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

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

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.

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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

ACE-Step can be used for:

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

The model serves as a foundation for:

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

The model should not be used for:

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

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

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

• 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

Users should:

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

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

@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}
}

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