From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/models/sond/encoder/fsmn_encoder.py | 79 +++++++++++++++------------------------
1 files changed, 31 insertions(+), 48 deletions(-)
diff --git a/funasr/models/sond/encoder/fsmn_encoder.py b/funasr/models/sond/encoder/fsmn_encoder.py
index fb87ee8..9ec9912 100644
--- a/funasr/models/sond/encoder/fsmn_encoder.py
+++ b/funasr/models/sond/encoder/fsmn_encoder.py
@@ -18,16 +18,17 @@
class FsmnBlock(torch.nn.Module):
def __init__(
- self,
- n_feat,
- dropout_rate,
- kernel_size,
- fsmn_shift=0,
+ self,
+ n_feat,
+ dropout_rate,
+ kernel_size,
+ fsmn_shift=0,
):
super().__init__()
self.dropout = nn.Dropout(p=dropout_rate)
- self.fsmn_block = nn.Conv1d(n_feat, n_feat, kernel_size, stride=1,
- padding=0, groups=n_feat, bias=False)
+ self.fsmn_block = nn.Conv1d(
+ n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False
+ )
# padding
left_padding = (kernel_size - 1) // 2
if fsmn_shift > 0:
@@ -53,14 +54,7 @@
class EncoderLayer(torch.nn.Module):
- def __init__(
- self,
- in_size,
- size,
- feed_forward,
- fsmn_block,
- dropout_rate=0.0
- ):
+ def __init__(self, in_size, size, feed_forward, fsmn_block, dropout_rate=0.0):
super().__init__()
self.in_size = in_size
self.size = size
@@ -69,9 +63,7 @@
self.dropout = nn.Dropout(dropout_rate)
def forward(
- self,
- xs_pad: torch.Tensor,
- mask: torch.Tensor
+ self, xs_pad: torch.Tensor, mask: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# xs_pad in Batch, Time, Dim
@@ -86,24 +78,24 @@
class FsmnEncoder(AbsEncoder):
- """Encoder using Fsmn
- """
+ """Encoder using Fsmn"""
- def __init__(self,
- in_units,
- filter_size,
- fsmn_num_layers,
- dnn_num_layers,
- num_memory_units=512,
- ffn_inner_dim=2048,
- dropout_rate=0.0,
- shift=0,
- position_encoder=None,
- sample_rate=1,
- out_units=None,
- tf2torch_tensor_name_prefix_torch="post_net",
- tf2torch_tensor_name_prefix_tf="EAND/post_net"
- ):
+ def __init__(
+ self,
+ in_units,
+ filter_size,
+ fsmn_num_layers,
+ dnn_num_layers,
+ num_memory_units=512,
+ ffn_inner_dim=2048,
+ dropout_rate=0.0,
+ shift=0,
+ position_encoder=None,
+ sample_rate=1,
+ out_units=None,
+ tf2torch_tensor_name_prefix_torch="post_net",
+ tf2torch_tensor_name_prefix_tf="EAND/post_net",
+ ):
"""Initializes the parameters of the encoder.
Args:
@@ -148,14 +140,9 @@
ffn_inner_dim,
num_memory_units,
1,
- dropout_rate
- ),
- FsmnBlock(
- num_memory_units,
dropout_rate,
- filter_size,
- self.shift[lnum]
- )
+ ),
+ FsmnBlock(num_memory_units, dropout_rate, filter_size, self.shift[lnum]),
),
)
@@ -167,7 +154,7 @@
num_memory_units,
1,
dropout_rate,
- )
+ ),
)
if out_units is not None:
self.conv1d = nn.Conv1d(num_memory_units, out_units, 1, 1)
@@ -176,10 +163,7 @@
return self.num_memory_units
def forward(
- self,
- xs_pad: torch.Tensor,
- ilens: torch.Tensor,
- prev_states: torch.Tensor = None
+ self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
inputs = xs_pad
if self.position_encoder is not None:
@@ -194,4 +178,3 @@
inputs = self.conv1d(inputs.transpose(1, 2)).transpose(1, 2)
return inputs, ilens, None
-
--
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