标题： MusicGen-Stem：通过自回归建模进行多音轨音乐生成和编辑 显示英文标题

标题： MusicGen-Stem: Multi-stem music generation and edition through autoregressive modeling

摘要： 虽然大多数音乐生成模型生成的是混合音轨（单声道或立体声），我们提出训练一个包含3个音轨（低音、鼓和其他）的多音轨生成模型，以学习它们之间的音乐依赖关系。 为了做到这一点，我们为每个音轨训练一个专门的压缩算法，将音乐转换为并行的标记流。 然后，我们利用音乐源分离任务的最新进展，在大规模数据集上训练一个多流文本到音乐的语言模型。 最后，由于一种特殊的条件方法，我们的模型能够编辑现有或生成的歌曲中的低音、鼓或其他音轨，以及进行迭代创作（例如，在现有的鼓音轨上生成低音）。 这为音乐生成算法提供了更大的灵活性，并且据我们所知，这是第一个开源的多音轨自回归音乐生成模型，能够进行高质量的生成和连贯的源编辑。 代码和模型权重将被发布，样本可在 https://simonrouard.github.io/musicgenstem/ 上查看。 显示英文摘要

摘要： While most music generation models generate a mixture of stems (in mono or stereo), we propose to train a multi-stem generative model with 3 stems (bass, drums and other) that learn the musical dependencies between them. To do so, we train one specialized compression algorithm per stem to tokenize the music into parallel streams of tokens. Then, we leverage recent improvements in the task of music source separation to train a multi-stream text-to-music language model on a large dataset. Finally, thanks to a particular conditioning method, our model is able to edit bass, drums or other stems on existing or generated songs as well as doing iterative composition (e.g. generating bass on top of existing drums). This gives more flexibility in music generation algorithms and it is to the best of our knowledge the first open-source multi-stem autoregressive music generation model that can perform good quality generation and coherent source editing. Code and model weights will be released and samples are available on https://simonrouard.github.io/musicgenstem/.