Synthesizes on brand voiceovers for marketing videos Ruby

Okay, here's a Ruby program example focused on synthesizing on-brand voiceovers for marketing videos. It's a simplified simulation to illustrate the concepts; a real-world implementation would be much more complex and likely involve external libraries and APIs for voice synthesis and style analysis.

```ruby
# Simulate a brand "RubySpark" with defined voice characteristics
class Brand
  attr_accessor :name, :target_audience, :voice_tone, :voice_style, :keywords

  def initialize(name:, target_audience:, voice_tone:, voice_style:, keywords:)
    @name = name
    @target_audience = target_audience
    @voice_tone = voice_tone # e.g., "Energetic", "Professional", "Friendly"
    @voice_style = voice_style # e.g., "Conversational", "Authoritative", "Humorous"
    @keywords = keywords # Important keywords for the brand
  end

  def to_s
    "Brand: #{@name}\nTarget Audience: #{@target_audience}\nVoice Tone: #{@voice_tone}\nVoice Style: #{@voice_style}\nKeywords: #{@keywords}"
  end
end

# Simulate a video with content that needs a voiceover.
class Video
  attr_accessor :title, :description, :script

  def initialize(title:, description:, script:)
    @title = title
    @description = description
    @script = script
  end

  def to_s
    "Video Title: #{@title}\nDescription: #{@description}\nScript:\n#{@script}"
  end
end


# A simple VoiceoverSynthesizer.  This is where the magic (simulation) happens.
class VoiceoverSynthesizer
  def initialize(brand)
    @brand = brand
  end

  def synthesize_voiceover(video)
    puts "Synthesizing voiceover for '#{video.title}'..."
    # In a real system, this is where AI/ML and external APIs would be used.
    # For this example, we'll just do some basic keyword insertion and tone adjustment.

    voiceover = video.script.dup # Start with the video script

    # 1. Keyword integration
    @brand.keywords.each do |keyword|
      if !video.script.include?(keyword)
        voiceover += " " + keyword # Add the keyword if missing, not efficiently
        puts "Added keyword: #{keyword}"
      end
    end

    # 2. Tone adjustment (VERY simplified)
    case @brand.voice_tone
    when "Energetic"
      voiceover = voiceover.upcase + "!" # Make it louder
      puts "Applying Energetic tone."
    when "Professional"
      voiceover = voiceover.gsub("!", ".") # Replace exclamation points with periods
      puts "Applying Professional tone."
    when "Friendly"
      voiceover = voiceover.gsub("we", "we all")  # make it inclusive.
      puts "Applying Friendly tone."
    end

    # 3. Style adjustment (again, very simplified)
    case @brand.voice_style
    when "Conversational"
      voiceover = voiceover.gsub(/([.?!])/, '\1... ') # Add ellipses for a conversational feel
      puts "Applying Conversational style."
    when "Humorous"
      voiceover = voiceover.gsub(/([.?!])/, '\1  (Just kidding!) ') # Add jokes.
      puts "Applying Humorous style."

    end

    puts "Synthesized Voiceover:\n#{voiceover}"
    return voiceover
  end
end


# Example Usage:
ruby_spark = Brand.new(
  name: "RubySpark",
  target_audience: "Developers, Tech Enthusiasts",
  voice_tone: "Energetic",
  voice_style: "Conversational",
  keywords: ["Ruby", "Development", "Innovation", "Efficiency"]
)

puts ruby_spark

promo_video = Video.new(
  title: "RubySpark: The Future of Ruby Development",
  description: "A short video showcasing RubySpark's new features.",
  script: "Welcome to RubySpark.  We're excited to announce our latest updates.  It's faster and better than ever!"
)

puts promo_video

synthesizer = VoiceoverSynthesizer.new(ruby_spark)
synthesized_voiceover = synthesizer.synthesize_voiceover(promo_video)

puts "\nFinal Voiceover:\n#{synthesized_voiceover}"
```

Key improvements and explanations:

* **Classes for `Brand` and `Video`:** Encapsulates the data related to the brand guidelines and the video content.  This makes the code more organized and easier to understand.
* **`VoiceoverSynthesizer` Class:** This class does the work of analyzing the video script and modifying it to match the brand's voice.  This is where a real AI/ML system would be integrated.  The current implementation uses simple string manipulations to simulate the process.
* **Keyword Integration:** The `synthesize_voiceover` method checks for the brand's keywords in the script and adds them if they are missing.  Crucially, this avoids infinite loops by only adding missing keywords.
* **Tone and Style Adjustment:**  The code simulates tone and style adjustment using simple string replacements (e.g., uppercasing for an energetic tone, adding ellipses for a conversational style).  This is obviously very basic, but it demonstrates the *concept* of what a real system would do.
* **Example Usage:** The example code creates a `Brand` and a `Video` object, then uses the `VoiceoverSynthesizer` to generate a voiceover.
* **Clear `puts` statements:** provides info regarding actions of the program.
* **`to_s` override:**  provides a way to print useful information about class objects.

How this program simulates the process:

1. **Brand Definition:** The `Brand` class stores the characteristics of the brand (name, target audience, voice tone, style, and keywords).
2. **Video Analysis:** The `VoiceoverSynthesizer` takes the video script as input.
3. **Voice Customization:** It then modifies the script based on the brand's voice tone, style, and keywords.  This is done using basic string manipulations.
4. **Output:** The `synthesize_voiceover` method returns the modified voiceover script.

To run this, save it as a `.rb` file (e.g., `voiceover_synthesizer.rb`) and execute it from your terminal:

```bash
ruby voiceover_synthesizer.rb
```

Remember, this is a simplified example. A real-world system would involve:

* **Natural Language Processing (NLP):** To analyze the sentiment, tone, and meaning of the video script.
* **Speech Synthesis (Text-to-Speech):** To generate the actual voice audio.  This would likely involve using an external API like Google Cloud Text-to-Speech, Amazon Polly, or similar.
* **Voice Cloning/Style Transfer:**  More advanced techniques could be used to clone a specific voice or transfer the style of one voice to another.
* **Machine Learning:**  Training models to predict the best voice style and tone for a given video script and brand.