In my work scenario, we handle a large volume of offline interview audio recordings from users every day. These recordings capture users’ feedback on products or services, and our task is to extract key tags from the content, such as points users think are well-done, areas that need improvement, and topics they are particularly focused on. This tagging process helps facilitate subsequent data analysis and provides valuable insights for optimization and improvement.
To streamline this process and reduce manual effort, I developed an automated tool specifically designed to extract these key tags from audio conversations. The tool aims to simplify workflows, enhance processing efficiency, and deliver more accurate and structured data for further analysis.