NotaGen is an innovative symbolic music generation model that enhances music generation quality through three stages: pre-training, fine-tuning, and reinforcement learning. Utilizing large language model technology, it can generate high-quality classical music scores, bringing new possibilities to music creation. The model's main advantages include efficient generation, diverse styles, and high-quality output. It is applicable in music creation, education, and research, with broad application prospects.

DiffRhythm is an innovative music generation model that utilizes latent diffusion technology to achieve fast and high-quality full-song generation. This technology breaks through the limitations of traditional music generation methods, eliminating the need for complex multi-stage architectures and cumbersome data preparation. Only lyrics and style prompts are needed to generate a complete song up to 4 minutes and 45 seconds in a short time. Its autoregressive structure ensures fast inference speed, greatly improving the efficiency and scalability of music creation. The model was jointly developed by the Audio, Speech, and Language Processing group (ASLP@NPU) at Northwestern Polytechnical University and the Big Data Institute of the Chinese University of Hong Kong (Shenzhen), aiming to provide a simple, efficient, and creative solution for music creation.

InspireMusic is an AIGC toolkit and model framework focused on music, songs, and audio generation, developed using PyTorch. It achieves high-quality music generation through audio tokenization and decoding processes, combining autoregressive transformers and conditional flow matching models. This toolkit supports multiple conditional controls such as text prompts, music styles, and structures, enabling the generation of high-quality audio at both 24kHz and 48kHz, as well as supporting long audio generation. Additionally, it offers convenient fine-tuning and inference scripts for users to adjust the model according to their needs. The open-source nature of InspireMusic aims to empower everyday users to enhance sound effects in their research through music creation.

YuE is a groundbreaking open-source foundational model series designed for music generation, capable of transforming lyrics into complete songs. It generates full songs featuring captivating vocals and accompanying instrumentals, supporting various music styles. Based on deep learning technology, it boasts powerful generating capability and flexibility, providing music creators with robust tool support. Its open-source nature also allows researchers and developers to conduct further research and development.

YuE is an open-source music generation model developed by the Hong Kong University of Science and Technology and a multimodal art projection team. It can generate full songs up to 5 minutes long, including vocals and accompaniment, based on given lyrics. The model addresses the complex issues of lyric-to-song generation through various technological innovations, such as semantic-enhanced audio taggers, dual-tagging technology, and lyrical chain thinking. The main advantages of YuE include its ability to produce high-quality musical works and support for multiple languages and music styles, offering strong scalability and controllability. The model is currently free and open-source, aimed at promoting the advancement of music generation technology.

RapBank is a dataset focused on rap music, collecting a large number of rap songs from YouTube and offering a meticulously designed data preprocessing workflow. This dataset is significant for the field of music generation as it provides a wealth of rap music content that can be used for training and testing music generation models. The RapBank dataset includes 94,164 song links, successfully downloaded 92,371 songs, totaling 5,586 hours of music, covering 84 different languages, with English songs accounting for the majority, approximately two-thirds of the total duration.

MelodyFlow is a high-fidelity music generation and editing model based on text control. It utilizes continuous latent representation sequences to avoid information loss associated with discrete representations. Built on a diffusion transformer architecture and trained with flow matching objectives, the model can generate and edit a diverse range of high-quality stereo samples while maintaining the simplicity of text descriptions. MelodyFlow also explores a novel regularized latent inversion method for text-guided editing in zero-shot testing, demonstrating its superior performance across various music editing prompts. The model has been evaluated using objective and subjective metrics, confirming that it matches the quality and efficiency of established benchmarks in standard text-to-music evaluations while surpassing previous state-of-the-art techniques in music editing.

Wondera is an AI music collaboration tool that can co-create music with users, offering creative inspiration and music production support. The product background is designed to enable users to collaborate with AI on creating unique musical works. It is suitable for music creators and enthusiasts. The price is free.

Lami AI Music Generator is an advanced AI tool that quickly converts text into original music and supports commercial use. It offers features like AI vocal elimination and audio track separation to lower the barriers to music creation.

LofiLab is a web application that allows you to explore and enjoy various forms of ambient music, music, and visual effects. With an intuitive interface and powerful features, you can create personalized, immersive experiences tailored to your preferences.

The AI rap generator is a tool that uses AI technology to turn text into rap music, capable of quickly producing unique rap music works. Its advantages include quick creation, helping to overcome creative blocks, providing free music, etc.

Lyria 2 is the latest music generation model capable of creating high-fidelity music in various styles, suitable for complex musical works. This model not only provides powerful tools for music creators but also drives the development of music generation technology, improving creative efficiency. Lyria 2 aims to make music creation simpler and more accessible, providing flexible creative support for both professional musicians and amateurs.

Mureka is an AI music generation platform designed to help users transform text or prompts into high-quality musical works. The product uses intelligent algorithms to process users' lyrics and music style choices, generating professionally produced songs ideal for music creators and enthusiasts. Mureka offers unlimited creations and guarantees that the generated music is royalty-free and suitable for any commercial use.

Stella is an AI tool designed for modern brands and marketers, aiming to improve marketing efficiency through efficient content generation and automation. It analyzes users' social media posts, competitor dynamics, and trends to generate brand-consistent content and supports multi-platform content scheduling. Developed by top engineers with MIT backgrounds and experienced marketers, this tool is suitable for businesses and brands of all sizes, helping them save time and costs while improving content quality and user engagement.

NotaGen is an innovative symbolic music generation model that enhances music generation quality through three stages: pre-training, fine-tuning, and reinforcement learning. Utilizing large language model technology, it can generate high-quality classical music scores, bringing new possibilities to music creation. The model's main advantages include efficient generation, diverse styles, and high-quality output. It is applicable in music creation, education, and research, with broad application prospects.

DiffRhythm is an innovative music generation model that utilizes latent diffusion technology to achieve fast and high-quality full-song generation. This technology breaks through the limitations of traditional music generation methods, eliminating the need for complex multi-stage architectures and cumbersome data preparation. Only lyrics and style prompts are needed to generate a complete song up to 4 minutes and 45 seconds in a short time. Its autoregressive structure ensures fast inference speed, greatly improving the efficiency and scalability of music creation. The model was jointly developed by the Audio, Speech, and Language Processing group (ASLP@NPU) at Northwestern Polytechnical University and the Big Data Institute of the Chinese University of Hong Kong (Shenzhen), aiming to provide a simple, efficient, and creative solution for music creation.