From 1ce704d8c09bd4d4c7e5ab087f951f31fad9fca6 Mon Sep 17 00:00:00 2001
From: nichongjia-2007 <nichongjia@gmail.com>
Date: 星期五, 07 七月 2023 15:47:19 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR

---
 funasr/bin/asr_infer.py |   14 ++++++++++----
 1 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index 259a286..02ca63d 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -280,6 +280,7 @@
             nbest: int = 1,
             frontend_conf: dict = None,
             hotword_list_or_file: str = None,
+            clas_scale: float = 1.0,
             decoding_ind: int = 0,
             **kwargs,
     ):
@@ -376,6 +377,7 @@
         # 6. [Optional] Build hotword list from str, local file or url
         self.hotword_list = None
         self.hotword_list = self.generate_hotwords_list(hotword_list_or_file)
+        self.clas_scale = clas_scale
 
         is_use_lm = lm_weight != 0.0 and lm_file is not None
         if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
@@ -439,16 +441,20 @@
         pre_token_length = pre_token_length.round().long()
         if torch.max(pre_token_length) < 1:
             return []
-        if not isinstance(self.asr_model, ContextualParaformer) and not isinstance(self.asr_model,
-                                                                                   NeatContextualParaformer):
+        if not isinstance(self.asr_model, ContextualParaformer) and \
+            not isinstance(self.asr_model, NeatContextualParaformer):
             if self.hotword_list:
                 logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
             decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds,
                                                                      pre_token_length)
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
         else:
-            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds,
-                                                                     pre_token_length, hw_list=self.hotword_list)
+            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, 
+                                                                     enc_len, 
+                                                                     pre_acoustic_embeds,
+                                                                     pre_token_length, 
+                                                                     hw_list=self.hotword_list,
+                                                                     clas_scale=self.clas_scale)
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
         if isinstance(self.asr_model, BiCifParaformer):

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