Show HN: Castrag – transcribe and semantically search a large podcast archive (github.com)

A new OpenAI-based tool called Castrag has been unveiled, designed to efficiently transcribe and semantically search large podcast archives. This solution can process around 145 hours of audio from 230 podcast episodes for only $33. It offers three main capabilities: structured episode summaries that provide quick overviews, a semantic search feature enabling rapid querying of relevant content, and a retrieval-augmented generation (RAG) approach for asking natural-language questions grounded in the transcripts. The system is built with a modular pipeline that allows users to resume processing at any stage, making it adaptable and user-friendly. The significance of Castrag lies in its ability to transform extensive audio collections into searchable and actionable data, addressing the common challenge of scalability in handling large transcripts within language models. By implementing techniques like chunking audio into manageable segments and storing embeddings in a simple numpy array for efficient searching, the tool avoids the need for complex vector databases, making it accessible even for smaller projects. This approach not only enhances the usability of auditory content but also sets a precedent for future tools focused on harnessing the potential of audio data through AI.