| 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 |