ACE Step V1 Chinese Rap LoRA

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

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

• Improved accuracy of Chinese pronunciation.
• Enhanced adherence to hip-hop and electronic styles.
• Greater diversity in hip-hop vocal expressions.

Check out audio examples here: https://ace-step.github.io/#RapMachine

🚀 Quick Start

✨ Features

This model can be used to:

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 more details.
   • Add experimental flavors (e.g., underground, street culture).
4. Fine-tune using the following 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

Although a Chinese rap LoRA may seem niche for non-Chinese communities, through such projects, we consistently demonstrate that ACE-step, as a music generation foundation model, has boundless potential. It not only improves pronunciation in one language but also 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 drive the evolution of musical history forward.

📚 Documentation

ACE-Step: A Step Towards Music Generation Foundation Model

Model Description

ACE-Step is a novel open-source foundation model for music generation. It 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.
• Editing 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), with limited style adherence and a 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 the originality of generated works.
• Disclose AI involvement.
• Respect cultural elements and copyrights.
• Avoid generating harmful content.

Model Details

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.