Qwen-Music技术报告

摘要

本报告介绍Qwen-Music，一款功能强大的音乐生成模型，能够生成具有完整人声演唱、高音乐表现力和高保真度的歌曲。Qwen-Music支持两项核心任务：文本到音乐生成（根据文本描述、歌词和音乐属性创作全新歌曲）以及翻唱生成（以不同风格和人声特征重新演绎现有歌曲）。在架构上，Qwen-Music集成了三个核心组件：Qwen-Music-Tokenizer、Qwen-Music-LLM和Qwen-Music-Render。Qwen-Music-Tokenizer将音频压缩为25 Hz的单码本流音乐语义标记，保留用于大语言模型预测的语义和旋律信息。基于这些标记，Qwen-Music-LLM执行自回归音乐语义建模，其关键创新在于基于旋律标记的思维链机制——在全曲生成前规划旋律，从而提升创意性、音乐性、结构连贯性，并支持基于参考音频的旋律克隆。为解决离散语义标记的保真度限制，Qwen-Music-Render执行生成式立体声渲染，丰富声学细节并生成高保真立体声波形。最后，我们在超过500万小时的多语言音乐数据（涵盖数百种语言）上训练Qwen-Music-LLM。首先应用质量感知的预训练课程，然后采用渐进式后训练，包括监督式初始化、离线DPO和在线GSPO，以进一步提升音乐性和指令遵循能力。在600条中英文提示词上，Qwen-Music在16项客观音乐性和音频质量指标中的13项上达到了最优结果。专业评估者相比主流商用系统也更偏好Qwen-Music。在翻唱生成任务中，Qwen-Music比主流商用系统更准确地保留参考旋律。

English

In this report, we introduce Qwen-Music, a powerful music generation model capable of producing highly musical and high-fidelity songs with complete vocal singing. Qwen-Music supports two core tasks: Text to Music Generation, which create entirely new songs from text descriptions, lyrics, and musical attributes, and Cover Song Generation, which reinterprets existing songs with different styles and vocal characteristics. Architecturally, Qwen-Music integrates three core components: Qwen-Music-Tokenizer, Qwen-Music-LLM, and Qwen-Music-Render. Qwen-Music-Tokenizer compresses audio into a 25 Hz single-codebook stream of Music Semantic Tokens that preserve semantic and melodic information for LLM prediction. Based on these tokens, Qwen-Music-LLM performs autoregressive music semantic modeling, with a key novelty being a melody-token-based chain-of-thought (Melody-CoT) mechanism that plans melodies before full-song generation, improving creativity, musicality, structural coherence, and reference-audio-based melody cloning. To overcome the fidelity limitations of discrete semantic tokens, Qwen-Music-Render performs generative stereo rendering, enriching acoustic details and producing high-fidelity stereo waveforms. Finally, we train Qwen-Music-LLM on more than 5 million hours of multilingual music data covering hundreds of languages. We first apply quality-aware pre-training curriculum, then use progressive post-training, comprising supervised initialization, offline DPO, and online GSPO, to further improve musicality and instruction-following ability. Across 600 Chinese and English prompts, Qwen-Music achieves state-of-the-art results in 13 of 16 objective musicality and audio-quality metrics. Professional evaluators also prefer Qwen-Music over leading proprietary systems. For cover song generation, Qwen-Music preserves reference melodies more accurately than leading proprietary systems.