Building a Voice Archive Search Tool with Deepgram’s STT, Cohere Embeddings, and Pinecone

The tutorial outlines the process of building a voice archive search tool using a combination of Deepgram's speech-to-text (STT) API, Cohere embeddings, and Pinecone vector search to facilitate semantic search over audio files. The application, built with FastHTML and HTMX, allows users to upload audio files in MP3 or WAV format or provide URLs, which are then transcribed and segmented with timestamps and speakers. The tool optionally redacts personally identifiable information before embedding the transcript into a vector space for indexing in Pinecone, enabling meaning-aware retrieval. The tutorial emphasizes the superiority of semantic search over keyword search for handling synonyms, phrasing, and accents. It provides a step-by-step guide for setting up the pipeline, which includes transcription, chunking, embedding, indexing, and querying, and highlights operational considerations such as scaling, privacy, and evaluation metrics like Word Error Rate (WER) and Recall@K. The app features a user-friendly interface with options to filter results, set similarity thresholds, and evaluate retrieval quality, making it suitable for various industries, including customer support, compliance, and HR.