Gamahea/lemmdata

🎵 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) project.

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📁 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
    └── ...

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🎨 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
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:

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")

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📊 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
2. Navigate to "Training" tab
3. Click "Import Dataset" → "From HuggingFace"
4. Select dataset
5. Use for training

Download via Code:

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")

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🚀 Contributing

Upload LoRA

Train a LoRA in LEMM Space:

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

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📝 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

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🔐 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

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📚 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

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🎓 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

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📄 License

MIT License - Free to use, modify, and share.

All contributed LoRAs and datasets inherit this license unless otherwise specified.

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🏷️ Tags

music-generation lora diffrhythm2 audio training-data datasets models lemm

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Last Updated: December 2025
Repository: https://huggingface.co/datasets/Gamahea/lemmdata
LEMM Space: https://huggingface.co/spaces/Gamahea/lemm-test-100