ACE-Step/ACE-Step-v1-chinese-rap-LoRA

🎤 Chinese Rap LoRA for ACE-Step (Rap Machine)

This is a hybrid rap voice model. We meticulously curated Chinese rap/hip-hop datasets for training, with rigorous data cleaning and recaptioning. The results demonstrate:

• —Improved Chinese pronunciation accuracy
• —Enhanced stylistic adherence to hip-hop and electronic genres
• —Greater diversity in hip-hop vocal expressions

Audio Examples see: https://ace-step.github.io/#RapMachine

Usage Guide

1. 1.Generate higher-quality Chinese songs
2. 2.Create superior hip-hop tracks
3. 3.Blend with other genres to:
4. 4.Produce music with better vocal quality and detail
5. 5.Add experimental flavors (e.g., underground, street culture)
6. 6.Fine-tune using these parameters:

Vocal Controls `vocal_timbre`

• —Examples: Bright, dark, warm, cold, breathy, nasal, gritty, smooth, husky, metallic, whispery, resonant, airy, smoky, sultry, light, clear, high-pitched, raspy, powerful, ethereal, flute-like, hollow, velvety, shrill, hoarse, mellow, thin, thick, reedy, silvery, twangy.
• —Describes inherent vocal qualities.

`techniques` (List)

• —Rap styles: mumble rap, chopper rap, melodic rap, lyrical rap, trap flow, double-time rap
• —Vocal FX: auto-tune, reverb, delay, distortion
• —Delivery: whispered, shouted, spoken word, narration, singing
• —Other: ad-libs, call-and-response, harmonized

Community Note

While a Chinese rap LoRA might seem niche for non-Chinese communities, we consistently demonstrate through such projects that ACE-step - as a music generation foundation model - holds boundless potential. It doesn't just improve pronunciation in one language, but spawns new styles.

The universal human appreciation of music is a precious asset. Like abstract LEGO blocks, these elements will eventually combine in more organic ways. May our open-source contributions propel the evolution of musical history forward.

ACE-Step: A Step Towards Music Generation Foundation Model

Model Description

ACE-Step is a novel open-source foundation model for music generation that overcomes key limitations of existing approaches through a holistic architectural design. It integrates diffusion-based generation with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer, achieving state-of-the-art performance in generation speed, musical coherence, and controllability.

Key Features:

• —15× faster than LLM-based baselines (20s for 4-minute music on A100)
• —Superior musical coherence across melody, harmony, and rhythm
• —full-song generation, duration control and accepts natural language descriptions

Uses

Direct Use

ACE-Step can be used for:

• —Generating original music from text descriptions
• —Music remixing and style transfer
• —edit song lyrics

Downstream Use

The model serves as a foundation for:

• —Voice cloning applications
• —Specialized music generation (rap, jazz, etc.)
• —Music production tools
• —Creative AI assistants

Out-of-Scope Use

The model should not be used for:

• —Generating copyrighted content without permission
• —Creating harmful or offensive content
• —Misrepresenting AI-generated music as human-created

How to Get Started

see: https://github.com/ace-step/ACE-Step

Hardware Performance

RTF (Real-Time Factor) shown - higher values indicate faster generation

Limitations

• —Performance varies by language (top 10 languages perform best)
• —Longer generations (>5 minutes) may lose structural coherence
• —Rare instruments may not render perfectly
• —Output Inconsistency: Highly sensitive to random seeds and input duration, leading to varied "gacha-style" results.
• —Style-specific Weaknesses: Underperforms on certain genres (e.g. Chinese rap/zh_rap) Limited style adherence and musicality ceiling
• —Continuity Artifacts: Unnatural transitions in repainting/extend operations
• —Vocal Quality: Coarse vocal synthesis lacking nuance
• —Control Granularity: Needs finer-grained musical parameter control

Ethical Considerations

Users should:

• —Verify originality of generated works
• —Disclose AI involvement
• —Respect cultural elements and copyrights
• —Avoid harmful content generation

Model Details

Developed by: ACE Studio and StepFun Model type: Diffusion-based music generation with transformer conditioning License: Apache 2.0 Resources:

• —Project Page
• —Demo Space
• —GitHub Repository

Citation

@misc{gong2025acestep,
  title={ACE-Step: A Step Towards Music Generation Foundation Model},
  author={Junmin Gong, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
  howpublished={\url{https://github.com/ace-step/ACE-Step}},
  year={2025},
  note={GitHub repository}
}

Acknowledgements

This project is co-led by ACE Studio and StepFun.