From d60306e7a435053a1ed626213f9fa6fe12af2b3e Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期五, 23 二月 2024 16:47:15 +0800
Subject: [PATCH] test

---
 funasr/models/lcbnet/model.py |   11 ++++++++---
 1 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/funasr/models/lcbnet/model.py b/funasr/models/lcbnet/model.py
index 9646e1e..555d4e6 100644
--- a/funasr/models/lcbnet/model.py
+++ b/funasr/models/lcbnet/model.py
@@ -21,7 +21,7 @@
 from funasr.utils import postprocess_utils
 from funasr.utils.datadir_writer import DatadirWriter
 from funasr.register import tables
-
+import pdb
 @tables.register("model_classes", "LCBNet")
 class LCBNet(nn.Module):
     """
@@ -89,8 +89,8 @@
         text_encoder = text_encoder_class(input_size=vocab_size, **text_encoder_conf)
         fusion_encoder_class = tables.encoder_classes.get(fusion_encoder)
         fusion_encoder = fusion_encoder_class(**fusion_encoder_conf)
-        bias_predictor_class = tables.encoder_classes.get_class(bias_predictor)
-        bias_predictor = bias_predictor_class(args.bias_predictor_conf)
+        bias_predictor_class = tables.encoder_classes.get(bias_predictor)
+        bias_predictor = bias_predictor_class(**bias_predictor_conf)
 
         if decoder is not None:
             decoder_class = tables.decoder_classes.get(decoder)
@@ -117,9 +117,13 @@
         self.specaug = specaug
         self.normalize = normalize
         self.encoder = encoder
+        # lcbnet
         self.text_encoder = text_encoder
         self.fusion_encoder = fusion_encoder
         self.bias_predictor = bias_predictor
+        self.select_num = select_num
+        self.select_length = select_length
+        self.insert_blank = insert_blank
 
         if not hasattr(self.encoder, "interctc_use_conditioning"):
             self.encoder.interctc_use_conditioning = False
@@ -409,6 +413,7 @@
             logging.info("enable beam_search")
             self.init_beam_search(**kwargs)
             self.nbest = kwargs.get("nbest", 1)
+        pdb.set_trace()
 
         meta_data = {}
         if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank":  # fbank

--
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