EchoSynth 🎙️➡️📝🖼️

EchoSynth transforms audio content into refined text and visual assets through a coordinated team of AI agents. It's designed for podcasters, content creators, educators, and anyone working with spoken media who wants to automatically generate high-quality derivative content.

🌟 Features

• Audio Transcription - Accurate text conversion from various audio formats
• Speech Refinement - Transforms raw transcripts into polished speeches
• Smart Summarization - Creates concise text summaries capturing key points
• Image Generation - Produces relevant visuals that match content themes
• Coordinated AI Agents - Specialized AI agents working together via CrewAI
• Flexible Pipeline - Modular architecture that supports customization
• JSON Output - Saves all generated content in structured JSON format

🛠️ Tech Stack

• CrewAI - Agent orchestration framework
• OpenAI Whisper - Speech-to-text transcription
• OpenAI GPT-4o - Text processing and refinement
• OpenAI DALL-E 3 - Image generation
• Python 3.9+ - Core programming language

📋 Prerequisites

• Python 3.9 or higher
• OpenAI API key with access to Whisper, GPT-4, and DALL-E models
• Audio files (.mp3, .mp4, .mpeg, .mpga, .m4a, .wav, or .webm)

🚀 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/EchoSynth.git
   cd EchoSynth

2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up your environment variables:

   export OPENAI_API_KEY="your_openai_api_key_here"

💻 Usage

Basic Example

from echo_synth.flows import EchoSynthFlow

# Initialize the flow with your audio file
flow = EchoSynthFlow(audio_file_path="data/your_audio_file.mp3")

# Run the processing pipeline
flow.run()

# Access the results
transcription = flow.state.transcribed_text
speech = flow.state.speech_text
summary = flow.state.summary_text
image_path = flow.state.image_file
results_json = flow.state.results_json

print(f"Processed {flow.audio_file_path}")
print(f"Generated image saved to: {image_path}")
print(f"Results JSON saved to: {results_json}")

Using Environment Variables

You can also set the audio file path via environment variables:

export AUDIO_FILE_PATH="data/your_audio_file.mp3"

from echo_synth.flows import EchoSynthFlow

# The audio path will be read from environment variables
flow = EchoSynthFlow()
flow.run()

🧠 Architecture

EchoSynth uses a multi-agent architecture powered by CrewAI:

1. Agent 1 (Text Transcribe) - Converts audio to accurate text using Whisper API
2. Agent 2 (Speech Writer) - Refines raw transcripts into polished, structured speech
3. Agent 3 (Summary for Image) - Creates descriptive content for image generation
4. Agent 4 (Summarizer) - Produces concise summaries of the key content
5. Agent 5 (Generate Image) - Creates visual representations using DALL-E
6. Agent 6 (Save data to JSON) - Compiles all outputs into a structured JSON file

The flow is coordinated through CrewAI's sequential pipeline, ensuring each agent receives the proper inputs from previous steps and all results are saved in a structured format.

Tools Used by Agents:

• Tool 1 (Whisper STT) - Used by Agent 1 for transcription
• Tool 2 (Sentiment Analysis) - Used by Agent 1 for audio content analysis
• Tool 3 (DALL-E) - Used by Agent 5 for image generation
• Tool 4 (FileWriter) - Used by Agent 6 to save outputs to JSON

🔍 Troubleshooting

Common Issues:

Audio File Not Found

Ensure your audio file exists and the path is correct. EchoSynth will search in:

• The absolute path provided
• The current working directory
• A data/ subdirectory in the working directory

API Key Issues

Make sure your OPENAI_API_KEY environment variable is correctly set and has access to the required models.

File Size Limits

OpenAI's Whisper API has a 25MB file size limit. For larger files, consider splitting them or using a different method.

JSON Output

If you encounter issues with JSON output:

• Check that all agent outputs are valid and complete
• Ensure the FileWriter tool has proper permissions to write to the output directory
• Verify the JSON structure matches your expected schema

🤝 Contributing

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

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

📄 License

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