README.md

---
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

```python
# 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

```bash
# 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
```python
# 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:

```python
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

---

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