Large-scale audio transcription leverages asynchronous batch processing to efficiently convert thousands of audio files into searchable text, using Python and the AssemblyAI SDK to handle concurrent job submission, status polling, and multi-format exports. This system can transcribe extensive audio libraries, such as podcast collections or years of meeting recordings, in parallel, minimizing total processing time to the length of the longest file rather than the cumulative duration of all files. The architecture supports unlimited file processing, speaker labeling, and text formatting, with polling and webhooks available for status monitoring. The approach allows exporting results in various formats, including JSON and SRT, while maintaining high accuracy in challenging audio conditions. The pricing model is straightforward, based on audio minutes, and offers cost optimization through selective feature use and automatic retry mechanisms, enabling scalable transcription without concurrency limits.