Show HN: Entropic — information-driven variable-rate media playback (github.com)

A developer has unveiled "Entropic," an innovative tool for variable-rate audio and video playback that dynamically adjusts speed based on the informational density of speech. Using token-level surprisal analytics from a local language model (LLM), Entropic slows down when encountering unfamiliar or high-information words while speeding through predictable segments. This is primarily achieved through two modes: Variable Bit Rate (VBR), which targets an average playback speed, and Skiplow, which maintains a consistent speech rate but compresses low-information segments and silences. This project is significant for the AI/ML community as it explores the intersection of natural language processing and multimedia presentation, offering new ways to enhance comprehension while accommodating varying listener familiarity with the subject matter. Entropic employs advanced techniques such as word transcriptions from WhisperX, word surprisal scoring from models like distilgpt2, and real-time audio manipulation via librubberband. By tailoring playback speed according to contextual information, the tool has the potential to improve media consumption experiences, especially in educational and informational contexts, highlighting the necessity of adapting tech to human cognitive factors in understanding language.