---
title: ACE-Step-v1-chinese-rap-LoRA
canonical_url: "https://www.modelscope.ai/models/ACE-Step/ACE-Step-v1-chinese-rap-LoRA"
md_url: "https://www.modelscope.ai/models/ACE-Step/ACE-Step-v1-chinese-rap-LoRA.md"
repository: ACE-Step/ACE-Step-v1-chinese-rap-LoRA
last_updated: 2026-11-09
license: apache-2.0
parameters: 261.9M
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
  - de
  - fr
  - es
  - it
  - pt
  - pl
  - tr
  - ru
  - cs
  - nl
  - ar
  - ja
  - hu
  - ko
  - hi
downloads: 286
stars: 0
tags:
  - music
  - text2music
---

# ACE-Step-v1-chinese-rap-LoRA

> ACE-Step-v1-chinese-rap-LoRA - An open-source model by ACE-Step on ModelScope. 🎤 Chinese Rap LoRA for ACE-Step (Rap Machine)

ACE-Step/ACE-Step-v1-chinese-rap-LoRA is a 261.9M-parameter machine learning model on ModelScope. licensed under apache-2.0.

- **Repository**: ACE-Step/ACE-Step-v1-chinese-rap-LoRA
- **License**: apache-2.0
- **Parameters**: 261.9M
- **Tags**: music, text2music
- **Downloads**: 286
- **Stars**: 0
- **Last updated**: 2026-11-09

Source: https://www.modelscope.ai/models/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. Generate higher-quality Chinese songs
2. Create superior hip-hop tracks
3. Blend with other genres to:
   - Produce music with better vocal quality and detail
   - Add experimental flavors (e.g., underground, street culture)
4. 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

![ACE-Step Framework](https://github.com/ACE-Step/ACE-Step/raw/main/assets/ACE-Step_framework.png)

## 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:**  
- [Project Page](https://ace-step.github.io/)
- [Demo Space](https://huggingface.co/spaces/ACE-Step/ACE-Step)
- [GitHub Repository](https://github.com/ACE-Step/ACE-Step)


## Citation

```bibtex
@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.