🎤 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

Generate higher-quality Chinese songs
Create superior hip-hop tracks
Blend with other genres to:
- Produce music with better vocal quality and detail
- Add experimental flavors (e.g., underground, street culture)
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

Device	27 Steps	60 Steps
NVIDIA A100	27.27x	12.27x
RTX 4090	34.48x	15.63x
RTX 3090	12.76x	6.48x
M2 Max	2.27x	1.03x

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:

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.

ACE-Step
/

ACE-Step-v1-chinese-rap-LoRA