Add comprehensive dataset card with folder structure documentation

README.md

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 ---
-license: apache-2.0
+title: LEMM Training Data Repository
+emoji: 🎵
+colorFrom: purple
+colorTo: pink
+sdk: static
+pinned: false
+license: mit
 ---
+
+# 🎵 LEMM Training Data Repository
+
+Central storage for all LEMM training artifacts:
+- **LoRA Adapters** - User-trained model adaptations
+- **Training Datasets** - Prepared and curated audio datasets
+
+Part of the [LEMM (Let Everyone Make Music)](https://huggingface.co/spaces/Gamahea/lemm-test-100) project.
+
+---
+
+## 📁 Repository Structure
+
+```
+lemmdata/
+├── loras/                          # LoRA Adapters (ZIP files)
+│   ├── jazz-v1.zip                # Example: Jazz style adapter
+│   ├── metal-data1.zip            # Example: Metal style adapter
+│   └── ...
+│
+└── datasets/                       # Training Datasets (ZIP files)
+    ├── gtzan_prepared.zip         # Example: GTZAN dataset
+    ├── user_dataset_123.zip       # Example: User-uploaded dataset
+    └── ...
+```
+
+---
+
+## 🎨 LoRA Adapters (`loras/`)
+
+Each LoRA is packaged as a ZIP file containing:
+
+- **`final_model.pt`** - Trained LoRA weights (PyTorch checkpoint)
+- **`config.yaml`** - Training hyperparameters and settings
+- **`metadata.json`** - Training statistics, timestamps, dataset info
+- **`README.md`** - Documentation and usage instructions
+
+### How to Use
+
+**Download in LEMM:**
+1. Go to [LEMM Space](https://huggingface.co/spaces/Gamahea/lemm-test-100)
+2. Navigate to "LoRA Management" tab
+3. Click "Sync from HuggingFace"
+4. Select LoRA from dropdown
+5. Use in generation or continue training
+
+**Download via Code:**
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+
+# Download LoRA ZIP
+zip_path = hf_hub_download(
+    repo_id="Gamahea/lemmdata",
+    repo_type="dataset",
+    filename="loras/jazz-v1.zip"
+)
+
+# Extract
+with zipfile.ZipFile(zip_path, 'r') as zipf:
+    zipf.extractall("./my_loras/jazz-v1")
+```
+
+---
+
+## 📊 Training Datasets (`datasets/`)
+
+Each dataset is packaged as a ZIP file containing:
+
+- **`dataset_info.json`** - Metadata (size, format, split ratios)
+- **`train/`** - Training audio files
+- **`val/`** - Validation audio files
+
+### Supported Formats
+
+- **Audio**: WAV, MP3, FLAC, OGG
+- **Sample Rate**: 44.1kHz or 48kHz recommended
+- **Channels**: Mono or Stereo
+
+### How to Use
+
+**Download in LEMM:**
+1. Go to [LEMM Space](https://huggingface.co/spaces/Gamahea/lemm-test-100)
+2. Navigate to "Training" tab
+3. Click "Import Dataset" → "From HuggingFace"
+4. Select dataset
+5. Use for training
+
+**Download via Code:**
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+
+# Download dataset ZIP
+zip_path = hf_hub_download(
+    repo_id="Gamahea/lemmdata",
+    repo_type="dataset",
+    filename="datasets/gtzan_prepared.zip"
+)
+
+# Extract
+with zipfile.ZipFile(zip_path, 'r') as zipf:
+    zipf.extractall("./my_datasets/gtzan_prepared")
+```
+
+---
+
+## 🚀 Contributing
+
+### Upload LoRA
+
+Train a LoRA in [LEMM Space](https://huggingface.co/spaces/Gamahea/lemm-test-100):
+1. Prepare or select a dataset
+2. Configure training parameters
+3. Start training
+4. LoRA automatically packaged as ZIP and uploaded to `loras/{your-lora-name}.zip`
+
+### Upload Dataset
+
+Prepare a dataset and export:
+1. Upload audio files to LEMM
+2. Use dataset preparation tools
+3. Export as prepared dataset
+4. Dataset packaged as ZIP and uploaded to `datasets/{your-dataset-name}.zip`
+
+---
+
+## 📝 Naming Conventions
+
+### LoRA Names
+- **Format**: `{style}-{variant}_{version}`
+- **Examples**: 
+  - `jazz-bebop_v1`
+  - `rock-heavy_v2`
+  - `classical-piano_v1`
+
+### Dataset Names
+- **Format**: `{source}_{description}`
+- **Examples**:
+  - `gtzan_prepared`
+  - `user_dataset_1702987654`
+  - `opensinger_vocals`
+
+---
+
+## 🔐 Authentication
+
+**Read Access**: Public (no authentication required)
+
+**Write Access**: Requires HuggingFace token
+- Only LEMM Space can upload
+- User-trained artifacts auto-upload
+- Token managed via HF Space secrets
+
+---
+
+## 📚 Related Resources
+
+- **LEMM Space**: https://huggingface.co/spaces/Gamahea/lemm-test-100
+- **GitHub Repo**: https://github.com/Gamahea/Angen
+- **DiffRhythm2**: Music generation model with vocals
+- **MuQ-MuLan**: Music style encoding
+
+---
+
+## 🎓 Training Best Practices
+
+### LoRA Training
+- **Dataset Size**: 100+ clips minimum
+- **LoRA Rank**: 8-32 for most styles
+- **Learning Rate**: 1e-4 to 1e-3
+- **Epochs**: 20-50 depending on dataset
+
+### Dataset Preparation
+- **Clip Length**: 10-30 seconds
+- **Audio Quality**: Clean, well-produced
+- **Consistency**: Similar genre/style
+- **Diversity**: Varied within target style
+
+---
+
+## 📄 License
+
+MIT License - Free to use, modify, and share.
+
+All contributed LoRAs and datasets inherit this license unless otherwise specified.
+
+---
+
+## 🏷️ Tags
+
+`music-generation` `lora` `diffrhythm2` `audio` `training-data` `datasets` `models` `lemm`
+
+---
+
+**Last Updated**: December 2025  
+**Repository**: https://huggingface.co/datasets/Gamahea/lemmdata  
+**LEMM Space**: https://huggingface.co/spaces/Gamahea/lemm-test-100