Tempolor v4.7 - Quwan Technology's Flagship AI Music Generation Large Model

What is Tempolor v4.7

Tempolor v4.7 is Quwan Technology's flagshipAI music generationlarge model, adopting a fourth-generation hierarchical progressive architecture, supporting 48kHz dual-channel high-quality output. The model focuses on controllable generation and secondary creation, enabling genre rewriting, cover songs, and fine-grained audio editing while preserving the core melody, moving from one-click song creation to modify-as-you-wish, while opening up to developers and ordinary users through OpenAPI and intelligent agentsTunee.

Main Features of Tempolor v4.7

• Remix Rewriting: Upload existing music material, preserve the core melody and motif, and redesign the genre, arrangement, and vocal performance.
• Cover Songs: Replace lyrics and vocal timbre, maintaining the original song's recognizability through "rapid motif regression" technology to avoid melody misalignment.
• Fine-Grained Audio Editing: Precisely adjust instrument timbre, harmony, vocals, and backing vocals through prompts.
• Reference Audio Style Transfer: Upload reference audio to generate new songs in the same style, while optimizing audio leakage to output more independent and clean sound.
• Multimodal Generation: Supports generating music from text, images, videos, and other input methods.
• Conversational Creation: Integrates the Tunee intelligent agent to complete music creation and visual generation through conversation.

Technical Principles of Tempolor v4.7

• Fourth-Generation Hierarchical Progressive Architecture: Splits music generation into three layers: musicality, semantics, and acoustics, completed sequentially from coarse to fine, first determining the theme motif and overall structure, then generating specific content, and finally adding vocal and instrumental details.
• Rapid Motif Regression: In cover scenarios, the model appropriately rewrites based on the new lyrics structure while converging back to the main melody at key positions, balancing creative freedom and original song recognizability.
• Reference Audio Leakage Optimization: When learning the original melody structure, the model reduces direct copying of the original vocals and timbre.
• Tag System Restructuring: Readjusts training data tags to improve recognition and response accuracy for instrument timbre, harmony, vocals, and other information.
• Continuous Acoustic Representation: Adopts a continuous audio representation scheme combining masked autoregression and diffusion models, balancing generation quality and local modification capability.

How to Use Tempolor v4.7

• Ordinary Users: VisitTuneeintelligent agent, and input creative requirements through conversation or uploading materials.
• Developers/Enterprises: Visit the Tianpule OpenAPI platform, call V4.7 capabilities through interfaces, and integrate into your own applications or workflows.
• Creators: Upload reference audio or input prompts, choose Remix, Cover, or completely new generation modes, and continuously modify and iterate on the output results.

Core Advantages of Tempolor v4.7

• Strong Controllability: The hierarchical architecture makes prompt responses more stable, with higher determinism in output results for the same input, reducing the cost of repeated generation and screening.
• Leading Secondary Creation: Domestic leading Remix rewriting and cover song capabilities, supporting free adjustment while preserving the core motif.
• Excellent sound quality: 48kHz dual-channel output, improved spatial separation of drums, bass, harmonies, and vocals, and more delicate emotional expression in slow-tempo styles.
• Multimodal input: The world's first large music generation model that supports text/image/audio/video multimodal input.
• Performance improvement: Overall performance improved by about 20%, with streaming generation first-packet response of about 20 seconds.

Comparison of Tempolor v4.7 with similar competitors

Application scenarios of Tempolor v4.7

• Short video soundtrack: Creators quickly generate or adapt BGM matching the video's mood, with support for multiple fine-tunings until satisfied.
• Advertising and film/TV: Generate customized music for brand advertisements and film/TV clips, and quickly adapt to different version requirements through Remix.
• Music education: Students upload practice pieces, and AI assists in adapting them to different styles, lowering the barrier to learning arrangement
• Independent musician creation: Musicians use AI-generated results as creative material, continuously revising and processing them to form complete works
• Game audio: Generate theme music for game scenes, and quickly adapt multilingual versions of theme songs through the Cover function