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README.md

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----
-license: apache-2.0
-tags:
-- music
-- text2music
-pipeline_tag: text-to-audio
-language:
-- en
-- zh
-- de
-- fr
-- es
-- it
-- pt
-- pl
-- tr
-- ru
-- cs
-- nl
-- ar
-- ja
-- hu
-- ko
-- hi
-library_name: diffusers
----
-
-# 🎤 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
-
-## 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.
-
+---
+license: apache-2.0
+---

config.json

@@ -0,0 +1,15 @@
+{
+    "r": 256,
+    "lora_alpha": 32,
+    "target_modules": [
+        "speaker_embedder",
+        "linear_q",
+        "linear_k",
+        "linear_v",
+        "to_q",
+        "to_k",
+        "to_v",
+        "to_out.0"
+    ],
+    "use_rslora": true
+}