Musenet

Introduction

Musenet is an AI company that specializes in developing advanced AI models to create music, images, and text. They have created a unique deep neural network that can generate a range of new musical pieces that reflect different genres and styles- from Mozart to the Beatles and everything in between. Additionally, the company offers training and guidance services to help others achieve success in AI development.

Musenet is dedicated to working towards building safe and beneficial AGI (Artificial General Intelligence) models that promote positive change in the world. Their mission is to foster collaboration and promote ethical decision-making in the field of AI, ensuring that their technology can bring immense benefits to everyone without compromising safety and transparency.

TLDR

Musenet is an AI company that offers AI-generated music, images, and text. Their unique AI tools and models provide fast and efficient content creation without compromising quality. Musenet uses cutting-edge machine learning techniques to create AGI models that ensure positive change in the world. Their state-of-the-art deep neural network enables the user to create entirely new musical compositions that reflect different genres and styles. Musenet guarantees a pricing structure that guarantees customers that they only pay for the services they need, making it easy and flexible to use.

Company Overview

Musenet is an AI company that is dedicated to building safe and beneficial AGI (Artificial General Intelligence). They believe that AI development requires people from a wide range of disciplines and backgrounds, which is why they have built a diverse team who work together to achieve this goal.

They don't just want to build AGI, but also to aid others in doing so as well. Musenet has developed unique AI tools and models that help users to generate music, images, and even text in a faster and more efficient way. Their aim is to help people create amazing content without spending countless hours on the production process.

Musenet uses state-of-the-art machine learning techniques to advance the capabilities of its AI models. Their AI tools allow creators to experiment with different styles, genres, and even come up with original compositions. As a result, they have gained popularity among artists, music composers, game developers, and many others who require high-quality content quickly and efficiently.

The company has a vision that goes beyond just AI services. Musenet aims to create positive change in the world by fostering collaboration and promoting ethical decision-making in the field of AI. They are dedicated to building AGI that is safe, transparent, and has the potential to bring immense benefits to everyone.

Overall, Musenet is a forward-thinking AI company that is working towards building disruptive technology for the betterment of humanity. Their unique AI tools are designed to help creators, automate the content creation process, and move the field of AI forward in a responsible way.

Features

Music Generation

Deep Neural Network for Music Generation

Musenet is a one-of-a-kind deep neural network designed to create 4-minute musical compositions utilizing 10 different instruments while combining styles ranging from country to Mozart to the Beatles. It is capable of producing completely new pieces of music without any explicit programming regarding our understanding of music. It was designed to learn patterns of harmony, rhythm, and style by initiating the prediction of the next token from hundreds of thousands of MIDI files. The same unsupervised technology that was used to develop the large-scale transformer model GPT-2 was used to train MuseNet.

Blending Different Musical Styles

The amazing MuseNet system is capable of blending generations harmoniously in novel ways because of its knowledge about many different styles. With MuseNet, one can choose one of the classical composers or pop styles, include an optional start of a renowned piece, and start generating new music compositions. One of the greatest things about this is that the user can listen to uncensored random samples that MuseNet has pre-generated, thanks to its simple mode. When utilizing advanced mode, users are provided with an opportunity to interact with the system directly, even though the completions will take longer to generate.

Generating Music with Limitations in Mind

Even though MuseNet is one of the most advanced music-generating AI systems available, it still has some limitations. To ensure greater control over the types of samples available for generation, the creator introduced composer and instrument tokens that were prepended to each sample during training time. During generation time, the system could be programmed to create samples in a chosen style by triggering it with a prompt similar to that used to generate samples in a specific Chopin piece or by Journey. Furthermore, the creator applied t-SNE to generate a 2-D map of the cosine similarity of a variety of musical composer and style embeddings to better understand what the system has learned.

Training

Collecting Training Data

The creator utilized different sources to collect training data for MuseNet, ranging from established sources like ClassicalArchives to popular sources like BitMidi. They also gathered several online collections, like jazz and pop styles, as well as African, Indian, and Arabic styles. Additionally, the MAESTRO dataset was used to gather data. During the training phase, the transformer was trained on sequential data, and given a set of notes to teach it to predict the upcoming note.

Inculcating Inner Critic During Training

To improve its performance during the training stage, MuseNet was designed with an inner critic. The system is tasked with predicting whether a presented sample is genuinely collected from the dataset or if it is one of the model's own previous creations. This score is then used to select MuseNet's generations during generation time.

The Role of Embeddings in Training

An essential aspect of MuseNet's training was the addition of several embedding types to enhance the model's structural context. Along with the standard positional embeddings, the creator added a learned embedding that tracked the passage of time. This means that all the notes that sound at the same time are given the same timing embedding. Additionally, an embedding was included for each note in a chord, reinforcing relative attention and making it simpler for the model to learn, such that note 4 needs to look at the previous chord's note 3 or note 4. Finally, two structural embeddings were included to inform the system of a given musical sample's instrumentation and position within the larger musical piece.

Benefits of MuseNet

Maintains Long-Term Structure in Pieces and Creates Musical Melodic Structures

Musenet is a remarkable addition to the world of AI-generated music because of its ability to maintain long-term structure like a human composer. Additionally, it can create musical melodic structures with ease, as evidenced by the samples shared on the platform. With a 72-layer network and 24 attention heads utilizing full attention on a 4096-token context, the system makes it possible to access a host of features that can enable users to craft entirely new pieces of music based on their individual preferences and styles.

Testing Domain for Sparse Transformer

The process of music generation is an excellent terrain for testing the Sparse Transformer, as it rests in a middle ground between images and texts. It has the fluent token structure of text, whereby users can look back at n tokens to find the previous row above, even though there is no fixed number for looking back to the previous measure in music. However, it is relatively easy to tell if a music model messes up the structure by changing the rhythm, although it is not always evident if a text model goes on a brief tangent.

Endless Possibilities

One of the exciting things about MuseNet is that it allows musicians and music lovers to create endless possibilities. By training MuseNet, the AI system can create music tracks in a variety of different genres, making it possible to explore and experiment with new sounds and styles. Because music is a crucial part of various cultures worldwide, the benefits of exploring music using MuseNet are virtually limitless.

Pricing

Musenet offers multiple models, offering a spectrum of capabilities and corresponding price points. The prices are determined per 1,000 tokens, which can be considered as pieces of words, with 1,000 tokens being roughly equivalent to 750 words. This ensures that customers are only paying for what they use and can choose the right model for the job.

For those who are new to the service and want to experiment, Musenet offers a free trial with $5 in free credit that can be used towards their first 3 months. This not only allows customers to try out the service but also provides a cost-effective option for those who only need Musenet on occasion or for small projects.

If a customer chooses to continue using Musenet after the free trial or free credit has expired, they only pay for the resources they use. Musenet aims to keep things simple and flexible, which is reflected in their pricing structure. This also ensures that customers are only paying for the services they need.

Overall, Musenet's pricing structure is designed to be customer-friendly, while also allowing them to create high-quality, AI-generated content that meets their specific needs without breaking the bank.