Qwen-Music Technical Report
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper introduces Qwen-Music, a state-of-the-art music generation system that effectively bridges semantic composition and acoustic rendering through a novel Melody-CoT mechanism and a high-fidelity generative renderer, achieving superior performance in both objective metrics and human preference tests.
Methodology
The paper presents a comprehensive three-stage architecture for music generation: a semantic tokenizer, an autoregressive language model (LLM), and a generative renderer. The core methodological contributions lie in the specific design choices for handling the complexity of full-song generation. First, the **Qwen-Music-Tokenizer** employs a four-stage training recipe (BestRQ pretraining, causal adaptation, multi-task SFT, and VQ insertion) to create a 25 Hz single-codebook stream. This is a significant engineering contribution, as it balances semantic richness with computational tractability for LLMs. Second, the **Melody-CoT** mechanism is a notable innovation. By explicitly planning melody tokens before generating full-mixture semantic tokens, the model addresses the "composition vs. rendering" mismatch. This chain-of-thought approach allows for better structural coherence and enables cover song generation by conditioning on reference melody tokens while allowing style/timbre changes. The use of relative MIDI representations for melody tokens is a smart design to prevent leakage of absolute pitch/key information that might conflict with style tags. Third, the **Qwen-Music-Render** utilizes a Diffusion Transformer (DiT) conditioned on both semantic tokens and textual descriptions, followed by a novel **Spec-VAE** and **Band-Mode Refiner**. The refiner’s frequency-adaptive correction (phase-only for low, joint for mid, magnitude-only for high) is a sophisticated touch to address specific spectral artifacts. The training pipeline is also rigorous, featuring a quality-graded pre-training curriculum (Q1-Q6 buckets) and a progressive post-training alignment (SFT -> Offline DPO -> Online GSPO), which is state-of-the-art practice for aligning generative models with human preference.
Results & evaluation
The evaluation is extensive and robust. The authors conduct blind A/B preference tests with 50 professional human raters, showing Qwen-Music outperforms leading proprietary systems (Suno V5.5, MiniMax, Mureka) with a 59.1% win rate. They also report performance on the Artificial Analysis leaderboard. Objective evaluations cover 16 metrics across SongBench, SongEval, and AudioBox-Aesthetic, where Qwen-Music leads in 13/16 metrics. The cover song generation evaluation is particularly strong, demonstrating superior melody preservation compared to baselines. The inclusion of a detailed acoustic data quality pipeline for training data filtering (detecting fake stereo, noise floors, cutoffs) adds credibility to the high-fidelity claims. The ablation studies on the renderer and the effectiveness of the Melody-CoT mechanism are well-supported by the results.
Reproducibility
The paper provides detailed architectural specifications, including layer counts, parameter sizes, and training objectives. The tokenizer training stages are clearly defined. The post-training pipeline (SFT, DPO, GSPO) is described with sufficient detail for replication by other research groups. However, the specific "quality-aware" reward models and the exact composition of the 5 million-hour dataset are proprietary and not fully disclosed, which limits exact reproducibility of the pre-training phase. The code for the tokenizer and renderer is not explicitly linked in the provided text, though the Qwen team typically releases code for their models.
Limitations
The paper relies heavily on proprietary baselines (Suno, MiniMax) for comparison, which makes independent verification difficult. The "quality-graded" pre-training assumes the availability of a reliable internal reward model, which may not be generalizable to all settings. The Melody-CoT mechanism, while effective, adds complexity and inference time. The model's performance on non-vocal music or highly abstract musical forms (e.g., free jazz, classical symphonies without lyrics) is less emphasized compared to vocal pop/rock genres, which dominate the training data. The reliance on a single codebook for the tokenizer might limit the expressiveness compared to multi-codebook approaches (like Encodec) in capturing fine-grained timbral details, although the renderer is designed to mitigate this.
Broader impact
Qwen-Music represents a significant step forward in accessible, high-fidelity music generation. By open-sourcing the technical report and potentially the model (implied by the Qwen lineage), it democratizes access to state-of-the-art music AI. The ability to generate cover songs and control musical attributes empowers creators. However, the potential for misuse in generating copyrighted material or deepfake vocals remains a concern, necessitating robust safety filters and watermarking, which are not detailed in this technical report. The high computational requirements for training such large models also contribute to the centralization of AI capabilities. This paper introduces Qwen-Music, a state-of-the-art music generation system that effectively bridges semantic composition and acoustic rendering through a novel Melody-CoT mechanism and a high-fidelity generative renderer, achieving superior performance in both objective metrics and human preference tests.
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