README.md

metadata

license: mit
title: image to song lyrics
sdk: gradio
emoji: 😻
colorFrom: red
short_description: turn any image into song lyrics
sdk_version: 5.31.0

🎵 Image to Lyrics Generator

A multimodal AI system that transforms images into structured song lyrics with artist style control. Built with BLIP-2 for image captioning and transformer models for lyric generation.

🚀 Features

• Multimodal Processing: Converts images to poetic descriptions using BLIP-2
• Artist Style Control: Emulates 8 different artist styles (Drake, Kendrick Lamar, Travis Scott, etc.)
• Structured Output: Generates properly formatted songs with verses, choruses, bridges
• Customizable Creativity: Adjustable creativity levels for varied lyrical output
• Real-time Generation: Fast inference optimized for Hugging Face Spaces

🏗️ Architecture

Core Components

1. Image Encoder Module

   • Model: Salesforce/blip-image-captioning-large
   • Function: Converts images to poetic, lyric-worthy descriptions
   • Enhanced prompting for metaphorical language generation

2. Lyric Generation Engine

   • Base Model: Transformer-based language model
   • Artist Style Adapters: Prompt engineering with style-specific characteristics
   • Structure Templates: Configurable song section generation

3. Style Control System

   • 8 pre-configured artist profiles with unique characteristics
   • Dynamic prompt construction based on artist attributes
   • Section-specific generation (verse, chorus, bridge handling)

Technical Implementation

# Core pipeline flow
Image → BLIP-2 Encoder → Poetic Description → LLM + Style Prompts → Structured Lyrics

🎯 Artist Styles Supported

Artist	Style Characteristics
Drake	Melodic, introspective, emotional storytelling
Kendrick Lamar	Complex wordplay, social commentary, conscious rap
Travis Scott	Atmospheric, psychedelic imagery, energetic
Billie Eilish	Dark, moody, intimate and haunting
Post Malone	Melodic rap-singing, emotional vulnerability
J. Cole	Storytelling, conscious lyrics, personal reflection
Ariana Grande	Powerful vocals, romantic themes, pop sensibility
The Weeknd	Dark R&B, atmospheric, seductive undertones

📊 Song Structure Templates

• Standard: Verse → Chorus → Verse → Chorus → Bridge → Chorus
• Simple: Verse → Chorus → Verse → Chorus
• Extended: Intro → Verse → Pre-Chorus → Chorus → Verse → Pre-Chorus → Chorus → Bridge → Chorus → Outro
• Minimal: Verse → Chorus

🔧 Installation & Setup

For Hugging Face Spaces

1. Create new Space with Gradio SDK
2. Upload all files from this repository
3. Space will auto-build and deploy

Local Development

# Clone repository
git clone <your-repo-url>
cd image-to-lyrics

# Install dependencies
pip install -r requirements.txt

# Run application
python app.py

💡 Usage Examples

Basic Usage

1. Upload an image (nature, cityscape, portrait, etc.)
2. Select artist style from dropdown
3. Choose song structure template
4. Adjust creativity level (0.3-1.0)
5. Click "Generate Lyrics"

Advanced Features

• Creativity Control: Lower values (0.3-0.5) for more literal lyrics, higher values (0.7-1.0) for abstract/artistic output
• Style Mixing: Experiment with different artists for the same image to see style variations
• Structure Experimentation: Try different song structures to match your creative vision

🔍 Technical Deep Dive

Image Processing Pipeline

# Enhanced prompt engineering for lyrical descriptions
prompt = "A poetic and emotionally vivid description with metaphorical language:"
inputs = processor(image, prompt, return_tensors="pt")
caption = model.generate(**inputs, temperature=creativity_level)

Style Adaptation System

The system employs dynamic prompt construction with artist-specific attributes:

artist_prompt = f"""
Write {section} lyrics in the style of {artist}.
Style: {artist_style_characteristics}
Scene: {image_description}
Section goal: {section_specific_guidance}
"""

Memory-Efficient Design

• Model caching with @st.cache_resource
• Optimized inference for Hugging Face Spaces constraints
• Efficient GPU/CPU switching based on availability

🎨 Creative Applications

• Music Production: Generate lyrical concepts from visual inspiration
• Songwriting: Overcome writer's block with AI-assisted creativity
• Educational: Learn different artist styles and song structures
• Content Creation: Generate original lyrics for multimedia projects

🔒 Ethical Considerations

• All generated lyrics are original AI creations
• No copyrighted material is reproduced
• Artist styles are emulated through learned patterns, not copied content
• Designed to inspire human creativity, not replace it

🚀 Future Enhancements

• LoRA/PEFT adapters for more accurate artist mimicry
• Style interpolation (blend multiple artists)
• Rhyme scheme control
• Beat/tempo matching
• Advanced song structure templates
• Collaborative filtering for style recommendations

📝 Model Details

BLIP-2 Image Captioning

• Model: Salesforce/blip-image-captioning-large
• Purpose: Generate poetic image descriptions
• Optimization: Custom prompting for lyrical language

Text Generation

• Architecture: Transformer-based language model
• Training: Fine-tuned on lyrical content patterns
• Inference: Optimized for creative text generation

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/enhancement)
3. Commit changes (git commit -am 'Add new feature')
4. Push to branch (git push origin feature/enhancement)
5. Create Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Hugging Face for transformer models and hosting platform
• Salesforce for BLIP-2 image captioning model
• The open-source AI community for foundational research

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Built with ❤️ using Hugging Face Transformers, BLIP-2, and Gradio