起始日期 | Start Date
7/8/2026
实现PR | Implementation PR
Executive Summary
While Qwen-Audio has established a strong foundation in audio understanding, there is a critical gap in high-fidelity audio generation. The current market leader, Suno, has set aggressive ELO benchmarks that the open-weight community urgently needs to match and exceed. We propose the development of Qwen-Music, a dedicated generative model built upon the Qwen2-Audio architecture, specifically optimized to surpass existing proprietary models in musical coherence, lyrical alignment, and structural fidelity.
Benchmark Targets & Competitive Analysis
To be viable, Qwen-Music must target the following public benchmark scores (ELO):
Suno v3.5: 845 ELO
Suno v4: 992 ELO
Target for Qwen-Music (v1.0): >1050 ELO
Stretch Goal (vs Suno v4.5): >1150 ELO
Achieving these scores requires moving beyond basic text-to-audio and implementing advanced conditional generation controls that current open models lack.
Required Core Functionalities
To compete with and surpass Suno v4/v4.5, Qwen-Music must natively support the following input modalities and generation capabilities:
Advanced Lyric-to-Melody Alignment: Precise phonetic and prosodic mapping of user-provided lyrics to generated melodies, ensuring natural vocal delivery and rhythmic accuracy without hallucination.
Granular Style Conditioning: Support for multi-tag style prompts (e.g., "cinematic orchestral, 120 BPM, minor key, female vocals") with strict adherence to genre-specific instrumentation and production quality.
Audio Reference Inpainting/Continuation: Ability to accept an uploaded audio reference (max 2:50 duration) as a structural and timbral anchor. The model must continue or vary the reference while maintaining key, tempo, and instrumental identity.
Natural Language Structural Instructions: Support for complex, multi-step generation instructions (e.g., "Start with a piano intro, build to a chorus at 0:45, add drums at 1:10, end with a fade-out") to enable precise compositional control.
High-Fidelity Output: Minimum 44.1kHz / 16-bit stereo output with low noise floor and professional-grade mastering characteristics.
Strategic Rationale
Developing Qwen-Music would not only close the generative gap in the Qwen ecosystem but also establish Alibaba as a leader in open-weight creative AI. The combination of Qwen’s strong language understanding with dedicated music generation training data could yield a model that outperforms closed-source alternatives in lyrical intelligence and instruction following—key differentiators where Suno still shows weaknesses.
Call to Action
We urge the Qwen team to prioritize this development track. The open-source community is ready to contribute datasets, evaluation frameworks, and fine-tuning efforts to make Qwen-Music a reality. Let’s build the first open model that doesn’t just compete with Suno—but redefines what’s possible in AI music generation.
相关Issues | Reference Issues
摘要 | Summary
Qwen-Music is a proposed next-generation generative audio model built upon the Qwen2-Audio architecture, specifically engineered to surpass current industry benchmarks in AI music generation. Targeting an ELO score of >1050 (exceeding Suno v4’s 992 and v3.5’s 845), this model bridges the gap between audio understanding and high-fidelity musical creation within the open-weight ecosystem.
Unlike generic text-to-audio models, Qwen-Music introduces advanced conditional generation capabilities essential for professional music production. Key features include precise lyric-to-melody alignment, granular multi-tag style conditioning, and audio reference inpainting (supporting uploads up to 2:50 minutes) for structural continuity. Additionally, it supports complex natural language instructions for dynamic compositional control, enabling users to dictate song structure, instrumentation, and transitions with unprecedented accuracy.
By leveraging Qwen’s superior language understanding alongside specialized music training data, Qwen-Music aims to redefine open-source audio generation—delivering studio-quality output that competes with and exceeds proprietary leaders like Suno v4.5 while remaining fully accessible for research, development, and creative applications.
基本示例 | Basic Example
Generate an upbeat synthwave track with female vocals.
Lyrics: "Neon lights are calling me, through the digital sea..."
Style tags: 120 BPM, retro 80s, driving bassline, ethereal pads
Reference audio: [uploaded_file.wav] (2:30 min) - Use this as intro and maintain key/tempo
Instructions: Start with the reference audio for 30 seconds, then transition into full vocals at 0:45. Add a guitar solo at 1:50. Fade out after 2:40.Qwen-Music Output:
✅ Generates 2:45 high-fidelity stereo track (44.1kHz/16-bit)
✅ Seamlessly continues from reference audio while preserving original key (A minor) and tempo (120 BPM)
✅ Vocals align perfectly with provided lyrics, natural prosody and rhythm
✅ Style tags accurately reflected: prominent synth bass, 80s drum machine, atmospheric pads
✅ Structural instructions followed precisely: intro → vocal entry at 0:45 → guitar solo at 1:50 → fade-out at 2:40
✅ ELO Score: 1087 (surpasses Suno v4 benchmark of 992)
缺陷 | Drawbacks
️ Key Challenges & Mitigation Strategies for Qwen-Music
- Computational Cost & Training Complexity
Challenge: High-fidelity music generation requires significantly more compute than audio understanding. Training a model to surpass Suno v4 (992 ELO) demands massive GPU clusters and specialized datasets (e.g., MusicCaps, AudioSet), increasing R&D costs exponentially.
Mitigation: Leverage Qwen2-Audio’s existing architecture as a foundation to reduce pre-training overhead. Use parameter-efficient fine-tuning (LoRA/QLoRA) on curated music datasets. Partner with academic institutions for shared compute resources.
- Copyright & Licensing Risks
Challenge: Training on copyrighted music exposes Alibaba to legal liability. Suno faces ongoing lawsuits; an open-weight model could amplify these risks if not carefully managed.
Mitigation: Train exclusively on licensed/royalty-free datasets (e.g., Free Music Archive, Jamendo). Implement watermarking and provenance tracking in generated outputs. Publish clear usage guidelines prohibiting commercial use of unlicensed derivatives.
- Lyrical Coherence vs. Musical Quality Trade-off
Challenge: Optimizing for both lyrical alignment and musical fidelity often leads to compromises. Models may generate perfect melodies but nonsensical lyrics, or vice versa.
Mitigation: Adopt a two-stage training pipeline: Stage 1 focuses on musical structure/instrumentation; Stage 2 fine-tunes on lyric-melody pairs with prosody-aware loss functions. Integrate Qwen’s language model as a separate lyric-refinement module.
- Reference Audio Limitations
Challenge: The 2:50-minute reference audio constraint may be insufficient for complex compositions. Short references can lead to mode collapse or failure to capture long-term structural dependencies.
Mitigation: Implement hierarchical attention mechanisms to extract global structure from short references. Allow users to upload multiple reference clips (e.g., intro + chorus) for better contextual grounding. Add a "structure sketch" input (text-based timeline) as a fallback.
- Benchmark Validity & Subjectivity
Challenge: ELO scores are inherently subjective and prone to bias. A model scoring >1050 ELO may still fail in real-world production scenarios due to overfitting to benchmark preferences.
Mitigation: Supplement ELO with objective metrics (FAD, KL-divergence, chroma similarity). Establish a diverse evaluation panel including musicians, producers, and listeners across genres. Release transparent benchmark methodology and raw evaluation data.
- Open-Weight Abuse Potential
Challenge: Releasing a powerful music generator openly enables misuse (deepfake vocals, copyright infringement, spam content). This contradicts Alibaba’s responsible AI principles.
Mitigation: Implement mandatory safety filters for vocal cloning and explicit content. Require user authentication for API access. Provide detection tools for identifying AI-generated music. Start with a gated release (research-only) before full open-weight deployment.
未解决问题 | Unresolved questions
¡Ex️ Key Challenges & Mitigation Strategies for Qwen-Music
- Computational Cost & Training Complexity
Challenge: High-fidelity music generation requires significantly more compute than audio understanding. Training a model to surpass Suno v4 (992 ELO) demands massive GPU clusters and specialized datasets (e.g., MusicCaps, AudioSet), increasing R&D costs exponentially.
Mitigation: Leverage Qwen2-Audio’s existing architecture as a foundation to reduce pre-training overhead. Use parameter-efficient fine-tuning (LoRA/QLoRA) on curated music datasets. Partner with academic institutions for shared compute resources.
- Copyright & Licensing Risks
Challenge: Training on copyrighted music exposes Alibaba to legal liability. Suno faces ongoing lawsuits; an open-weight model could amplify these risks if not carefully managed.
Mitigation: Train exclusively on licensed/royalty-free datasets (e.g., Free Music Archive, Jamendo). Implement watermarking and provenance tracking in generated outputs. Publish clear usage guidelines prohibiting commercial use of unlicensed derivatives.
- Lyrical Coherence vs. Musical Quality Trade-off
Challenge: Optimizing for both lyrical alignment and musical fidelity often leads to compromises. Models may generate perfect melodies but nonsensical lyrics, or vice versa.
Mitigation: Adopt a two-stage training pipeline: Stage 1 focuses on musical structure/instrumentation; Stage 2 fine-tunes on lyric-melody pairs with prosody-aware loss functions. Integrate Qwen’s language model as a separate lyric-refinement module.
- Reference Audio Limitations
Challenge: The 2:50-minute reference audio constraint may be insufficient for complex compositions. Short references can lead to mode collapse or failure to capture long-term structural dependencies.
Mitigation: Implement hierarchical attention mechanisms to extract global structure from short references. Allow users to upload multiple reference clips (e.g., intro + chorus) for better contextual grounding. Add a "structure sketch" input (text-based timeline) as a fallback.
- Benchmark Validity & Subjectivity
Challenge: ELO scores are inherently subjective and prone to bias. A model scoring >1050 ELO may still fail in real-world production scenarios due to overfitting to benchmark preferences.
Mitigation: Supplement ELO with objective metrics (FAD, KL-divergence, chroma similarity). Establish a diverse evaluation panel including musicians, producers, and listeners across genres. Release transparent benchmark methodology and raw evaluation data.
- Open-Weight Abuse Potential
Challenge: Releasing a powerful music generator openly enables misuse (deepfake vocals, copyright infringement, spam content). This contradicts Alibaba’s responsible AI principles.
Mitigation: Implement mandatory safety filters for vocal cloning and explicit content. Require user authentication for API access. Provide detection tools for identifying AI-generated music. Start with a gated release (research-only) before full open-weight deployment.
起始日期 | Start Date
7/8/2026
实现PR | Implementation PR
Executive Summary
While Qwen-Audio has established a strong foundation in audio understanding, there is a critical gap in high-fidelity audio generation. The current market leader, Suno, has set aggressive ELO benchmarks that the open-weight community urgently needs to match and exceed. We propose the development of Qwen-Music, a dedicated generative model built upon the Qwen2-Audio architecture, specifically optimized to surpass existing proprietary models in musical coherence, lyrical alignment, and structural fidelity.
Benchmark Targets & Competitive Analysis
To be viable, Qwen-Music must target the following public benchmark scores (ELO):
Suno v3.5: 845 ELO
Suno v4: 992 ELO
Target for Qwen-Music (v1.0): >1050 ELO
Stretch Goal (vs Suno v4.5): >1150 ELO
Achieving these scores requires moving beyond basic text-to-audio and implementing advanced conditional generation controls that current open models lack.
Required Core Functionalities
To compete with and surpass Suno v4/v4.5, Qwen-Music must natively support the following input modalities and generation capabilities:
Advanced Lyric-to-Melody Alignment: Precise phonetic and prosodic mapping of user-provided lyrics to generated melodies, ensuring natural vocal delivery and rhythmic accuracy without hallucination.
Granular Style Conditioning: Support for multi-tag style prompts (e.g., "cinematic orchestral, 120 BPM, minor key, female vocals") with strict adherence to genre-specific instrumentation and production quality.
Audio Reference Inpainting/Continuation: Ability to accept an uploaded audio reference (max 2:50 duration) as a structural and timbral anchor. The model must continue or vary the reference while maintaining key, tempo, and instrumental identity.
Natural Language Structural Instructions: Support for complex, multi-step generation instructions (e.g., "Start with a piano intro, build to a chorus at 0:45, add drums at 1:10, end with a fade-out") to enable precise compositional control.
High-Fidelity Output: Minimum 44.1kHz / 16-bit stereo output with low noise floor and professional-grade mastering characteristics.
Strategic Rationale
Developing Qwen-Music would not only close the generative gap in the Qwen ecosystem but also establish Alibaba as a leader in open-weight creative AI. The combination of Qwen’s strong language understanding with dedicated music generation training data could yield a model that outperforms closed-source alternatives in lyrical intelligence and instruction following—key differentiators where Suno still shows weaknesses.
Call to Action
We urge the Qwen team to prioritize this development track. The open-source community is ready to contribute datasets, evaluation frameworks, and fine-tuning efforts to make Qwen-Music a reality. Let’s build the first open model that doesn’t just compete with Suno—but redefines what’s possible in AI music generation.
相关Issues | Reference Issues
摘要 | Summary
Qwen-Music is a proposed next-generation generative audio model built upon the Qwen2-Audio architecture, specifically engineered to surpass current industry benchmarks in AI music generation. Targeting an ELO score of >1050 (exceeding Suno v4’s 992 and v3.5’s 845), this model bridges the gap between audio understanding and high-fidelity musical creation within the open-weight ecosystem.
Unlike generic text-to-audio models, Qwen-Music introduces advanced conditional generation capabilities essential for professional music production. Key features include precise lyric-to-melody alignment, granular multi-tag style conditioning, and audio reference inpainting (supporting uploads up to 2:50 minutes) for structural continuity. Additionally, it supports complex natural language instructions for dynamic compositional control, enabling users to dictate song structure, instrumentation, and transitions with unprecedented accuracy.
By leveraging Qwen’s superior language understanding alongside specialized music training data, Qwen-Music aims to redefine open-source audio generation—delivering studio-quality output that competes with and exceeds proprietary leaders like Suno v4.5 while remaining fully accessible for research, development, and creative applications.
基本示例 | Basic Example
Generate an upbeat synthwave track with female vocals.
Lyrics: "Neon lights are calling me, through the digital sea..."
Style tags: 120 BPM, retro 80s, driving bassline, ethereal pads
Reference audio: [uploaded_file.wav] (2:30 min) - Use this as intro and maintain key/tempo
Instructions: Start with the reference audio for 30 seconds, then transition into full vocals at 0:45. Add a guitar solo at 1:50. Fade out after 2:40.Qwen-Music Output:
✅ Generates 2:45 high-fidelity stereo track (44.1kHz/16-bit)
✅ Seamlessly continues from reference audio while preserving original key (A minor) and tempo (120 BPM)
✅ Vocals align perfectly with provided lyrics, natural prosody and rhythm
✅ Style tags accurately reflected: prominent synth bass, 80s drum machine, atmospheric pads
✅ Structural instructions followed precisely: intro → vocal entry at 0:45 → guitar solo at 1:50 → fade-out at 2:40
✅ ELO Score: 1087 (surpasses Suno v4 benchmark of 992)
缺陷 | Drawbacks
️ Key Challenges & Mitigation Strategies for Qwen-Music
Challenge: High-fidelity music generation requires significantly more compute than audio understanding. Training a model to surpass Suno v4 (992 ELO) demands massive GPU clusters and specialized datasets (e.g., MusicCaps, AudioSet), increasing R&D costs exponentially.
Mitigation: Leverage Qwen2-Audio’s existing architecture as a foundation to reduce pre-training overhead. Use parameter-efficient fine-tuning (LoRA/QLoRA) on curated music datasets. Partner with academic institutions for shared compute resources.
Challenge: Training on copyrighted music exposes Alibaba to legal liability. Suno faces ongoing lawsuits; an open-weight model could amplify these risks if not carefully managed.
Mitigation: Train exclusively on licensed/royalty-free datasets (e.g., Free Music Archive, Jamendo). Implement watermarking and provenance tracking in generated outputs. Publish clear usage guidelines prohibiting commercial use of unlicensed derivatives.
Challenge: Optimizing for both lyrical alignment and musical fidelity often leads to compromises. Models may generate perfect melodies but nonsensical lyrics, or vice versa.
Mitigation: Adopt a two-stage training pipeline: Stage 1 focuses on musical structure/instrumentation; Stage 2 fine-tunes on lyric-melody pairs with prosody-aware loss functions. Integrate Qwen’s language model as a separate lyric-refinement module.
Challenge: The 2:50-minute reference audio constraint may be insufficient for complex compositions. Short references can lead to mode collapse or failure to capture long-term structural dependencies.
Mitigation: Implement hierarchical attention mechanisms to extract global structure from short references. Allow users to upload multiple reference clips (e.g., intro + chorus) for better contextual grounding. Add a "structure sketch" input (text-based timeline) as a fallback.
Challenge: ELO scores are inherently subjective and prone to bias. A model scoring >1050 ELO may still fail in real-world production scenarios due to overfitting to benchmark preferences.
Mitigation: Supplement ELO with objective metrics (FAD, KL-divergence, chroma similarity). Establish a diverse evaluation panel including musicians, producers, and listeners across genres. Release transparent benchmark methodology and raw evaluation data.
Challenge: Releasing a powerful music generator openly enables misuse (deepfake vocals, copyright infringement, spam content). This contradicts Alibaba’s responsible AI principles.
Mitigation: Implement mandatory safety filters for vocal cloning and explicit content. Require user authentication for API access. Provide detection tools for identifying AI-generated music. Start with a gated release (research-only) before full open-weight deployment.
未解决问题 | Unresolved questions
¡Ex️ Key Challenges & Mitigation Strategies for Qwen-Music
Challenge: High-fidelity music generation requires significantly more compute than audio understanding. Training a model to surpass Suno v4 (992 ELO) demands massive GPU clusters and specialized datasets (e.g., MusicCaps, AudioSet), increasing R&D costs exponentially.
Mitigation: Leverage Qwen2-Audio’s existing architecture as a foundation to reduce pre-training overhead. Use parameter-efficient fine-tuning (LoRA/QLoRA) on curated music datasets. Partner with academic institutions for shared compute resources.
Challenge: Training on copyrighted music exposes Alibaba to legal liability. Suno faces ongoing lawsuits; an open-weight model could amplify these risks if not carefully managed.
Mitigation: Train exclusively on licensed/royalty-free datasets (e.g., Free Music Archive, Jamendo). Implement watermarking and provenance tracking in generated outputs. Publish clear usage guidelines prohibiting commercial use of unlicensed derivatives.
Challenge: Optimizing for both lyrical alignment and musical fidelity often leads to compromises. Models may generate perfect melodies but nonsensical lyrics, or vice versa.
Mitigation: Adopt a two-stage training pipeline: Stage 1 focuses on musical structure/instrumentation; Stage 2 fine-tunes on lyric-melody pairs with prosody-aware loss functions. Integrate Qwen’s language model as a separate lyric-refinement module.
Challenge: The 2:50-minute reference audio constraint may be insufficient for complex compositions. Short references can lead to mode collapse or failure to capture long-term structural dependencies.
Mitigation: Implement hierarchical attention mechanisms to extract global structure from short references. Allow users to upload multiple reference clips (e.g., intro + chorus) for better contextual grounding. Add a "structure sketch" input (text-based timeline) as a fallback.
Challenge: ELO scores are inherently subjective and prone to bias. A model scoring >1050 ELO may still fail in real-world production scenarios due to overfitting to benchmark preferences.
Mitigation: Supplement ELO with objective metrics (FAD, KL-divergence, chroma similarity). Establish a diverse evaluation panel including musicians, producers, and listeners across genres. Release transparent benchmark methodology and raw evaluation data.
Challenge: Releasing a powerful music generator openly enables misuse (deepfake vocals, copyright infringement, spam content). This contradicts Alibaba’s responsible AI principles.
Mitigation: Implement mandatory safety filters for vocal cloning and explicit content. Require user authentication for API access. Provide detection tools for identifying AI-generated music. Start with a gated release (research-only) before full open-weight deployment.