State-of-the-art music generation models like ACE-Step 1.5 are technical marvels, but they carry a known systemic blindspot: they were trained predominantly on Western 4/4 structures. When you prompt foundation models for traditional South ...
State-of-the-art music generation models like ACE-Step 1.5 are technical marvels, but they carry a known systemic blindspot: they were trained predominantly on Western 4/4 structures.
When you prompt foundation models for traditional South Indian acoustic percussion like Parai, Thappu, or high-energy Dappankuthu, the output falls flat. The reason is musical and mathematical: our folk drumming relies on 6/8 compound time signatures (tha-ki-tta, tha-ki-tta) and rapid acoustic transient spikes that standard cross-attention layers fail to resolve on standard binary grids.
To solve this, I fine-tuned and open-sourced SouthIndiaParaiLoRA on top of the ACE-Step 1.5 foundation architecture.
🔬 Engineering & Training Setup
Base Architecture: ACE-Step 1.5 (acestep-v15-x1-base & turbo compatible)
Dataset: 393 studio-quality stems of authentic Parai, Thappu, Thappattam, and Urumi loops (80 – 172 BPM)
Hardware: 30 epochs on an NVIDIA A40 (48GB VRAM) via RunPod
LoRA Configuration: Rank 64, Alpha 128
Optimization: Slashed final loss by 75.5% (from a 1.97 baseline down to 0.48)
🛠️ How to Run in ACE-Step Playground & ComfyUI
The model weights are released in standard .safetensors format:
1. ACE-Step Playground
Clone the engine: ACE-Step-1.5-PlayGround--Engine
Drop adapter_model.safetensors and adapter_config.json into:
checkpoints/SouthIndiaParaiLoRA/final/
Launch the WebUI and select SouthIndiaParaiLoRA from the LoRA dropdown.
2. ComfyUI Workflow
Load adapter_model.safetensors directly into your standard ACE-Step 1.5 LoRA loader node.
🎯 Example Prompt
Prompt: High energy South Indian dappankuthu street festival drumming, massive thappu ensemble, authentic 6/8 compound folk rhythm at 145 BPM, urumi and parai dynamic groove
Lyrics: [Instrumental]
LoRA Scale: 0.85
🎧 Model Weights & 7 Audio Demos
The adapter weights and 7 pre-rendered audio demos across different tempos and rhythmic variations are available under Apache 2.0 on Hugging Face:
👉 [Hugging Face Model Card & Demos]
(https://huggingface.co/boobalanit/ACE-Step-1.5-SouthIndiaParaiLoRA)
I'd love to hear feedback from fellow researchers, audio engineers, and developers experimenting with ACE-Step or regional music synthesis!