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MiniMax Unveils Music3: A New AI Model That Turns Lyrics and Descriptions Into 5-Minute Songs
MiniMax has introduced MiniMax-Music3, a new music generation model capable of creating full songs up to 5 minutes in length. Users simply provide lyrics and a musical description, and the system outputs a 32kHz, 16-bit stereo WAV file.
Powered by a hierarchical autoregressive architecture, the system employs two specialized models working in tandem. The Global LLM, with 8B parameters, predicts the first RVQ codebook frame by frame to capture long-term semantics and structural shifts, initialized from Qwen3-8B. During training, its embedding and output layers are first adapted to musical semantic tokens before jointly modeling all codebooks with the local model. Meanwhile, the Local LLM, with just 0.6B parameters, predicts the remaining acoustic codebooks for each frame, gradually adding fine-grained audio details. This clear division of labor allows a large model to handle the structural skeleton while a smaller one fills in the sound nuances.
According to the developers, the model consistently maintains musical themes, rhythm, vocal identity, and arrangement progression throughout long audio tracks. It accurately preserves all structural elements, including preludes, verses, pre-choruses, choruses, bridges, instrumental interludes, and codas. The model page is now live on GitHub, complete with sample songs for listening.
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MiniMax has introduced MiniMax-Music3, a new music generation model capable of creating full songs up to 5 minutes in length. Users simply provide lyrics and a musical description, and the system outputs a 32kHz, 16-bit stereo WAV file.
Powered by a hierarchical autoregressive architecture, the system employs two specialized models working in tandem. The Global LLM, with 8B parameters, predicts the first RVQ codebook frame by frame to capture long-term semantics and structural shifts, initialized from Qwen3-8B. During training, its embedding and output layers are first adapted to musical semantic tokens before jointly modeling all codebooks with the local model. Meanwhile, the Local LLM, with just 0.6B parameters, predicts the remaining acoustic codebooks for each frame, gradually adding fine-grained audio details. This clear division of labor allows a large model to handle the structural skeleton while a smaller one fills in the sound nuances.
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