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title: LyricLoop v2.0 |
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emoji: 🎤 |
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colorFrom: indigo |
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colorTo: blue |
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sdk: streamlit |
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app_file: app.py |
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pinned: false |
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short_description: AI studio for structured lyrics, fine-tuned on Gemma-2b. |
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license: gemma |
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sdk_version: 1.53.0 |
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--- |
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NAME |
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LyricLoop LLM |
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PROJECT OBJECTIVE |
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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, including Electronic, Pop, Rock, and Hip-Hop. |
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LANGUAGE / STACK |
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Python | PyTorch, Hugging Face (Transformers, PEFT, TRL), Streamlit |
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TECHNICAL METHODOLOGY |
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- Fine-Tuning: Implemented Low-Rank Adaptation (LoRA) to specialize the model in rhythmic patterns while preserving base reasoning. |
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- Optimization: Used 4-bit Quantization (QLoRA) via bitsandbytes to reduce the memory footprint during training. |
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- Instruction Tuning: Supervised Fine-Tuning (SFT) with custom templates to enforce structural and genre constraints. |
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PROJECT STRUCTURE |
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- app.py: main streamlit application entry point and UI logic. |
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- src/lyricloop/: core modular package containing engine logic: |
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- config.py: global constants and path management. |
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- data.py: prompt engineering and dataset preprocessing. |
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- environment.py: hardware-aware setup (MPS/CPU/CUDA). |
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- metrics.py: inference execution and perplexity scoring. |
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- viz.py: standardized plotting and visual utilities. |
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- notebooks/: development playground, training workflows, and EDA. |
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- reports/: written technical documentation and project summaries. |
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- assets/: visual artifacts and plots used in documentation. |
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- requirements.txt: dependency management for environment parity. |
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DATA & SOURCE |
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- Corpus: 5mm+ Song Lyrics (Genius Dataset). |
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- Metadata: Artist mapping via Pitchfork Reviews. |
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- Stack: Python, Hugging Face (Transformers, PEFT, TRL), PyTorch, and Google Colab (L4 GPU). |
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EXTERNAL RESOURCES |
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- Full Project Workspace (Google Drive): [Access the Notebooks & Raw Data](https://drive.google.com/drive/folders/1M5SJRaaK8OaskUgEsBupgGVN_-fQS3i4?usp=sharing) |
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- Training Environment: Google Colab (L4 GPU) |
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STUDIO GUIDE |
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- App URL: https://lxtung95-lyricloop.hf.space/ |
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- 1. Details: Enter a song title and an Artist Aesthetic (e.g., Taylor Swift) to set the tone. |
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- 2. Genre: Select your target genre to adjust rhythmic density. |
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- 3. Compose: Use the Creativity (Temperature) slider to control experimental word choice. |
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- 4. Export: Download the final composition as a .txt file for your creative workflow. |
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SUPPORT |
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Visit my GitHub repository for the latest scripts and downloads: |
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https://github.com/lxntung95 |
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