From 3fb0a431704a18c9d04230b07a1d75b7ea159970 Mon Sep 17 00:00:00 2001 From: marcoyang1998 <45973641+marcoyang1998@users.noreply.github.com> Date: Thu, 27 Jul 2023 12:36:05 +0800 Subject: [PATCH] Fix conflict (#1187) Resolve conflict --- docs/source/decoding-with-langugage-models/rescoring.rst | 4 ---- 1 file changed, 4 deletions(-) diff --git a/docs/source/decoding-with-langugage-models/rescoring.rst b/docs/source/decoding-with-langugage-models/rescoring.rst index de7e700d0..ee2e2113c 100644 --- a/docs/source/decoding-with-langugage-models/rescoring.rst +++ b/docs/source/decoding-with-langugage-models/rescoring.rst @@ -4,11 +4,7 @@ LM rescoring for Transducer ================================= LM rescoring is a commonly used approach to incorporate external LM information. Unlike shallow-fusion-based -<<<<<<< HEAD methods (see :ref:`shallow_fusion`, :ref:`LODR`), rescoring is usually performed to re-rank the n-best hypotheses after beam search. -======= -methods (see :ref:`shallow-fusion`, :ref:`LODR`), rescoring is usually performed to re-rank the n-best hypotheses after beam search. ->>>>>>> 80d922c1583b9b7fb7e9b47008302cdc74ef58b7 Rescoring is usually more efficient than shallow fusion since less computation is performed on the external LM. In this tutorial, we will show you how to use external LM to rescore the n-best hypotheses decoded from neural transducer models in `icefall `__.