From 8706e767affc6bdc8cb7a67ca3a20a62779ff048 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期三, 17 五月 2023 15:45:46 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
funasr/models/encoder/conformer_encoder.py | 22 ++++++++--------------
1 files changed, 8 insertions(+), 14 deletions(-)
diff --git a/funasr/models/encoder/conformer_encoder.py b/funasr/models/encoder/conformer_encoder.py
index c837cf5..434f2a4 100644
--- a/funasr/models/encoder/conformer_encoder.py
+++ b/funasr/models/encoder/conformer_encoder.py
@@ -30,7 +30,6 @@
StreamingRelPositionalEncoding,
)
from funasr.modules.layer_norm import LayerNorm
-from funasr.modules.normalization import get_normalization
from funasr.modules.multi_layer_conv import Conv1dLinear
from funasr.modules.multi_layer_conv import MultiLayeredConv1d
from funasr.modules.nets_utils import get_activation
@@ -308,7 +307,7 @@
feed_forward: torch.nn.Module,
feed_forward_macaron: torch.nn.Module,
conv_mod: torch.nn.Module,
- norm_class: torch.nn.Module = torch.nn.LayerNorm,
+ norm_class: torch.nn.Module = LayerNorm,
norm_args: Dict = {},
dropout_rate: float = 0.0,
) -> None:
@@ -895,7 +894,7 @@
return x, cache
-class ConformerChunkEncoder(torch.nn.Module):
+class ConformerChunkEncoder(AbsEncoder):
"""Encoder module definition.
Args:
input_size: Input size.
@@ -940,7 +939,6 @@
default_chunk_size: int = 16,
jitter_range: int = 4,
subsampling_factor: int = 1,
- **activation_parameters,
) -> None:
"""Construct an Encoder object."""
super().__init__()
@@ -961,7 +959,7 @@
)
activation = get_activation(
- activation_type, **activation_parameters
+ activation_type
)
pos_wise_args = (
@@ -991,9 +989,6 @@
simplified_att_score,
)
- norm_class, norm_args = get_normalization(
- norm_type,
- )
fn_modules = []
for _ in range(num_blocks):
@@ -1003,8 +998,6 @@
PositionwiseFeedForward(*pos_wise_args),
PositionwiseFeedForward(*pos_wise_args),
CausalConvolution(*conv_mod_args),
- norm_class=norm_class,
- norm_args=norm_args,
dropout_rate=dropout_rate,
)
fn_modules.append(module)
@@ -1012,11 +1005,9 @@
self.encoders = MultiBlocks(
[fn() for fn in fn_modules],
output_size,
- norm_class=norm_class,
- norm_args=norm_args,
)
- self.output_size = output_size
+ self._output_size = output_size
self.dynamic_chunk_training = dynamic_chunk_training
self.short_chunk_threshold = short_chunk_threshold
@@ -1028,6 +1019,9 @@
self.jitter_range = jitter_range
self.time_reduction_factor = time_reduction_factor
+
+ def output_size(self) -> int:
+ return self._output_size
def get_encoder_input_raw_size(self, size: int, hop_length: int) -> int:
"""Return the corresponding number of sample for a given chunk size, in frames.
@@ -1151,7 +1145,7 @@
x = x[:,::self.time_reduction_factor,:]
olens = torch.floor_divide(olens-1, self.time_reduction_factor) + 1
- return x, olens
+ return x, olens, None
def simu_chunk_forward(
self,
--
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