Clipify

An AI-powered video processing toolkit for creating social media-optimized content with automated transcription, captioning, and thematic segmentation.

🌟 Key Features

Content Processing

• Video Processing Pipeline
  • Automated audio extraction and speech-to-text conversion
  • Smart thematic segmentation using AI
  • Mobile-optimized format conversion (9:16, 4:5, 1:1)
  • Intelligent caption generation and overlay

AI Capabilities

• Advanced Analysis
  • Context-aware content segmentation
  • Dynamic title generation
  • Smart keyword and hashtag extraction
  • Sentiment analysis for content optimization

Platform Options

• Desktop Application

  • Intuitive graphical interface
  • Drag-and-drop functionality
  • Real-time processing feedback
  • Batch processing capabilities

• Server Deployment

  • RESTful API integration
  • Asynchronous processing with webhooks
  • Multi-tenant architecture
  • Containerized deployment support

🚀 Quick Start

Desktop Application

🚀 Check out our full project based on Clipify on https://github.com/adelelawady/Clipify-hub 🚀

Download and install the latest version:

Python Package Installation

# Via pip
pip install clipify

# From source
git clone https://github.com/adelelawady/Clipify.git
cd Clipify
pip install -r requirements.txt

💻 Usage Examples

Basic Implementation

from clipify.core.clipify import Clipify

# Initialize with basic configuration
clipify = Clipify(
    provider_name="hyperbolic",
    api_key="your-api-key",
    model="deepseek-ai/DeepSeek-V3",
    convert_to_mobile=True,
    add_captions=True
)

# Process video
result = clipify.process_video("input.mp4")

# Handle results
if result:
    print(f"Created {len(result['segments'])} segments")
    for segment in result['segments']:
        print(f"Segment {segment['segment_number']}: {segment['title']}")

Advanced Configuration

clipify = Clipify(
    # AI Configuration
    provider_name="hyperbolic",
    api_key="your-api-key",
    model="deepseek-ai/DeepSeek-V3",
    max_tokens=5048,
    temperature=0.7,
    
    # Video Processing
    convert_to_mobile=True,
    add_captions=True,
    mobile_ratio="9:16",
    
    # Caption Styling
    caption_options={
        "font": "Bangers-Regular.ttf",
        "font_size": 60,
        "font_color": "white",
        "stroke_width": 2,
        "stroke_color": "black",
        "highlight_current_word": True,
        "word_highlight_color": "red",
        "shadow_strength": 0.8,
        "shadow_blur": 0.08,
        "line_count": 1,
        "padding": 50,
        "position": "bottom"
    }
)

AudioExtractor

from clipify.audio.extractor import AudioExtractor

# Initialize audio extractor
extractor = AudioExtractor()

# Extract audio from video
audio_path = extractor.extract_audio(
    video_path="input_video.mp4",
    output_path="extracted_audio.wav"
)

if audio_path:
    print(f"Audio successfully extracted to: {audio_path}")

SpeechToText

from clipify.audio.speech import SpeechToText

# Initialize speech to text converter
converter = SpeechToText(model_size="base")  # Options: tiny, base, small, medium, large

# Convert audio to text with timing
result = converter.convert_to_text("audio_file.wav")

if result:
    print("Transcript:", result['text'])
    print("\nWord Timings:")
    for word in result['word_timings'][:5]:  # Show first 5 words
        print(f"Word: {word['text']}")
        print(f"Time: {word['start']:.2f}s - {word['end']:.2f}s")

VideoConverter

from clipify.video.converter import VideoConverter

# Initialize video converter
converter = VideoConverter()

# Convert video to mobile format with blurred background
result = converter.convert_to_mobile(
    input_video="landscape_video.mp4",
    output_video="mobile_video.mp4",
    target_ratio="9:16"  # Options: "1:1", "4:5", "9:16"
)

if result:
    print("Video successfully converted to mobile format")

VideoConverterStretch

from clipify.video.converterStretch import VideoConverterStretch

# Initialize stretch converter
stretch_converter = VideoConverterStretch()

# Convert video using stretch method
result = stretch_converter.convert_to_mobile(
    input_video="landscape.mp4",
    output_video="stretched.mp4",
    target_ratio="4:5"  # Options: "1:1", "4:5", "9:16"
)

if result:
    print("Video successfully converted using stretch method")

VideoProcessor

from clipify.video.processor import VideoProcessor

# Initialize video processor with caption styling
processor = VideoProcessor(
    # Font settings
    font="Bangers-Regular.ttf",
    font_size=60,
    font_color="white",
    
    # Text effects
    stroke_width=2,
    stroke_color="black",
    shadow_strength=0.8,
    shadow_blur=0.08,
    
    # Caption behavior
    highlight_current_word=True,
    word_highlight_color="red",
    line_count=1,
    padding=50,
    position="bottom"  # Options: "bottom", "top", "center"
)

# Process video with captions
result = processor.process_video(
    input_video="input_video.mp4",
    output_video="captioned_output.mp4",
    use_local_whisper="auto"  # Options: "auto", True, False
)

if result:
    print("Video successfully processed with captions")

# Process multiple video segments
segment_files = ["segment1.mp4", "segment2.mp4", "segment3.mp4"]
processed_segments = processor.process_video_segments(
    segment_files=segment_files,
    output_dir="processed_segments"
)

The VideoProcessor provides powerful captioning capabilities:

• Customizable font styling and text effects
• Word-level highlighting for better readability
• Shadow and stroke effects for visibility
• Automatic speech recognition using Whisper
• Support for batch processing multiple segments

VideoCutter

from clipify.video.cutter import VideoCutter

# Initialize video cutter
cutter = VideoCutter()

# Cut a specific segment
result = cutter.cut_video(
    input_video="full_video.mp4",
    output_video="segment.mp4",
    start_time=30.5,  # Start at 30.5 seconds
    end_time=45.2     # End at 45.2 seconds
)

if result:
    print("Video segment successfully cut")

SmartTextProcessor

from clipify.core.text_processor import SmartTextProcessor
from clipify.core.ai_providers import HyperbolicAI

# Initialize AI provider and text processor
ai_provider = HyperbolicAI(api_key="your_api_key")
processor = SmartTextProcessor(ai_provider)

# Process text content
text = "Your long text content here..."
segments = processor.segment_by_theme(text)

if segments:
    for segment in segments['segments']:
        print(f"\nTitle: {segment['title']}")
        print(f"Keywords: {', '.join(segment['keywords'])}")
        print(f"Content length: {len(segment['content'])} chars")

📦 Project Structure

clipify/
├── clipify/
│   ├── __init__.py           # Package exports and version info
│   ├── core/
│   │   ├── __init__.py       # Core module exports
│   │   ├── clipify.py        # Main Clipify class implementation
│   │   ├── processor.py      # Content processing and segmentation
│   │   ├── text_processor.py # Text analysis and theme detection
│   │   └── ai_providers.py  # AI providers (OpenAI, Anthropic, Hyperbolic)
│   ├── video/
│   │   ├── __init__.py       # Video module exports
│   │   ├── processor.py      # Video captioning and effects
│   │   ├── converter.py      # Mobile format with blur background
│   │   ├── converter_stretch.py  # Stretch-based format conversion
│   │   └── cutter.py         # Video segment extraction
│   ├── audio/
│   │   ├── __init__.py       # Audio module exports
│   │   ├── extractor.py      # FFmpeg-based audio extraction
│   │   └── speech.py         # Whisper speech recognition
├── scripts/
│   ├── build.sh              # Package build script
│   └── publish.sh            # PyPI publishing script
├── .gitignore                # Git ignore patterns
├── LICENSE                 # MIT License
├── MANIFEST.in             # Package manifest
├── README.md               # Project documentation
├── requirements.txt        # Project dependencies
└── setup.py  # Package configuration

🛠️ Configuration Options

AI Providers

• hyperbolic: Default provider with DeepSeek-V3 model
• openai: OpenAI GPT models support
• anthropic: Anthropic Claude models
• ollama: Local model deployment

Video Formats

• Aspect Ratios: 1:1, 4:5, 9:16
• Output Formats: MP4, MOV
• Quality Presets: Low, Medium, High

Caption Customization

• Font customization
• Color schemes
• Position options
• Animation effects
• Word highlighting

🤝 Contributing

We welcome contributions! Here's how you can help:

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

Please read our Contributing Guidelines for details.

📄 License

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

🌐 Support

• Enterprise Support: Contact [email protected]
• Community Support: GitHub Issues
• Documentation: Wiki

🙏 Acknowledgments

• FFmpeg for video processing
• OpenAI for AI capabilities
• PyTorch community
• All contributors and supporters

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