stable audio 2
InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation
Zhang, Chong, Ma, Yukun, Chen, Qian, Wang, Wen, Zhao, Shengkui, Pan, Zexu, Wang, Hao, Ni, Chongjia, Nguyen, Trung Hieu, Zhou, Kun, Jiang, Yidi, Tan, Chaohong, Gao, Zhifu, Du, Zhihao, Ma, Bin
We introduce InspireMusic, a framework integrated super resolution and large language model for high-fidelity long-form music generation. A unified framework generates high-fidelity music, songs, and audio, which incorporates an autoregressive transformer with a super-resolution flow-matching model. This framework enables the controllable generation of high-fidelity long-form music at a higher sampling rate from both text and audio prompts. Our model differs from previous approaches, as we utilize an audio tokenizer with one codebook that contains richer semantic information, thereby reducing training costs and enhancing efficiency. This combination enables us to achieve high-quality audio generation with long-form coherence of up to $8$ minutes. Then, an autoregressive transformer model based on Qwen 2.5 predicts audio tokens. Next, we employ a super-resolution flow-matching model to generate high-sampling rate audio with fine-grained details learned from an acoustic codec model. Comprehensive experiments show that the InspireMusic-1.5B-Long model has a comparable performance to recent top-tier open-source systems, including MusicGen and Stable Audio 2.0, on subjective and objective evaluations. The code and pre-trained models are released at https://github.com/FunAudioLLM/InspireMusic.
- Media > Music (1.00)
- Leisure & Entertainment (1.00)
Stable Audio Open
Evans, Zach, Parker, Julian D., Carr, CJ, Zukowski, Zack, Taylor, Josiah, Pons, Jordi
Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.
Stability AI's audio generator can now crank out 3 minute 'songs'
Stability AI just unveiled Stable Audio 2.0, an upgraded version of its music-generation platform. This system lets users create up to three minutes of audio via text prompt. Just imagine the fake birthday song you could make in the style of that one Rob Thomas/Santana track. The tool is free and publicly available through the company's website, so have at it. Introducing Stable Audio 2.0 – a new model capable of producing high-quality, full tracks with coherent musical structure up to three minutes long at 44.1 kHz stereo from a single prompt.
- Media > Music (0.53)
- Leisure & Entertainment (0.53)