  ASR systems have become increasingly widespread in recent years. However,
their textual outputs often require post-processing tasks before they can be
practically utilized. To address this issue, we draw inspiration from the
multifaceted capabilities of LLMs and Whisper, and focus on integrating
multiple ASR text processing tasks related to speech recognition into the ASR
model. This integration not only shortens the multi-stage pipeline, but also
prevents the propagation of cascading errors, resulting in direct generation of
post-processed text. In this study, we focus on ASR-related processing tasks,
including Contextual ASR and multiple ASR post processing tasks. To achieve
this objective, we introduce the CPPF model, which offers a versatile and
highly effective alternative to ASR processing. CPPF seamlessly integrates
these tasks without any significant loss in recognition performance.
