YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
NAME
LyricLoop LLM
PROJECT OBJECTIVE
LyricLoop bridges the gap between semantic LLM text and professional musical phrasing. This framework fine-tunes Google's Gemma-2b-it to generate lyrics adhering to specific structures (Verse, Chorus, Bridge) and genre-specific stylings (Electronic, Pop, Rock, Hip-Hop).
LANGUAGE / STACK
Python | PyTorch, Hugging Face (Transformers, PEFT, TRL), Streamlit
TECHNICAL METHODOLOGY
- Fine-Tuning: Implemented Low-Rank Adaptation (LoRA) to specialize the model in rhythmic patterns while preserving base reasoning.
- Optimization: Used 4-bit Quantization (QLoRA) via bitsandbytes to reduce the memory footprint during training.
- Instruction Tuning: Supervised Fine-Tuning (SFT) with custom templates to enforce structural and genre constraints.
PROJECT STRUCTURE
- app.py: main streamlit application entry point and UI logic.
- src/lyricloop/: core modular package containing engine logic:
- config.py: global constants and path management.
- data.py: prompt engineering and dataset preprocessing.
- environment.py: hardware-aware setup (MPS/CPU/CUDA).
- metrics.py: inference execution and perplexity scoring.
- viz.py: standardized plotting and visual utilities.
- notebooks/: development playground, training workflows, and EDA.
- reports/: written technical documentation and project summaries.
- assets/: visual artifacts and plots used in documentation.
- requirements.txt: dependency management for environment parity.
DATA & SOURCE
- Corpus: 5mm+ Song Lyrics (Genius Dataset).
- Metadata: Artist mapping via Pitchfork Reviews.
- Stack: Python, Hugging Face (Transformers, PEFT, TRL), PyTorch, and Google Colab (L4 GPU).
EXTERNAL RESOURCES
- Full Project Workspace (Google Drive): [Access the Notebooks & Raw Data](https://drive.google.com/drive/folders/1M5SJRaaK8OaskUgEsBupgGVN_-fQS3i4?usp=sharing)
- Training Environment: Google Colab (L4 GPU)
STUDIO GUIDE
- Run on Hugging Face lxtung95/lyricloop
- App URL: https://lxtung95-lyricloop.hf.space/
1. Details: Enter a song title and an Artist Aesthetic (e.g., Taylor Swift) to set the tone.
2. Genre: Select your target genre to adjust rhythmic density.
3. Compose: Use the Creativity (Temperature) slider to control experimental word choice.
4. Export: Download the final composition as a .txt file for your creative workflow.
SUPPORT
Visit my GitHub repository for the latest scripts and downloads:
https://github.com/lxntung95
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support