From bc723ea200144bd6fa8a5dff4b9a780feda144fc Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 29 六月 2023 18:55:01 +0800
Subject: [PATCH] dcos

---
 funasr/models/decoder/contextual_decoder.py |   10 ++++------
 1 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/funasr/models/decoder/contextual_decoder.py b/funasr/models/decoder/contextual_decoder.py
index 32f550a..0e69c44 100644
--- a/funasr/models/decoder/contextual_decoder.py
+++ b/funasr/models/decoder/contextual_decoder.py
@@ -7,7 +7,6 @@
 
 from funasr.modules.streaming_utils import utils as myutils
 from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
-from typeguard import check_argument_types
 
 from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
 from funasr.modules.embedding import PositionalEncoding
@@ -74,7 +73,7 @@
         return x, tgt_mask, x_self_attn, x_src_attn
 
 
-class ContexutalBiasDecoder(nn.Module):
+class ContextualBiasDecoder(nn.Module):
     def __init__(
         self,
         size,
@@ -83,7 +82,7 @@
         normalize_before=True,
     ):
         """Construct an DecoderLayer object."""
-        super(ContexutalBiasDecoder, self).__init__()
+        super(ContextualBiasDecoder, self).__init__()
         self.size = size
         self.src_attn = src_attn
         if src_attn is not None:
@@ -102,7 +101,7 @@
 
 class ContextualParaformerDecoder(ParaformerSANMDecoder):
     """
-    author: Speech Lab, Alibaba Group, China
+    Author: Speech Lab of DAMO Academy, Alibaba Group
     Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
     https://arxiv.org/abs/2006.01713
     """
@@ -126,7 +125,6 @@
         kernel_size: int = 21,
         sanm_shfit: int = 0,
     ):
-        assert check_argument_types()
         super().__init__(
             vocab_size=vocab_size,
             encoder_output_size=encoder_output_size,
@@ -186,7 +184,7 @@
             ),
         )
         self.dropout = nn.Dropout(dropout_rate)
-        self.bias_decoder = ContexutalBiasDecoder(
+        self.bias_decoder = ContextualBiasDecoder(
             size=attention_dim,
             src_attn=MultiHeadedAttentionCrossAtt(
                 attention_heads, attention_dim, src_attention_dropout_rate

--
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