI Jukebox for yourself, you will need to download it from its official website or GitHub repository. You will also need to install some dependencies and requirements before you can run it.


openai jukebox code


openai jukebox samples


openai jukebox github


openai jukebox paper


openai jukebox blog


openai jukebox explorer


openai jukebox colab


openai jukebox tutorial


openai jukebox music generation


openai jukebox lyrics


openai jukebox neural net


openai jukebox model weights


openai jukebox installation


openai jukebox sampling


openai jukebox training


openai jukebox audio quality


openai jukebox genres and styles


openai jukebox songs


openai jukebox review


openai jukebox demo


openai jukebox api


openai jukebox dataset


openai jukebox reddit


openai jukebox youtube


openai jukebox soundcloud


openai jukebox license


openai jukebox requirements


openai jukebox documentation


openai jukebox issues


openai jukebox alternatives


openai jukebox comparison


openai jukebox limitations


openai jukebox challenges


openai jukebox future work


openai jukebox research


openai jukebox publications


openai jukebox presentations


openai jukebox podcasts


openai jukebox interviews


openai jukebox feedback


openai jukebox updates


openai jukebox improvements


openai jukebox tips and tricks


openai jukebox use cases


openai jukebox applications


openai jukebox projects


openai jukebox examples


openai jukebox testimonials


The official website of OpenAI Jukebox is [here](^1^). You can find the paper, blog post, code, samples, explorer tool, and colab notebook on this site. You can also listen to some curated samples of music generated by OpenAI Jukebox in different genres and styles.


The GitHub repository of OpenAI Jukebox is [here](^6^). You can find the source code, instructions, issues, pull requests, and license on this site. You can also clone or fork the repository to your own machine.


To install OpenAI Jukebox, you will need the following requirements:


- Python 3.7.5 or higher - PyTorch 1.4 or higher - CUDA 10.0 or higher - MPI4Py 3.0.3 or higher - AV 7.0.01 or higher You can install these requirements using conda or pip commands. You will also need to install the jukebox package using pip install -e . command in the jukebox directory.


To run OpenAI Jukebox, you will need to use the jukebox/sample.py script. You can use different commands to sample from different models and modes. For example:



# To sample from scratch using the 5b_lyrics model python jukebox/sample.py --model=5b_lyrics --name=sample_5b --levels=3 --sample_length_in_seconds=20 \ --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125 # To sample # To sample from a given genre using the 1b_lyrics model python jukebox/sample.py --model=1b_lyrics --name=sample_1b --levels=3 --sample_length_in_seconds=20 \ --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125 \ --genre=hiphop # To sample from a given artist using the 5b model python jukebox/sample.py --model=5b --name=sample_5b_artist --levels=3 --sample_length_in_seconds=20 \ --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125 \ --artist=Lady Gaga # To sample from a given lyrics using the 5b_lyrics model python jukebox/sample.py --model=5b_lyrics --name=sample_5b_lyrics --levels=3 --sample_length_in_seconds=20 \ --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125 \ --lyrics="I'm on the edge of glory" # To continue an existing song using the 5b model python jukebox/sample.py --model=5b --name=sample_5b_continue --levels=3 --sample_length_in_seconds=20 \ --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125 \ --mode=continue --audio_file=path/to/song.wav # To re-render an existing song using the 5b model python jukebox/sample.py --model=5b --name=sample_5b_rerender --levels=3 --sample_length_in_seconds=20 \ --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125 \ --mode=rerender --audio_file=path/to/song.wav


You can find more details and examples of the commands in the README file of the GitHub repository.


How to use OpenAI Jukebox




Once you have downloaded and installed OpenAI Jukebox, you can start using it to generate music in various ways. You can choose from different models and modes, depending on your preferences and goals.


The models available are:


- 1b: A smaller model with 1 billion parameters that can generate music in 10 genres and 10 artists per genre. - 1b_lyrics: A variant of the 1b model that can also generate lyrics conditioned on genre and artist. - 5b: A larger model with 5 billion parameters that can generate music in over 6000 genres and artists. - 5b_lyrics: A variant of the 5b model that can also generate lyrics conditioned on genre and artist. The modes available are:


- ancestral: A mode that generates music by sampling from the prior distribution of the model, without any conditioning or input. - primed: A mode that generates music by sampling from the conditional distribution of the model, given some input parameters such as genre, artist, lyrics, or audio. - continue: A mode that generates music by continuing an existing song, given its audio file as input. - rerender: A mode that generates music by re-rendering an existing song, given its audio file as input. The input parameters and options are:


- genre: A parameter that specifies the genre of the music to be generated, such as rock, pop, jazz, etc. - artist: A parameter that specifies the artist of the music to be generated, such as Beatles, Taylor Swift, Frank Sinatra, etc. - lyrics: A parameter that specifies the lyrics of the music to be generated, as a string or a text file. - audio_file: A parameter that specifies the audio file of an existing song to be continued or re-rendered. - prompt_length_in_seconds: An option that specifies how long the input audio or lyrics should be used to prime the model. - sample_length_in_seconds: An option that specifies how long each sample of music should be generated. - total_sample_length_in_seconds: An option that specifies how long the total output of music should be generated. - sr: An option that specifies the sampling rate of the audio in Hz. - n_samples: An option that specifies how many samples of music should be generated for each input. - hop_fraction: An option that specifies how much overlap there should be between different levels of the model. The output formats and quality are:The output formats and quality are:


- .wav: A format that stores the raw audio data as waveforms, without any compression or encoding. This format preserves the highest quality and fidelity of the music, but also takes up more space and bandwidth. - .mp3: A format that compresses and encodes the audio data using a lossy algorithm that reduces the file size and bandwidth, but also introduces some distortion and noise. This format sacrifices some quality and fidelity of the music, but also saves more space and bandwidth. - .mid: A format that stores the musical notes and instructions as MIDI messages, without any audio data. This format allows the music to be played by different synthesizers and instruments, but also depends on their quality and compatibility. The quality of the music generated by OpenAI Jukebox depends on several factors, such as the model size, the input parameters, the sampling rate, the hop fraction, and the random seed. Generally, larger models produce better quality music, but also require more computational resources and time. Higher sampling rates and lower hop fractions produce higher fidelity music, but also increase the file size and bandwidth. Different input parameters and random seeds produce different variations of music, but also introduce some uncertainty and diversity.


Limitations and alternatives of OpenAI Jukebox




While OpenAI Jukebo


About

Welcome to the group! You can connect with other members, ge...

Members

bottom of page