Recently, an open source music generation model called NotaGen was officially released, quickly becoming a hot topic in the field of technology and art. With its outstanding classical music generation capabilities as its core highlight, this model also supports pop music creation, demonstrating the unlimited potential of artificial intelligence in the music field.
NotaGen adopts a training method similar to the Big Language Model (LLM) and pre-trains based on a huge database of more than 1.6 million music works to ensure that the music generated reaches professional level. This technological breakthrough not only provides new possibilities for music creation, but also opens up a new path for the integration of artificial intelligence and art.
It is worth mentioning that NotaGen's models and code have been fully open source, providing free use opportunities for music lovers, developers and researchers around the world. This open source not only includes the basic version, but also launched an enhanced model called Notagen-X. This model requires 24G video memory when deployed locally, which is suitable for users who pursue the ultimate experience. However, it should be noted that the audio files generated by NotaGen are not directly playable, but symbolic scores output in ABC and XML file formats, which facilitate users to perform subsequent editing or conversion.
The core advantage of NotaGen lies in its high degree of controllability and professionalism. Users can generate scores that meet their needs by specifying the periodic style of the music (such as Baroque, Classical, or Romantic) or selecting a specific instrument type (such as keyboard or orchestral instrument). To further enhance the quality of classical music generation, the model has also been fine-tuned on 8948 classical scores and a professional dataset covering 152 composers. The test results show that the quality of the music score it generates is close to the professional composition level, with rich details and accurate style.
Open source measures have made NotaGen's application scenarios more extensive. It is an efficient tool for inspiration for professional composers; for amateurs, it lowers the barriers to music creation. Some comments pointed out that NotaGen's method of generating music scores through the prompt condition of "Time-Compassor-Instrument" is not only simple to operate, but also meets diverse creative needs. In addition, its performance in the field of pop music is also highly anticipated, and more music styles may be supported in the future.
The release of NotaGen marks a new stage in AI music generation technology. It not only injects technological vitality into artistic creation, but also promotes community collaboration and innovation through an open source model. Whether it is the inheritance of classical music or the exploration of pop music, NotaGen shows infinite possibilities and deserves continuous attention.