Index

• FrameDeployer
  • Overview
  • Features
  • Installation
    • Prerequisites
• Example of usage
• Documentation

FrameDeployer

Overview

FrameDeployer is a tool designed to automatically create videos based on current trends. It utilizes sentiment analysis, text-to-speech, and multimedia processing techniques to generate engaging and informative videos. This project aims to simplify video content creation by automating various steps, from information retrieval to final video production.

Features

• Trend Analysis: Extracts and summarizes articles based on current trends.
• Text-to-Speech: Converts summarized texts into audio using advanced speech synthesis techniques.
• Subtitle Generation: Creates synchronized subtitles for the generated videos.
• Image Search: Searches and downloads relevant images based on the summarized text.
• Music Selection: Chooses background music based on the emotion of the text.

sequenceDiagram
    participant ResourceManager
    participant web_scraper
    participant editing
    participant sentiment_analysis
    participant subtitles
    participant text_to_speech
    participant summarize
    participant media_finder
    
    Note right of ResourceManager: Initiates resource generation\nfor a given trend.
    
    ResourceManager->>web_scraper: get_trends()
    web_scraper-->>ResourceManager: trends
    
    ResourceManager->>web_scraper: get_trend_contents(trend_number, number_of_articles_to_read)
    web_scraper-->>ResourceManager: contents
    
    ResourceManager->>summarize: apply_summarization_article_on_trend(contents, text_length)
    summarize-->>ResourceManager: text_script, tags
    
    ResourceManager->>summarize: apply_summarization_article_on_trend(desc_contents_scrapped, desc_length)
    summarize-->>ResourceManager: description, _
    
    ResourceManager->>media_finder: searchAndDownloadImage(trend[trend_number], text_script)
    media_finder-->>ResourceManager: images
    
    ResourceManager->>sentiment_analysis: get_summarization_emotion(text_script)
    sentiment_analysis-->>ResourceManager: emotion
    
    ResourceManager->>text_to_speech: get_text_to_speech(text_script, path, language)
    text_to_speech-->>ResourceManager: audio_file
    
    ResourceManager->>subtitles: generate_srt(audio_file, path)
    subtitles-->>ResourceManager: srt_file
    
    ResourceManager->>media_finder: selectMusicByEmotion(emotion)
    media_finder-->>ResourceManager: music_path
    
    ResourceManager-->>editing: create_video_with_data(output)
	editing->>ResourceManager: video.mp4

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Installation

To set up this project, follow these steps:

Prerequisites

Ensure you have Python 3.6+ installed on your system.

Install the required packages listed in the requirements.txt file. You can do this by running the following command:

pip install -r requirements.txt

Next, download the necessary NLTK data:

import nltk
nltk.download('wordnet')
nltk.download('framenet_v17')
nltk.download('punkt')

Now you have to install ImageMagick clicking here. If you're on Windows you have to link the path of the executable you've installed in conf.py:

IMAGEMAGICK_BINARY = r"INSERT/YOUR/PATH/HERE.exe"

You have also to add your API key of assemblyAI transcriber engine in data/var.env:

aai="your-api-key"

Example of usage

Note

The project is currently under development. The example of usage will come when the final build will be released.

Documentation

You can check the documentation by clicking: here.