From cf2f14345aa2c4f168ee51c200b8081c748980b8 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 12 一月 2024 00:01:25 +0800
Subject: [PATCH] funasr1.0 fsmn-vad streaming
---
funasr/models/fsmn_vad/encoder.py | 18 +++++++++---------
1 files changed, 9 insertions(+), 9 deletions(-)
diff --git a/funasr/models/fsmn_vad/encoder.py b/funasr/models/fsmn_vad/encoder.py
index 54410ac..a0a379d 100755
--- a/funasr/models/fsmn_vad/encoder.py
+++ b/funasr/models/fsmn_vad/encoder.py
@@ -125,12 +125,12 @@
self.affine = AffineTransform(proj_dim, linear_dim)
self.relu = RectifiedLinear(linear_dim, linear_dim)
- def forward(self, input: torch.Tensor, in_cache: Dict[str, torch.Tensor]):
+ def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
x1 = self.linear(input) # B T D
cache_layer_name = 'cache_layer_{}'.format(self.stack_layer)
- if cache_layer_name not in in_cache:
- in_cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
- x2, in_cache[cache_layer_name] = self.fsmn_block(x1, in_cache[cache_layer_name])
+ if cache_layer_name not in cache:
+ cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
+ x2, cache[cache_layer_name] = self.fsmn_block(x1, cache[cache_layer_name])
x3 = self.affine(x2)
x4 = self.relu(x3)
return x4
@@ -140,10 +140,10 @@
def __init__(self, *args):
super(FsmnStack, self).__init__(*args)
- def forward(self, input: torch.Tensor, in_cache: Dict[str, torch.Tensor]):
+ def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
x = input
for module in self._modules.values():
- x = module(x, in_cache)
+ x = module(x, cache)
return x
@@ -199,19 +199,19 @@
def forward(
self,
input: torch.Tensor,
- in_cache: Dict[str, torch.Tensor]
+ cache: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Args:
input (torch.Tensor): Input tensor (B, T, D)
- in_cache: when in_cache is not None, the forward is in streaming. The type of in_cache is a dict, egs,
+ cache: when cache is not None, the forward is in streaming. The type of cache is a dict, egs,
{'cache_layer_1': torch.Tensor(B, T1, D)}, T1 is equal to self.lorder. It is {} for the 1st frame
"""
x1 = self.in_linear1(input)
x2 = self.in_linear2(x1)
x3 = self.relu(x2)
- x4 = self.fsmn(x3, in_cache) # self.in_cache will update automatically in self.fsmn
+ x4 = self.fsmn(x3, cache) # self.cache will update automatically in self.fsmn
x5 = self.out_linear1(x4)
x6 = self.out_linear2(x5)
x7 = self.softmax(x6)
--
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