AudioAnalyzer

Inspiration

Call centers generate thousands of hours of audio every day, but manually reviewing these calls for quality and compliance is time-consuming and error-prone. We wanted to automate this process using the latest AI technologies, making it scalable, language-agnostic, and easy to integrate into modern cloud workflows. AWS Lambda’s serverless architecture inspired us to build a solution that is both cost-effective and highly scalable, allowing organizations to process audio at any scale without managing servers.

What it does

AudioAnalyzer automatically processes audio files from call center campaigns, transcribes them using AI, and checks if the conversations meet predefined requirements (such as greetings, compliance statements, or specific questions). It supports multiple languages and campaigns, and can run both as a standalone Node.js app or as an AWS Lambda function triggered by new audio uploads to S3. The results, including detected issues and compliance status, are stored for further review, and problematic audios are flagged for manual inspection.

How we built it

• Core Logic: Developed in Node.js, with modular components for campaign management, audio transcription, semantic analysis, and result storage.
• Transcription: Utilizes OpenAI Whisper for accurate, multilingual speech-to-text conversion. Semantic Analysis: Uses OpenAI GPT models to analyze transcriptions and verify if required intentions or statements are present.
• AWS Lambda Integration: Refactored the codebase to support both local execution and AWS Lambda, with a dedicated Lambda handler and S3 event triggers.
• Storage: Uses Amazon S3 for audio input/output and result storage, ensuring scalability and durability.
• Error Handling: Robust error management moves failed files to a dedicated S3 folder and logs errors for later review.
• Documentation: Bilingual (Spanish/English) documentation and clear project structure for easy onboarding and collaboration.

Challenges we ran into

• Lambda Resource Limits: Adapting AI models and dependencies to fit within Lambda’s memory and deployment package size constraints.
• Audio Processing Time: Ensuring that audio files are processed within Lambda’s execution time limits, especially for longer files.
• S3 Event Handling: Managing S3 triggers and ensuring idempotent processing to avoid duplicate analyses.
• Error Recovery: Designing a robust system that gracefully handles API failures, network issues, and corrupted files without losing data.

Accomplishments that we're proud of

• Seamless Serverless Integration: Successfully running advanced AI audio analysis in a fully serverless environment.
• Robust Error Handling: Ensuring that no audio is lost and that all errors are logged and recoverable.
• Bilingual Documentation: Making the project accessible to both Spanish and English-speaking teams.
• Flexible Architecture: Allowing the same codebase to run both locally and in the cloud with minimal changes.

What we learned

• Serverless Best Practices: How to optimize Node.js applications for AWS Lambda, including cold start mitigation and efficient dependency management.
• AI Integration: Practical experience integrating state-of-the-art AI models (Whisper, GPT) into real-world workflows.
• Cloud Automation: Automating end-to-end workflows using S3 triggers, Lambda, and environment variables.
• Internationalization: The importance of supporting multiple languages and clear documentation for global teams.
• Resilience: Building systems that are fault-tolerant and easy to monitor and debug.

What's next for AudioAnalyzer

• Real-time Analysis: Integrate with Amazon Transcribe Streaming for near real-time call analysis.
• Dashboard & Analytics: Develop a web dashboard for visualizing results, trends, and compliance metrics.
• Customizable Intents: Allow users to define custom checklists and analysis criteria via a web interface.
• Deeper Language Support: Expand support for more languages and dialects, and improve accuracy for low-resource languages.
• Integration with CRM/QA Tools: Connect results directly to customer relationship management and quality assurance platforms.
• Cost Optimization: Explore model distillation and edge processing to further reduce costs and latency.

Built With

• amazon-web-services
• lambda
• node.js
• openai
• whisper