AI Speech-to-Text Platform
A scalable platform for real-time audio transcription with speaker diarization, sentiment analysis, and keyword extraction. Used for meeting transcription, call center analytics, and content accessibility.
🎯Problem
Organizations needed accurate, real-time transcription of meetings and calls with actionable insights, but existing solutions were expensive or inaccurate in Turkish.
💡Solution
Built a custom pipeline around OpenAI Whisper with post-processing NLP steps for speaker identification, sentiment analysis, and automatic summarization.
🏗️Architecture
WebSocket server receives audio streams in chunks, queues them in Redis for processing, and runs Whisper inference on GPU. NLP pipeline extracts entities, sentiments, and generates summaries. Results are streamed back to clients in real-time via WebSocket.
⚠️Challenges
Real-time processing with Whisper required careful chunking strategy to balance accuracy and latency. Speaker diarization was complex and required training custom embeddings.
📚Lessons Learned
GPU resource management is critical for cost efficiency. Proper audio preprocessing (noise reduction, normalization) dramatically improves Whisper accuracy.