Feed as Context in OpenAI Whisper: Understanding the Mechanism and Applications

Introduction

OpenAI’s Whisper model represents a significant leap forward in automatic speech recognition (ASR) technology. One of its key features is the ability to use “feed as context,” a mechanism that allows the model to leverage past inputs to improve accuracy and coherence in transcription. This article delves into the concept of feed as context in Whisper, explaining its function, benefits, and potential applications.

Understanding Feed as Context

The term “feed as context” in Whisper refers to the model’s ability to retain and utilize previous segments of audio or text as a reference when processing new inputs. This mechanism enables Whisper to maintain context over longer sessions, reducing errors and enhancing the overall quality of the transcription.

In traditional ASR systems, each segment of audio is often treated independently, leading to potential disconnects in the transcription, especially when dealing with complex sentences or conversations spanning multiple segments. Whisper, however, can “remember” prior inputs and use them to inform its understanding of subsequent ones. This context retention is crucial for maintaining coherence, especially in scenarios where the conversation or narrative develops over time.