v2.1.4 Release

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")

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 initialization and version
│   ├── core/
│   │   ├── __init__.py
│   │   ├── clipify.py             # Main Clipify class
│   │   ├── processor.py           # Content processing logic
│   │   ├── text_processor.py      # Text analysis and segmentation
│   │   └── ai_providers.py        # AI provider implementations
│   ├── video/
│   │   ├── __init__.py
│   │   ├── cutter.py             # Video cutting functionality
│   │   ├── converter.py          # Mobile format conversion
│   │   ├── converterStretch.py   # Alternative conversion method
│   │   └── processor.py          # Video processing and captions
│   ├── audio/
│   │   ├── __init__.py
│   │   ├── extractor.py          # Audio extraction from video
│   │   └── speech.py             # Speech-to-text conversion
│   └── utils/                    # Utility functions
│       ├── __init__.py
│       └── helpers.py
├── .gitignore                   # Git ignore rules
├── LICENSE                      # MIT License
├── MANIFEST.in                  # Package manifest
├── README.md                    # Project documentation
├── requirements.txt             # Dependencies
└── setup.py                     # Package setup

🛠️ 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 adel50ali5b@gmail.com
• Community Support: GitHub Issues
• Documentation: Wiki

🙏 Acknowledgments

• FFmpeg for video processing
• OpenAI for AI capabilities
• P...

2.1.1

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")

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 initialization and version
│   ├── core/
│   │   ├── __init__.py
│   │   ├── clipify.py             # Main Clipify class
│   │   ├── processor.py           # Content processing logic
│   │   ├── text_processor.py      # Text analysis and segmentation
│   │   └── ai_providers.py        # AI provider implementations
│   ├── video/
│   │   ├── __init__.py
│   │   ├── cutter.py             # Video cutting functionality
│   │   ├── converter.py          # Mobile format conversion
│   │   ├── converterStretch.py   # Alternative conversion method
│   │   └── processor.py          # Video processing and captions
│   ├── audio/
│   │   ├── __init__.py
│   │   ├── extractor.py          # Audio extraction from video
│   │   └── speech.py             # Speech-to-text conversion
│   └── utils/                    # Utility functions
│       ├── __init__.py
│       └── helpers.py
├── .gitignore                   # Git ignore rules
├── LICENSE                      # MIT License
├── MANIFEST.in                  # Package manifest
├── README.md                    # Project documentation
├── requirements.txt             # Dependencies
└── setup.py                     # Package setup

🛠️ 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 adel50ali5b@gmail.com
• Community Support: GitHub Issues
• Documentation: Wiki

🙏 Acknowledgments

• FFmpeg for video processing
• OpenAI for AI capabilities
• P...

v0.7.0

A powerful Python tool for processing video content into social media-friendly segments with automated transcription, captioning, and thematic segmentation.

Features

• 🎥 Video Processing

  • Extracts audio from video files
  • Converts speech to text with timing information
  • Segments videos by theme and content
  • Converts videos to mobile-friendly format (9:16 aspect ratio)
  • Adds auto-generated captions

• 🤖 AI-Powered Content Analysis

  • Intelligent thematic segmentation
  • Smart title generation
  • Keyword extraction
  • Sentiment analysis
  • Hashtag generation

• 📝 Transcript Processing

  • Generates accurate transcripts with timing information
  • Processes transcripts into coherent segments
  • Maintains timing alignment for precise video cutting

Prerequisites

• Python 3.8+
• FFmpeg installed and in PATH
• NLTK resources
• Required Python packages (see requirements.txt)
• API key for content processing services

Clone the repository:

Installation

install from pip

pip install clipify

install from source

git clone https://github.com/adelelawady/Clipify.git
cd Clipify

Install the dependencies:

pip install -r requirements.txt

Usage

1. Basic video processing:

from clipify.core.clipify import Clipify
    # Initialize Clipify with Hyperbolic or OpenAI or Anthropic AI and specific model
    clipify = Clipify(
        provider_name="hyperbolic",
        api_key="api-key",
        model="deepseek-ai/DeepSeek-V3",  # Specify model
        convert_to_mobile=True,
        add_captions=True,
        mobile_ratio="9:16"
    )
    
    # Process a video
    result = clipify.process_video("path/to/video.mp4")
    
    if result:
        print("\nProcessing Summary:")
        print(f"Processed video: {result['video_path']}")
        print(f"Created {len(result['segments'])} segments")
        
        for segment in result['segments']:
            print(f"\nSegment #{segment['segment_number']}: {segment['title']}")
            if 'cut_video' in segment:
                print(f"Cut video: {segment['cut_video']}")
            if 'mobile_video' in segment:
                print(f"Mobile version: {segment['mobile_video']}")
            if 'captioned_video' in segment:
                print(f"Captioned version: {segment['captioned_video']}")

Project Structure

clipify/
├── clipify/
│ ├── init.py
│ ├── content_processor.py
│ ├── video_processor.py
│ └── utils/
│ ├── audio.py
│ ├── captions.py
│ └── transcription.py
├── tests/
├── requirements.txt
├── setup.py
└── README.md

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

v0.6.0

A powerful Python tool for processing video content into social media-friendly segments with automated transcription, captioning, and thematic segmentation.

Features

• 🎥 Video Processing

  • Extracts audio from video files
  • Converts speech to text with timing information
  • Segments videos by theme and content
  • Converts videos to mobile-friendly format (9:16 aspect ratio)
  • Adds auto-generated captions

• 🤖 AI-Powered Content Analysis

  • Intelligent thematic segmentation
  • Smart title generation
  • Keyword extraction
  • Sentiment analysis
  • Hashtag generation

• 📝 Transcript Processing

  • Generates accurate transcripts with timing information
  • Processes transcripts into coherent segments
  • Maintains timing alignment for precise video cutting

Prerequisites

• Python 3.8+
• FFmpeg installed and in PATH
• NLTK resources
• Required Python packages (see requirements.txt)
• API key for content processing services

Clone the repository:

Installation

install from pip

pip install clipify

install from source

git clone https://github.com/adelelawady/Clipify.git
cd Clipify

Install the dependencies:

pip install -r requirements.txt

Usage

1. Basic video processing:

from clipify.core.clipify import Clipify
    # Initialize Clipify with Hyperbolic or OpenAI or Anthropic AI and specific model
    clipify = Clipify(
        provider_name="hyperbolic",
        api_key="api-key",
        model="deepseek-ai/DeepSeek-V3",  # Specify model
        convert_to_mobile=True,
        add_captions=True,
        mobile_ratio="9:16"
    )
    
    # Process a video
    result = clipify.process_video("path/to/video.mp4")
    
    if result:
        print("\nProcessing Summary:")
        print(f"Processed video: {result['video_path']}")
        print(f"Created {len(result['segments'])} segments")
        
        for segment in result['segments']:
            print(f"\nSegment #{segment['segment_number']}: {segment['title']}")
            if 'cut_video' in segment:
                print(f"Cut video: {segment['cut_video']}")
            if 'mobile_video' in segment:
                print(f"Mobile version: {segment['mobile_video']}")
            if 'captioned_video' in segment:
                print(f"Captioned version: {segment['captioned_video']}")

Project Structure

clipify/
├── clipify/
│ ├── init.py
│ ├── content_processor.py
│ ├── video_processor.py
│ └── utils/
│ ├── audio.py
│ ├── captions.py
│ └── transcription.py
├── tests/
├── requirements.txt
├── setup.py
└── README.md

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.